E ntr e p r e n e u r ial Fi nan ce
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Entrepreneurial
Finance
Venture Capital, Deal
Structure & Valuation,
Second Edition
Janet Kiholm Smith and
Richard L. Smith
Stanford Business Books
An imprint of Stanford University Press
Stanford, California
stanford university press
Stanford, California
© 2011, 2019 by the Board of Trustees of the Leland Stanford Junior University.
All rights reserved.
No part of this book may be reproduced or transmitted in any form or by any
means, electronic or mechanical, including photocopying and recording, or in any
information storage or retrieval system without the prior written permission of
Stanford University Press.
Printed in the United States of America on acid-free, archival-quality paper
Library of Congress Cataloging-in-Publication Data
Names: Smith, Janet Kiholm, author. | Smith, Richard L., author.
Title: Entrepreneurial finance : venture capital, deal structure & valuation /
Janet Kiholm Smith and Richard L. Smith.
Description: Second edition. | Stanford, California : Stanford Business Books,
an imprint of Stanford University Press, 2019. | Includes bibliographical
references and index.
Identifiers: LCCN 2018060867 (print) | LCCN 2019001437 (ebook) |
ISBN 9781503609129 (e-book) | ISBN 9781503603219 (cloth : alk. paper)
Subjects: LCSH: Venture capital. | New business enterprises—Finance. |
Entrepreneurship.
Classification: LCC HG4751 (ebook) | LCC HG4751 .S597 2019 (print) |
DDC 658.15/224—dc23
LC record available athttps://lccn.loc.gov/2018060867
Cover design by Kevin Barrett Kane
Cover photograph by Anna Om
Typeset by Newgen in Times New Roman MT Std 10/14
Contents
List of Illustrations xiii
Abbreviations xix
Preface xxiii
Acknowledgments xxix
About the Authors xxxi
Part 1 Getting Started
Chapter 1 Introduction 3
1.1What Makes Entrepreneurial Finance Different
from Corporate Finance? 3
1.2
Entrepreneurship and the Entrepreneur 9
1.3
Hypothesis-Driven Entrepreneurship 17
1.4
The Stages of New Venture Development 23
1.5Financial Performance and Stages of New Venture
Development 25
1.6
The New Venture Business Model 28
1.7
Summary 30
Review Questions 31
Notes 32
References and Additional Reading 33
vi
Contents
Chapter 2 New Venture Financing: Considerations and Choices 36
2.1
The Sequence of New Venture Financing 37
2.2
Sources of New Venture Financing 39
2.3What’s Different About Financing Not-for-Profit Ventures? 59
2.4
Organizational Form and Financing Choices 61
2.5
Regulatory Considerations 62
2.6
International Differences in Financing Options 66
2.7
Recap: Considerations in the Choice of Financing 67
2.8
How Financial Distress Affects Financing Choices 71
2.9
Summary 73
Review Questions 74
Notes 74
References and Additional Reading 77
Part 2 Financing of High-Risk, High-Growth Ventures
Chapter 3 Venture Capital and Angel Investing 83
3.1
Development of the Venture Capital Market 84
3.2
The Organization of Venture Capital Firms 94
3.3
Investment Returns and Compensation 103
3.4
Impact of Compensation on Investment Selection 107
3.5
Aspects of the VC Industry Structure 108
3.6
How Venture Capitalists Can Add Value 110
3.7Luck Versus Skill: What Accounts for Venture Capital
Success? 112
3.8
The Role of Reputation in the Venture Capital Market 113
3.9
Angel Investing 114
3.10
Summary 118
Review Questions 119
Notes 119
References and Additional Reading 122
Contents vii
Chapter 4 Venture Deals 126
4.1
The Economic Framework for Financial Contracting 127
4.2
Essentials of Contract Design 132
4.3
Elements of VC Deal Structure 138
4.4
Analysis of Key Term Sheet Provisions 149
4.5
Deal Structures of Angel Investments 156
4.6
Summary 159
Review Questions 161
Notes 162
References and Additional Reading 163
Part 3 Financial Aspects of Strategic Planning
Chapter 5 New Venture Strategy and Real Options 169
5.1
Product-Market, Financial, and Organizational Strategy 169
5.2
The Interdependence of Strategic Choices: An Example 171
5.3
What Makes a Plan or Decision Strategic? 172
5.4
Financial Strategy 172
5.5
Deciding on the Objective 173
5.6
Strategic Planning for New Ventures 175
5.7
Recognizing Real Options 177
5.8
Strategic Planning and Decision Trees 182
5.9
Decision Trees and Contract Negotiation 191
5.10
Rival Reactions and Game Trees 192
5.11
Real Options with Continuous Distributions 197
5.12
Summary 199
Review Questions 199
Notes 200
References and Additional Reading 201
Chapter 6 Developing Venture Strategy Using Simulation 204
6.1
Use of Simulation in Business Planning: An Example 205
viii
Contents
6.2
Who Relies on Simulation? 207
6.3
Simulation in New Venture Finance 207
6.4
Simulation: An Illustration 209
6.5
Simulating the Value of a Financial Option 214
6.6
Describing Risk 216
6.7
Using Simulation to Evaluate a Strategy 219
6.8
Valuing Real Options and Comparing Strategic Choices 228
6.9
Summary 239
Review Questions 239
Notes 240
References and Additional Reading 242
Part 4 Financial Forecasting and Assessing Financial Needs
Chapter 7 Revenue Forecasting 245
7.1
Principles of Financial Forecasting 246
7.2
Forecasting Revenue 247
7.3
Estimating Uncertainty 254
7.4Building a New Venture Revenue Forecast: An Illustration 256
7.5Introducing Uncertainty to the Forecast: Continuing the
Illustration 259
7.6Calibrating the Development Timing Assumption: An
Example 267
7.7
Summary 268
Review Questions 269
Notes 270
References and Additional Reading 270
Chapter 8 Financial Modeling 271
8.1
An Overview of Financial Statements 272
8.2
Working Capital, Growth, and Financial Needs 277
8.3
Developing Assumptions for the Financial Model 283
Contents ix
8.4
Building a Financial Model of the Venture 291
8.5
Adding Uncertainty to the Financial Model 303
8.6
NewCo: Building an Integrated Financial Model 308
8.7
Summary 318
Review Questions 319
Notes 320
References and Additional Reading 320
Chapter 9 Assessing Cash Needs 321
9.1
Cash Flow Breakeven Analysis 322
9.2
Sustainable Growth 327
9.3Planning for Financial Needs When the Desired Growth Rate
Exceeds the Sustainable Rate 332
9.4
Planning for Product-Market Uncertainty 334
9.5Assessing Financial Needs with Sensitivity/Scenario
Analysis 337
9.6
Assessing Financial Needs with Simulation 341
9.7
Summary 348
Review Questions 348
Notes 349
References and Additional Reading 350
Part 5 Valuation
Chapter 10 Foundations of New Venture Valuation 353
10.1
Perspectives on the Valuation of New Ventures 354
10.2
Myths About New Venture Valuation 355
10.3
An Overview of Valuation Methods 359
10.4
Discounted Cash Flow Valuation 363
10.5
The Relative Value Method 374
10.6
Valuation by the Venture Capital Method 378
10.7
Valuation by the First Chicago Method 380
x
Contents
10.8
Reconciliation with the Pricing of Options 381
10.9
Required Rates of Return for Investing in New Ventures 384
10.10
The Entrepreneur’s Opportunity Cost of Capital 386
10.11
Matching Cash Flows and Discount Rates 392
10.12
Summary 396
Review Questions 398
Notes 398
References and Additional Reading 401
Chapter 11 New Venture Valuation in Practice 405
11.1
Criteria for Selecting a New Venture Valuation Method 405
11.2
Implementing the Continuing Value Concept 407
11.3
Implementing DCF Valuation Methods 413
11.4
New Venture Valuation: An Illustration 428
11.5
The Cost of Capital for Non-U.S. Investors 442
11.6
Some Practical Caveats on Implementation 443
11.7
Summary 444
Review Questions 445
Notes 445
References and Additional Reading 446
Chapter 11 Appendix: The Entrepreneur’s Perspective on Value 448
11A.1
Estimating the Entrepreneur’s Commitment to a Venture 448
11A.2
Valuing Partial-Commitment Investments 453
11A.3
Implementation: Partial Commitment 455
11A.4
Shortcuts and Extensions 459
11A.5
Summary 464
References and Additional Reading 465
Chapter 12 Designing and Valuing Staged Investment with Real Options 467
12.1
Staged Investment: The Venture Capital Method 467
12.2Staged Investment: CAPM Valuation with Discrete
Scenarios 474
Contents xi
12.3
Using Simulation to Design Financial Contracts 482
12.4
Valuing Different Types of Financial Claims 493
12.5
Summary 494
Review Questions 495
Notes 495
References and Additional Reading 496
Part 6 Harvesting and Beyond
Chapter 13 Harvesting 501
13.1
Going Public 502
13.2
Acquisition 519
13.3
Valuing Private Transactions 522
13.4
Management Buyout 526
13.5
Employee Stock Ownership Plans 527
13.6
Roll-Up IPO 531
13.7
The Choice of Harvesting Method 532
13.8
Harvesting Choices of VC-Backed Firms 536
13.9
Summary 538
Review Questions 538
Notes 539
References and Additional Reading 545
Chapter 14 The Future of Entrepreneurial Finance 551
14.1
Completing the Circle 552
14.2Public Policy and Entrepreneurial Activity: An International
Comparison 557
14.3
Breaking New Ground 569
Notes 574
References and Additional Reading 576
Index 579
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I l l u s t r at i o n s
Figures
1.1
1.2
1.3
1.4
1.5
1.6
2.1
2.2
2.3
2.4
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
4.1
Survival rates of new ventures 11
Private sector establishment births and deaths 12
Entrepreneurial involvement and motivation by country 14
Global differences in supportiveness for starting and doing business 15
Stages of new venture development 24
Financial performance and stages of new venture development 26
Sources of new venture financing 38
New VC investments by sector 46
Top 10 states for VC investing: 2016 46
Number of independent VC deals and deals with CVC involvement 49
New commitments of VC in the U.S.: 1969–2017 86
VC investments in the U.S.: 1970–2017 89
European new VC commitments: 2008–2017 90
New VC investments in Europe as percentages of country GDP 91
U.S. VC investments by stage of development: 1980–2017 92
Corporate VC involvement in U.S. VC deals: 2007–2017 93
Organizational structure of VC funds 95
The VC investment process 97
Net returns by quarter to limited partners of VC funds: 1981–2016 104
Depiction of a VC waterfall provision 105
Liquidation preference: $1MM investment with a 3X preference or
convertible to 20% of common equity 150
xiii
xiv
Illustrations
4.2
4.3
5.1
5.2
5.3
5.4
5.5
5.6
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10
6.11
6.12
6.13
6.14
7.1
7.2
7.3
7.4
7.5
7.6
8.1
8.2
Liquidation preference: $1MM investment with a 3X preference or
convertible to 20% of common stock, versus an alternative contract of 25%
of common stock 151
Comparison of four alternative terms for $1MM investment: 3X liquidation
preference or convertible to 20% of common stock; 1X preference and
full participation with 20% ownership; 2X liquidation preference with 20%
participation up to a 4X cap; and 25% of common stock 152
Financial implications of product-market and organizational strategic
choices 176
Expiration date values of call and put options on Spacely.com stock 178
Decision tree for accept/reject decision to invest in the venture 185
Decision tree for investing in a venture, with the option to delay investing
until uncertainty about market demand is resolved 187
Decision tree for investing in a facility, with the option to expand the initial
investment 189
Entry decision game tree 195
Venture NPV estimated based on the expected value of each input 210
Venture NPV estimated by simulation using the distribution of each
input 212
Simulation model of stock price and put and call option expiration
values 215
Simulation of 12 months of stock price performance 215
Illustrations of @RISK statistical distributions 218
Assumptions and statistical processes of the large-facility model 223
Unconditional simulation results 224
Distribution of market size estimates generated by simulation 226
Histogram of unit sales simulation results 227
Convergence of the entrepreneur’s average NPV 227
Small facility: NPV to entrepreneur 233
Large facility overlaid with small: NPV to entrepreneur 234
Subtree for venture learning option with simulated uncertainty 235
Subtree for venture expansion option with simulated uncertainty 237
NewCo revenue assumptions 257
NewCo revenue forecast 258
NewCo revenue simulation assumptions 263
Discrete probability approach to simulating development timing 264
NewCo simulated distribution of the start of revenue generation 266
NewCo revenue forecast: Sample trial results 267
Working capital policy template 279
Morebucks simulation of profitability and surplus cash 307
Illustrations xv
8.3
8.4
9.1
9.2
9.3
9.4
9.5
9.6
9.7
10.1
10.2
10.3
10.4
11.1
11.2
11.3
12.1
13.1
13.2
13.3
13.4
13.5
13.6
14.1
14.2
14.3
Tables
2.1
2.2
3.1
3.2
4.1
4.2
NewCo integrated financial model assumptions 309
NewCo expected financial performance 314
Net income and cash flow breakeven point, cash needs assessment, and
investment value 324
Sustainable growth model template 328
Dynamic sustainable growth for an early-stage venture 331
iFree scenario analysis 340
NewCo total cash need as of quarter end 342
NewCo NPV results from simulation 344
NewCo simulation results for development completion timing (based on
1,000 trials) 344
Distribution of VC fund average annual IRRs (vintage years
1969–2013) 355
VC returns to limited partners by vintage year 357
How portfolio risk depends on the number of assets in the portfolio 368
The Capital Asset Pricing Model (CAPM) 369
Using continuing value to estimate the value of a new venture 407
Historical price/earnings ratios of the S&P 500 Index 412
Using arithmetic or geometric average returns 417
Staged investment decision model 477
IPO issue pricing process for a firm commitment underwriting 505
Average IPO underpricing by year (measured as initial return) 508
Average IPO underwriter spread by year 509
Structure of a private leveraged ESOP 529
Roll-up IPO 531
Numbers of exits of VC-backed firms via IPO and M&A and the NASDAQ
Index by quarter 537
Early-stage entrepreneurial activity and GDP per capita 558
Gross expenditures on R&D by country (percentage of GDP) 559
Early-stage entrepreneurial activity for factor-driven, efficiency-driven, and
innovation-driven countries 560
U.S. angel and VC investments: 2006–2016 44
Key U.S. securities market regulations for entrepreneurs and investors 63
Summary of terms in a VC limited partnership private placement
memorandum (PPM) 99
Key covenants in a VC limited partnership agreement (LPA) 102
Standard provisions of a VC term sheet for convertible preferred
investment 140
Simple capitalization table 148
xvi
Illustrations
4.3
5.1
7.1
8.1
8.2
8.3
8.4
8.5
8.6
8.7
8.8
8.9
8.10
8.11
8.12
8.13
8.14
9.1
9.2
10.1
10.2
11.1
11.2
11.3
11.4
11.5
11.6
11.7
11A.1
11A.2
11A.3
Comparison of antidilution provisions: Full ratchet and weighted average
ratchet 155
Examples of real options 181
Yardstick company data 251
Income statement (year ended 12/31/2018) 273
Balance sheet (year ended 12/31/2018) 275
Cash flow statement (year ended 12/31/2018) 276
Key business ratios for eating and drinking establishments 285
Coffee People, Inc. financials prior to IPO 287
Coffee People, Inc. common size statements 288
Coffee People, Inc. financial ratios 289
Step 1 of pro forma financial model for Morebucks: Income statement
assumptions 292
Step 2 of pro forma financial model for Morebucks: Balance sheet
assumptions 296
Step 3 of pro forma financial model for Morebucks: Investment
assumption 300
Pro forma financial model for Morebucks with simulation 304
Morebucks: Summary of simulation results (based on 1,000 trials) 306
NewCo pro forma financial statements 311
NewCo simulation assumptions 316
NewCo cumulative NPV by first quarter of revenue generation 345
NewCo cumulative NPV by first quarter of revenue and revenue growth
rate 347
Measures of expected cash flow 393
Matching cash flows to discount rates for various financial claims 395
Historical stock and bond returns 415
Average beta estimates for selected sectors 419
Beta estimates and market correlations for newly public, VC-backed
firms 422
Standard deviation of market returns (S&P 500) 423
Valuation Template 1: Valuation by the RADR method based on discrete
scenario cash flow forecast 431
Valuation Template 2: Valuation by the CEQ method based on discrete
scenario cash flow forecast 433
Valuation at various discount rates by the Venture Capital Method 439
Valuation Template 3: Present value of the entrepreneur’s wealth and
commitment 453
New venture simulation model ($000) 457
Valuation Template 4: Partial commitment of the entrepreneur 458
Illustrations xvii
11A.4
11A.5
12.1
12.2
12.3
12.4
12.5
12.6
12.7
12.8
12.9
12.10
12.11
12.12
13.1
13.2
Valuation Template 5: Entrepreneur’s cost of capital 463
Underdiversification premium to required rate of return 465
Valuation Template 6: Single-stage investment—Venture Capital
Method 468
Valuation Template 7: Multi-stage investment—Venture Capital
Method 470
Investment and payoff assumptions 474
Venture Capital Method single-stage valuation 475
Valuation of unstaged investment: VC investor 478
Valuation of staged investment: VC investor 480
Valuation-based contracting model of VC ownership shares 481
New venture simulation with conditional second-stage investment 485
New venture simulation: Incremental effects of conditional second-stage
investment 488
Summary of simulation investment and return results 489
Valuing the investor’s second-stage real option 490
Valuing the investor’s financial claim by discounting all expected cash
flows 491
IPO direct costs and underwriter spread by issue size 509
Private placement discounts compared to equity market value 525
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A b b r e v i at i o n s
A
AP
AR
BDC
BEP
BS
BV
CAPM
CEQ
CF
CML
COC
COGS
D
D&A
DCF
D/E
D/V
DPO
E
E/V
EBIT
assets
accounts payable
accounts receivable
business development company
breakeven point
balance sheet
book value
Capital Asset Pricing Model
certainty equivalent
cash flow
capital market line
cash on cash (net multiple)
cost of goods sold
debt or dividend per share (depending on context)
depreciation and amortization
discounted cash flow
debt to equity ratio
debt to value ratio
direct public offering
equity
equity to value ratio
earnings before interest and taxes
xix
xx
Abbreviations
EBITDA
EBT
ERISA
ESOP
FDA
FY
g*
GAAP
GDP
GEM
GP
ICA
ICO
IFRS
INT
IPO
IRR
IS
IT
JOBS
LBO
LP
M&A
MBO
MV
NAICS
NAV
NI
NPV
NVCA
NWC
NYSE
OCF
OECD
OEM
OPM
OTC
earnings before interest, taxes, depreciation, and amortization
earnings before tax
Employee Retirement Income Security Act
employee stock ownership plan
Food and Drug Administration
fiscal year
sustainable growth rate
generally accepted accounting principles
gross domestic product
Global Entrepreneurship Monitor
general partner/partnership
Investment Company Act of 1940
Initial coin offering
International Financial Reporting Standards
interest expense
initial public offering
internal rate of return
income statement
information technology
Jump Start Our Business Startups Act of 2012
leveraged buyout
limited partner/partnership
merger and acquisition
management buyout
market value
North American Industry Classification System
net asset value
net income
net present value
National Venture Capital Association
net working capital
New York Stock Exchange
operating cash flow
Organization for Economic Cooperation and Development
original equipment manufacturer
Black-Scholes Option Pricing Model
over-the-counter
Abbreviations xxi
P
P&L
P/E
PEG
PP&E
PV
R
R&D
RADR
ROA
ROR
ROS
RP
RV
SAFE
SAFT
SBA
SBIC
SBIR
SEC
SEO
SG&A
SIC
SME
SML
TY
VC
WACC
price per share
profit and loss
price to earnings ratio
price earnings to growth
property, plant, and equipment
present value
retention ratio (1 − dividend payout ratio)
research and development
risk-adjusted discount rate
return on assets
rate of return
return on sales
risk premium
relative value
simple agreement for future equity
simple agreement for future tokens
Small Business Administration
Small Business Investment Company
Small Business Innovation Research
Securities and Exchange Commission
seasoned equity offering
selling, general, and administrative expenses
Standard Industrial Classification
small and medium-size enterprise
security market line
tax year
venture capital/capitalist
weighted average cost of capital
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P r e fac e
H i s to ry a b o u n ds w it h examples of extraordinary entrepreneurs whose
new ideas and products have changed the world. Many people are enamored
with the idea of creating new products and starting businesses. Accompanying
the interest in venture creation, there is broad interest in venture capital, investment banking, and careers related to new venture financing, deal structuring,
and harvesting.
Our primary motivation for writing this book is to empower students and
practitioners to be more successful in developing and financing their ideas.
Our overriding orientation is to apply the theory and methods of finance and
economics to the rapidly evolving field of entrepreneurial finance.
This book is unique in several ways. First, it builds on and significantly extends the tools and methods of corporate finance and financial economics to
approach the difficult and important financial problems associated with starting
and growing new ventures. Building on the foundations of financial economics
makes the lessons more general and memorable and the applications easier to
implement in varied settings. Mastery of the framework facilitates clearer and
more defensible evaluation of opportunities and choices than is possible based
on heuristics and intuition. With reliable and rigorous tools, you can be more
confident that the decisions you make will be the right ones.
Second, while many books address aspects of entrepreneurship (writing business plans, leading and managing new ventures, etc.), this book has a unique
and direct focus on the question of how new venture financing choices can add
value and turn marginal opportunities into valuable ones. We emphasize value
creation as the objective of each financial choice that an entrepreneur or investor
makes—including recent developments in new venture contracting, staging, deal
xxiii
xxiv
Preface
structuring, real options, risk, diversification, and exit. Understanding the effects
of each choice has the potential to add tremendous value to ideas and innovations.
Third, in contrast to other books on entrepreneurial finance, we specifically
address the influences of risk and uncertainty on new venture success, with
particular attention to risky ventures with significant upside potential. We use
discrete scenario and simulation analysis throughout the book to evaluate alternative strategies, assess financial needs, assess risk and expected cash flows as
elements of valuation, and compare different deal structures and contract terms.
Fourth, because both valuation and assessment of cash needs depend on
projections of cash flow, we devote significant attention to methods of forecasting the pro forma financial statements of new ventures in an integrated fashion. Integrated financial modeling enables the entrepreneur or investor to use
financial forecasting to conduct scenario analysis and to simulate such things
as how growth rates that are faster or slower than expected affect cash needs.
Fifth, we provide a comprehensive survey of approaches to new venture valuation with an emphasis on applications. Our approach to valuation is more
comprehensive than most because we approach valuation from a contracting
perspective that is affected by the different assessments of the entrepreneur and
the investor. We recognize that the entrepreneur’s unique circumstances can lead
to value conclusions that are different from those of a well-diversified investor.
Why Study Entrepreneurial Finance?
Whether you see yourself as an entrepreneur, a corporate financial manager
dealing with new projects, an asset manager, an investor, or a social entrepreneur, a solid understanding of entrepreneurial finance can help you make better decisions. Couple this with the estimate that over 50% of new businesses
fail within a few years, and the value of understanding new venture finance
becomes clear. Perhaps more telling is that even if a business survives, the entrepreneur may not. In a majority of venture capital-backed start-ups, nonfounders are appointed CEO within a few years of the start of operation.
How can the hazards and pitfalls of forming new ventures be avoided or
mitigated? The answer is to understand the financial economic foundations
and use the best available decision-making tools and methods. A new venture
should not be undertaken unless the expected reward is high enough to compensate for the value of forgone opportunities. Investing personal resources
and time in a venture that should never have been pursued is just as serious an
error as failing to invest in a good venture. Throughout the book, we reiterate
Preface xxv
and demonstrate through examples that this trade-off of risk and return is not
easy to assess intuitively. Rather, this is an area where analytical rigor can add
considerable value.
Even the best initial projections, however, can prove to be overly optimistic as
the future unfolds. It is important to base the decision to continue or abandon
a venture on the same kind of rigorous analysis that was used in making the
original decision. It is all too easy to continue investing time and resources in a
venture that is destined for mediocre long-run performance or to give up on a
venture that has experienced a temporary setback but still offers the potential
for substantial gain.
It is a rare individual who is good at both recognizing an opportunity and
managing the venture to capitalize on that opportunity. Careful design of the
organization at the outset helps ensure that a venture does not fail just because
the visionary was not well suited to manage the day-to-day operations. Careful
design also can help ensure that the entrepreneur does not lose control of the
venture unnecessarily.
What’s New About This Book?
This book builds on our previous books, Entrepreneurial Finance (2000,
2004) and Entrepreneurial Finance: Strategy, Valuation and Deal Structure
(2011). Like the earlier books, it ties the applications to the underlying disciplines of finance and economics and makes clear in what ways the study
of entrepreneurial finance is distinct from corporate finance. In contrast to
other books, this book focuses less on small business entrepreneurship and
focuses more on issues facing high-risk ventures that have significant growth
potential and are candidates for external equity capital such as angel and
venture capital investing. We emphasize key considerations such as new developments in financial contracting and negotiation, hypothesis-driven entrepreneurship, adding value through deal structuring and staging of investment, the choice and timing of financing, and valuation of risky ventures,
including valuation of real options.
Coming on the heels of the JOBS Act of 2012, there have been a number of
innovations in the ways new ventures are financed and the contract terms that are
used. Worldwide, there has been considerable growth in both angel and venture
capital investing. There is more variety in the sources of financing available to
entrepreneurs, ranging, as examples, from crowdfunding, to initial coin offerings (ICOs), to mini-IPOs. Technological changes have resulted in reduced costs
xxvi
Preface
of experimenting with new venture ideas, leading to an explosion of start-ups
seeking early-stage funding. The result has been an increase in the prevalence
of early-stage “nonpriced” financing in the form of sophisticated convertible
notes and an increase in the number of funding rounds.
We emphasize the usefulness of hypothesis-driven entrepreneurship by tying it to an intuitive discussion, using decision trees, of how real options can
be created and exploited. The chapter on venture deals (Chapter 4) reflects the
centrality of understanding how financial contacts can mitigate information
costs and align incentives of the entrepreneur and investor. We examine venture
contracting terms in detail (such as liquidation preferences, conversion rights,
and antidilution terms) and analyze the terms of new-style convertible notes
that now are commonly used by angels and some VCs in early stages. We reflect
uncertainty of the real option values by combining decision trees and simulation
(Chapter 5). The analysis is step-by-step in the context of specific examples. We
devote significant attention to financial forecasting and the construction of integrated pro forma financial statements, as both are key to valuation, contracting,
and assessment of cash needs.
We break new ground in several areas by presenting material and analytical
approaches that extend the frontier of academic research related to entrepreneurial finance. The book focuses on U.S. institutions but also provides international
perspective by illustrating, throughout, the many similarities and the differences
in analysis for U.S. ventures versus international ventures.
The book concludes with a summary of major themes and a discussion of
international differences in institutions and public policies aimed at encouraging innovation and entrepreneurship. We also identify open questions regarding
the future of entrepreneurial finance. A number of technological changes have
the potential to dramatically change the landscape for finance, including, as
examples, blockchain and cryptocurrency, crowdfunding, artificial intelligence
and machine learning, and internet connectivity (IoT).
Intended Audience
This book can be used as a text in an advanced finance or entrepreneurship
course and by scholars who are interested in research related to entrepreneurial finance. However, its intended audience is broader. The book is appropriate for students, entrepreneurs, practitioners involved with new ventures, and
corporate financial managers looking for ways to encourage corporate venturing. We have designed the book for readers who are familiar with the basic
concepts and tools of corporate finance, accounting, economics, and statis-
Preface xxvii
tics, and who seek a rigorous and systematic approach to adding value to new
ventures through financing and deal structuring. On the companion website,
we provide brief reviews for those who feel the need for a refresher on some
key background concepts.
The book is appropriate for graduate students, advanced undergraduates
in business and economics, and executive MBA students. In our own teaching,
we use the book and related materials with students at all levels. Because entrepreneurial finance builds on and integrates all areas of management, a course
developed around the book can serve as a capstone integrative experience to
the MBA or an undergraduate business degree.
The book can be used effectively in an entrepreneurial finance course or in
a course on venture capital and private equity. It is designed to be used either
as a stand-alone resource or in conjunction with cases or a business planning
exercise. Each chapter includes end-of-chapter review questions. The book’s
website has end-of-chapter problems that are designed to give hands-on opportunities to apply the lessons of each chapter.
The book can be used in a variety of different course formats:
Some users like our use of simulation throughout the book. For those, we
rely on readily available software (@RISK) and also provide files on the website
that contain examples and problem solutions that are prepared using @RISK
and also Oracle Crystal Ball, another popular software package.
For those who are oriented to case method teaching, we have provided a
­series of our own interactive cases that correspond to the book chapters and
have developed a list of commercially available cases that work well with the
book.
For those who see value in linking the coverage of entrepreneurial finance to
a business planning exercise, the organization of the book follows the normal
organization of the thought processes and financial contents of a business plan.
A Note About the Website and Internet Resources
The book is designed to be used most effectively in conjunction with resources
we provide on the book’s website, https://www.sup.org/entrepreneurialfinance.
Much of the book relates to software, spreadsheets, templates, simulation applications, and interactive cases and tutorials that are available for download.
For those teaching from the book, we also provide PowerPoint presentations
by chapter. Instructor-specific resources are password protected. Instructors
can gain access to teaching materials that accompany the book by contacting
Stanford University Press at info@www.sup.org.
xxviii
Preface
The website contains problems and interactive cases, along with solutions,
that complement the book material. All Excel figures in the book, including
charts, are available as downloadable files so that the user can review the spreadsheet structure and cell formulas. When we use quantitative examples in discussion, the website normally includes a file that contains the backup spreadsheet
analysis.
Simulation
Because of the usefulness of simulation to evaluate risk and uncertainty, both
prominent features of new venture development, we incorporate it into the
book. Many readers will be familiar with commercial simulation packages
such as @RISK and Oracle Crystal Ball. We primarily rely on @RISK in the
book examples but provide both @RISK and Oracle Crystal Ball versions of
the simulation analysis on the book’s website. When an Excel syntax is used in
a simulated cell in a spreadsheet, we use the @RISK syntax. If you are a user
of another commercial package you can study the parallel syntax by opening
and modifying our example files.
Spreadsheets and Templates
The website contains soft copies of the figures and tables in the book, including several templates that you can use to study your own new venture valuation questions (i.e., you can easily edit the template to study and value your
own cash flow projections). We have provided copies of spreadsheets that are
imbedded with simulation formulae as well as versions that do not require
simulation.
Ac k n o w l e d g m e n t s
I n p r e pa r i n g t h e b o o k , we have benefited from numerous comments
from colleagues, students, venture capitalists, business angels, entrepreneurs,
and friends.
Over the years, an impressive group of reviewers and adopters have provided comments that have sharpened the presentation. These include Robin
Anderson (University of Nebraska), Sanjai Bhagat (University of Colorado),
Carol Billingham (Central Michigan University), Carol Marie Boyer (Clarkson
University), Daniel Donoghue (Discovery Group), Fernando Fabre (Endeavor),
Samuel Gray (New Mexico State University), Phil Greenwood (University of
Wisconsin, Madison), Ilan Guedj (University of Texas), Thomas Hellmann
(Stanford University), Glenn Hubbard (Columbia University), William C. Hudson (St. Cloud University), Steve Kaplan (University of Chicago), Cenk Karahan
(Bogazici University), Jill Kickul (DePaul University), Frank Kerins (Montana
State University), Sandy Klasa (University of Arizona), Kenji Kutsuna (Kobe
University), Daniel McConaughy (California State University, Northridge),
James Nelson (Florida State University), Bill Petty (Baylor University), Edward
Rogoff (Baruch College), Chip Ruscher (University of Arizona), Bob Schwartz
(Silver Fox Advisors), James Seward (University of Wisconsin, Madison), Jeffrey
Sohl (University of New Hampshire), Howard Van Auken (Iowa State University), Nikhil Varaiya (San Diego State University), and Edward Williams (Rice
University). We also thank our many colleagues at the Claremont Colleges and
UC Riverside.
Thanks also to those students in our classes who read and worked through
early drafts of the book and website. These include undergraduate and MBA
xxix
xxx
Acknowledgments
students from our schools. We very much appreciate their goodwill and thoughtful suggestions.
A number of practitioners, including venture capitalists, venture funds managers, entrepreneurs, and angel investors, were very generous in sharing their
experiences and providing ideas for cases and other book material. Special
thanks to Andy Horowitz, Richard Chino, Wayne Cantwell, Arielle Zuckerberg,
Matthew Goldman, Russ Shields, John Sibert, Richard Sudek, Luann Bangsund, Luis Villalobos, John Jasper, John Kensey, Thomas Gephart, Kazuhiko
Yamamoto, and Yoshi Bunya. In particular, their insights on valuation practices
and deal negotiations and their willingness to challenge academic theory have
added institutional richness to the book.
We are grateful to our former co-author, Richard Bliss, Babson College, for
many discussions and insights about venture finance and pedagogy. The team at
Stanford University Press, including Steve Edward Catalano and Olivia Bartz,
was a pleasure to work with. We are indebted to Margo Beth Fleming, formerly
of Stanford University Press, who saw the value in developing an advanced applied book on entrepreneurial finance.
Janet Kiholm Smith
Claremont, California
Richard L. Smith
Riverside, California
About th e Autho rs
Ja n e t Kih o l m S m it h is the Von Tobel Professor of Economics and found-
ing dean of the Robert Day School of Economics and Finance, Claremont Mc­
Kenna College. She was formerly department chair and director of the Financial
Economics Institute of CMC. She currently is director of the Center for Innovation & Entrepreneurship at CMC, which supports research, courses, and cocurricular programs related to innovation. She teaches courses on new venture
finance, economics of strategy, industrial organization, and research methods.
She is the author of numerous journal articles on topics ranging from IPO
pricing to corporate philanthropy to managerial risk-taking behavior, including
publications in finance and economics journals including Journal of Finance;
Journal of Corporate Finance; Journal of Financial and Quantitative Analysis;
Journal of Banking and Finance; Journal of Law and Economics; and Journal of
Law, Economics, and Organization. Current research interests include the rise
of private equity markets and the important role that working capital plays in
financing new ventures.
Smith consults for matters related to business plan advising, working capital
management, contracts, and antitrust issues. She has served as a consultant for
major corporations and the Federal Trade Commission on complex business
litigation.
Rich a r d L . S m i t h is the Philip L. Boyd Chair in Finance at the A. Gary
Anderson Graduate School of Management of the University of California,
Riverside. He regularly teaches courses on new venture finance and strategic risk
management. Before joining the UC Riverside faculty, Smith held the Leatherby
xxxi
xxxii
About the Authors
Chair in Entrepreneurship at Chapman University and prior to that, he was
director of the Venture Finance Institute at Claremont Graduate University. He
regularly teaches courses on new venture finance and strategic risk management.
Smith has authored over 50 articles appearing in the Journal of Financial
Economics, Journal of Finance, and Review of Financial Studies, among others.
His research interests include venture capital, initial public offerings, and contracting and valuation issues related to private equity and entrepreneurial firms.
He has served on the university investment committees of Arizona State University and Claremont Graduate University and is a former Chair of the Investment Advisory Council of the Arizona State Retirement System. He is a former
member of Tech Coast Angels and has consulted extensively on valuation and
deal structuring issues for venture capitalists, angel groups, and entrepreneurs.
He has consulted or served as an expert witness on numerous cases involving
financial contracting, securities litigation, valuation, and antitrust.
E ntr e p r e n e u r ial Fi nan ce
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C h a p t e r One
I ntro du c tio n
T h o u s a n d s o f b u s i n e s s ventures are started every year. Many fail
within a short period. Of those that survive, most achieve only meager success,
some achieve rates of return high enough to justify the initial investment, and
a few achieve phenomenal success. What distinguishes the successes from the
failures? There is no single answer. A new venture based on a good idea can fail
because of poor implementation or bad luck. One that is based on a bad idea
can fail despite excellent implementation. Many that survive but do not thrive
should never have been undertaken. Sometimes, even when a venture is hugely
successful, early financing mistakes limit the entrepreneur’s ability to share in
the rewards.
This is a book on financial decision making for new ventures. It provides the
fundamentals for thinking analytically about whether a new venture opportunity is worth pursuing and about how to apply the tools of financial economic
theory to enhance the expected value of the undertaking. Although our focus
is on for-profit ventures, not-for-profit entrepreneurial ventures face similar
challenges. Where there are important differences, we discuss the application
of these tools to not-for-profit venturing.
1.1 What Makes Entrepreneurial Finance
Different from Corporate Finance?
The guiding principles of financial decision making—in both large, established companies and start-ups—can be stated succinctly:
3
4
Chapter One
• More of a good is preferred to less.
• Present wealth is preferred to future wealth.
• Safe assets are preferred to risky assets.
These principles drive choices of resource allocation, including investment
decisions and financing decisions. Investment decisions concern the acquisition of assets, which can be tangible, like a machine; intangible, like a patent;
or simply an option to take some action in the future. The worth, or value, of
an investment depends on its ability to generate cash flows for investors in the
future and on the riskiness of those cash flows. Financing decisions concern
the choice of financing (debt, equity, or some hybrid such as preferred stock),
how much financing should come from investors, and how financial contracts
should be structured.
The range of decisions that can be approached using the finance paradigm is
much broader than may be apparent at first glance. Financial considerations are
applicable, for example, to the choice of organizational form (e.g., sole proprietorship, partnership, or corporation) and to the design of financial contracts to
align the incentives of investors and entrepreneurs. Similarly, issues such as the
choices of scale and scope of a venture can be analyzed as financial investment
decisions (i.e., larger scale requires more outside financing).
If the financial theories and decisions facing all ventures are similar, it is
natural to wonder why entrepreneurial finance is worthy of special study—why
aren’t the principles of corporate finance directly applicable in an entrepreneurial setting? After all, a basic course in corporate finance concerns investment
and financing decisions of large public corporations and generally introduces
valuation techniques such as discounted cash flow and cost of capital analysis. The limitation, however, is that corporate finance theory assumes away a
number of issues that are of secondary importance in a large corporate setting
but are critical to decision making for new ventures. These distinctions make
entrepreneurial finance an intellectually challenging area worthy of special
study. The focus on entrepreneurship and early-stage ventures dramatically
changes the way the finance paradigm is applied.
Moreover, certain techniques of entrepreneurial finance (such as thinking
about investment opportunities as portfolios of real options), while they are
particularly useful in a new venture setting, are also useful in the context of a
large public corporation. They normally, however, do not receive much attention
in corporate finance courses.
We highlight 8 important differences:
1. The inseparability of new venture investment decisions from financing
decisions
Introduction 5
2. The role of necessary underdiversification as a determinant of investment value
3. The extent of managerial involvement by investors in new ventures
4. The effects of substantial information problems on the firm’s ability to
undertake a project
5. The role of contracting to resolve incentive and information problems in
entrepreneurial ventures
6. The critical importance of real options in determining project value
7. The importance of harvesting (exit) as an aspect of new venture valuation and the investment decision
8. The distinction between maximizing value for the entrepreneur and maximizing shareholder value
Interdependence Between Investment and
Financing Decisions
In corporate finance, investment decisions and financing decisions are treated
as independent of each other. The manager selects investments by comparing
return on investment to the market interest rate for projects of equivalent risk.
The manager typically does not need to convince investors of the merits of
the investment and does not need to consider simultaneously how ownership
of the assets will be financed or whether the firm’s shareholders prefer highdividend payouts or capital gains.
For start-up businesses, however, the interdependencies between investment
and financing decisions are much more complex and important. Among other
things, the entrepreneur will not be able to pursue the venture without convincing an investor of the merits and will probably place a very different value
on the new venture than will well-diversified investors. These differences are
important because the entrepreneur cannot normally sell shares of a private
venture to generate funds for current consumption (analogous to receiving a
dividend). Therefore, simple adjustments to net present value (NPV) cannot
be used to address the divergence of valuations between the entrepreneur and
the investor.
More generally, some investment choices are contingent on certain financing choices. For example, rapid growth may be possible only with substantial outside financing, whereas a large corporation may be able to finance the
entire project with internally generated funds. The link between investment
choices and financing choices creates complexity that does not arise in corporate finance.
6
Chapter One
Diversifiable Risk and Investment Value
In corporate finance, the NPV of an investment (project) is determined by applying a discount factor to expected future cash flows.1 Corporate finance proposes that the discount factor depends only on nondiversifiable risk. But this
proposition relies on the assumption that investors can diversify at low cost.
Although the assumption holds for many investors in a new venture (e.g., the
investors who participate in venture capital [VC] funds, wealthy angel investors, or shareholders of large lenders), it categorically does not hold for the
entrepreneur. In fact, the entrepreneur often must invest a large fraction of his
or her financial wealth and human capital in the venture. This difference between entrepreneurs and investors in ability to diversify results in the project
value for entrepreneurs being different from the project value for investors.
In corporate finance, because only nondiversifiable risk matters, the values of different financial claims are additive. At the firm level, value additivity
implies that allocation of financial claims among different kinds of investors
such as owners and managers does not affect the decision to accept a project.
New ventures can also be considered “projects.” But for new ventures, because
entrepreneurs and investors view risk differently, each ascribes a different value
to the same risky asset. As a result, value additivity does not hold and the allocation of financial claims becomes important.
Managerial Involvement of Investors
In public corporations, investors (stockholders and creditors) generally are
passive and do not contribute managerial services. Nor do they normally have
access to significant inside information. In contrast, some investors in new
ventures (e.g., venture capitalists [VCs] and angel investors) frequently provide
managerial and other services that can contribute to the venture’s success.
Typically, these investors will have access to inside information that they gain
as a result of their continuing investment in the venture and will be involved in
important decisions about the venture.
Information Problems and Contract Design
Separate from differences in valuation that arise from underdiversification
(­differences even when the entrepreneur and investors agree about the expected future cash flows and know that they agree), differences in value can
arise because of information problems between the parties. Although information gaps also exist between insiders and outsiders of public corporations,
the gaps need not materially affect the investment decisions of public corpora-
Introduction 7
tion m
­ anagers. Public corporations generally can, and often do, make investment decisions without much immediate regard to the question of how investors perceive the value of the investment.
Generally, corporate managers need not convince investors, lenders, or employees that a project is worth undertaking, at least in the short run. The situation is very different in the case of a start-up business that requires outside
financing. In the latter case, investors look specifically to the venture to provide
a return on their investments. Moreover, there is often no easy way for the entrepreneur to communicate her true beliefs about the potential for success of a
new venture. From a financial perspective, this places considerable emphasis on
finding ways to signal the entrepreneur’s confidence in the venture.
Incentive Alignment and Contract Design
Incentive contracting clearly plays a role in the large public corporation. On
the positive side, managerial stock options and performance bonuses are intended to align the interests of managers and investors. On the negative side,
debt covenants and similar provisions are designed to discourage reliance on
risky debt financing that can lead to inefficient investment decisions and other
agency costs associated with heavy reliance on debt. The issues are similar for
start-up businesses, but reliance on incentive contracts is, in some respects,
more compelling. In contrast to the managers of public corporations, investors generally keep the entrepreneur on a short leash.
There are compelling reasons to find ways to invest in the projects of unproven
entrepreneurs. An investor who is good at identifying untested entrepreneurs
who are likely to succeed can profitably participate in new ventures that would
have been rejected by a less astute investor. The result is that investors use a
variety of contractual devices to supplement their ability to identify and motivate
high-quality entrepreneurs.
The Importance of Real Options
Students and practitioners of corporate finance know to value projects by
discounting expected future cash flows back to NPV. Even in the corporate
setting, this approach is oversimplified, except with respect to the most basic independent investment projects. The more difficult valuation challenges
are those for projects that include important real options. A real option is the
right to undertake certain business initiatives, such as deferring, abandoning,
or expanding a capital investment project after the initial investment has been
made. In contrast to a financial option, such as a call or put on a share of
stock, the underlying asset is tangible and the option reflects a change in how
8
Chapter One
the real asset is used. The reality is that most investing involves a process of
acquiring, retaining, exercising, and abandoning real options. Nonetheless,
the common practice of ignoring real options in corporate investment decisions suggests that they often are of secondary importance to the decision.
The values of real options associated with an investment depend on the degree
of uncertainty surrounding the investment. For projects such as an investment
in research and development or an investment in a new industry, uncertainty
levels are likely very high. This uncertainty adds to the importance of considering embedded option values. Nowhere is the importance of option values more
central to investment decision making than for a start-up business. Staging of
capital infusions, abandonment of the project, growth rate acceleration, and a
variety of other choices all involve real options and contribute to the need for a
process of investment decision making that focuses on recognizing and valuing
the real options that are associated with the project.
Harvesting the Investment
In corporate finance, investment opportunities are evaluated based on their
capacity to generate free cash flow for the corporation. The investment decision does not depend on when the cash flows are distributed to investors,
except that corporations generally will not retain cash that they cannot invest
profitably.
In their decisions to invest in the shares of a public corporation with liquid
shares, investors normally are not focused on when they will sell or on the anticipated valuation at the time of sale or on the costs associated with selling.
Investing in new ventures is different. New venture investments normally are not
liquid and often do not generate any significant free cash flow for several years.
Most investors in new ventures, and many entrepreneurs, have finite investment
horizons. To realize returns on their investments, a public offering of equity or
another kind of liquidity event must occur (e.g., an acquisition of the venture for
cash or freely tradable shares of the acquirer). Such harvesting opportunities are
among the main ways investors in new ventures realize returns on their investments. Because of the importance of liquidity events, they generally are forecasted
explicitly. The forecasts are formally factored into valuation of the investment.
Value to the Entrepreneur
The final difference between start-ups and public corporations is the focus on
the entrepreneur. In the public corporation, the focus of decision making is on
investment returns to shareholders. In a start-up, the true residual claimant is
Introduction 9
the entrepreneur. In the corporate setting, maximum shareholder value is the
most frequently espoused financial objective. In contrast, the objective of the
entrepreneur in deciding whether to pursue the venture and how to structure
the financing is to maximize the value of the financial claims and other benefits that the entrepreneur is able to retain as the business grows.
It is easy to envision cases in which an objective of maximizing share value
would not be in the entrepreneur’s best interest. This is particularly true if the
entrepreneur is unable to convince investors of the true value of the project
and would therefore have to give up too large a fraction of ownership, or if the
entrepreneur values other considerations besides share value.
While the principles in this book are often developed and presented from
the perspective of the entrepreneur (by structuring the investor’s claim to have
an NPV of zero), they are no less valuable for investors. The analysis can easily
be reformulated around the objective of maximizing NPV for an investor, such
as a VC or angel investor. With regard to valuation and contracting, because
both parties can benefit from knowing how the other views the venture, we study
these topics from both perspectives.
1.2 Entrepreneurship and the Entrepreneur
The term “entrepreneur” is of French origin. Its literal translation is simply
“undertaker,” in the sense of one who undertakes to do something. In the
early 1700s, the English banker Richard Cantillon coined the use of the word
in a managerial context. He emphasized the notion of the entrepreneur as a
bearer of risk, particularly with respect to provision of capital. This early usage, however, does not adequately characterize our current understanding of
what it means to be an entrepreneur.
In the early 1800s, the French economist J. B. Say described the entrepreneur
as a person who seeks to shift economic resources from areas of low productivity to areas of high productivity. Although Say’s notion points us in a useful
direction, it is too general. Most purposeful human activity can be described
as shifting economic resources to higher-valued uses (or at least attempting to
do so).2
The contributions of Cantillon and Say gained renewed attention in the early
1900s through the writings of two other economists. Joseph Schumpeter (1912)
viewed the entrepreneur as actively seeking opportunities to innovate. In his
view, the entrepreneur is the driver of economic progress, continually seeking to
disturb the status quo in a quest for profits from deliberate and risky efforts to
combine society’s resources in new and valuable ways. Current use of the term
10
Chapter One
“­entrepreneurship” derives from these views and from more recent thinking by
management scholars such as Peter Drucker. Drucker, who was a personal friend
of Schumpeter, describes entrepreneurs as individuals who “create something
new, something different; they change or transmute values.”3
Today, entrepreneurship is most often described as the pursuit of opportunities to combine and redeploy resources, without regard to current ownership
or control of the resources. This notion clearly draws on the definition offered
by Schumpeter but adds structure by recognizing that the entrepreneur is not
constrained by current control of resources.
Thinking of entrepreneurship in this way suggests a multidimensional process. The entrepreneur must:
• Perceive an opportunity to create value by redeploying society’s
resources
• Devise a strategy for marshaling control of the necessary resources
• Implement a plan of action to bring about the change
• Harvest the rewards that accrue from the innovation
This definition is broad enough to encompass entrepreneurship that arises
in the for-profit sector, including extant corporations, as well as in the not-forprofit sector, including in universities and charitable foundations.
The first step is to “perceive an opportunity to create value.” What are the
characteristics of a good entrepreneurial opportunity? In general, the opportunity should:
• Address a need or solve a problem for the customer
• Identify a new product, service, process, or market
• Demonstrate a sustainable competitive advantage
• Offer the potential to be profitable and create value for the parties
involved
Our focus in this book is on assessing the last point, but the other factors
are integral to the venture’s likelihood of success and to value creation for the
entrepreneur.
Survival and Failure Rates of New Businesses
To be successful, an entrepreneur needs to maintain a clear focus on how
strategic choices and implementation decisions are likely to affect rewards.
While this may increase the likelihood of success, many new ventures do not
succeed and all entrepreneurial activity takes place in a dynamic economic
environment.
Introduction 11
Fi g u r e 1 .1
Survival rates of new
ventures
https://​w ww​.bls
​.gov/​b dm/​entrepreneur
ship/​b dm​_ chart3​.htm.
100%
90%
The figure shows survival
rates of new business
establishments that were
initiated in 1995, 2000,
2005, and 2010.
Percentage of ventures surviving
source:
80%
1995
70%
2000
2005
60%
2010
50%
40%
30%
20%
10%
0%
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21
Years since starting
Figure 1.1 shows the survival rates of new ventures from a U.S. Department
of Labor longitudinal study of business ventures that were launched from 1994
through 2016. The figure provides data for ventures started in 1995, 2000, 2005,
and 2010. Based on the data, 50% of ventures survive for at least 5 years and
about 35% survive for at least 10 years. There is almost no difference in survival
rates across the four starting years in spite of the fact that they encompass varied
periods of economic growth.
Survival cannot be equated to success, nor does nonsurvival mean failure. In
fact, in a Small Business Administration (SBA) study based on data compiled
by the Census Bureau, one third of the entrepreneurs of businesses that did
not survive reported that they considered the venture a success.4 Among other
possibilities of successful closure, nonsurviving businesses may have been established to take advantage of transitory opportunities, may have been closed
in one location and reopened in another, or may have been acquired. This would
imply a “failure” rate significantly lower than suggested by the nonsurvival data
in Figure 1.1.
Although survival rates are consistent over time, the pace of business creation
and termination can vary significantly with economic conditions. Figure 1.2
shows data on private sector establishment births and deaths between 2006 and
2017. Over the entire period, there were approximately 9.0 million firm births and
8.5 million deaths, for a net creation of about 500,000 additional establishments.
Looking at subperiods reveals a different story. In the two years leading up to
the recession of 2008, there was a net increase of approximately 175,000 new
12
Chapter One
Fi g u r e 1 . 2
260
Private sector
establishment births
and deaths
240
230
220
210
200
190
180
Births
Deaths
170
March 2006
August 2006
January 2007
June 2007
November 2007
April 2008
September 2008
February 2009
July 2009
December 2009
May 2010
October 2010
March 2011
August 2011
January 2012
June 2012
November 2012
April 2013
September 2013
February 2014
July 2014
December 2014
May 2015
October 2015
March 2016
August 2016
January 2017
June 2017
The figure shows the number in thousands of births
and deaths of establishments between 2006 and
2017.
Number of firms, thousands
source: https://​w ww​.bls
​.gov/​news​.release/​c ewbd
​.t08​.htm.
250
businesses. For the following two years, 2008 and 2009, deaths exceeded births
by 212,000. As the economy began to pick up steam, births again surpassed
deaths. For the last eight quarters of available data, there was a net creation of
over 200,000 new establishments.
The dynamism of the new venture landscape is expected. Entrepreneurs constantly come up with new and innovative ideas, but the pursuit and successful
execution of the ideas is fraught with uncertainty. In addition, as firms evolve,
decisions to merge, sell, or abandon the venture are a normal part of any evolving economy and, in fact, are necessary if assets are going to be consistently put
to highest and best use.5
Types of Entrepreneurship
Replicative versus innovative. There is a useful distinction between
“replicative” and “innovative” entrepreneurship. Schumpeter wrote extensively
about innovative entrepreneurs, who “act as destabilizing influences triggering
‘creative destruction’—the simultaneous creation of new industries through
innovation and elimination of sectors of prior economies.” Innovative entrepreneurship has the potential to add huge value to economies and is often highrisk/high-return. Amazon​.com, Netflix, Uber, and Airbnb were all founded by
innovative entrepreneurs who challenged prevailing business models. William
Introduction 13
Baumol, a leading researcher in the area, calls them “bold and imaginative
deviators from established business patterns and practices” (2002). Innovative
entrepreneurship is the type most associated with Silicon Valley.
Replicative entrepreneurs, on the other hand, function as efficient coordinators of resources. They start and maintain businesses that mimic predecessors.
As population grows, the economy must provide more goods and services. Economies need more grocery stores, home improvement stores, dry cleaners, and
donut shops. Many of these needs are filled by replicative entrepreneurs. Replicative businesses often stay small and don’t export their products or services
outside the geographic area they serve. Many communities and local economies
try to encourage this type of entrepreneurship and do quite well, as indicated by
the numbers of new businesses started and people employed by them.
Opportunity-based versus necessity-based. A related distinction is
between “opportunity-based” and “necessity-based” entrepreneurship, terms
coined by the Global Entrepreneurship Monitor (GEM) consortium.6 Innovative entrepreneurship is virtually all opportunity based, whereas replicative
entrepreneurship is divided between opportunity and necessity. Necessitybased entrepreneurship represents people driven to entrepreneurship by lack
of alternatives. Necessity-based ventures may be simple businesses, including
microbusinesses that require negligible capital, have no employees other than
the entrepreneur, and produce near-subsistence-level earnings.
Figure 1.3 shows results of the 2017–2018 GEM survey, which assesses the
overall level of entrepreneurial activity and the motivation behind it by country.
Because opportunities vary depending on economic circumstances, perceptions of what constitutes opportunity-based entrepreneurship may vary across
countries. There are some overall trends in Figure 1.3. The highest overall
levels of engagement in entrepreneurship generally are found in countries
that are less well-developed, such as some countries in Latin America and
Africa. These countries also tend to report relatively high levels of necessitydriven entrepreneurship and are likely to view basic replicative activity as
opportunity-driven. In contrast, many European countries and other countries
that have focused economic development around large well-established businesses report low levels of total entrepreneurial activity and also low levels of
necessity-based entrepreneurship. Countries such as the U.K., Ireland, and
Luxembourg have overall rates of entrepreneurial activity that are close to the
average across all reporting countries. In these countries, entrepreneurship
is largely opportunity-driven and the levels of necessity-based entrepreneurship are low.
14
Chapter One
Fi g u r e 1 . 3
30
Entrepreneurial
involvement and
motivation by country
Based on data
from Global Entrepreneurship Monitor, 2017–2018;
available at http://​w ww
​.gemconsortium​.org/​d ata.
Necessity
25
Percentage of working-age population
20
15
10
5
0
Ecuador
Guatemala
Peru
Lebanon
Chile
Vietnam
Madagascar
Malaysia
Thailand
Brazil
Estonia
Canada
Colombia
Panama
Uruguay
Latvia
Mexico
United States
Egypt
Iran
Korea
Israel
Australia
Slovakia
Saudi Arabia
Kazakhstan
South Africa
Puerto Rico
China
Netherlands
India
Luxembourg
United Arab Emirates
Croatia
Ireland
Poland
Morocco
Taiwan
Switzerland
United Kingdom
Indonesia
Qatar
Cyprus
Sweden
Slovenia
Spain
Argentina
Germany
Greece
Japan
Italy
Bosnia and Herzegovina
France
Bulgaria
The figure shows the
percentage of a country’s workforce that is
involved in entrepreneurial
ventures. The workforce
is defined as the working
population between ages
18 and 64. Entrepreneurial
involvement is classified
as opportunity based or
necessity based. Data are
compiled by GEM consortium members through
surveys of their local
economies.
Opportunity
Unknown
source:
Figure 1.3 shows clearly that entrepreneurial activity occurs everywhere,
including once-closed economies like China and some of the world’s leastdeveloped countries. Competition for ideas and for financing has increased
dramatically in recent decades. Several developments have contributed to the
global growth of entrepreneurship. The personal computer, the wireless phone,
and the Internet allow investors to reach markets that were inaccessible a few
years ago. The importance of entrepreneurship in creating jobs and dynamic
economies is now recognized by leading international entities like the European
Union (EU), the United Nations (UN), and the World Bank, all of which sponsor initiatives and provide financial support for entrepreneurship. Governments
worldwide are enacting policies and providing financial subsidies that they hope
will encourage entrepreneurship.
The World Bank ranks countries according to the supportiveness of their
environments for starting and doing business, and this information provides
additional context for the level and type of entrepreneurial activity. Figure 1.4
shows the most recent rankings of large-population countries. Ease of doing
business (plotted in the figure) is based on 10 factors, including availability of
financing, legal environment, and availability of employees. Ease of starting a
business is one of the 10 factors; we plot it separately in Figure 1.4 because it is
the factor most closely related to stand-alone entrepreneurial activity.
Introduction 15
80
Ease of doing business
Ease of starting a business
70
Rank —Highest number is best (Max = 74)
60
50
40
30
20
0
New Zealand
Canada
Hong Kong SAR, China
Singapore
Australia
Ireland
Korea, Rep.
Estonia
Sweden
United Kingdom
Taiwan, China
Belgium
Norway
Netherlands
France
Finland
Lithuania
Russian Federation
Oman
Serbia
Denmark
Thailand
Israel
Greece
Panama
Portugal
United States
United Arab Emirates
Ukraine
Iceland
Uruguay
Chile
Italy
Luxembourg
Switzerland
Hungary
Turkey
Czech Republic
Spain
Croatia
Qatar
Mexico
China
Colombia
Iran, Islamic Rep.
Zambia
Egypt, Arab Rep.
Jordan
Japan
Malaysia
Germany
Peru
Kenya
Austria
Poland
Vietnam
Costa Rica
Saudi Arabia
South Africa
El Salvador
Pakistan
Iraq
India
Argentina
Yemen, Rep.
Uganda
Comoros
Ecuador
West Bank and Gaza
Brazil
Zimbabwe
Somalia
Haiti
Venezuela, RB
10
Fi g u r e 1 . 4
Global difference in supportiveness for starting and doing business
source:
http://​w ww​.doingbusiness​.org/​rankings.
The figure shows country ranks (highest being best) for ease of starting a business and ease of doing business based on ten equal-weighted
factors that are assessed by the World Bank based on midyear 2017 data.
Countries that rank high on both dimensions, such as the first 4 in the figure,
tend to be those that can support high-growth start-ups. Those that are high for
ease of doing business but low for ease of starting a business, such as Germany,
Japan, and Mexico, tend to be countries where high-growth entrepreneurial
activity is conducted mainly through established corporations and business
groups. Those where ease of starting a business is high but ease of doing business is low, such as Russia, Ukraine, and Iran, tend to be relatively unregulated
environments with weak infrastructures. Those where both are low, such as
Haiti, Somalia, and Venezuela, tend to be turbulent environments with political
unrest and dictatorships or militaristic factions.
Corporate Venturing
The international evidence highlights the point that in certain countries
much entrepreneurial activity occurs within established businesses or in
strategic partnerships among established businesses. Corporate venturing is
16
Chapter One
­ articularly common for ventures that require large and complex research
p
teams and use of generic testing equipment and where development times are
long. For example, large pharmaceutical firms almost exclusively pursue pharmaceutical innovation. Similarly, introductions of new large commercial aircraft are normally pursued as strategic partnerships involving airframe manufacturers, engine manufacturers, and avionics manufacturers.
Corporate venturing, while easier in some respects, is more challenging in
others. One obvious difficulty is that of designing appropriate incentives to motivate entrepreneurial effort in large organizations without creating perceived
equity imbalances among employees. Sometimes the venturing activities are
so central to the core business that they are pursued as part of the company’s
overall research and development (R&D) effort. Other times, corporations create separate entities that are wholly owned subsidiaries and operate much like
independent VC firms.
Social Venturing
There is considerable confusion surrounding the term “social venturing.”
Most entrepreneurs and most VC and angel investors believe that what they
are doing is beneficial to society—that they are “doing good” in the pursuit
of “doing well.” Further, some aspects of the endeavors of many for-profit enterprises are explicitly viewed as intended to have some sort of social impact.
Patagonia, for example, designates a small fraction of its revenues for environmental charities and promotes environmentally friendly clothing manufacturing, and TOMS Shoes provides free shoes to people living in poverty. Both of
these companies are for-profit enterprises that have been certified as B Corporations by B Lab, which is a not-for-profit organization that was established in
the U.S. to promote socially responsible business practices to for-profit corporations. The B Corporation designation has no separate legal standing in the
U.S., but is recognized in most states.
The B Corporation designation indicates that a venture adheres to certain
principles established by B Lab but does not indicate that the venture is forgoing returns to investors for the pursuit of social objectives. This is one reason
that the term “social venturing” can be confusing. For example, a for-profit
business might decide to emphasize a social orientation or to seek B Corporation status because doing so appeals to a group of potential customers that the
venture hopes to attract.
The term “not-for-profit” can be similarly confusing. Narrowly, it means only
that the venture does not have stockholders and is exempt from paying income
taxes. Such a venture may still be seeking to maximize a residual of revenue,
donations, or other inflows, minus expenses.
Introduction 17
Because of the potential confusion, we think of social venturing as involving entrepreneurial efforts where financial returns are traded off against social
objectives—a true “double bottom line.” The distinguishing characteristic of a
social venture versus a commercial one is the elevation of the objective to address social issues to a point where financial returns are sacrificed for a social
objective. Profits are typically used partly to sustain the organization’s ability
to provide a social benefit.
Because assessing these trade-offs is often a matter of judgment, it can be
unclear whether an enterprise is a true social venture or an effective marketer.
An example is TOMS, a for-profit company founded by serial entrepreneur
Blake Mycoskie in 2006 with $500,000 of proceeds from selling his online driver
education business. TOMS (through TOMS Roasting Co.) began selling shoes
using a “one-for-one” model, which meant donating a pair to children in poor
countries for every pair sold. By 2012, the company had donated over two million
pairs of shoes around the world. As of 2013, it had established manufacturing
facilities in Haiti, Kenya, India, and Ethiopia, providing employment but also
low-cost manufacturing. In 2014 TOMS expanded its “one-for-one” model to
coffee, where each purchase results in a one-week supply of clean water for a
person in need. It is clear that the enterprise is not purely social as later that year,
Bain Capital acquired 50% of TOMS, of which Mycoskie was the sole owner,
at a reported valuation of $625 million.7
The evolution of TOMS from opportunity identification to exit by acquisition parallels many successful entrepreneurial ventures. And the estimated $300
million that the founder received in the buyout shows that the financial returns
to incorporating a social objective can be substantial. As with many successful
innovations, the one-for-one business model has proliferated, with at least 40
companies adopting a similar approach as of 2016.8
Data suggest that social venturing will grow in importance. On the funding
side, a large number of foundations and philanthropists are using more traditional for-profit benchmarks and analyses to assess the impact of their charitable giving. Finally, many established VC firms are creating funds specifically
targeted to social impact ventures.
1.3 Hypothesis-Driven Entrepreneurship
The previous discussion highlights that entrepreneurial activity is driven by
many factors and takes many forms around the world. In this book, we include examples of replicative and innovative types of entrepreneurship but
focus primarily on innovative entrepreneurship. New ventures that involve
18
Chapter One
innovations with uncertain potential pose significant challenges for strategy,
forecasting, valuation, contracting, and financing choices—greater challenges
than those faced by ventures that build on established business models and
ventures that can be financed with small investments, traditional borrowing,
and operating cash flows. By focusing on innovative entrepreneurship and
high growth, we tackle the most challenging issues that entrepreneurs and investors may confront. The differences, however, are of degree rather than substance. All entrepreneurs and investors can benefit from better understanding
of concepts like milestones, staging, and real options. These ideas fit well with
the hypothesis-driven approach to evaluating and advancing entrepreneurial
opportunities.9
Formulating, testing, and evaluating hypotheses is at the core of the scientific
method. In recent years this approach to discovery has been successfully applied
to new ventures. If done correctly and if the results are interpreted objectively,
the approach can add tremendous value. Just as with any type of research project, the key is asking the right questions, formulating hypotheses based on those
questions, systematically testing the hypotheses, and making evidence-based
decisions to proceed, abandon, or pivot by modifying the hypothesis.
As illustrated shortly, this modern approach to entrepreneurship breaks a
business model into a set of hypotheses: design the method to test them, collect
relevant data, and evaluate the results. Generally these hypotheses are sequenced
in an economically sound way that is designed to quickly validate missioncritical assumptions and limit the resources that are devoted to ventures that
may sound good but are destined to fail.
You might, for example, first test the hypothesis that you can build a prototype with basic functionality in one month. This could make sense as an
initial hypothesis if the potential for success depends critically on your ability
to quickly develop a working prototype and if not being able to do so would be
likely to leave the door open to a competitor who could execute more quickly.
The question should be one that is falsifiable and quantifiable—either you find
support for your hypothesis or you do not. The data must be of sufficient quality that you have confidence in the results and you must maintain objectivity
when interpreting them.
Implementation of Hypothesis-Driven Entrepreneurship
To illustrate the value of hypothesis-driven entrepreneurship, consider the
early stages of Dropbox, a downloadable app that allows users to share, sync,
and store files using the Internet rather than their hard drives. At the time of
its introduction, Dropbox had several advantages over cloud storage competi-
Introduction 19
tors, including speed and simplicity. Users could access their files through office computers, laptop PCs, and mobile devices. As with most new ventures,
there was considerable uncertainty surrounding the early stages of the company. Several crucial questions come to mind: Could a working prototype
be developed in a reasonable amount of time? Who are the customers for the
product—businesses, individuals, or both? What features would customers
find most desirable? What would be the most effective way to attract customers? How much would it cost to acquire and retain a customer? How would
the product make money? Each of these can be considered a research question
where the underlying uncertainty can be reduced by hypothesis testing.
As a first step, the Dropbox founders created a prototype for Windows PC
users. Next, they needed to test the hypothesis that Dropbox would appeal to
potential users. Rather than simply launching the product in an expensive rollout and taking the chance that it might underperform in some crucial way, they
posted a brief product demo on the Hacker News website to solicit opinions and
to identify a set of beta users for subsequent tests. After validating the product in
this way, they moved on to the question of marketing. They were able to quickly
rule out their initial hypothesis that they could benefit by a traditional B2B direct
personal selling approach, and instead they turned to an innovative grassroots
approach of marketing directly to individual users. The approach of marketing
to consumers effectively turned consumers into marketing agents for the business
applications of Dropbox. This process of testing and modifying and retesting
allowed them to improve the prototype and successfully launch the product.
The sequential approach to learning also creates opportunities to “stage” the
venture, to set out observable milestones, and possibly to raise financing that
is associated with achieving specific milestones rather than trying to fully fund
the venture at the outset.
Measuring Progress with Milestones
Hypothesis-driven entrepreneurship is closely linked to the notion of measuring new venture progress in terms of the attainment of important milestones.
Rather than thinking of staging in terms of time intervals such as months or
years, orienting around milestones is more useful. At early stages of venture
development the uncertainty of future free cash flows (i.e., cash flows available
to investors) is extremely high. As a result, raising capital at a very early stage
is difficult and expensive for ventures that ultimately are successful. Relying
on milestones enables the parties to engage in a structured program of hypothesis testing and to postpone financial commitments until they are needed. As
milestones are achieved, uncertainty is reduced, so that ­investment ­valuations
20
Chapter One
are higher and the fraction of ownership that can be retained by the entrepreneur is increased.
Each major milestone functions as a working hypothesis about the venture.
In scientific terms, achieving a particular milestone means that the hypothesized outcome was not rejected and enables the entrepreneur to move ahead
to the next. Falling short of a successful test is cause for concern but does
not necessarily mean that the venture is a failure and should be abandoned.
Understanding the reasons for failing to meet a milestone is important. If, for
example, an entrepreneur believes that a prototype of the idea can be completed
in 6 months but instead it takes 9, the delay indicates that some aspects of the
venture need to be reexamined. There are several possible reasons for failing
to achieve a milestone on time. Perhaps the entrepreneur underestimated the
technical difficulties; alternatively, the entrepreneur may have mismanaged the
project. The distinction is critical to deciding how, or even whether, the venture
should continue. In any case, milestones enable the entrepreneur and investors
to sharpen their expectations about ultimate success or failure.
Milestones also help identify ways to enhance the expected benefits of the
project. Appropriate milestones differ with circumstances. For some kinds of
ventures, the first significant milestone is concept testing, such as what the Dropbox team did before actually rolling out the product. The objective of concept
testing is to do enough fieldwork to determine whether a market opportunity
exists and whether there is enough upside potential to warrant continued investment. Reaching this milestone helps resolve some of the uncertainty and contributes to convergence of expectations between the entrepreneur and investors.
A second milestone might be the completion of a prototype. Normally, a prototype is an early-stage working model of the envisioned product—for example,
a beta website for an Internet company. Completing the prototype forces the
entrepreneur to anticipate and encounter a variety of issues related to product
development. By doing so, the entrepreneur can gain increased understanding
of technological bottlenecks, manufacturing costs, and materials availability. In
addition to resolving uncertainties about the product, reaching this milestone
may help establish a first-mover advantage for the entrepreneur so that it is easier
to control development of the idea. It also provides early tangible evidence of
the entrepreneur’s managerial ability.
The Dropbox founders, as noted, actually pursued these two milestones in the
opposite order—they first developed the prototype, then used the prototype in a
video to test the concept that helped it identify beta testers and preempt possible
competitors. The appropriate sequencing of milestone objectives is situationspecific. If a working prototype would be time-consuming and expensive, it could
Introduction 21
make sense to devote more initial effort to assessing market potential before
committing resources to prototype development. Conversely, if a prototype can
be developed quickly and inexpensively, the quality of information about the
market could be enhanced by using the prototype to test the concept.
The nature of the venture determines which milestones afford the most potential to assess progress and to facilitate the venture’s development. The following
are some additional examples of milestones:
Completing a minimally viable product (MVP). An MVP is one with
enough features to appeal to beta testers/early adopters. Dropbox used the
feedback from its demonstration video to design and limit the features of its
launch product.
Making a key hire. A key hire is a person who can help build the management team, bring essential expertise, and potentially signal to rivals, customers,
and financial backers that the company is on track for success. In 2007, Facebook
made a major stride toward becoming an economically viable company when
it was able to hire Sheryl Sandberg as chief operating officer (COO), taking her
away from a well-paying position at Google. Facebook became profitable under
her leadership while other social media sites languished, had to pivot, or failed.
Securing the commitment of an essential supplier. When Amazon​
.com launched in 1994 with an initial focus on book distribution, it did so with
almost no book inventory and no significant facilities for storing or shipping
books. Before it could do so, it needed commitments from major book wholesale distributors, especially Ingram, the gorilla of book distribution, to supply
Amazon​.com on terms comparable to those it offered to other retail distributors.
Attraction of first independent financing. Oculus VR is a company
specializing in virtual reality hardware and software. It received its first independent funding in 2012 from a Kickstarter crowdfunding round that raised
$2.4 million, almost 10 times the $250,000 objective for the campaign. The contributors did not receive financial claims, but rather, if the contribution was
large enough, they were promised a “dev-kit” version of the product. However,
even though there was not financial commitment from investors, the round did
demonstrate the intensity of demand for virtual reality, and the company soon
attracted an independent VC funding round of $75 million. In 2014, Oculus VR
was acquired by Facebook for $2.3 billion.
22
Chapter One
Production start-up. Following a noteworthy failure by General Motors
(GM) to introduce an electric automobile, Tesla was launched in 2003 with the
intent of developing a commercially viable electric automobile. After year of
development efforts, in 2009, Tesla initiated production of the Roadster, the
first production automobile to use lithium-ion battery cells and the first electric
vehicle with a range greater than 200 miles per charge.
Completing a bellwether sale. Snapchat, an online image-sharing application, was launched on a small scale in 2011 and grew to many users over
the next few years but with no specific plan for developing a revenue stream. It
began to monetize the network in 2014, with a sponsored story from Samsung.
By the time of the Snap, Inc. initial public offering (IPO) in 2017, additional key
marketers included Starbucks, bareMinerals, Trolli Candy, 20th Century Fox,
and Under Armour.
First competitive reaction. One indication that a business model is working is when an important competitor decides that the upstart new venture is a
significant threat. Amazon​.com, as an Internet-based book distributor attempting to compete against brick-and-mortar bookstores, is a particularly good
example. At the time of Amazon’s launch, it was not clear whether online book
distribution would displace brick-and-mortar, function as a weak substitute, or
even complement physical distribution. When Amazon began to significantly
draw sales from brick-and-mortar stores, Barnes & Noble, the largest retail book
chain, responded with a combination of introducing its own online service and
discounting book prices. The competitor’s response is evidence that the potential
for success is substantial, and, in the case of Amazon, created opportunities to
test additional hypotheses related to repeat purchase potential and the price
sensitivity of Amazon users.
First pivot, redesign, or redirection. The company now named Groupon
was initially launched as The Point in 2007 to implement the idea of its chief
executive officer (CEO) and founder, Andrew Mason, of using social media
to organize people into collective groups to pursue specific causes or goals.
The original idea achieved only modest success. Responding to an opportunity
identified by a group of the users, another of the founders, Eric Lefkofsky, encouraged the company to pivot and focus on group buying. The company was
renamed Groupon. Groupon’s pivot is an example of how a venture can adjust
when its original hypothesized value proposition is rejected but, in the process,
a different focus is identified.
Introduction 23
Relationship Between Hypothesis Testing and Real Options
When we analyze new ventures in a scientific manner so that key business
model hypotheses are formulated and tested, we create opportunities for the
entrepreneur to learn and to either validate the assumptions or refute them.
Each of the examples of milestones presented earlier offered the entrepreneur
a way to learn about the venture and its market and to modify (or pivot) accordingly, or to abandon. Hypothesis-driven entrepreneurship, in effect, creates real options, or rights to undertake decisions in the future about an underlying nonfinancial asset. Because they limit downside risk and increase upside
potential, real options are valuable. In Chapters 5 and 6 we discuss more formal modeling of real options and approaches to how they can be valued.
1.4 The Stages of New Venture Development
Although there is no typical life cycle for a new venture, firms do go through
stages of development: they come into existence; they may undergo episodes
of rapid growth, slow growth, or stagnation; and they may fail. But, after
coming into existence, a venture can go through stages in any order and
can go through one or more stages several times. A firm can even fail more
than once.
With this caveat, Figure 1.5 depicts the stages of new venture development
from inception of the opportunity through its development and eventual harvesting—the stage where the investors and maybe the entrepreneur can successfully
unwind their positions. To avoid overgeneralizing, the figure represents a hightech, single-product venture for a product that gains rapid market acceptance
after being introduced. The diagram shows the various stages of development,
identifies the types of decisions and actions that typically are made at each stage,
indicates the types of real options associated with each stage, and in the final
row describes the stage in general terms.
As the diagram implies, once an opportunity is recognized, some nonfinancial
activities can begin immediately. Filing for patents, searching for personnel, assessing the size of the market, and identifying actual and potential competitors
are examples of activities that need to be performed regardless of any decisions
about development strategy.
From a decision-making standpoint, the entrepreneur’s first action is to generate a short list of alternative venture strategies. For some kinds of ventures,
only one such strategy may be feasible. For many others, it should be possible
Stages
Actions
Real
Options
Description
Opportunity
Development
Start-up
Early growth
Expansion
Exit
Obtain seed financing
Assess opportunity
Assess strategic alternatives
Determine organizational structure
Determine organizational form
Prepare business plan/model
Obtain R&D financing
Build research team
Conduct R&D activities, e.g.:
Secure patent
Develop prototype
Build website
Test market/market research
Assess/update business model
Obtain start-up financing
Acquire facilities and equipment
Initiate production
Build starting inventory
Build sales and marketing team
Initiate revenue generation
Assess/update business model
Obtain early-growth financing
Work toward break even revenue
Expand team as needed
Expand facilities as needed
Assess/update business model
Obtain expansion financing
Work toward proven viability
Expand team as needed
Expand facilities as needed
Build track record for harvest
Assess/update business model
Obtain continuing financing:
IPO
Acquisition
Buyout
Early investors harvest
Assess/update business model
Continue to next stage
Modify concept
Abandon
Continue to next stage
Extend stage/financing
Modify R&D strategy
Abandon
Continue to next stage
Modify production/financing
Modify marketing/financing
Abandon
Continue to next stage
Extend stage/financing
Abandon
Continue to next stage
Extend stage/financing
Choose form of exit
All activities through preparation of
business plan and before incurring
significant expense.
All research and development
activity that must be completed
before revenue generation can
commence.
All activities related to start of
production and marketing and
initiation of revenue-generating
activities.
All activities during the period
before the venture reaches a level
of sales sufficient for cash-flow
breakeven.
All activities during the period
after breakeven and before
sustainable viability is
established.
All activities related to
establishing continuing financing
and enabling early investors to
harvest.
Fi g u r e 1 . 5
Stages of new venture development
The figure shows the standard progression of development of a new venture from opportunity identification though stages culminating in exit. At each stage, the figure indicates the kinds of actions that normally are associated with the stage, as well as some of the real options the entrepreneur is likely to have.
Introduction 25
to represent the array of sensible alternatives in terms of a small number of
discrete strategic scenarios.
At each stage, the entrepreneur needs to identify a limited set of real options for implementation. These real options are choices such as continuing to
the next stage; waiting until the potential of the opportunity becomes clearer;
abandoning the venture altogether; or modifying the R&D strategy, which
typically involves revising the business model and may affect financing as well.
This analysis is done for one real option structure at a time, matched with each
financing structure that would make sense for that strategy. The real option
structure and financing structure are interdependent. Thus, the entrepreneur
needs to search for the most valuable financing structure to complement a
particular real option structure.
Performance evaluation/learning is a continuous process, and is reflected
in Figure 1.5 as “assess/update business model.” Reassessment of the venture’s
progress occurs throughout its development. If implementation and financing
results align generally with expectations, then all that may be necessary is to periodically refine and update the business plan. If not, then the strategy is suspect
and the entrepreneur should revisit the strategic plan, considering alternatives
ranging from major refocusing to abandonment.
As indicated in Figure 1.5, the final stage for a successful venture is exit, which
allows investors and the entrepreneur to harvest their investments.
1.5 Financial Performance and Stages of
New Venture Development
Figure 1.6 shows how financial performance can be used to delineate the
stages of new venture development for a typical venture that supplies a physical product and makes a positive investment in net working capital. The figure
reflects the same stages shown in Figure 1.5 after the opportunity has been
identified and a decision to proceed has been made. The horizontal axis measures time; the vertical axis, dollars. Time 0 represents initiation of sales. The
three curves in the figure are revenue, net income, and cash flow available to
investors. Revenue and net income are measured in the conventional ways for
a firm that uses accrual-based accounting. Cash flow available to investors is
defined in the figure as cash flow from operations after tax and before interest
expense, less the net new investments in working capital and fixed assets that
are needed to support the revenue levels and prepare for future growth. If cash
Chapter One
Dollars
26
0
Revenue
Net income
Cash flow
0
Development
Start-up
Time
Early Growth
Expansion
Exit
Fi g u r e 1 . 6
Financial performance and stages of new venture development
The figure reflects five stages that are typical of new venture development. During the development stage, the venture generates no revenues
and both net income and cash flow are negative. Start-up begins when the firm acquires the facilities, equipment, and employees required
to produce the product. During early growth, revenue is growing, but both net income and cash flow available to investors are negative.
Expansion is the last stage during which external financing is required. During exit, the rate of growth declines to the point where cash flow
available to investors is positive.
flow available to investors is negative, the venture must finance the shortfall. If
it is positive, the firm can distribute the surplus to investors, including interest
payments on debt, debt redemption, dividends, and share repurchase, or it can
pursue other investment opportunities.
Development
This stage encompasses all activities leading up to the start of revenue generation. During this stage, the entrepreneur develops prototypes, performs beta
testing, and invests in any capital equipment needed for product development.
The venture generates no revenues during this period. Net income is negative
and may become increasingly negative as the number of people and other resources involved in product development increases. Cash flow initially is very
negative as the firm invests in capital equipment needed for development. Under existing U.S. accounting conventions, firms must record the depreciation
expense of equipment over time. As a result, net income is not as negative as
cash flow.
Introduction 27
Start-Up
The demarcation between development and start-up is when the firm acquires
the facilities and equipment required to produce the product and invests in the
employees and marketing that support production. The decline in cash flow
preceding revenue generation reflects investment in production equipment, facilities, and net working capital. This pattern would be somewhat different for
a venture that does not supply a physical product, such as one that receives
payment in advance for software or a subscription-type service or one that
could operate with negative net working capital (e.g., if accounts payable is
higher than accounts receivable). For such ventures, cash flow could be higher
than net income.
Early Growth
Revenue is growing (possibly rapidly) in the third stage. Both net income and
cash flow available to investors are negative. Cash flow of a conventional venture exceeds net income, essentially because periodic depreciation expenses
are larger than the increase of investment in working capital and fixed assets needed to support the growth of revenue. This is referred to as the earlygrowth stage because, although growth may be rapid in percentage terms,
the base from which we calculate revenue growth is low. When we consider
the forms of financing that are available to ventures at different development
stages, it will be useful to think of the early-growth stage as one where, if the
venture were to stop growing and to spend and invest accordingly, operating
income would still be negative and the venture would not be viable as a going
concern.
Expansion
There is no clear line of demarcation between the early-growth stage and expansion. Many new ventures experience growth in the early stage but fizzle
out before they achieve sales that are sufficient to sustain the business. Hence,
entrepreneurs can benefit by being cognizant of signs that the venture has
reached the expansion stage. As shown in Figure 1.6, net revenues are increasing but are at a level where, if growth were to stop and the revenue and expenditures were to be adjusted to the no-growth scenario, the venture would have
positive operating income and be a viable entity. For a product-based business, rapid sales growth during this stage puts heavy demands on the entrepreneur to locate the financing needed to sustain the corresponding growth of
28
Chapter One
working capital and fixed assets. For subscription businesses and other businesses with negative net working capital, rapid growth can provide positive
cash flow during expansion.
Exit
The exit stage requires that there be a viable market for the company’s shares
either via an acquisition or merger or via an IPO. This implies that investors,
either buying in the IPO or making the acquisition offer, believe that insiders
(the entrepreneur and existing investors) are representing the company in an
accurate way so that outsiders are not skeptical of the reason insiders want to
divest (e.g., selling shares because they believe that future performance will be
poor). This does not imply that all uncertainty is reduced or even that the firm
is profitable (in fact, many high-tech and biotech firms are acquired or go public with negative net income), but it does suggest that the growth rate and track
record of the firm have reached a point where future growth can be forecasted.
As long as outsiders do not believe they are at a serious information disadvantage, successful exit, even from very risky ventures, is feasible. Generally, as
Figure 1.6 indicates, during the exit stage, the venture’s rate of growth declines
to the point where cash flow available to investors is positive. The venture can
provide returns to debt and equity investors without needing to increase outside financing. This is an obvious time for investors to harvest and realize the
returns on their investments.
1.6 The New Venture Business Model
Formal written business plans are not as common as they once were. Instead,
entrepreneurs are encouraged by investors to think of the business model as a
means to identify the value proposition, explain why the founders are the ones
who should pursue the venture, clarify the assumptions underlying their business strategy, and lay out expectations of what is achievable if the assumptions
are validated. Because of the importance of the test-learn-pivot approach, the
modern business model is dynamic and flexible, evolving over time as the results of the experimentation and testing unfold and the venture progresses.
It is easier to attract investors with a business model that sets out explicit
financial projections and milestones than with one that is vague. By making the
model specific and by including documentation and support, the entrepreneur
invites oversight and evaluation and provides a mechanism that makes it easier
for investors to reevaluate their investments as the venture progresses. Investors
Introduction 29
can use the milestones and financial projections to test the entrepreneur’s beliefs.
The model can also facilitate reaching an initial investment agreement if there
are contractual terms that tie the entrepreneur’s ownership share or control to
attainment of milestones such as growth targets or landing a critical customer.
New venture business plans serve multiple purposes. In contrast, for an established enterprise, the business plan may be strictly an internal document.
Decisions by outside investors, such as the decision to continue financing an
established enterprise, are likely to be based on experience with management,
track record of the business, and current financial health. Investors in established
ventures are not likely to depend significantly on review of the company’s projections. However, for a start-up, the reverse is true. Consequently, the business plan
for a start-up must be prepared with an expectation that it will be scrutinized
by outsiders who may not be very familiar with the business.
Many things that are unnecessary to include in the plan of an established
enterprise are essential in a plan that outside investors expect to see and on which
they will rely. The common features of a business plan include identification
of the problem to be solved, the “solution” or value proposition, the market
potential, the competition, the team, financial metrics, accomplishments to
date, a time line, and (if appropriate) the financing “ask” and explanation of
how the funds will be used.
It sometimes is appropriate to have multiple (but consistent) plans, with each
plan containing information that is tailored for the specific information needs
of a particular audience. Plans can be circulated to potential customers, investors, partners, and founders.
What Makes a Business Plan Convincing?
Some entrepreneurs prepare and circulate business plans in an effort to test
investor interest in an idea. In such a case, the entrepreneur operates with the
perception that the critical success factor for the venture is the idea, and that
investors, recognizing the merits of the idea, will come forth with funding.
Obviously, this approach—of looking for investors before committing to the
project—reduces risk for the entrepreneur. Unfortunately, it also reduces the
potential for obtaining financing. Credible evidence of the entrepreneur’s commitment and beliefs about the validity of projections presented in the business
model is critical to securing funding.
Credible evidence of commitment. Although a good idea is necessary,
investors are looking to the plan for evidence that the entrepreneur (along with
key members of the team) is committed to the venture. The most convincing
30
Chapter One
evidence is the investment of effort and capital that the entrepreneur has already
made and is continuing to make in the venture and for which a return cannot
be realized unless the project goes forward and is successful.
There are many ways to demonstrate such investments. The key element for
their functioning as credible commitments to the project is that they are sunk,
meaning that they are not recoverable or will not generate a return for the entrepreneur unless the entrepreneur continues to commit effort to the project.10
It is not the action per se that matters but the irreversibility of the action. The
loss of salary that comes with resignation of current employment is credible as
a signal only if the entrepreneur would have difficulty finding new employment
of equal value.11
Evidence of reputation and certification. Experienced entrepreneurs
sometimes attract investment without the need to make credible demonstrations of commitment to individual projects.12 Later in the book, we explore the
connection between past success and reputation, and we will see other ways in
which reputation is important in entrepreneurial settings.
What about the first-time entrepreneur who has yet to establish a reputation or an entrepreneur whose track record is less than perfect?13 We’ve already
seen that the entrepreneur may be able to make a credible commitment to the
venture, but without a reputation the commitment alone may not be sufficient
to attract investors.14 One solution for the first-time entrepreneur is to rely on
the reputations of others.15 Important suppliers or customers who have publicly
committed to transact with the venture may provide the level of certification that
is important for attracting investment, as can relational partners who become
involved in implementing the business plan.
1.7
Summary
Having completed this chapter, you should now have a clear sense of the important ways that entrepreneurial finance is difference from finance for a
public corporation. You should also recognize what it means to be an entrepreneur and that entrepreneurship exists in a variety of forms, including endeavors ranging from replicative ventures that are intended to remain small to
visionary enterprises that are intended to disrupt the status quo and change
the ways we do things. You will have seen that the failure rates of new ventures
are quite high, but also that even some ventures that last only a few years were
Introduction 31
designed to capitalize on transitory opportunities. While our main focus is
on the difficult challenges that face stand-alone, high-tech ventures with the
potential for rapid growth, we also note that much entrepreneurial activity occurs within established firms and that some entrepreneurial activity is driven
partly by the desire of the entrepreneur to pursue a social agenda.
Perhaps the most important lesson from this introductory chapter is that good
entrepreneurship has an intrinsically scientific nature that is designed to help
steer resources to the places where they can be most valuable. Entrepreneurial
finance at the early stages in the life of a venture is a progression of successively
setting out and testing hypotheses about the merits of the idea and the ability of
the entrepreneur to bring the idea to fruition. Hypothesis-driven entrepreneurship is an approach that can quickly and inexpensively identify ideas that are
not worth pursuing and can continue to nurture and refine those that are on a
path toward success.
This chapter develops some important terminology and concepts that are
related to the maturation of successful ventures. As a venture matures, it progresses through a number of developmental stages. At each stage, the kinds of
hypotheses that are being explored change, and the kinds of resources that will
be available to support the venture change.
The chapter concludes with a brief discussion of how business planning is
different for entrepreneurial ventures than for established firms.
Review Questions
1. What are the key distinctions between corporate finance and entrepreneurial finance?
2. What, according to this chapter, is an “entrepreneur”?
3. What makes a venture a social venture?
4. How is the distinction between innovative and replicative entrepreneurship related to the distinction between opportunity-based and necessitybased entrepreneurship?
5. What is hypothesis-driven entrepreneurship and how does it relate to
real options? What are some examples of real options?
6. What are the main stages of new venture development? What kinds of
things go on during the different stages?
7. How are the stages of development related to the financial performance
of the venture? How do the stages relate to availability of financing?
8. Explain how milestones are related to development stages and to real
options.
32
Chapter One
9. What are some important differences between new venture business
plans and business plans of established firms?
10. How can entrepreneurs make their business plans more convincing?
Notes
1. A “project” is the fundamental building block of corporate finance. In
basic courses it is treated a black box where cash flow forecasts are unbiased
and the task for the manager is to assess risk and choose the right discount
rate when determining net present value. In more advanced courses, projects
are analyzed with techniques such as sensitivity analysis, simulation, and other
computer-assisted techniques that look more objectively at the uncertainty associated with cash flows. See, for example, Brealey, Myers, and Allen (2017).
2. Kirzner (1979) discusses early views of entrepreneurship.
3. Drucker (1985), p. 20. Bull and Willard (1994) survey definitions of entrepreneurship and conclude that most are permutations of, or derivative of,
Schumpeter’s view.
4. See Headd (2003).
5. For insights on returns to entrepreneurship, see Hall and Woodward
(2010), who study returns to an important class of entrepreneurs. They find
that the typical venture-capital-backed entrepreneur received an average of
$5.8 million in exit cash. Almost three quarters of entrepreneurs receive nothing
at exit and a few receive over a billion dollars. See also Moskowitz and VissingJorgenson (2002), who find evidence of a “private equity premium puzzle” in
that returns to entrepreneurs who invest in their own ventures would have done
just as well if they had invested in public equity instead. However, Kartashova
(2014) examines a longer time span and finds that the private equity puzzle occurs in only some time periods. All of the authors point out the riskiness of
entrepreneurship and the nondiversified position that entrepreneurs face.
6. GEM is a not-for-profit academic research consortium that makes international data on entrepreneurial activity publicly available.
7. Mycoskie used a portion of his proceeds from the sale to establish the
TOMS Social Entrepreneurship Fund, which invests in other social entrepreneurs. http://​w ww​.reuters​.com/​article/​us​-toms​-baincapital/​exclusive​-bain​
-capital​-to​-invest​-in​-shoemaker​-toms​-sources​-idUSKBN0GK1ZZ20140820.
8. https://​w ww​.inc​.com/​magazine/​201605/ ​leigh​-buchanan/​toms​-founder​
-blake​-mycoskie​-social​- entrepreneurship​.html.
9. A large number of incubators and applied courses on entrepreneurship
use this approach when guiding new venture development. Useful reference
books for practitioners include Blank and Dorf (2012) and Ries (2011).
Introduction 33
10. See Williamson (1985) and Ghemawat (1991) for seminal work on the
role of sunk investment in strategic settings.
11. Levesque and MacCrimmon (1997) report that one study (of the owners of Inc. magazine’s list of fastest-growing companies) found that, on average, the entrepreneurs kept their existing jobs for four months beyond the
founding of the new venture. They offer a theoretical model of the choice of
when to resign, based on the tolerance for continuing to work and the marginal productivity of devoting effort to the venture. They indicate that timing
is often affected by the entrepreneur’s need for cash to defray living expenses
or to help fund the venture. The evidence is generally consistent with a view
that resignations tend to occur at a point when the entrepreneur perceives the
specific opportunity to be more attractive than continued current employment
and when resignation would lend credibility to the entrepreneur’s capital­
raising efforts.
12. Gompers, Kovner, Lerner, and Scharfstein (2007) show that entrepreneurs with track records of success are more likely to succeed than firsttime entrepreneurs or those who have previously failed. They also find that
VCs are more likely to identify and invest in first-time entrepreneurs who are
likely to become serial entrepreneurs.
13. Wright, Robbie, and Ennew (1997) study VC investment decisions
and find a strong preference for investing in projects of entrepreneurs who
have played major roles in previous successful ventures.
14. For an overview on the role of reputation, see Milgrom and Roberts
(1992). Klein and Leffler (1981) provide a formal analysis of reputation formation, the role of sunk investment as a bonding mechanism, and the effect of
reputational capital on product price.
15. A model of certification, applied to new issue underwriting, is presented in Booth and Smith (1986). Hsu (2004) evaluates the certification benefit that entrepreneurs gain by accepting financing from reputable VCs. The
entrepreneurs expect to gain credibility by associating with reputable VCs,
in part through their access to networks of relationships that they otherwise
would not have. He finds that high-reputation VCs acquire start-up equity at a
10 to 14% discount relative to less reputable counterparts, implying that entrepreneurs see the relationship as value-enhancing.
References and Additional Reading
Baumol, W. 2002. The Free-Market Innovation Machine: Analyzing the Growth
Miracle of Capitalism. Princeton, NJ: Princeton University Press.
Blank, S., and B. Dorf. 2012. The Startup Owner’s Manual: a Step-by-Step
Guide for Building a Great Company. Pescaderno, CA: K&S Ranch.
34
Chapter One
Block, Z., and I. C. MacMillan. 1985. “Milestones for Successful Venture
Planning.” Harvard Business Review 63 (5): 184–​96.
Booth, J. R., and R. L. Smith. 1986. “Capital Raising, Underwriting, and the
Certification Hypothesis.” Journal of Financial Economics 15: 261–81.
Brealey, R. A., S. C. Myers, and F. Allen. 2017. Principles of Corporate Finance,
12th ed. New York: McGraw-Hill.
Bull, I., and G. Willard. 1994. “Towards a Theory of Entrepreneurship.” Journal of Business Venturing 9: 183–​95.
Chemmanur, T. J., and P. Fulghieri. 2014. “Entrepreneurial Finance and Innovation: An Introduction and Agenda for Future Research.” Review of
Financial Studies 27: 1–19.
Drucker, P. F. 1985. Innovation and Entrepreneurship: Practice and Principles.
New York: HarperCollins.
Ghemawat, P. 1991. Commitment: The Dynamics of Strategy. New York: Free
Press.
Headd, B. 2003. “Redefining Business Success: Distinguishing Between Closure and Failure.” Small Business Economics 21: 51–61.
Gompers, P., A. Kovner, J. Lerner, and D. Scharfstein. 2007. “Performance
Persistence in Entrepreneurship.” Journal of Financial Economics 62:
731–64.
Hall, R. E., and S. E. Woodward. 2010. “The Burden of the Nondiversifiable
Risk of Entrepreneurship.” American Economic Review 100: 1163–1194.
Hsu, D. H. 2004. “What Do Entrepreneurs Pay for Venture Capital Affiliation?” Journal of Finance 59: 1805–44.
Kartashova, K. 2014. “Private Equity Premium Puzzle Revisited.” American
Economic Review 104: 3297–3334.
Kirzner, I. M. 1979. Perception, Opportunity, and Profit. Chicago: University
of Chicago Press.
Klein, B., and K. B. Leffler. 1981. “The Role of Market Forces in Assuring
Contractual Performance.” Journal of Political Economy 89: 615–41.
Levesque, M., and K. R. MacCrimmon. 1997. “On the Interaction of Time
and Money Invested in New Ventures.” Entrepreneurship Theory and
Practice 22: 89–110.
Moskowitz, T. J., and A. Vissing-Jorgensen. 2002. “The Returns to Entrepreneurial Investment: A Private Equity Premium Puzzle?” American Economic Review 92: 745-78.
Milgrom, P., and J. Roberts, 1992. Economics, Organization and Management.
Englewood Cliffs, NJ: Prentice-Hall.
Ries, E. 2011. The Lean Startup. New York: Crown.
Introduction 35
Schumpeter, J. A. 1912. Theorie der Wirtschaftlichen Entwicklung. Leipzig:
Dunker and Humblot. Translated by Redvers Opie as The Theory of Economic Development. Cambridge, MA: Harvard University Press, 1934.
Von Mises, L. 1966. Human Action, 3rd rev. ed. Chicago: Henry Regnery.
Williamson, O. 1985. The Economic Institutions of Capitalism. New York: Free
Press.
Wright, M., K. Robbie, and C. Ennew. 1997. “Venture Capitalists and Serial
Entrepreneurs.” Journal of Business Venturing 12: 227–49.
Wulf, J., and J. Lerner. 2007. “Innovation and Incentives: Evidence from Corporate R&D.” Review of Economics and Statistics 89: 634–​44.
C h a p t e r T wo
N e w Ve ntu r e Fi nan ci n g :
Co n s i d e r atio n s an d Ch oice s
T h e p r i n t i n g p r e s s , the automobile, the microchip, satellites, and
the Internet are inventions that have transformed markets and changed the
course of history. But how are such great ideas and inventions financed and
brought to market? Financing needs vary considerably depending on the underlying technology. Automobile manufacturing, for example, is extremely
capital intensive, whereas many mobile apps can be produced at little cost.
The stage of development also affects the menu of available financing options.
A pre-revenue venture has limited options and cannot, for example, access
traditional debt markets, but a later-stage company may well consider debt
as an option.
The choice of financing method is pivotal to whether an idea or product
reaches the market quickly and successfully. Financing decisions during early
stages of development also dramatically affect the value the entrepreneur can
derive from the endeavor. The choice of exit financing (e.g., acquisition vs. IPO)
can also dramatically affect value.
In this chapter, we provide an overview of financing sources for new ventures
and the regulatory considerations that relate to financing choices. In addition
to introducing the institutions and terminology, we examine determinants of
the choice.
There are four important questions to keep in mind as we survey financing
choices:
1. Is the need for financing immediate (urgent) or is there time to shop the
alternatives?
2. How large is the near-term financing need?
36
New Venture Financing 37
3. Is the near-term financing need permanent or transitory?
4. How does the near-term financing need relate to the cumulative need?
The answers depend on the development stage of the company. A profitable
later-stage venture with stable revenue growth has different options than a
very early-stage company with high growth and negative cash flow and net
income.
Capital intensity is important as well. As an illustration, over the last decade, start-ups gained the ability to take advantage of outsourced cloud-based
computing instead of making large capital investments in hardware and storage. At the same time, the availability of early-stage financing by VCs increased
dramatically. The innovation in cloud computing significantly reduced financing
needs for start-ups that rely on these services compared to the financing needs of
more asset-intensive ventures like biotech and driverless cars. Ewens, Nanda, and
Rhodes-Kropf (2017) attribute the changes in financing choices to the technological shift marked by the introduction of Amazon Web Services (AWS). AWS and
competitors have allowed cloud-based start-ups to economize on physical space
and development tools and allowed them to scale as needed rather than buying
or developing their own hardware and software.
2.1 The Sequence of New Venture Financing
The stages of development discussed in Chapter 1 are also reflected in the top
row of Figure 2.1. The figure illustrates how the realistic financing options
vary as the firm evolves. At an early stage, the entrepreneur looks for ways
to finance the venture from bootstrap sources such as personal savings and
borrowing sources that do not rely on venture success. In some cases, development efforts are subsidized through grant-based financing from government
programs directed to universities and other research organizations. As development progresses, the entrepreneur may look to other sources of financing,
including angel investors and VCs.
Customary venture financing terminology reflects the correspondence between feasible financing choices and development stages. The earliest external
financing is known as seed financing. Seed financing consists of small amounts of
money to support exploration of a concept. It may cover such things as the cost
of assessing market size and customer validation. For high-technology ventures,
seed financing may provide initial funds for R&D. In cases where R&D efforts
38
Chapter Two
Fi g u r e 2 .1
Sources of new
venture financing
Dark gray shading
indicates primary focus of
investor type; light gray
shading indicates secondary focus, or focus of a
subset of investors.
Entrepreneur
Friends and family
Angel investors
Corporate strategic partner
Venture capital
Government programs
Asset-based lender
Venture leasing
Franchising
Trade credit/vendor financing
Factoring
Commercial bank lending
Mezzanine lender
Public debt
IPO
Acquisition, LBO, MBO
Development
Start-up
Early growth
Expansion
Exit
are expensive and protracted, R&D financing could be required beyond what
is typically regarded as seed financing. The critical risk exposure in such cases
is that development efforts could fail.
Start-up financing covers activities around the initiation of sales. Generally,
start-up financing is provided when a concept appears to be worth pursuing,
key members of the team are in place, and most of the risks related to initial
development have been resolved. Start-up financing includes such things as
investment in infrastructure related to manufacturing and financing of inventory. For software companies it includes financing for investment in platforms
and systems for distribution of products. At this point, the main risk exposure
is related to whether a cost-effective approach to manufacturing/distributing
technology can be put in place.
Later-stage financing is associated with the early-growth and expansion stages
of development. VCs sometimes refer to financing rounds by number, such as
“round 1 financing,” “round 2 financing,” or by letter, such as “A-round financing,” “B-round financing.” These terms are somewhat arbitrary but do imply
sequencing based on meeting a particular milestone and moving to the next. To
be consistent with terminology, we refer to later-stage financing as encompassing
two general types of financing:
1. Financing provided to a company that is generating revenue but (normally)
has not yet achieved profitability. The firm has a marketable product, but
substantial uncertainty remains as to achievable sales and profitability.
The critical element of risk is whether the venture can reach a level of
sales sufficient to attract and compensate investors in an exit.
2. Financing to support the continuing growth of a venture that is operating
around the breakeven point of profitability. Many profitable ventures are
not yet generating sufficient cash flow to support anticipated expansion.
Uncertainty remains about ultimate market potential and profitability.
New Venture Financing 39
In practice, a venture may go through many rounds of growth financing.
The limiting factors on the number of times the venture can “go to the well”
are practicality and the desire of the investor to maintain a close monitoring
relationship. The downside of increasing the number of rounds, particularly if
the investments are being made by a VC or someone else with fiduciary responsibility, are that each negotiation can be a distraction from pursuit of the venture
and each round requires a valuation. One venture with which we are familiar
went through 16 rounds of VC-backed financing, with a valuation at each stage!
Referring again to Figure 2.1, the exit stage is where the founders and other
investors attempt to harvest their investments. Harvesting techniques include
taking the company public with an IPO, arranging for an acquisition or management buyout (MBO), and other approaches discussed later.
2.2 Sources of New Venture Financing
Self, Friends, and Family
Bootstrap financing does not depend on investor assessment of the merits
of the opportunity or whether venture assets are sufficient to secure a loan.
Bootstrap financing includes financing from personal resources, family, and
friends. Family and friends generally have years of experience with the entrepreneur and probably have a sense of the entrepreneur’s reliability, trustworthiness, and ability to handle adversity. They may be incapable of assessing
the merits of the opportunity and are investing because they believe in the
entrepreneur or feel compelled by family relationships.
The obvious starting point for the entrepreneur is to use personal resources
to advance the project to a point where outside financing is feasible. The entrepreneur’s resources include not only personal savings and assets but also
debt capacity. Stories of entrepreneurs whose earliest financing was achieved
by taking out second mortgages on their homes and “maxing out” credit card
borrowing are common.1
A study of nascent entrepreneurs indicates that personal savings is the most
important early financing source. The evidence indicates that more than 90%
of entrepreneurs rely on personal savings, followed by personal loans and credit
cards (28%) and loans from friends and family (7%). Approximately 5% rely on
equity investments by friends and family.2
In dollar terms, entrepreneur-backed bank loans, such as home mortgages,
represent the primary sources of financing during the first year.3 Bootstrap
sources rely on the entrepreneur’s reputation and credit history and not on the
40
Chapter Two
merits of the venture. Accordingly, personal debt capacity gives the entrepreneur
access to capital without the need to convince investors that the opportunity
is worth pursuing.
Many ventures are legendary for their success in spite of getting started with
bootstrap financing. Steve Jobs and his partner, Steve Wozniak, sold a Volks­
wagen and a programmable calculator to raise the $1,350 they used to build the
first Apple computer. Bill Gates started his venture with Paul Allen from Gates’s
dorm room in 1975 and later relocated to a hotel room in Albuquerque.4 They
funded the start-up from savings and built a shoestring operation into Microsoft,
a company with more than $110 billion in 2018 sales. But many more ventures
begun by bootstrapping have failed miserably. Some undoubtedly were based
on good ideas but were underfinanced.
Accelerators and Incubators
Accelerators offer aspiring entrepreneurs work space, mentoring, networking
opportunities, and relatively small amounts of funding (typically $25,000 to
$150,000), in exchange for equity (usually 4–8%). Access to accelerators is competitive (e.g., Y Combinator, DreamIt, and Techstars); accelerators are cohortbased, offer involvement for a fixed amount of time (3–6 months), and usually
culminate in a “demo day” where participating entrepreneurs have an opportunity to pitch their ideas to potential investors and others. Most accelerators
are attracted to technology-based, early-stage ventures with high-growth potential that do not require large investments. Accelerators have proliferated in
recent years, increasing from very low numbers to over 500 worldwide.5
Incubators also offer resources for early-stage entrepreneurs. They, too, have
competitive selection, but they work with a broader array of ventures. They offer
longer-term assistance (1–5 years) than accelerators and may offer workspace,
access to networks, assistance with rounding out the management team, and
help with obtaining external equity financing. They are distinct from accelerators in several ways, with the primary distinction being that incubators usually
do not invest directly in the company, although they may take an equity stake
for their efforts and consulting services.6
Crowdfunding
It is important to distinguish between donation-based crowdfunding and
equity crowdfunding. Platforms like Kickstarter and GoFundMe are donation based. They are designed to enable people to support projects in which
they are interested, most commonly artistic endeavors like music, film, video
New Venture Financing 41
games, or unique product ideas. Financial support is provided in exchange for
tickets to prospective performances or merchandise. Donation-based crowdfunding can function as a means of validating the level of interest in a venture
that would help attract angel or VC funding at a later stage.7
Crowdfunding has evolved to offer funding and marketing exposure for a
broader array of early-stage ventures in exchange for equity. Equity crowdfunding allows a large number of online investors to contribute small amounts of
money to a company in exchange for equity. They can do so through online
platforms approved by the Securities and Exchange Commission (SEC) such as
AngelList and CircleUp. As explained shortly, there are regulatory constraints
on the fund-raising and issuing equity through such sites, including investor
maximum investment amounts and minimum wealth restrictions.
Initial Coin Offerings (ICOs)
An initial coin offering is a new kind of capital-raising approach that is similar
to crowdfunding. It is difficult to generalize about ICOs because the approach
is evolving rapidly. Currently, ICOs are not generally used for raising equity
but rather are used for building a network or ecosystem of users for platformbased business. Such ventures could include those focused on a software utility, such as a blockchain technology or a platform to assign and transfer cloud
storage capacity. The ventures seek to use an ICO to build a network of users
and can use an ICO to raise funds for working capital to support early development efforts and to provide information about the extent of interest in the
idea. They do that by offering utility tokens to be used on the platform. In
contrast to an IPO, where the offering document is a prospectus, an offering
document of an ICO often is a Simple Agreement for Future Tokens (SAFT).
Tokens are normally tradable rights to use the utility the entrepreneur seeks
to develop. The agreement is for “future tokens” because at the time of the
ICO the utility normally is still in development (and development success is
far from certain). In contrast to ownership shares, utility tokens convey usage
rights. In many ICOs, purchasers of tokens can pay either currency or cryptocurrency, such as Bitcoin. ICOs in the U.S. are regulated by the SEC but the
nature of the regulation is still evolving, as the SEC is catching up with this
new approach to funding entrepreneurial activity.
Angel Investors
For ventures based on concepts that require lengthy development efforts, the
earliest source of outside financing is often from high-net-worth individuals
42
Chapter Two
who invest their own capital either independently or through an organized angel investor group. These angel investors generally are freelancers who are interested in investing relatively small amounts of money ($25,000 to $500,000)
in early-stage projects.8
Angel investors often provide seed capital to develop an idea to the point
where more formal outside financing becomes feasible. Many are willing to
invest over horizons of 5 to 10 years. In contrast, traditional VC funds usually
prefer larger investments and somewhat shorter investment horizons. Angels
seek to add value by identifying ventures with high potential for success and
helping them to progress. They usually seek to realize a return by taking
equity in the venture.
Most estimates put the amount of angel capital invested in the U.S. in recent
years at around $20 billion annually, excluding capital provided by friends and
family. According the University of New Hampshire Center for Venture Research, 64,000 entrepreneurial ventures received angel financing in 2016, with
an average round size of $330,000.
Many angel investors work alone; others are affiliated with angel networks.
In the late 1980s, angels began to organize into informal groups that could
share deal flow and due diligence. Participants co-invest with each other, enabling the group to make larger commitments and bring more experience to
each deal. There are several well-known groups in geographic areas known for
high start-up activity: the Band of Angels (Menlo Park), Tech Coast Angels
(eight locations in Southern California), and New York Angels (New York
City). Angel groups range in size from 40–100 members. There also are global
angel groups, such as Keiretsu Forum, which has groups on three continents
and 2,500 members. Networking organizations, like the Angel Capital Association (ACA), facilitate connections among angel groups. The ACA has 400
angel groups in its database, and there are many more around the world. Some
angel groups, such as Investors’ Circle, focus on social venturing while others,
like Golden Seeds, focus on specific types of ventures—namely women-led
early-stage ventures.
The sizes of angel group investments in start-ups vary widely. The 2016 Halo
Report of the Angel Resource Institute reports that the median angel group
investment reached $127,000 in 2016. Round sizes are larger because groups
sometimes co-invest with other angel groups, VCs, or other investors.
The ACA reports that average group size in 2016 was around 42 members,
with total annual investments around $2 million in 10 deals per year. While each
group has distinct membership and goals, angel groups share common characteristics: they meet regularly to review business proposals, select entrepreneurs
to make presentations to the membership, decide individually whether to invest
New Venture Financing 43
in each venture, work together to conduct due diligence to validate the plans,
and evaluate the entrepreneurial team.
While precise numbers are hard to come by, Panel A of Table 2.1 shows a time
series of statistics provided by the Center for Venture Research (CVR). The CVR
estimates that U.S. angels invested about $21.3 billion in 2016, in over 64,000
deals; many of these investments were in seed/start-ups (a combined category in
the CVR data) or early-stage ventures. The time-series statistics in Panel A show
that the total size of the angel investment market was negatively affected by the
2008 financial market crisis, mainly through smaller investments and a reduced
focus on seed and start-up investments in more seasoned ventures (defined in the
CVR data as post-seed/start-up), as well as a reduced focus on initial funding in
favor of follow-on funding in ventures that had already received funding. While
the total amount of angel investments has recovered since 2009, and the number
of angel deals has increased, average deal size has remained lower than in the
precrisis period and the focus on seed and start-up (in favor of early-growth) and
initial angel investments has remained low.
While angels may jointly investigate and discuss opportunities, U.S. securities
laws dictate that each investor must make an independent investment decision
and take individual responsibility for her own due diligence. It is common for
individuals in these groups to invest $25,000 to $50,000 in a company.
Some angel investors are interested strictly in return on investment and do
not want to become deeply involved with the companies in which they invest.
Others are more active, often motivated by desire to add value to the companies
beyond their capital commitments. These angels typically focus on the types
of companies with which they have experience. They can be good sources of
information concerning financing and strategy.
Angel syndicates. In addition to organizing into groups, angel investors
sometimes form syndicates to invest collectively. In contrast to the group structure, the syndicate structure is a special-purpose vehicle that is created to invest
in a single deal. Typically a syndicate has one lead investor who is experienced
in attracting deal flow, conducting due diligence, and providing ongoing moni­
toring of portfolio companies. In recent years notable angel investors that often
act as lead investors in angel syndicates have increasingly relied on website platforms like AngelList to identify individual angels who are interested in joining
the lead investor’s syndicates. Angel investors who join syndicates must meet
U.S.-accredited investor standards (discussed later) and be accepted by the syndicate leader. Many syndicated deals are led by well-known investors, including
partners in top VC firms. Angel syndication is a recent development, and when
VC partners lead syndicates, it appears that they may be pursuing opportunities
44
Chapter Two
Tab le 2 .1 US angel and VC investments: 2006–2016
Panel A
US Angel Investments
Angel
Investors
Total Investments
($Billion)
Deals Funded
Average Deal Size
($Thousand)
Percent of Deals
Seed/Startup
Percent Initial
Funding
2006
$25.60
51,000
$502.0
46.0%
63.0%
2007
$26.00
57,120
$455.2
39.0%
63.0%
2008
$19.20
55,480
$346.1
45.0%
63.0%
2009
$17.60
57,225
$307.6
35.0%
47.0%
2010
$20.10
61,900
$324.7
31.0%
41.0%
2011
$22.50
66,230
$339.7
42.0%
52.0%
2012
$22.91
67,030
$341.8
35.0%
50.0%
2013
$24.81
70,730
$350.8
45.0%
50.0%
2014
$24.11
73,400
$328.5
25.0%
50.0%
2015
$24.56
71,110
$345.4
28.0%
44.0%
2016
$21.26
64,380
$330.2
41.0%
43.0%
Percent of Deals
Seed/Angel
Percent Initial
Funding
Panel B
US Venture Capital Investments
VC
Investors
Total Investments
($Billion)
Deals Funded
Average Deal Size
($Thousand)
2006
$29.17
3,320
$8,785.2
13.1%
38.7%
2007
$35.46
4,291
$8,263.3
17.4%
39.0%
2008
$37.25
4,730
$7,876.1
19.1%
37.3%
2009
$26.57
4,456
$5,963.0
26.1%
37.3%
2010
$32.00
5,403
$5,922.5
31.2%
38.5%
2011
$44.66
6,775
$6,591.9
38.4%
41.7%
2012
$40.72
7,986
$5,098.5
44.7%
41.7%
2013
$44.89
9,325
$4,813.4
50.1%
38.5%
2014
$68.66
10,565
$6,498.6
52.2%
35.6%
2015
$79.04
10,483
$7,539.6
54.4%
32.4%
2016
$70.83
8,469
$8,363.0
50.4%
29.3%
source :
Center for Venture Research, University of New Hampshire and National Venture Capital Association, 2017 Yearbook.
that would not be good prospects for their VC funds or may be prospecting for
opportunities that could attract VC funding in a subsequent round.9
Venture Capital
Venture capital (VC), along with angel investing and other nonpublic financing
sources, is part of the private equity (PE) market. VC funds are organized by
New Venture Financing 45
VC firms, where the VC firm serves as the general partner (GP) of each VC fund
it organizes. VC funds in the U.S. are limited partnerships in which the limited
partners (LPs) provide almost all of the capital and the GP is responsible for
managing the fund, including investment selection, working with entrepreneurs,
and harvesting the investments. VC funds have finite lives and are normally established to last for about 10 years. While some VC firms establish seed-stage
funds for funding start-ups, most focus on funding ventures that are about to
embark on the start-up stage with the potential for high growth that will follow.
Because VC funds involve small numbers of LP investors (financial institutions and wealthy individuals) that are presumed to be sophisticated enough
not to require government oversight, VC funds are exempt from the key federal
regulations of the securities industry. As PE has grown in importance and more
companies have opted out of the public capital markets, PE has increasingly
attracted regulatory scrutiny in the U.S. and elsewhere.
VC-backed ventures generally require few tangible assets or other assets that
could serve as collateral and provide a basis for secured lending; they also have
the potential to achieve high growth with high profitability but with the commensurate high risk. Because both angel investing and VC are key to enabling
high-growth entrepreneurship, we devote Chapter 3 to a comprehensive description of the institutions. Here we focus more narrowly on identifying some of
the considerations that help entrepreneurs decide when to seek VC financing,
as opposed to alternatives.
Panel B of Table 2.1 provides statistics on the size of the U.S. VC market since
2006. Total VC investment has increased from being about the same as the level
of angel funding in 2006 to over $70 billion—more than 3 times the size of the
angel market. Still, there are many more angel-funded deals than VC-funded
deals. Offsetting the numbers of deals, the average size of VC deals is many
times greater, indicating an important difference in the primary foci of angels
and VCs. It is noteworthy, however, that over time the two have grown closer.
Seed/angel-stage VC deals are now more than half of all VC deals, but angels
are still the primary sources of small early investments; in 2016, for example,
26,400 angel-backed deals were classified as seed/start-up, whereas 4,300 deals
were classified by VCs as seed/angel stage. VCs are also less inclined than angels
to undertake initial funding of a venture.
From the entrepreneur’s perspective, a number of factors help determine
whether VC is appropriate and which VC firm to select. First, timing is important. The venture must be developed to a point where the VC firm can expect to
add value, not just money, and the firm must expect to earn a return that will
compensate for the VC’s human capital investment and where the entrepreneur
recognizes the potential value of VC involvement.
46
Chapter Two
Fi g u r e 2 . 2
New VC investments
by sector
National Venture
Capital Association, PitchBook 2018-Q1 Report.
source:
100%
90%
Other
80%
Software
Pharma & biotech
70%
Media
60%
IT hardware
Health care services & systems
50%
Health care devices & supplies
40%
Energy
30%
Consumer goods & recreation
Commercial services
20%
10%
0%
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Fi g u r e 2 . 3
50%
Capital invested
Investee companies
40%
30%
20%
10%
s
te
sta
O
th
er
lo
ra
d
o
a
Co
ns
yl
va
ni
U
ta
h
Pe
n
in
oi
s
Ill
n
to
ng
s
a
hi
W
as
Fl
or
id
xa
Te
ts
sa
ch
us
et
Y
or
k
M
N
ew
as
a
0%
Ca
lif
or
ni
source: National Venture
Capital Association, 2017
Yearbook, PitchBook
data.
60%
Percentages of capital invested and investee companies
Top 10 states for VC
investing: 2016
Second, the venture must be in an industry sector where the VC has expertise.
Figure 2.2 shows the sectors in which VC funds typically have invested. Their
focus is on sectors with potential high-growth, high-return opportunities.
Third, VCs have tended to concentrate investments in particular geographic
areas and to focus on opportunities that are “close to home.” While, historically,
New England has been the focus of much VC activity, in more recent years the
geographic focus has shifted. The National Venture Capital Association (NVCA)
reports that over the 2014 to 2016 period, 57.1% of the value of VC investments
New Venture Financing 47
was on the West Coast, followed by 16.1% in the Mid-Atlantic. Figure 2.3 identifies the top 10 states for VC investment.
Fourth, the investment horizon and investment objectives of VC funds make
VC better suited for some projects than others. In part, because of their agreements with LPs, VCs are likely to focus on projects with potential for very high
returns but low probabilities of success and that can be harvested during the
planned life of the VC fund. They are not likely to invest in projects that can
generate positive cash flows quickly but have limited growth potential or in those
that are likely to be harvested outside a 3-to-8-year window.
Corporate Venturing
A number of prominent corporations have, at one time or another, implemented an in-house VC program to aid in developing new business opportunities. Some corporations view continuous innovation as essential to their sustainability and growth. Such firms have found creative ways to supply capital
to support ideas that do not “fit” with their traditional product or service lines
but may have market potential.
Corporate venture funds can help to retain creative employees and provide
some assurance that great ideas will not “escape.” A well-known example of an
idea that escaped is the establishment of Hewlett-Packard by former employees of Bell Labs who became frustrated with the corporate research process
at Bell Labs. Subsequently, two employees of HP left to form Intel. Former
Google employees have founded a number of start-ups. Perhaps in response to
concerns with losing talent and losing out on opportunities, in 2009 Google’s
parent company (Alphabet) formed GV (formerly Google Ventures) as its VC
investment arm. GV provides seed- and growth-stage funding to technology
companies and operates independently from Google. In contrast, Apple is a
technology company where talented employees have left and founded start-ups
of their own; however, Apple does not have a VC arm.
The primary motivation for corporate venture capital (CVC) is strategic, but
CVC can be driven by several motivations. One is defensive—a way for firms to
keep track of emerging competitors and innovative technologies. CVC can also
be an offensive strategy by investing and potentially acquiring or partnering
with ventures that provide complementary services or products. CVC activity
can also be a way to keep and incentivize employees who are entrepreneurial and
would otherwise leave to start their own firms and rely on private VC for funding.
CVC is more likely in firms that depend on innovation to sustain competitive
advantage. For 2016, the CBInsights Global CVC Report indicates that there
were 752 investments by U.S. corporations and 1412 worldwide.
48
Chapter Two
The average CVC deal size in the U.S., according to the Global CVC Report,
was $21.4 million, much larger than the average VC investment. However, there
is not a bright line between CVC activity and innovative activity that occurs
within firms. Firms like Apple have engaged in numerous acquisitions of innovative firms, and they devote considerable internal resources to cutting-edgetechnology development. Similarly, the R&D investments of pharmaceuticals
firms are largely internal because of such considerations as shared resources
and the extensive testing required for new drug treatments.
There is considerable variation in the way CVC activity is set up within the
corporate organization. Drover, Busenitz, Matusik, Townsend, Anglin, and
Dushnitsky (2017), in their review, indicate that some units are organized like
independent VC firms that are staffed by experienced VCs, receive funding from
a dedicated capital pool, and have discretion over investment selection and support. Incentives for performance are similar to those of independent VC firms.
Others are in-house units, staffed by salaried corporate employees who most
likely are looking for strategic insights or complementarities with the parent
and are not as concerned with harvesting per se.
The financial performance of CVCs is mixed. Some studies document that
strategic alliance formation can positively impact firm value but that quantitative assessment of the financial benefits to the parent firm is difficult. Drover
et al. (2017) report that very few CVC units have generated “top tier” returns.
While CVC can benefit start-ups, entrepreneurs need to be aware that corporate
incentives may not align with those of the entrepreneur, especially in cases where
ownership of intellectual property is not clear-cut and appropriation of ideas is a
concern.10 If the entrepreneur’s innovation is complementary to the corporation’s
product, this is less of a concern. Empirically, based on their review, Drover et
al. conclude that start-ups that accept CVC funding appear to perform at least
as well as VC-backed peers.
The dollar magnitude and number of CVC investment varies widely from year
to year. Based on data from the NVCA Yearbook, Figure 2.4 shows total U.S. CVC
investment relative to total VC investment. Corporate venturing is pro-cyclical, as
a percentage of total VC investment and CVC peaks when total VC investment is
highest. The average size of the CVC deals is larger than traditional VC deals. In recent years in the U.S., corporate venture fund investments represent around 10–15%
of all VC deals (including CVC), but around 30–45% of all VC deal dollar value.
Venture Debt
Debt financing for entrepreneurial firms encompasses a variety of forms.
Term lengths can vary, loans may be collateralized or not, and returns may be
interest-only or a combination of interest and warrants or convertible to ac-
New Venture Financing 49
Fi g u r e 2 . 4
12,000
Number of
independent VC deals
and deals with CVC
involvement
The table shows, by year,
the total number of VC
deals, the number of independent VC deals, and the
number of deals with CVC
involvement, 1999–2017.
8,131
7,473
Total number of deals
source: National Venture
Capital Association, PitchBook 2018-Q1 Report.
9,129 9,090
10,000
8,000
5,790
7,282
7,002
5,996
6,000
4,207
4,801
4,008
3,473
4,000
3,624
2,516
2,450
2,505 2,550
3,968
2,766
2,000
2,123
1,301
0
1,005
577
461
567
578
568
680
699
497
577
738
863
1,468 1,351 1,370
1,095 1,338
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
CVC deals
Independent VC deals
quire ­equity. Lenders include venture debt funds, sovereign wealth funds, specialized banks, and institutions such as pension funds, and others that make
direct loans to ventures.
Asset-based lending. Asset-based lenders provide debt capital to ventures
that have tangible or intangible assets that can serve as collateral. The lender
is not relying on the venture’s cash flow for repayment but on the ability of the
venture to liquidate business assets, if necessary, for debt servicing. Loans may
be secured by accounts receivable, inventory, equipment, real estate, royalty
rights to intellectual property, or other assets with verifiable market/liquidation values. Taking the collateral is not something the lender wants to do. Accordingly, asset-based lenders can also be expected to be concerned about the
financial health of the venture. Based on the Commercial Finance Association’s
Annual Asset-Based Lending and Factoring Survey, 2016 U.S. commitments
from responding lenders totaled $199 billion. The actual number is likely to be
much higher because of nonresponses.
Debt financing and business development companies. Venture debt
is a form of early-stage financing represented by loan agreements between earlystage ventures and the providers of the loans. These loans generally are not
collateralized as they are with asset-based lending. There are four categories of
50
Chapter Two
providers of venture debt: private debt funds, direct lenders, business development companies (BDCs), and enterprise banks like Silicon Valley Bank. The
loans are complementary to equity investments in that they sometimes fill gaps
in funding between rounds of equity so that the firm has funds to enable it to
reach the next milestone.
The loans can work like mandatory redeemable preferred stock with a premium since the provider is making a bet that the firm will be able to get future
financing or operating cash flow that will enable the debt to be paid off. The interest on the debt can accrue or be paid off each month. The loans can be short- or
long-term. Those intended as bridge financing between equity rounds or prior
to an IPO or acquisition may have terms as short as a few months. Interest rates
on unsecured loans that do not include equity sweeteners can be high—in the
11–14% range or higher. The loans usually include warrants, but these normally
represent a tiny fraction of the capitalization of the company. One attractive
feature of the financing is that a reputable provider can help the borrower with
introductions, networking, and so on. Many such loans can be restructured if
the borrower runs into trouble.11
BDCs are active participants in this market. They were authorized by the
U.S. Congress in 1980 under a modification to the 1940 Investment Company
Act to provide funding to small and medium-sized enterprises, especially during
early stages of development. BDCs are set up as closed-end investment funds
and many are publicly held firms, with shares traded on major stock exchanges.
The investments that BDCs make in emerging firms are a mix of debt (primarily), equity, and hybrid securities like convertible debt.12 BDCs, like VCs, are
closely involved with providing advice to their portfolio companies, which are
usually private firms. To qualify as a BDC, an investment company must invest
at least 70% of its assets in U.S. firms with market values less than $250 million.
BDCs are pass-through investment vehicles that are able to qualify as tax exempt
as long as they distribute over 90% of their profits to BDC shareholders. BDCs
comprise a small fraction of the total market for new venture financing. As of
2017, the Closed-End Fund Association reports that there are 52 publicly traded
BDCs with a total market value of approximately $35 billion.
Revenue-based financing. For firms that are growing rapidly and have
solid income statements, revenue-based financing has emerged to fill the gap
between bank debt and VC. Loans for this category are generally in the $4–$10
million range. More traditional forms of venture debt and bank debt generally include liquidity covenants, which may not be appropriate for start-ups or
growing firms, both of which will almost certainly have liquidity issues. VC, in
New Venture Financing 51
contrast, involves the investor taking an equity stake in the firm. Recognizing
a gap in the financing menu, revenue-based financing emerged as a new vehicle
offered by limited partnerships that are organized like VC firms, but where
the portfolio companies are growth firms with (relatively) predictable revenue
growth and are not the typical high-growth-potential firms that VCs pursue.
Revenue-based financing is a structured debt instrument where the investor
does not receive equity or warrants.
Venture Leasing
Venture leasing is an alternative to asset-based lending. In asset-based lending,
the venture owns the asset and the asset serves as collateral on the loan. In venture leasing, the lessor continues to own the asset and the venture leases it. The
periodic lease payment is a substitute for debt service. Unlike most leases, leases
to ventures are often subject to considerable risk. To compensate for potential
loss, the lessor’s return may be tied to the financial performance of the venture,
so that if the venture does well, the lessor realizes more than the expected return,
and conversely.
Venture leasing usually involves assets that are key to the operation of the
venture. For example, in past years a number of ventures were launched to
provide whole-body computerized tomography (CT) scans. The entrepreneurs
in these ventures had to buy or lease a CT scanner, which, if purchased, was a
multimillion-dollar investment. With venture leasing, the entrepreneur’s initial
investment is reduced, but some of the upside is shared with the lessor. Normally,
the providers of leases are the companies that produce and distribute the equipment, as they are the ones who can most easily redeploy them if the venture fails.
There can also be tax advantages to leasing as compared to owning, especially
for a venture that has yet to reach a profitable level of operation. Depending
on its organizational form, if the venture were to purchase, it might not have
sufficient income to be able to realize the tax benefit of recording depreciation
expense related to the outlay for the equipment. In contrast, a profitable lessor
who buys the machine and leases it to the lessee can realize the tax benefit of
depreciation. If lessors compete to supply the equipment, some of the tax benefit
should make its way to the venture in the form of more favorable lease terms.
Government Programs
Many countries have established government-supported programs to provide
loans and other financing for start-ups, small firms, and firms with growth potential, and to support R&D. In the U.S., Congress created the Small ­Business
52
Chapter Two
Administration (SBA) in 1953 to foster innovation and growth of small businesses.13 The SBA is primarily a guarantor of loans made by banks and other
financial institutions and does not lend directly to small businesses.14 In addition to the SBA programs described here, government supports innovation
in other ways such as grants to support scientific research, research labs, and
even incubators.
The SBA’s Small Business Investment Companies. Most SBA-guaranteed financing is provided through Small Business Investment Companies
(SBICs). SBICs are privately owned and managed, for-profit organizations that
are licensed by the SBA. With their own capital (raised from private sources including banks, corporations, individuals, and others) and with borrowed funds,
SBICs provide capital to new and established small businesses.15 SBIC financing
usually takes the form of interest-bearing loans that are guaranteed by the SBA.
Because of government support, the capital that SBICs invest is obtained at
below-market rates. An SBIC that is faced with competition to supply funds to
a venture may be compelled to pass along some of these savings. More recently,
the program has broadened to enable financing that is more like equity, such
as loans that include warrants where the total return is tied to venture success.
While SBICs have invested in such companies as Apple, Costco, FedEx, and
Whole Foods, the impact of SBA loan guarantee programs on entrepreneurial
activity is small. In 2016, the SBA Office of Advocacy reports that its loan/guarantee portfolio totaled $124 billion. However, the effect of the loan guarantees
is mainly to subsidize the institutions that are the actual lenders by reducing the
cost of default risk, with the expectation that at least a portion of the subsidy
gives rise to more loans and lower interest rates for borrowers.16
The SBA also offers some grant programs, but the grants generally support
not-for-profit organizations, lending institutions, and state and local government entities that provide small business management or assistance to small
businesses.
Although SBICs are involved in financing at about the same stages as VC,
the normal interest-bearing structure of the financing makes it better suited for
firms that are less risky than typical VC-backed firms. SBICs prefer firms with
more limited growth potential that need financing for working capital and have
the ability to achieve profitable operations quickly.
The Small Business Innovation Research program. If the venture
involves technology, several government agencies sponsor grants under the
­auspices of the Small Business Innovation Research (SBIR) program. Partici-
New Venture Financing 53
pants include the Departments of Agriculture, Commerce, Defense, Education,
Energy, and Health and Human Services, as well as the National Aeronautics
and Space Administration (NASA) and the National Science Foundation. SBIR
Plan I research grants provide funding defined by milestones; in Phase I, they
provide seed capital and funding for up to $150,000 to explore commercial
feasibility, while Phase II grants are usually around $1,000,000 for up to two
years. The goal is to expand R&D and further assess viability. Phase III involves
transitioning from the lab to the market, during which time the firm searches
for follow-on private sector financing or other government programs; the SBIR
does not fund this phase.
Government programs in other countries. Most developed economies provide government-based assistance grants and other means to facilitate
creation and growth of new businesses. Prominent examples include Canada’s
Small Business Finance Centre and the U.K.’s Department for Business, Energy and Industrial Strategy (BEIS). Among the programs of the BEIS is a
multimillion-pound equity finance program, Enterprise Capital Funds. The
program is designed to provide up to £5 million of initial funding for ventures
that require funding within what some have termed the equity gap—more than
what angel investors generally supply but less than what VCs would consider.
Trade Credit
Trade credit, or vendor financing, is the largest source of external short-term
financing for firms in the U.S. and is even more important in emerging economies, where risk capital can be scarce. Vendor financing arises whenever a
business makes a purchase from a supplier that offers trade credit. For example, a venture that buys supplies on terms of net 30 receives the supplies
right away but does not need to pay until 30 days later.17 In effect, the supplier
is providing the venture a zero-interest-rate loan for 30 days. Trade credit appears on the borrower’s balance sheet as an account payable. The same loan
appears on the supplier’s balance sheet as an account receivable. The difference between a firm’s accounts payable and its accounts receivable is its net
trade credit. The net trade credit balance defines the firm in terms of whether
trade credit functions as a net source or net use of funds. If accounts payable
exceeds accounts receivable, trade credit is a net source—that is, borrowing.
Not all trade credit is free. When it is not, heavy reliance on trade credit can
be very expensive. For example, terms of 2/10 net 30 are common. The buyer
is offered a 2% discount if the invoice is paid within 10 days; otherwise the full
payment is due in 30 days. If the buyer decides to forgo the discount, there is a
54
Chapter Two
sizable opportunity cost. In effect, the firm borrows the invoiced amount for 20
extra days by paying 2% more. The compounded implicit interest rate on such
a loan is 44% per annum.18 Managers of any new venture must consider this
cost when deciding whether to pay during the net period or take advantage of
the discount. If the need for cash is transitory or bank loans are not available,
forgoing the discount could make sense.19
Entrepreneurs may not have much actual control over the decision to offer
trade credit to customers. Credit terms and availability are determined by competition and tend to be uniform in a given industry. A venture that is competing
against established firms may find that it must offer trade credit to get customers
to try the product, whereas the venture’s suppliers may insist on receiving cash
until the venture has proven itself.
Factoring
When a business offers trade credit, it generates accounts receivable. In lieu
of borrowing to finance the receivables, the venture may be able to sell the receivables to a factor. A factor is a specialist who buys receivables and manages
the collection activities. Factoring comes in two basic types: with and without
recourse. If factoring is without recourse and a customer does not pay its bill,
the factor absorbs the loss. Factoring with recourse means that if the customer
does not pay, the factor can collect from the venture directly.
The three basic parts of a factoring transaction are (1) the advance (the factor
advances a percentage of the face value of the receivables to the seller), (2) the
reserve (the remainder of the total invoice amount that the factor holds until it
receives payment), and (3) the fees associated with the transaction, which are
deducted from the reserve. The advance generally ranges from 70% to 90%. The
fee is calculated as a percentage of the face value of the receivables. It includes
compensation for handling the collection and (for a nonrecourse transaction)
compensation for assuming the risk of nonpayment. Normal fees can be 2–6%
and can include implicit interest for the period over which the funds are advanced or an additional component for explicit interest over the period. Research indicates that factoring is more common for smaller sellers and is most
beneficial for sellers with geographically dispersed buyers and those with a few
repeat customers. Although the cost of factoring may seem high, the terms are
competitively determined. Factoring may be attractive to a cash-poor venture
until it grows to a size that makes integrating the collections function more
economical.20
Factoring is a large and international institution. FCI, a trade association of
many of the world’s factoring firms, estimates that 2016 total factoring volume
New Venture Financing 55
was €2.375 trillion, concentrated mainly in Europe (67.05%) and Asia-Pacific
(23.38%), with North America in fourth place (4.0%). In a number of European
countries and a few Asian countries, the World Bank reports that the ratio of
factoring volume to gross domestic product (GDP) in 2015 was greater than 10%,
whereas in the U.S. it was less than 1% of GDP. While its use in the U.S. has
been static, factoring is a growing and significant source of financing worldwide.
This trend may reflect differences in institutional constraints on bank lending
that make factoring more appealing in some countries, whereas in others, bank
loans that are secured by receivables are more common. It may also reflect differences in typical firm size and the composition of industries.
Fintech
Bank loans, factoring, and trade credit are long-lived traditional institutions
that have been challenged on many fronts by financial technology (fintech)
ventures that are looking to disrupt old ways to providing credit to start-ups
and small and medium-size enterprises (SMEs). These include peer-to-peer
(P2P) lending firms, firms that have adopted business models that streamline
SBA loans, online lenders (e.g., Kabbage), innovations in creating markets and
auctions for trade credit balances (e.g., C2FO​.com), and electronic markets for
corporate receivables (e.g., NYSE Euronext). All these innovations are forms
of disintermediation and are aimed at more efficient, speedier financing for
firms. While most of these innovations in financing have focused on the larger
markets in the U.S. and Europe, there is a global movement toward nonbank
lending as well.
Franchising and Independent Contracting
Franchising is another way to finance growth. In business format franchising,
such as a fast-food restaurant, the franchisor establishes a business format and
offers franchising opportunities to prospective franchisees. The franchisee invests in a facility in a particular locality. The facility carries the franchisor’s
brand and must conform to the general standards of the franchise network.
Franchisors provide a range of services that can include site selection, training, product supply, marketing, and assistance in arranging financing. The
franchisee normally pays a franchise fee and makes periodic payments that
are partly based on revenues.
Similarly, companies like Uber and FedEx use independent contractors in
at least some components of their enterprises. Uber drivers and FedEx Ground
operators acquire their own vehicle and, in some cases, may employ drivers.
56
Chapter Two
Uber and FedEx coordinate the activities of the independent contractors and
establish service standards.
Reasons for franchising and use of independent contractors are not limited to
financing advantages, but the arrangement does allow the franchisor to expand
the size and geographic reach of the business rapidly, without having to raise all
the capital by itself.21 Among other things, the franchisee may be responsible for
identifying the local market opportunity and acquiring the land and facilities,
which, if the outlet were company owned, would have been costs borne by the
franchisor.
Franchising is common in a number of industries such as quick-service restaurants, lodging, car rental, automobile dealerships, and full-service restaurants.
About one third of all retail sales in the U.S. are made through franchised outlets (including car dealers and gas stations).22 The practice has also increased
the international reach of well-known consumer brand names. McDonald’s,
for example, is so pervasive that the “Big Mac Index” is sometimes used as an
indicator of purchasing power parity across countries. There are also companies
that do not “franchise” in a formal legal sense, but instead enter into licensing
agreements for operation (e.g., Shake Shack). Still, the licensing revenue stream
provides the parent firm with financing. The legal distinctions between licensing and franchising are nuanced and important to consider before choosing to
adopt one approach versus the other.
Mezzanine Capital
“Mezzanine financing” usually refers to capital raised after the firm has established a record of positive net income with revenues approaching $10–$20 million or more. Some VC firms and other private equity firms offer this type of
financing. The financing generally is a hybrid of senior debt and common equity. A frequently used instrument is subordinated debt with an equity “sweetener” or warrants. Warrants entitle the holder to buy shares of the firm’s common stock at a stated price for a period of time. For example, each $1,000
of borrowing might provide the mezzanine lender with warrants to purchase
20 shares of stock at $5 each.
Private Debt
Debt financing may make sense for a rapidly growing venture. There are two
primary reasons for using debt to finance growth. First, because interest payments are tax deductible, debt may be less expensive than equity for a firm
that has consistent earnings. Second, debt holders usually cannot vote. There-
New Venture Financing 57
fore, equity owners do not lose voting control when debt is issued. However,
debt is a contractual obligation, so in the event of bankruptcy, bondholders
have priority over equity owners and may effectively gain control.
Private debt, such as commercial bank debt, sometimes is appropriate for
small businesses, particularly those with steady, verifiable cash flows, where the
business is not expected to grow rapidly. This type of financing is more likely
for replicative types of entrepreneurial ventures, such as those that employ established business models and are created to serve a growing population—such
as independently owned dry cleaners, bakeries, restaurants, and health clubs.
The venture should be in a position to make the interest payments and to take
advantage of the tax deductibility of interest payments.
Publicly Traded Debt
Small and new firms do not have access to the public bond market. Investment
banking firms seldom underwrite bond issues smaller than $10 million in gross
proceeds. Unless a firm has a substantial asset base and steady cash flows and
is in need of a significant amount of capital, it is unlikely to be able to arrange
a bond issue. New ventures are more likely to borrow from commercial banks,
life insurance companies, or an SBIC or other government-related entity.
Private Placements of Equity and Debt
Sometimes a firm would like to raise a specific amount of capital quickly and
would prefer to avoid the cost and time required to complete a public offering.
This can be accomplished by private placement of debt or equity. The private
placement market generally is more attractive than the public market for small
equity or debt issues or for debt issues backed by complex security arrangements where flexibility is desired. Angel and VC financing are equity private
placements at early stages. The term “private placement,” however, applies
more broadly to any sale of equity or debt securities to a selected group of
investors by means other than a public offering.
In the U.S., public offerings of debt and equity are regulated by the SEC.
Similar regulatory bodies oversee public offerings in other countries. One advantage of private placements is that the venture avoids the complex, ongoing
reporting that would be required if it were to raise capital via public offering.
In addition, an entrepreneur can use private placement to limit the number of
people who gain access to strategic information about the venture.
While privately placed securities avoid SEC registration and reporting requirements, only certain types of offerings qualify as private. Important ­factors
58
Chapter Two
that bear on the choice are the number of offerees, the relationship of the offerees to the issuer, the offering size, and the manner of offering. Often more
important, however, is the sophistication of the investors and their access to
information about the issuer.
Stakeholders in the new venture, including employees, distributors, retailers,
suppliers, and so on, are potential investors in equity private placements, as are
insurance companies, pension funds, and high-net-worth individuals. Institutional investors such as insurance companies and pension funds are candidates
for buying privately placed debt.
Often, private equity is structured as convertible preferred stock. Preferred
stock typically converts to common at an IPO. Privately placed debt may have
some advantages as compared to a public issue. As with equity, the costs of a
private placement of debt tend to be lower and the placement can be quicker.
Also, it is possible to negotiate greater flexibility in the terms than would be
possible for a public offering. A significant advantage of a privately placed issue
is that it may facilitate monitoring. A public debt issue normally has many investors. The resulting diffuse ownership encourages free riding in terms of investor
effort devoted to monitoring the company. In contrast, if a debt or equity issue
is placed privately, ownership is concentrated and there are significant incentives to monitor management. The small number of investors also facilitates
renegotiating the terms of the investment agreement if circumstances change.
Initial Public Offering
In an IPO, the issuing company raises capital by selling registered equity
shares to the public via a formal offering process. Since the IPO gives rise to
a liquid market for company shares, it can be a convenient exit or harvesting
mechanism for VCs and other investors.23 The importance of VC in the IPO
market has increased over the past few decades. For the period from 1980 to
2016, 37% of IPOs were VC-backed, but the level rose to an average of 55%
from 2010 to 2016.24
Going public provides a way for the venture to raise equity capital and for early
investors to realize returns on their investments, achieve liquidity, and diversify.25
An IPO also provides a market-determined valuation that can be used as a basis
for negotiating merger and acquisition (M&A) transactions. Where the venture’s
track record is clear, large amounts of capital are needed, and potential synergies
with other firms are absent, an IPO may bring a higher share price than a private
placement. Public ownership also provides a way to create equity incentives for
employees. The stock price is a barometer of market expectations and can provide information about how investors expect economic events and managerial
New Venture Financing 59
decisions to affect future earnings. Finally, a publicly traded firm may be able to
raise additional capital more quickly and cheaply than if it were private.
On the cost side, IPOs are expensive in terms of time and costs of the offering
and ongoing compliance with SEC regulations and reporting requirements.26
Already-scarce managerial time must be diverted from the business and devoted
to the IPO process and ongoing reporting. The presence of a visible stock price
may also induce management to be unduly concerned with short-term stock
price fluctuations. In recent years, the private equity market has developed to
the point where some of the historical benefits of being public are less apparent.
Direct Public Offering
Between an IPO and an equity private placement, U.S. securities regulations
provide a variety of safe harbors whereby a firm can issue equity directly to
small numbers of investors without going through the formal public offering
process. Generally, the safe harbors relate to raising capital from small numbers of investors, employees, and sophisticated investors. SEC safe harbors
operate on the premise that the investors’ specialized or inside knowledge will
protect them sufficiently even in the absence of SEC oversight.
As an example, in 1984 Ben & Jerry’s, a company founded in Vermont, built
its first ice cream plant by selling $750,000 of equity directly to Vermont residents
in a direct public offering (DPO). The firm later raised $6.5 million in a public
offering. This experience suggests that DPOs can make sense for a firm that has
a loyal customer and employee base.
Later-Stage Financing Alternatives
The list of financing sources could be extended considerably. Other possibilities identified in Figure 2.1 include merger and acquisition (M&A), leveraged
buyout (LBO), and management buyout (MBO). We address these alternatives
later in the text when we analyze the choice of how early-stage investors harvest their investments. M&A, for example, is a much more likely exit outcome
for a successful start-up than is an IPO.
2.3 What’s Different About Financing
Not-for-Profit Ventures?
Ventures that are organized as not-for-profit entities can compete in the same
market as for-profit ventures. The main difference for our purposes is that
60
Chapter Two
they have different access to financing sources. For example, there are specific
federal and state grant programs and foundation grant programs that target
nonprofits. There also are sponsored competitions that focus on areas of specific social concern (such as improving water quality, education, or health).27
While not-for-profit and for-profit firms have similar access to many financing
sources, especially debt financing sources such as trade credit and private and
public debt offerings, not-for-profits cannot raise capital by issuing equity. In
some cases, not-for-profits have established for-profit subsidiaries to carry out
activities that do not directly fit within the not-for-profit mission but can provide
resources to support the nonprofit mission. For example, Mozilla Foundation
is the nonprofit provider of the well-known browser Firefox. The foundation’s
mission, broadly stated, is to provide free access to the Internet and to develop
software to advance that mission. Firefox does not provide its own search engine
but instead uses a third-party provider as its default search engine. In 2005,
Mozilla Foundation established Mozilla Corporation as a for-profit subsidiary
to contract for search engine services. Until late 2014, Mozilla Corporation was
contracting with and receiving payments from Google and was using Google
as its default search engine. In 2012, for example, CNET reported that Mozilla
Corporation received $311 million in revenue from Google. In 2014, Mozilla
switched to Yahoo. As part of the deal, Yahoo agreed to support Do Not Track
technology, which would enable users of Firefox to prevent tracking of their web
use for advertising purposes.28 While Mozilla Corporation is wholly owned by
Mozilla Foundation, a potential benefit of establishing a for-profit subsidiary
is that the subsidiary can raise capital by selling shares to outside investors.
Although nonprofits cannot issue equity directly, they can offer debt claims
that have payoff structures that are somewhat similar to equity. For example,
a nonprofit can raise capital by issuing revenue participation rights or other
creative financing instruments that combine the properties of equity and debt.
A quasiequity debt security is particularly useful for ventures that are legally
structured as nonprofits since they cannot issue equity. Revenue participation
rights are technically debt, but with returns that are linked to the organization’s financial performance. The investor in such an instrument does not have
a direct claim on the voting control or ownership of the enterprise, but the loan
is designed to provide greater returns if the nonprofit venture does well.
The Bridges Social Entrepreneurs Fund in the U.K., for example, committed
£1 million as a social loan to HCT Group, which uses surpluses from its commercial London buses and related services to provide community transportation
for people who are unable to use conventional public transportation. The loan
has a quasi equity feature in that the investor receives a percentage of revenues,
thereby sharing some of the business risk and gains.29
New Venture Financing 61
2.4 Organizational Form and Financing Choices
One of the early decisions an entrepreneur must make concerns the organizational form of the venture. The choice has implications for a variety of factors,
including ability to attract financing, tax treatment, liability, succession, and
ability to attract employees. The organizational form choice can be addressed
most effectively by considering the growth potential of the venture and the
availability of financing sources.
Answering a few key questions can aid the process: Are not-for-profit status
and the attendant tax exemption worthwhile? Should liability be limited, or
should losses be passed on to the company’s owners? Is it important to be able
to switch corporate forms easily as the company evolves? How important is
it to avoid corporate-style taxation (i.e., double taxation)? Who are the best
monitors of the firm—owners, investors, or managers? How will the monitors
themselves be monitored?
In the U.S. the most common organizational forms for for-profit entities are
sole proprietorship, general partnership, limited partnership, limited liability
company (LLC), S corporation (S corp), and C corporation (C corp). In the U.S.,
many not-for-profit firms are organized under Section 501(c)(3) of the Internal
Revenue Code, which allows the company to avoid paying tax on earnings in
excess of expenses. The rationale is that the excess is not distributed to owners
but rather is retained to fund the ongoing activities of the enterprise.
The organizational form choice can be changed, but not without cost. A venture at an early stage might be organized as an S corp or LLC because operating
losses are expected and these forms allow losses to be passed through to owners.
Later, when the venture needs to access larger capital sources and early owners
are looking to exit, it may be reorganized as a C corp. Even a venture that starts
as a 501(c)(3) firm can be reorganized as a taxable entity, but doing so is difficult.
While tax exemption may seem like a tremendous advantage, when income
is a small fraction of revenue the economic advantage can also be small. A forprofit venture has advantages associated with equity financing and more effective monitoring of managers. These benefits can more than make up for the tax
benefits associated with not-for-profit or pass-through status.
Assuming the venture under consideration is to be operated for profit, it is
important to consider long-run capital needs. C corps and limited partnerships
can raise large amounts of money from investors whose involvement with the
enterprise may be passive. The other forms all restrict capital-raising ability
by limiting the number of investors or limiting investment to parties who are
actively involved in the venture. Important distinctions between a C corp and
a limited partnership relate to transferability of ownership and tax treatment
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of earnings. A C corp, particularly if it is publicly held, can most easily raise
capital from diverse groups of investors, and organizing as a C corporation
facilitates transfer of ownership. Limited partnership interests are less easily
transferable. However, the limited partnership structure avoids taxation of earnings at the entity level and facilitates structures that allocate taxable gains and
losses among partners. Both forms offer limited liability to investors who are
not actively involved in the venture.
Limited liability is essential to large-scale fund-raising from investors who
wish to play passive roles in the venture. If raising a large amount of capital is
not important, a venture may be organized as a general partnership. Doing so
subjects the partners to unlimited liability but enables venture earnings to flow
through to the partners untaxed. Sometimes the number of partners can be so
large and the activities of the enterprise so diffuse that the pure partnership
form is an impediment to growth. In that case, an LLC form can preserve the
tax advantages of partnership and still offer some protection against liability.
Protection from liability can break down, however, if partners act negligently
in monitoring the activities of the venture.
The early choice of organizational form can be of strategic importance to
a new venture. Sole proprietorships and small partnerships are relatively easy
to convert to other forms, but as the venture grows and more parties become
involved as partners or owners, ability to transition from one form to another
decreases. If the firm is planning to raise venture capital or angel financing,
then a C corp makes sense (and may be required by the investors). If there is no
plan to raise equity, then an S corp or LLC is a good choice as it has tax benefits
relative to a C corp and retains the liability protections.30
2.5 Regulatory Considerations
Entrepreneurs and investors are confronted with a myriad of securities laws
when considering how to finance a venture and whether to raise capital in the
public markets. The laws governing access to the public capital markets, and
to operating as a corporation with publicly tradable securities, are complex
and fluid. In Table 2.2, we identify the main legislation that affects entrepreneurial ventures in the U.S. While we focus on the U.S., all countries have laws
that affect public market access and that require regular financial disclosure.
We identify the major components of U.S. federal securities laws; discuss state
regulations (known as “blue sky laws”) that affect security issuance; describe
the SEC’s securities registration process; and review some exemptions and safe
harbors that may affect financing choices.
New Venture Financing 63
Tab le 2 . 2
Key U.S. securities market regulations for entrepreneurs and investors
Regulation
Description
Relevance to Entrepreneurs and Investors
Securities Act of 1933
Regulates the offering and sale of securities, establishes the law related to securities fraud, and sets requirements
to register securities formally with the
federal government. It delegates to the
SEC the authority to enforce and interpret the 1933 act.
Securities and Exchange Act
of 1934
Regulates aftermarket trading of public
securities. It also establishes the law on
insider trading and buying on margin.
Regulates the organization of companies that engage primarily in investing
and trading securities. The Act includes
disclosure requirements including valuations and investment objectives.
Reforms aimed at enhancing corporate
responsibilities and financial disclosure.
Pertains to the primary offering of securities by a company and the ongoing disclosure of information once the securities are issued.
Firms wishing to issue public securities must file a prospectus (Form S-1, or
SB-1 for issues less than $10 million).
Relates to the exchange of publicly
traded securities after they have been issued, including reporting requirements.
Requires closed-end mutual funds to report fair value of non-public securities,
discouraging funds from investing in
non-public firms.
Investment Company Act
(ICA) of 1940
Sarbanes-Oxley Act (SOX)
of 2002
Jumpstart Our Business
Startups (JOBS) Act of
2012
Aims to help emerging growth companies raise funds in public capital markets by streamlining regulatory requirements for certain firms and offering
sizes.
Reporting requirements are substantial
and greatly increase the cost of being a
public firm, which may impact the decision to go public, especially for smaller
firms.
Reg. A+ allows for public equity offerings for up to $50 million with
streamlined regulations and reporting
requirements.
The “equity crowdfunding” provisions allow companies to sell securities through open platforms (e.g., Kickstarter, Indiegogo). The Act led to
Regulation Crowdfunding in 2016.
The business failures and personal bankruptcies of the U.S. Great Depression led to enactment of the two federal laws that form the basis of regulation
concerning the offering, sale, and trading of securities. The Securities Act of
1933 is the more relevant to start-ups, as it pertains to the primary public offering of securities by a company and the ongoing disclosure of information
once the securities are issued. The Securities and Exchange Act of 1934 relates
to exchange of publicly traded securities among investors after the securities
have been issued.
The 1933 Act seeks to foster transparency at the time of a company’s public
offering of securities and afterward. Under it, corporations that desire to issue
securities to the general public can do so only by filing a registration statement
with the SEC (Form S-1, or, for small businesses seeking to raise $10 million
or less, Form SB-1). The Act mandates dissemination of a disclosure document known as a prospectus and creates a complex liability scheme concerning
64
Chapter Two
misinformation in the registration statement or the prospectus or during the
distribution of the securities to public investors. The Act also places specific
limitations on the timing and content of pre-issuance communications. The
1933 Act has been amended numerous times and has become expansive in its
reach and vision.
The issuer’s responsibilities do not end with filing a registration statement
and circulating a prospectus. After the IPO or public debt offering, the company is subject to ongoing reporting requirements under the 1933 Act and under
Sarbanes-Oxley. Public companies must provide timely annual reports that
include audited financial statements and are subject to other reporting rules
that are intended to help level the playing field for investors. The cost of ongoing
reporting can be a significant burden for a small company, a consideration that
prevents all but the most promising ventures from going public.
In the U.S., the Jumpstart Our Business Startups (JOBS) Act of 2012 was a
major policy effort aimed at encouraging funding of small businesses by easing
securities regulations. The changes include creating a way for companies to use
crowdfunding to issue securities, which was not previously permitted. The JOBS
Act includes changes in Regulation A (“Reg A”) by expanding the regulation
into two tiers and increasing the threshold for its applicability. Under Reg A+
there are two tiers for securities offerings—tier 1 for offerings up to $20 million
and tier 2 for offerings up to $50 million. The JOBS Act streamlined regulations
and reporting requirements for firms making Reg A+ offerings.
Securities sold and traded within a single state are insulated from federal
regulation but still are subject to state law. The state laws that pertain to companies with publicly traded securities are known as “blue sky laws.” The phrase
derives from the concern that shady operators would try to sell unsuspecting
investors pieces of clear blue sky. As of now, every state in the U.S. has securities
laws. Unlike federal securities laws, which constitute a disclosure-based regulatory system, some states use merit-based regulation—the individual states are
empowered to decide whether an offering deserves the attention of investors.
The state laws do not regulate securities exchanges, interstate trading markets,
or disclosure by public companies in connection with shareholder trading or
voting.
Exemptions from Registration in the U.S.
Registration of securities can be very costly in terms of out-of-pocket expenses
for both lawyers and underwriters and because of potential liability. The 1933
Act exempts from registration those transactions for which registration may
New Venture Financing 65
be unduly expensive given investor sophistication or the issuer’s modest financial needs. But even if a security is exempt, securities laws apply. Thus,
the SEC can proceed against any issuer if there is misrepresentation. Exempt
transactions include purely intrastate offerings, private placements, and certain small public offerings by the issuer.
In 1982, the SEC consolidated its exemptions for small and private offerings
into a set of rules known as Regulation D (“Reg D”), which were subsequently
modified by the JOBS Act. Reg D contains three exemptions from the federal
registration requirements, and thereby facilitates private placements of equity.
1. An exemption for small offerings (Rule 504). Companies can offer and
sell up to $1 million of their securities to “accredited investors” in any
12-month period without registration. Accredited investors are high-networth individuals who are presumed not to need the investor protections
of the 1933 Act.
2. An exemption for offerings of restricted securities to accredited investors
or limited numbers of investors (Rule 505). Companies can offer and sell,
without registration, up to $5 million of their securities in any 12-month
period to an unlimited number of accredited investors and up to 35 other
persons. Investors receive “restricted securities,” meaning that they cannot be sold for six months or longer without first being registered.
3. An exemption for private placements (Rule 506). Under the private offering exemption, companies can raise an unlimited amount of capital without registration and can advertise to the general public, provided they
sell only to accredited investors and take reasonable steps to verify that
all purchasers of an offering are accredited. Securities acquired through
private placement cannot be sold for at least a year without being registered. Unlike the second exemption, all investors must be sophisticated—
that is, they must have sufficient knowledge of and experience with financial and business matters to make them capable of evaluating the merits
and risks of the prospective investment.
An additional exemption from registration was created under Regulation
Crowdfunding in 2016. This exemption covers offerings up to $1,000,000 (which
is adjusted up over time with inflation) and requires that the offering be made
through an SEC-registered online platform. Investors in crowdfunded issues
are subject to limitations on the fractions of their net worth they can invest.
Under this regulation there is a requirement for annual reporting to the SEC,
but an audited financial statement is not required. Securities purchased via
crowdfunding generally cannot be resold for a year.
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Chapter Two
2.6 International Differences in Financing Options
Resources to fund entrepreneurial ventures vary dramatically around the
globe, in terms of both the availability of funds and the types of financing.
The reasons for these differences include such infrastructure factors as enforceability of contracts, protection of minority interests, institutionalization
of money management, and the economic wealth of the society.
Financing sources are also affected by the nature of the entrepreneurial
activity that is prevalent in the country and, conversely, source availability
affects the nature of entrepreneurial activity. In the U.S., where a significant
fraction of the activity is focused on high tech with the potential for very
high returns, VC has emerged as an effective funding mechanism, and the
availability of VC funding has fostered entrepreneurship. Even so, total funds
provided to entrepreneurs by VC are quite small compared to, for example,
bank loans. In emerging economies, entrepreneurial activity is more focused
on launching and growing traditional enterprises that are relatively safe and
can generate positive cash flow quickly.
Because the mix of entrepreneurial activity varies so much, it is difficult to generalize about availability of financing sources. Based on survey data compiled by
the World Bank to examine global patterns of financing of SMEs, external financing as a fraction of total financing does not depend on country infrastructure, but
the mix of external financing does.31 Enterprises in less-developed countries are
not as able to obtain external debt and equity financing and, therefore, rely more
on operations-based financing such as trade credit. Greater reliance on vendor
financing in emerging economies is not surprising since such economies tend to
have limited availability of financing from other sources.
According to World Bank data, in most low-income countries, loans are the
only institutional source of financing, but the amount of loan financing is small
in relation to GDP, which, in low-income countries, is also quite small on a per
capita basis. For the most part, if entrepreneurs need funding in such countries, they are compelled to rely on non-institutional sources, such as personal
wealth, friends and family, and vendors and strategic partners who are willing
to extend credit. But from where do the companies that extend credit obtain
their funds if bank lending in such countries is low? One important answer is
that providers of credit are often foreign and have access to financing sources
that local businesses do not.
The outlook for entrepreneurs is much better for the high-income countries.
Bank lending is high compared to GDP, but in most countries it is dominated
by private and public debt issues and equity offerings. Of course, these are also
the countries where financing sources such as trade credit, angel investment, and
New Venture Financing 67
VC are relatively high and where bootstrap funding and funding from friends
and family are likely to be more significant.
2.7 Recap: Considerations in the Choice of Financing
We began this chapter with 4 questions regarding the choice of financing: How
urgent is the financing need? How large is it? Is it permanent or transitory?
How does it relate to the cumulative need for financing? Now that we’ve reviewed the varied sources of new venture financing, it should be clear why
these questions are important. One could rely, for example, on trade credit
with expensive terms to finance a small, temporary financing need until the
company can obtain external financing, but if the cost of paying late is high, it
does not make sense to rely on trade credit for a sustained period. Mezzanine
loans prior to an IPO are also a temporary form of financing and are viable
only when a company is nearing an exit that will fund repayment of the loan.
To add structure to the decision-making process, a useful starting point is
to assess the current stage and condition of the venture. The realistic menu of
financing alternatives is constrained by these factors. Additional considerations
include the value of outside advice, the asset intensity of the venture, track record, the level and stability of earnings and cash flow, and influences of existing
financing.
Stage of Development
Financing typically begins with bootstrapping. Seed capital normally is external financing, such as angel investment, although as we have seen, angel
investment can go beyond seed financing. Start-up financing provides funds to
initiate operations. Later-stage financing options are used to finance growth
before the venture is profitable or during the early-growth and expansion
stages. Mezzanine financing and other forms of debt are best suited for ventures that are generating taxable income and approaching the conditions for
harvesting. Exit financing, including buyout financing and public debt or equity issues, is designed to enable the entrepreneur and early-stage investors to
realize returns on their investments.
Asset Intensity of the Venture
The ability to collateralize assets is an important characteristic that affects
the choice of financing. Even a high-risk venture with no track record and an
68
Chapter Two
­ nproven entrepreneur can attract debt financing if assets are available to seu
cure the loan. These advantages to the lender translate into lower borrowing
costs, because they commit the entrepreneur to dealing reasonably with future
adversity and because they signal confidence. An offsetting concern is that a
secured lender has little interest in the success of the venture and is unlikely to
provide much useful advice other than ways to ensure that the loan is repaid.
Immediacy of Financing Needs
If financial needs are immediate, most government-based financing is precluded, given the length of time to process applications. Similarly most equity
funding sources are precluded, unless the entrepreneur has an established relationship with the investor. VC firms and angel investor groups can have lengthy
due diligence and approval processes, especially for initial investments. The
same is true of corporate venture funds. Similarly, late-stage financing like an
IPO or an acquisition would take months of planning to complete. Even more
expedient methods of raising equity, such as private placements, are unlikely
prospects if needs are urgent.
The most realistic sources of immediate financing are those for which little or
no negotiation or approval process is required or for which financing is preapproved. For early-stage firms, the realistic alternatives are the personal resources
of the entrepreneur, friends and family, loans secured by personal guarantees,
and possibly existing shareholders. Early-stage ventures that are producing
revenues can approach customers or suppliers for funds, but it is unlikely that
a suitable agreement can be reached on short notice since such relationships are
not sufficiently strong at that point. Temporary use of trade credit (by forgoing
prompt payment discounts) and factoring are feasible if the firm is at a stage
where it is producing revenue.
The Influence of Near-Term Financing Needs
Two primary considerations influence the venture’s choice of near-term financing. First, the choice of near-term financing depends on the permanency of the
need. Transitory needs are generally best financed with short-term sources.
Second, the choice depends on the amount the venture is seeking. For small
amounts, likely sources include the SBA, crowdfunding, banks, and private
placements of equity with a few individuals or a group of angel investors. For
larger amounts, private placements of debt with institutional lenders, VC investors, and strategic partners gain importance. If required financing is very large,
the entrepreneur could consider a registered public offering of debt or equity.
New Venture Financing 69
The cost of short-term financing can be volatile. If the financing is used to
acquire long-term assets, it may increase the uncertainty of cash flows or even
threaten venture survival. Equity and long-term debt are the obvious sources
for meeting long-term needs of large and established firms. The issue cost percentages clearly favor debt issues. Offsetting the lower cost of issuing debt are
two important considerations: first, the issuance costs of debt are recurring
whereas equity is a source of permanent financing; and second, compared to
equity issues, debt issues limit flexibility.
The Influence of Cumulative Financing Needs
If the entrepreneur expects that the venture will soon begin to generate positive cash flow sufficient to fund growth, then he can make near-term financing
choices with little regard to their potential effect on the availability or cost of
future financing. The important scenarios, however, are the extremes. How
should near-term financing be arranged if the entrepreneur’s cumulative needs
may be substantially higher or substantially lower than the venture’s ability to
generate free cash flow?
If cumulative needs are expected to be higher than present needs, current
financing must not seriously impair the ability to raise capital later. Investor
options to make additional investments at pre-agreed prices, or rights of first
refusal for new financing, or financing arrangements that include other options
and rights can be problematic for the new venture in the future. If such terms
are accepted, their effects may be offset by other provisions, such as call options
on the financing or buyout options that enable the venture to restore flexibility,
such as by paying off the existing investors or lenders. If the entrepreneur expects
future needs to be lower than near-term needs, the arrangements should enable
the venture to pay off the financing with little or no penalty as cash becomes
available. Call options and buyout rights are useful under these conditions, as
well as when the venture is expected to grow rapidly.
Other Considerations That Affect Financing Choices
Diversification of risk. Outside investors are generally capable of more
fully diversifying risk. In contrast, entrepreneurs tend to be highly underdiversified, as they put much of their human and financial capital into their ventures.
Because investors who can diversify do not need to be compensated for bearing idiosyncratic risk, outside investors can justify lower expected returns for a
given level of total risk. The implication for choice of financing is compelling.
All else equal, greater reliance on external financing can increase the value of
70
Chapter Two
the entrepreneur’s ­ownership stake. An offsetting consideration is the cost associated with managing relations with the investors, including reporting and
access to information and the possible loss of control that can occur.
Taxes. Not all organizations generate taxable income, and the applicable tax
rate depends in part on organizational form. For example, tax rates and other
aspects of taxation differ for corporations versus proprietorships. Also, the tax
treatment of interest payments on debt differs from that of returns to equity
investors. Debt is not attractive for many new ventures, partly because if they
are not generating taxable income they may be unable to benefit from the tax
deductibility of interest payments.
The value of outside advice. An important consideration in the choice of
early-stage financing is whether the venture can benefit from active involvement
of an outside investor.32 Angel investors and VCs are active investors. Active
involvement may be limited to monitoring the operation and serving on the
board. Or it may be as extensive as stepping in as CEO for a period to help get
the venture on track, making key strategic and tactical decisions, and helping to
build the management team.33 In most cases, the active investor’s return comes
mainly through ownership in the venture.
The cost of any monitoring services provided by outside investors ultimately
must be borne by the entrepreneur in the form of reduced ownership share. There
are several reasons why it might be worthwhile for the entrepreneur to incur
the cost of subjecting the venture to monitoring. One pragmatic reason is that
monitoring is a precondition of getting outside capital from an angel or VC firm
and that alternatives that do not involve monitoring are usually unavailable to
early-stage, high-risk ventures. Overall, the empirical evidence on the value of
monitoring suggests that both angels and VCs can add value beyond the benefits
associated with just the financing.
Assessing the Effects of Financing Choices
The effects of these financing considerations on the value of the entrepreneur’s
claim are complex and sometimes counterintuitive. A sensible approach to
making the financing choice is to model the venture, including revenue growth
projections and underlying cost structure, and study the effects of alternatives
on the value of the entrepreneur’s claim. More often than not, the realistically
available choices are limited to a few. But even if the entrepreneur has few op-
New Venture Financing 71
tions, he will want to evaluate such variables as the amount of the funding, the
form of the commitment the investor plans to make (e.g., debt versus equity,
passive versus active commitment), and the effects of restrictions the investor
imposes on future actions (through restrictive covenants or by other means).
2.8 How Financial Distress Affects Financing Choices
The financial condition of a venture affects its financing opportunities. A
profitable business with steady cash flow can arrange financing more easily
than one that is struggling. For many new ventures, however, at some stages of
development, struggling to meet financing needs is normal. The term “financial distress” means more than simply that a venture is in need of cash to fund
its operations. A distressed firm is one that has fundamentally disappointed
investors. If the investors are creditors, a distressed firm may have violated
important debt covenants or may have defaulted on its repayment obligations
or be about to do so. For equity-financed ventures, financial distress can mean
that the venture is so short of cash that it is unable to carry out its business
plan. The cash shortage can be symptomatic of missed milestones or other
disappointing performance. Such a firm may be compelled to terminate or
significantly scale back operations and may be at risk of losing key employees.
Why Turnaround Financing Is Different
Financing distressed firms is different from financing high-risk start-ups for two
reasons. First, distress means that the entrepreneur has already failed to achieve
a level of success consistent with earlier projections. That failure can undermine
the entrepreneur’s credibility. Although it is common to search for external
causes for underperformance, the evidence suggests that the usual causes are
fundamental failures of management. Management issues include lack of critical skills, excessive turnover, and similar deficiencies. Inadequate financial planning and controls can contribute to unanticipated cash shortages, poor pricing
decisions, lack of control over costs, and similar problems.
Given that the venture has failed in fundamental dimensions, why should
investors assume that projections in a revised plan are achievable or that the
entrepreneur is capable of managing the operation? In the U.S., an entrepreneur
who has encountered trouble can take advantage of a legal presumption that
the cause of financial distress, if not completely external, is curable. A Chapter
11 bankruptcy offers a window of protection against creditors’ demands for
72
Chapter Two
repayment of their loans. The underlying concept is that if a venture is fundamentally viable, then creditors, as a group, are better off remaining on board.
The statute gives the entrepreneur time to try to achieve a reorganization plan
that creditors find acceptable.
This leads to the second reason why financing of distressed firms is different.
The financial structures of most firms are based on a premise that the venture will
be successful. Creditors, including trade creditors and even employees, expect that
they will be paid. When a venture gets into trouble, the orientations of creditors
change. Secured creditors begin to look more intently at the value of collateral as
a source of repayment. They worry that if the entrepreneur is allowed to continue
to operate the business, the collateral may depreciate. They may press for liquidation as a means of repayment. Suppliers to distressed firms no longer assume that
they will eventually be paid for merchandise they sell on credit; they may demand
cash payment. Employees may assume that their jobs are at risk and try to protect
themselves by seeking new employment. Even customers may become concerned
and stop purchasing.
In the U.S., once a company declares bankruptcy, individual creditors can no
longer attach the company’s assets. Instead, the assets become part of a common
pool, and they are not dispersed unless the creditors reach an understanding
about how the various debts will be settled. For example, the creditors as a group
might conclude that the assets should be liquidated and the creditors paid out
of the proceeds, or they might decide to refinance the failing company.
The objective in turnaround financing is to devise a new financial structure
such that each party expects to be better off by maintaining or increasing its
investment than by collecting the liquidation value of its financial claims. Financial restructuring can involve significant changes in the financial claims held
by different investors. Claims of creditors, for example, may be exchanged for
common stock or for securities convertible to common. In general, restructuring
must achieve three results: it must reduce the cash needs of the venture for servicing existing debt; it must reallocate the going-concern value toward creditors
and away from the entrepreneur and other equity investors; and it must provide
a means of raising enough cash to restart the venture.34
For some kinds of ventures, the real costs of financial distress are small. Business assets, though they may be specialized to a particular use or geographic
location, are readily marketable, and success or failure of the venture does not
depend on the special knowledge or skills of employees. In such cases, financial
distress can result in little more than a reallocation of ownership claims and may
not materially affect the revenue or profit stream of the venture.
For other kinds of ventures, even the rumor of financial distress can have
important real consequences. Key employees, who would be difficult to replace,
New Venture Financing 73
may resign. Customers may switch to alternative suppliers to avoid the possible
consequences of dealing with a firm that might fail. Managers who anticipate
failure may engage in excessive risk taking or may depreciate the assets of the
venture. When the potential costs of financial distress would be large, earlier
financing choices should be based, in part, on the value of avoiding financial
distress. Among other things, this could imply raising a higher level of capital
at the outset and using a financial structure that limits the risk of defaulting or
violating debt covenants.
2.9 Summary
Investing in new ventures is very different from investing in the shares of a
public company. Because of the high degree of uncertainty about the potential
for survival and success, financing commitments tend to be made in stages
that are related to development of the venture. Customarily, development is
described in terms of hypothesis testing and milestones that correspond to
resolutions of significant uncertainty. Related to this approach to entrepreneurship, maximum value for the entrepreneur will not be achieved by raising
all of the anticipated financing needs at one time. As business model hypotheses are tested and validated, the riskiness of successful ventures declines, and
staging can produce higher expected value for the entrepreneur.
Appropriate forms of financing depend on the stage of new venture development. Financing typically begins with bootstrapping (tapping the resources
of the entrepreneur and friends and family). Seed capital normally is external
financing, such as angel investment, although angel investment can go beyond
seed financing. High-tech ventures with long development stages may require
specific R&D financing. Start-up financing provides funds to initiate operations.
Financing options, such as VC, are used to finance growth before the venture is
profitable or during the early growth and expansion stages. Mezzanine financing
and other forms of debt are best suited for ventures that are generating taxable
income. Exit financing, including buyout financing, acquisition, and public debt
or equity issues, is designed to enable early-stage investors to realize the returns
on their investments.
This chapter describes some of the main financing choices available to the
entrepreneur, defines terms and concepts that are used frequently in the industry, and identifies considerations that bear on the choice, such as securities
regulations, and institutional differences in capital markets across countries.
We also provide an approach for assessing financing needs and the timing of
those needs.
74
Chapter Two
Review Questions
1. Identify types of financing that are associated with each of the following
stages of new venture development: research and development, start-up,
early growth, rapid growth, and exit.
2. Why are the following important factors when choosing among financing sources: immediacy of financing need, the size of the need, the permanency of the need, and the cumulative need?
3. What, besides providing financial capital, are the advantages to a new
venture of attracting angel group financing? Venture capital financing?
4. What factors should an entrepreneur consider when differentiating between CVC and VC investors?
5. When is vendor-provided credit (trade credit) an important form of financing for new ventures and how do the credit terms affect the cost of
using trade credit?
6. How do government programs encourage new venture development?
Give examples of government programs that facilitate financing of new
ventures.
7. Why do small firms, especially new ones, have limited access to the public debt market? Under what conditions are ventures likely to be able to
access the bond market?
8. What are the differences in financing choices for for-profit firms and
not-for-profit firms?
9. Identify the basic U.S. securities laws that affect new venture financing
options. How has the JOBS Act (2012) affected new venture financing?
10. What are accredited investors and why are they important to new ventures seeking registration of their equity securities?
11. How is turnaround financing different from financing associated with a
healthy growing firm?
Notes
1. Chatterji and Seamans (2012) illustrate the importance of credit cards
to entrepreneurial activity. They study the impact of credit card deregulation
and find that deregulation increases the probability of entrepreneurial entry,
with a particularly strong effect for black entrepreneurs.
2. See Stouder (2002), who analyzes data on 74 entrepreneurs.
3. Reported by Robb and Robinson (2014), who base their work on the
large-scale Kauffman Foundation Firm Survey.
New Venture Financing 75
4. Gates (1995).
5. Drover, Busenitz, Matusik, Townsend, Anglin, and Dushnitsky (2017).
6. For comparison of accelerators and incubators, see Atkins (2011).
Universities and colleges also offer incubating services, which allows them
to leverage access to faculty and student research. The scale of support and
terms of investment vary considerably across programs. As examples of prominent incubators, see the University of Texas, https://​ati​.utexas​.edu/, and the
University of Chicago, https://​polsky​.uchicago​.edu/​programs​- events/​polsky​
-incubator/.
7. Strauz (2017) models the trade-off between the benefits that crowdfunding provides entrepreneurs by reducing demand uncertainty and allowing them to screen more effectively for valuable projects, and to gauge the expected costs associated with the entrepreneur’s incentives for moral hazard.
8. A few angels may commit to significantly larger amounts ($500,000 to
$3 million). Angels in this category include people like Mitch Kapor, founder
of Lotus; Paul Allen, cofounder of Microsoft; and Ron Conway, a Silicon Valley angel investor who was an early-stage investor in Google, Ask Jeeves, and
PayPal.
9. See Itenberg and Smith (2017) for further discussion.
10. See Hellmann (2002) concerning the effects of conflicts of interest
arising in corporate venturing. He suggests that VC firms are less prone to
such risks.
11. Kwan and Carleton (2010) report that in a sample of privately placed
loans by corporations, a large fraction were renegotiated. In fact, one reason
for a firm to place a large loan privately is that, compared to public debt, privately placed debt adds the flexibility of being able to renegotiate covenants.
See also Boot, Gopalan, and Thakor (2006).
12. Cornelli and Yosha (2003) study the role of convertible securities
in new ventures with staged financing. They suggest that with straight debt,
the entrepreneur may have an incentive to “window dress” to attract the next
round of financing. Financing with convertible securities reduces the entrepreneur’s incentive to engage in such actions because the lender shares in equity
returns.
13. Research by Brown and Earle (2017) shows that each million dollars
of loans supplies an increase of 3–3.5 jobs. They estimate that taxpayer cost
per job created is in the range of $21,000–$15,000 per job.
14. In addition to the SBA, the Farmers Home Administration, the
Department of Commerce, the Department of Energy, the Department of
Housing and Urban Development, and the Department of the Interior all offer loans or loan-guarantee programs. Other state and federal government
76
Chapter Two
­ nancing sources include state business and industrial development corporafi
tions, the Export-Import Bank of the United States, and the Small Business
Innovation Research (SBIR) program.
15. SBICs generally invest their own money, along with borrowed funds
covered by SBA loan guarantees. Investments that can generate steady streams
of cash flow are most appropriate for these sources.
16. Craig, Jackson, and Thomson (2009) find evidence of a small positive impact of the SBA’s guaranteed lending programs on the financial performance of companies that receive guaranteed loans.
17. See Petersen and Rajan (1997). Ng, Smith, and Smith (1999) analyze
trade credit terms across industries.
18. The effective annual rate is calculated as:
(1 + discount percent)(365/credit period) − 1.
19. Huyghebaert, Van de Gucht, and Van Hulle (2007) find that entrepreneurs prefer trade credit over bank debt, even if it is more expensive. They
argue that suppliers are more willing to negotiate with borrowers in the event
of default, rather than compelling them to liquidate.
20. Mian and Smith (1992) and Smith and Schnucker (1994) examine
firms’ choices to factor their receivables. Factors are more likely to be used
by firms when information costs about buyers and monitoring costs are high.
21. Some states have classified Uber drivers and FedEx Ground operators
as employees for specific purposes such as requiring the employer to provide
benefits. The legal determination of employment status, however, does not affect the fundamental economic nature of the relationship where the venture is
funding its activities partly through resources provided by the operators.
22. Brickley and Dark (1987).
23. See Gompers (1995); Barry, Muscarella, Peavy, and Vetsuypens
(1990); and Lin and Smith (1998).
24. See https://​site​.warrington​.ufl​.edu/​r itter/​ipo​- data/.
25. Early-stage investors are often precluded from selling shares they
own in or during the IPO. For the most part, they harvest by selling in the
public market after the shares have been trading for several months.
26. The cost of a public offering has three components: the spread between the offer price and net proceeds to the issuer (which constitutes the
underwriter’s fee), issue costs borne directly by the issuing firm, and underpricing. For equity issues, in addition to the fee and expenses, it is standard
practice for the underwriter to set the offering price to around 15% below
what the market price is expected to be at the time of the offering. If the underpricing is considered to be part of the issue cost, then the total cost of an
New Venture Financing 77
IPO, as a percentage of net proceeds to the issuer, could be in the 15–​30%
range, depending on actual underpricing, issue size, and other factors.
27. Many online sources provide listings of nonprofit funding opportunities, including corporate funding; see http://​
w ww​
.fundraiserhelp​
.com/​
corporate​-grants​-source​-list​.htm.
28. For links to relevant references see, https://​
en​
.wikipedia​
.org/​
w iki/​
Mozilla​_Foundation.
29. See Bugg-Levine, Kogut, and Kulatilaka (2012).
30. In terms of absolute numbers of business entities, small proprietorships dominate, especially those with annual receipts less than $1 million.
But C corps become more prevalent as total business receipts increase and
account for the majority of total business receipts. S corps, LLCs, and partnerships, the flow-through entities, are more prevalent in the midsize receipt
classes. See the IRS’s Statistics of Income Bulletin (annual).
31. See Beck, Demirgüç-Kunt, and Maksimovic (2008).
32. Bettignies and Brander (2007) model the entrepreneur’s choice between bank and VC financing. Their model implies that VCs cannot survive
purely as financial intermediaries and that VC funding will be most attractive
to entrepreneurs who value the skill set or industry knowledge provided by
the VC. Ueda (2004) compares bank and VC investment decision making and
monitoring. See also Winton and Yerramilli (2008).
33. Inderst and Mueller (2009) model the impact of active investor involvement on new venture growth. They observe that involvement by active
investors can create a competitive advantage over rivals. The advantage comes
through better and faster information conveyance, quicker shutdown of less
promising ventures, and faster growth of ventures with better prospects.
34. Pindado, Rodrigues, and de la Torre (2006) find that for nondistressed firms, the amount of long-term debt is related to both tax shields and
insolvency costs. These relationships do not hold for distressed firms. They
conclude that equity issues and renegotiation with creditors are used by distressed firms to reduce leverage.
References and Additional Reading
Atkins, D. 2011. “What Are the New Seed or Venture Accelerators?” http://​
www​.nbia​.org/​resource​_ library/​review​_ archive/​0611​_01​.php.
Barry, C., C. Muscarella, J. Peavy, and M. Vetsuypens. 1990. “The Role of
Venture Capital in the Creation of Public Companies: Evidence from the
Going Public Process.” Journal of Financial Economics 27: 447–​71.
78
Chapter Two
Beck, T., A. Demirgüç-Kunt, and V. Maksimovic. 2008. “Financing Patterns
Around the World: The Role of Institutions.” Journal of Financial Economics 89: 467–​87.
Bettignies, J., and J. Brander. 2007. “Financing Entrepreneurship: Bank Finance Versus Venture Capital.” Journal of Business Venturing 22: 808–32.
Boot, A. W. A., R. Gopalan, and A. Thakor. 2006. “The Entrepreneur’s Choice
between Private and Public Ownership.” Journal of Finance 61: 803-836.
Brickley, J., and F. Dark. 1987. “The Choice of Organizational Form: The
Case of Franchising.” Journal of Financial Economics 18: 401–20.
Brown, J. D. and J. Earle. 2017. “Finance and Growth at the Firm Level: Evidence from SBA Loans.” Journal of Finance 72: 1039–80.
Bugg-Levine, A., B. Kogut, and N. Kulatilaka. 2012. “A New Approach to
Funding Social Enterprises.” Harvard Business Review (January–February): 118–123.
Chatterji, A., and R. C. Seamans. 2012. “Entrepreneurial Finance, Credit
Cards, and Race.” Journal of Financial Economics 106: 182–95.
Cornelli, F., and O. Yosha. 2003. “Stage Financing and the Role of Convertible Securities.” Review of Economic Studies 70: 1–32.
Coyle, J. F., and J. M. Green. 2014. “Contractual Innovation in Venture Capital.” Hastings Law Journal 66: 133–83.
Craig, B. R., W. E. Jackson III, and J. B. Thomson. 2009. “The Economic
Impact of the Small Business Administration’s Intervention in the Small
Firm Credit Market: A Review of the Research Literature.” Journal of
Small Business Management 47: 221–​31.
Dempwolf, C., J. Auer, and M. D’Ippolito. 2014. Innovation Accelerators: Defining Characteristics Among Startup Assistance Organizations. Report issued by the SBA Office of Advocacy. http://​w ww​.sba​.gov/​advocacy.
Drover, W., L. Busenitz, S. Matusik, D. Townsend, A. Anglin, and G. Dushnitsky. 2017. “A Review and Road Map of Entrepreneurial Equity Financing Research: Venture Capital, Corporate Venture Capital, Angel
Investment, Crowdfunding and Accelerators.” Journal of Management 43:
1820–53.
Ewens, M., R. Nanda, and M. Rhodes-Kropf. 2017. “Cost of Experimentation
and the Evolution of Venture Capital.” Harvard Business School working
paper 15-070. Journal of Financial Economics, forthcoming.
Gates, B. 1995. The Road Ahead. New York: Viking.
Gompers, P. 1995. “Optimal Investment, Monitoring, and the Staging of Venture Capital.” Journal of Finance 50: 1461–​89.
Gompers, P., and J. Lerner. 2001. “The Venture Capital Revolution.” Journal
of Economic Perspectives 15: 145–​68.
New Venture Financing 79
Hellmann, T. A. 2002. “A Theory of Strategic Venture Investing.” Journal of
Financial Economics 64: 285–​314.
Huyghebaert, N., L. Van de Gucht, and C. Van Hulle. 2007. “The Choice Between Bank Debt and Trade Credit in Business Start-Ups.” Small Business Economics 29: 435–​52.
Inderst, R., and H. M. Mueller. 2009. “Early-Stage Financing and Firm
Growth in New Industries.” Journal of Financial Economics 93: 276–​91.
Itenberg, O., and E. Smith. 2017. “Syndicated Equity Crowdfunding: The
Trade-off Between Deal Access and Conflicts of Interest.” Simon Business
School working paper FR 17-06. https://​papers​.ssrn​.com/​sol3/​papers​.cfm​
?abstract​_id​=​2933822.
Kerr, W. R., R. Nanda, and M. Rhodes-Kropf. 2014. “Entrepreneurship as
Experimentation.” Journal of Economic Perspectives 28: 25–48.
Kerr, W. R., and R. Nanda. 2015. “Financing Innovation.” Annual Review of
Financial Economics 7: 445–62.
Kwan, S. H., and W. T. Carleton. 2010. “Financial Contracting and the Choice
Between Private Placement and Publicly Offered Bonds.” Journal of
Money Credit and Banking 42: 907–29.
Lerner, J., A. Schoar, S. Sokolinski, and K. Wilson. 2018. “The Globalization
of Angel Investments: Evidence Across Countries.” Journal of Financial
Economics 127: 1–20.
Lin, T. H., and R. L. Smith. 1998. “Insider Reputation and Selling Decisions:
The Unwinding of Venture Capital Investments During Equity IPOs.”
Journal of Corporate Finance 4: 241–​63.
Mian, S. L., and C. W. Smith Jr. 1992. “Accounts Receivable Management
Policy: Theory and Evidence.” Journal of Finance 47: 169–​200.
Ng, C., J. K. Smith, and R. L. Smith. 1999. “Evidence on the Determinants of
Credit Terms Used in Interfirm Trade.” Journal of Finance 54: 1109–​29.
Petersen, M. A., and R. G. Rajan. 1997. “Trade Credit: Theories and Evidence.” Review of Financial Studies 10: 661–​91.
Pindado, J., L. Rodrigues, and C. de la Torre. 2006. “How Does Financial Distress Affect Small Firms’ Financial Structure?” Small Business Economics
26: 377–​91.
Robb, A., and D. Robinson. 2014. “The Capital Structure Decisions of New
Firms.” Review of Financial Studies 27: 153–79.
Smith, J. K., and C. Schnucker. 1994. “An Empirical Examination of Organizational Structure: The Economics of the Factoring Decision.” Journal of
Corporate Finance 1: 119–​38.
Stouder, M. D. 2002. “The Capital Structure Decisions of Nascent Entrepreneurs.” PhD dissertation, Rutgers University.
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Strauz, R. 2017. “A Theory of Crowdfunding: A Mechanism Design Approach
with Demand Uncertainty and Moral Hazard.” American Economic Review 107: 1430–76.
Ueda, M. 2004. “Banks Versus Venture Capital: Project Evaluation, Screening, and Expropriation.” Journal of Finance 59: 601–​21.
Winton, A., and V. Yerramilli. 2008. “Entrepreneurial Finance: Banks Versus
Venture Capital.” Journal of Financial Economics 88: 51–​79.
C h a p t e r Th r ee
Ve ntu r e Capital an d
An g e l I nve sti n g
T h e t e r m “ v e n t u r e capital” is sometimes used in a generic sense to mean
capital invested in new ventures. It more accurately refers to capital supplied by
a specific type of financial institution, the venture capital (VC) firm. VC firms
have a unique organizational structure and focus on a specific market niche of
high-risk ventures with potential for rapid and significant growth. Most new
ventures are not suitable for VC investing, and accordingly, most capital supplied to new ventures does not come from VC firms. On the other hand, VC
has been an early source of financing for many successful companies and is an
important driver of economic growth.
As discussed in Chapter 2, angel investing historically has focused on
seed-stage companies to help finance their early development, and VC investing generally follows as firms reach a rapid-growth stage and require larger
amounts of financing to sustain growth. In recent years, however, the lines
have blurred, as some VC firms have funds that focus on seed-stage financing
and some angel investors, through organized groups and syndicates, have
increased their presence in seed-stage investing, but also delve into growthstage investments. Accordingly, in this chapter, we discuss both angel and VC
investing. We review the evidence on the structure of the markets in which VCs
and angels participate, their business practices, their financial performance,
and the impact of their involvement with young firms. Chapter 4 provides an
economic analysis of contractual terms used in the venture “deals” between
entrepreneurs and VCs and angels, and the types of financing instruments and
provisions they use.
Understanding the natures of VC firms and angel organizations is important
for any entrepreneur who plans to undertake a venture that has the potential
83
84
Chapter Three
for rapid growth and for anyone considering making an angel investment or an
investment in a VC fund. The entrepreneur’s concerns are where to search most
effectively for financing and what to expect from the relationship. Entrepreneurs
whose ventures are not well suited for VC financing, for example, can easily waste
time and effort seeking funding from firms that, because of their orientation,
are unlikely to find the entrepreneur’s enterprise attractive.
VC firms, angel groups, and syndicates are of interest for other reasons. First,
the contract structures they use, both when they invest and when they raise
capital from investors, illustrate market-based solutions to many of the information and incentive problems that are endemic to new venture financing. Second,
new venture financing structures and terms are evolving rapidly. Being aware
of the markets for early-stage investing can provide a useful frame of reference
for examining and evaluating new organizational forms and practices and for
evaluating opportunities for innovation in entrepreneurial finance.
VC is a particular type of private equity. Private equity refers to any investment in equity where the financial claims cannot be readily traded on an organized exchange. This includes VC-style investments in early-stage companies
but can also include investments in unregistered shares of public companies that
have freely tradable registered shares. Firms that are engaged as general partners
in VC funds may also be engaged in other kinds of private equity funds such as
later-stage growth funds. Private equity funds that are focused on buyouts may
also allocate a portion of their investment capital to VC deals.
Investments in private equity can take the form of leveraged buyouts, VC,
distressed debt investments, mezzanine capital, and so forth. With a VC investment, the firm typically invests in young start-ups with growth potential and
generally does not obtain majority control. In contrast, when a private equity
firm undertakes a leveraged buyout (LBO), the firm seeks to acquire majority
control of an existing firm. Given that we are most interested in financing arrangements for new ventures, our focus is on VC as a subcategory of private
equity.
3.1 Development of the Venture Capital Market
The first modern VC fund in the U.S. was organized in 1946 by American Research and Development (ARD). ARD’s fund was organized as a closed-end
mutual fund. In contrast to the VC funds of today, it was open to investment
by any investor. In contrast to open-end funds, where new investors can join at
any time by purchasing new shares of the fund, the total capital investment in
Venture Capital and Angel Investing 85
a closed-end fund is fixed. After initial sale of the shares by the fund, a person
who wishes to invest must buy existing shares from someone else who owns
them and wants to sell.
ARD established the practice, which persists today, of searching for high-risk
deals with the potential for big wins. The fund’s early-stage investment in just
one of its ventures, Digital Equipment Company, accounted for roughly half
of the entire return to fund investors over a period of more than two decades.1
Several other funds that were launched during the 1950s and 1960s imitated the
structure and orientation of ARD.
As documented later in this chapter, early growth of the VC industry in the
U.S. was slow, restricted by regulation, tax policy, case law, and professional
investment management practices.
The Investment Company Act and the SEC
In the U.S., mutual funds and other investment companies are regulated under provisions of the Investment Company Act of 1940 (ICA), enforced by
the SEC. Based on ARD’s early success, it might appear that mutual funds
would be a viable vehicle for VC investment. However, because of SEC interpretations of some provisions of the ICA in the late 1960s, the ARD style of
closed-end mutual fund was (and still is) largely precluded for VC investment.
Specifically, the SEC required that the “fair values” of fund investments be
determined “in good faith by the fund’s board of directors.” Thus, reliance
on consultants to determine fair value would not absolve the board of potential liability for reporting valuations later determined to be inconsistent with
fair value. Particularly detrimental to VC, the SEC equated fair value with
liquidation value, even with regard to assets where the fund has no near-term
intent to liquidate, and the SEC prohibited reliance on formulaic approaches
to estimating fair value.
The SEC’s emphasis on liquidation value and good-faith liability of fund directors precipitated a withdrawal of closed-end funds from investing in private
equity. Over most of the following decade, the VC market languished. Closedend funds effectively were foreclosed as vehicles for investing in VC.
Consistent with the impact of the SEC’s interpretations of the ICA fair value
standard, Figure 3.1 shows that new capital commitments to VC generally declined from 1969 through 1977.2 Capital commitments are reputation-enforced
promises to invest up to a specified amount in the fund. The decline over this
period is 77%, from $171 million in 1969 to $39 million in 1977. To show percentage volatility more clearly, the figure is presented in log scale in Panel A.
Panel B is in non-log scale.
86
Chapter Three
Panel A
$1,000,000
Millions of dollars (log scale)
$100,000
$10,000
$1,000
$100
1995
1997
1999
2001
2003
2005
2007
2009
1997
1999
2001
2003
2005
2007
2009
2017
1993
1995
2015
1991
1993
2013
1989
1991
2011
1987
1989
1985
1987
1983
1981
1979
1977
1975
1973
1971
$1
1969
$10
Panel B
$120,000
Millions of dollars
$100,000
$80,000
$60,000
$40,000
2017
2015
2013
2011
1985
1983
1981
1979
1977
1975
1973
1971
$0
1969
$20,000
Fi g u r e 3 .1
New commitments of VC in the United States: 1969–2017
Data through 2005 are drawn from the following sources: U.S. Census Bureau, Statistical Abstract of the United States (various
issues); Thomson Reuters Venture Capital data (http://​thomsonreuters​.com); Sahlman (1990); National Venture Capital Association (http://​
www​.nvca​.org). Data from 2006 on are from the 2018 PitchBook-NVCA Venture Monitor.
sources:
Panel A is presented in log scale to show percentage volatility more clearly. Panel B is in levels.
VCs could avoid being subject to SEC interpretations of the ICA by taking
advantage of safe harbor provisions in the SEC Acts (discussed in Chapter 2).
The safe harbor provisions drove the firms that were interested in raising capital
for investment in nonpublic companies to organize as limited partnerships and
Venture Capital and Angel Investing 87
to only raise capital from investors who are deemed by the SEC to be sophisticated and not requiring the protections of the SEC Acts.
The VC limited partnership structure that is common in the U.S. today is
designed to fit within the safe harbors. To be able to invest in a VC fund and
other alternative private equity funds, an investor either must be a qualified
institutional buyer such as a pension fund, endowment, or insurance company
or must be an accredited individual investor. To be considered an accredited
investor, an individual must qualify under either an income test or a wealth
test that only about 7% of the U.S. population can pass. Thus, overwhelmingly,
individual investors are precluded from investing in VC.
Recognizing that many individuals would like to allocate a portion of their
wealth to VC and private equity, in recent years, several prominent private equity
firms (e.g., KKR and Blackstone) have completed IPOs by selling a partial ownership interest in their management companies. Investing in the management
company provides somewhat of a workaround of the ICA and allows individual
investors to access alternative asset classes. By investing in the management
company, the investor shares some of the management fees and carried interest
associated with the firms’ private equity funds.
The Prudent Investor Standard and the Employee
Retirement Income Security Act (ERISA)
An additional problem impeded the early growth of formal VC limited partnerships. To achieve their intended purpose, VC funds need access to patient
capital from investors who are able to (1) accept illiquidity and high risk in
assets with long holding periods and (2) reliably commit to providing investment capital when called upon to do so. The natural sources of such funds
are wealthy individuals and large institutional investors such as pension funds
and endowments. Historically, however, professional asset managers with fiduciary liability viewed themselves as being largely precluded from investing
in high-risk or nonmarket assets. The constraints were derived from an array
of old-fashioned state and federal regulations, legal precedents, and investment policies.
One specific constraint was the traditional “prudent person” standard of fiduciary responsibility. Under this standard, responsibility to clients was based on
the risk of each investment separately, without regard to portfolio diversification.
Using this standard, courts would attempt to assess whether each investment was
appropriate for the manager’s client (e.g., an employee participant in a retirement
plan). Investment managers who were held to such a standard generally avoided
investing in anything but safe securities such as “blue chip” stocks.
88
Chapter Three
Significant changes in the VC market in the U.S. began to occur in the late
1970s. First, in 1979 the Department of Labor reinterpreted the prudence standards governing investments by pension funds. The reinterpretation enabled
managers to view the risk of an individual investment in the context of its contribution to the overall risk of the pension fund portfolio. Under the new prudent
investor standard, pension funds could invest in securities that traditionally had
been viewed as too risky (imprudent). The result was a rapid increase in the level
of pension fund investments in VC funds. Other asset managers with fiduciary
responsibilities similar to those of pension fund managers, such as managers of
endowment funds, have adopted the new prudent investor standard.
Second, reduction of the capital gains tax rate in the Revenue Act of 1978
spurred an increase in entrepreneurial activity. Workers had more incentive to
leave salaried employment to pursue new ventures in which the principal form
of compensation would be a capital gain on sale of the venture’s stock. The
rate reduction also increased incentives to invest in financial instruments that
produce capital gains.3
Corresponding to the capital gains tax rate reduction, annual new commitments to VC funds increased from $39 million in 1977 to $600 million in 1978.
After 1978, as shown in Figure 3.1, the level increased dramatically, reaching
$106.1 billion in 2000 before dropping precipitously to $3.9 billion in 2002. New
commitments grew again during the mid-2000s, reaching $35.5 billion by 2007
before again starting to decline. In 2016, the industry had its best fund-raising
year in more than a decade, raising $40.6 billion across 277 funds, still well
below the high water mark in 2000.
The level of new VC commitments each year is related to what is happening
in the public equity markets. The early 1980s and much of the 1990s were periods of rising stock market values, high levels of new investment in the capital
markets, and high levels of IPO activity. The late 1980s was a period of lower
economic growth and a generally less active capital market. The decline beginning in 2000 corresponds to the collapse of the dot-com industry. The growth
of new commitments during the 2000s and the decline in 2008 parallel stock
market performance during the period, as does the recent uptick in venture
commitments.
Despite its rapid growth, the VC market in the U.S. remains small relative
to public equity and debt markets. Investment-grade bonds issued in the U.S.
in 2016, for example, were $1.27 trillion. Moreover, VC commitments in 2016
were comparable in size to SBA loan guarantees to small businesses, which in
the 2017 U.S. budget totaled $46 billion.
VC funding can dry up quickly when the equity capital market declines. The
data underlying Figure 3.1 shows that new commitments dropped precipitously
Venture Capital and Angel Investing 89
$120
12,000
10,000
$100
Dollars invested (billions)
8,000
$60
6,000
$40
4,000
$20
2,000
Number of companies
Dollars invested (billions)
Number of companies
$80
0
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1995
1990
1985
1980
1970
$0
Fi g u r e 3 . 2
VC investments in the United States: 1970–2017
Data through 2005 are drawn from the following sources: National Venture Capital Association (2009); PricewaterhouseCoopers/
National Venture Capital Association MoneyTree Report (https://​w ww​.pwcmoneytree​.com), based on data from Thomson Reuters. Data
from 2006 on are from the 2018 PitchBook-NVCA Venture Monitor.
sources:
The figure shows the total dollars invested by VCs and the number of companies invested in over time.
in 2009, down by 60% from 2008. However, the impact on the flow of investment
capital from new commitments to new investments can be spread over several
years. Figure 3.2 shows the decline in new investments from 2008 to 2009. This
28% decline is much less dramatic than the decline in new commitments.
Worldwide Growth of Venture Capital
Institutional factors similar to those that slowed the early growth of organized
VC in the U.S. have restrained growth in other parts of the world. The pension
funds that invest in VC in the U.S., for example, are defined-benefit plans in
which asset allocation choices are made by a board that acts as the agent of plan
participants. In many countries, defined-benefit retirement plans do not exist
or account for only a small fraction of retirement savings. Some countries rely
primarily on redistribution systems similar to the U.S. Social Security system,
which is unfunded; instead, current beneficiaries receive payments from the contributions paid in by current workers. In some countries, retirement plans have
formally imposed restrictions on investment choices or are required to invest
heavily in the sovereign debt of the country or securities selected for political
reasons. Similar restrictions sometimes limit the ­investment choices available to
insurance companies, another important source of VC funding in the U.S.
90
Chapter Three
In general, the factors that limit availability of funding to VC are more onerous in other countries than in the U.S. One response has been the introduction
of VC funds that raise capital from U.S. investors for the purpose of investing
in ventures in developing economies. Another has been that VC firms outside
the U.S. tend to raise funds from traditional financial institutions, such as commercial banks. When they do, the management practices of the VC firms tend to
be more conservative and based partly on the collateral or personal guarantee
of the entrepreneur.
Venture capital in Europe. Many factors influence the level of VC activity in
a country or region. Generally, the level of formal activity is higher in developed
economies than in emerging economies. Europe ranks second in overall VC activity. As shown in Figure 3.3, VC new commitments in 2008 reached over €9 billion
and declined by more than half during the financial market collapse. Similarly
to the U.S. experience, the 2016 level of new commitments to VC in Europe, at
almost $10 billion, modestly exceeds the level in 2008. As a comparison, in 2016
the level of commitments in Europe is about one fourth of the level in the U.S.4
In general, the level of VC activity is positively related to the economic development and financial health of a country. Figure 3.4 shows investments by VCs
in portfolio companies that are located in Europe, expressed as percentages of
€10
€9
€8
Billions of euros
€7
€6
€5
€4
€3
Fi g u r e 3 . 3
European new VC
commitments:
2008–2017
PitchBook 2017
European Venture Annual
Report.
source:
€2
€1
€0
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Venture Capital and Angel Investing 91
0.07%
0.06%
0.05%
0.04%
0.03%
0.02%
Greece
Ukraine
Romania
Luxembourg
Czech Republic
Italy
Poland
Portugal
Austria
Bulgaria
Netherlands
Baltic Countries
Norway
Hungary
Belgium
Germany
United Kingdom
Spain
Denmark
0.00%
France
0.01%
Sweden
The data are compiled by
the portfolio company and
not by the location of the
VC fund.
0.08%
Switzerland
source: Invest Europe:
https://​w ww​.investeurope
​.eu/​media/​651727/​i nvesteurope ​-2016 ​- european
​-private ​- equity​-activity
​-final​.pdf.
0.09%
Ireland
New VC investments in
Europe as percentages
of country 2016 GDP
Finland
Fi g u r e 3 . 4
country GDP. Investors can be local or international. Standardizing by GDP allows
us to evaluate the importance of new venture activity to each country. While the
UK, Germany, and the Netherlands, as examples, have much larger absolute VC
investments, Ireland has outpaced other countries in VC investments as a percentage of GDP. Ireland’s climate for new companies is very favorable, as reflected in
its corporate tax rate, which is one of the lowest in Europe, and in other policies
aimed at attracting and retaining new ventures. The primary areas of investment
are life science and tech, including fintech, traveltech, and edtech.5
The Changing Role of Venture Capital
In Chapter 2 (Figure 2.2), we presented evidence that the sector focus of VC
investment has changed over time in response to fluctuations in technological
innovation and changes in the need for investment capital for high-risk ventures.
The stage focus has also changed. In the U.S. in the 1980s and early 1990s, as
shown in Figure 3.5, around 40% of investment dollars went predominantly to
seed/start-up and early-stage ventures. In more recent years, after the 2000 tech
rally ended, VC funds have focused their resources more on late-stage investments. For the period after 2000, the proportion of resources devoted to seed/
start-up and early-stage investments has averaged around 35%.
Although the percentage allocation of investment dollars has remained small
in recent years, the number of seed and early-stage investments by VC funds
has increased. In 2008, VC funds invested $1.5 billion in seed/start-up ventures,
Chapter Three
100%
Fi g u r e 3 . 5
80%
Late-stage
70%
Early-stage
60%
Seed/start-up
50%
40%
30%
20%
10%
2016
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
0%
1980
Data through
2005 are drawn from
National Venture Capital
Association, Yearbook
(various years); PricewaterhouseCoopers/National
Venture Capital Association MoneyTree Report
(https://​w ww​.pwcmoney
tree​.com). Data from 2006
on are from the 2018-Q1
PitchBook-NVCA Venture
Monitor.
sources:
90%
Percentage of new commitment dollars
U.S. VC investments
by stage of
development:
1980–2017
1982
92
compared to $9 billion invested by angels. More recently, the number of seedstage investments has increased. This reflects changes in technology and the
emphasis on experimentation at the development stage. Ewens, Nanda, and
Rhodes-Kropf (2017) describe the change in investment strategy as “spray and
pray,” where VCs and other early-stage investors provide small amounts of
funding and limited oversight to a large number of start-ups, most of which
they abandon when the experiment fails, but a few of which are very successful. They associate this change in strategy with the advent of inexpensive cloud
computing. This innovation has allowed start-ups that leverage the cloud to shift
their larger capital requirements to later stages, when some of the early-stage
uncertainty has been resolved.
Another notable change over time is the increasing presence of corporate
venture capital in the VC market. Figure 3.6 provides information on CVC
involvement over the 12 years ending in 2017. While the percentage of VC deals
with CVC involvement has remained in the 10–15% range, CVC has increasingly
focused on the larger deals so that the number of deals with CVC involvement
had risen from 24% of total VC investment dollars in 2009 to 46% in 2016.
Who Invests in Venture Capital Funds?
VC requires investors who are able to commit substantial amounts of capital
over long periods and who do not regard the illiquidity of their VC investments as an important cost. As of 2003, the latest available year, the primary
sources of funding for VC in the U.S. were pension funds (43%), endowments
Venture Capital and Angel Investing 93
Fi g u r e 3 . 6
50%
Corporate VC
involvement in U.S. VC
deals: 2007–2017
45%
source: PitchBook-NVCA
Venture Monitor, 2018-Q1.
The figure shows the percentage of VC deals that
have CVC involvement
and the percentage of total
deal value that includes
CVC involvement. Total
CVC investment dollars is
not reported.
VC deals with CVC involvement (% of all VC deal value)
Number of deals with CVC involvement (% of all VC deals)
40%
35%
30%
25%
20%
15%
10%
5%
0%
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
(25%), and insurance companies (20%). Other investors in VC include highnet-worth (accredited) individuals, family offices, and corporations.6
Earlier, we indicated that VC in countries other than the U.S. has been
restrained by limited access to the kinds of institutions that are the primary
sources of VC in the U.S. Europe is second to the U.S. in terms of the size of the
VC industry. Yet even there, we can see the impact of limited access to institutional sources of patient risk capital. In sharp contrast to the sources of U.S.
funding, the most important sources for European VCs in order of importance
(in 2016) are government agencies (25%), corporations (15%), private individuals
(12%), funds-of-funds (10%), and family offices (8%). Endowments and foundations, insurance companies, and pension funds collectively account for only
15%, and “other” accounts for 15%.7
A relatively new source of VC funding is country-sponsored Sovereign Wealth
Funds (SWFs). These funds may invest directly in new ventures or indirectly as
limited partners of VC funds. In recent years there has been an increase in SWF
interest in investing in high-profile new ventures, including “unicorns,” defined
as VC-backed companies valued at $1 billion or more. SWFs invested in 6 of
the 10 most highly valued start-ups in 2017. Temasek, a SWF in Singapore, for
example, has participated in 88 rounds of 74 start-ups since 1999.8
The Impact of Venture Capital on the Economy
Early-stage VC investment is small compared to corporate and government
R&D investments. For example, during 2015, VC firms received $78.6 ­billion
94
Chapter Three
in new VC commitments, which was a ten-year high. This represents less than
a small fraction of the total $462 billion in R&D expenditures in the U.S.
in 2015.9
Though VC is a small industry, it has a large economic impact. Many of
today’s largest and most research-active corporations received early-stage funding from VC firms. Gornall and Strebulaev (2015) study how firms that were
VC-financed in early stages have profoundly changed the U.S. economy. Public companies with VC backing employ 4 million people and account for one
fifth of the market capitalization and 44% of the R&D spending of U.S. public
companies. A longer-term perspective shows that of all U.S. public companies
that were founded after 1974, 42% were VC-backed. These firms now represent
63% of U.S. market capitalization and 85% of total R&D. Five of the largest
companies in the U.S. in terms of market cap are Apple, Alphabet (Google),
Microsoft, Amazon, and Facebook, all of which were VC-backed.10
Sedlacek and Sterk (2017) show that employment created by start-ups is volatile and pro-cyclical, meaning that successful large firms tend to be born during
periods of booming consumer demand, when it is relatively easy for firms to
acquire new customers. Interestingly, the positive impact of start-ups on employment persists over time (rather than being mean-reverting), as highly scalable
businesses need time to reach their full potential.
Why is the impact of VC so great? The answer lies in the specialized market
niche that VC serves. VC investors seek young companies that have the potential
for rapid and substantial growth and that will need significant capital investments
to finance that growth. VC thereby fills a niche between very early-stage private
investment by entrepreneurs, their friends, and angel investors, and the market for
public capital or private corporate acquisition. Firms with limited product markets
and slower growth potential generally are not candidates for VC. Such firms tend
to have more stable capital needs and may be better suited for debt financing.
3.2 The Organization of Venture Capital Firms
Successful VC investing depends on finding solutions to an array of problems.
The fund manager must sort through a plethora of business plans, each describing a venture with a negligible operating history. In addition to identifying the few ventures that have some potential for success, the investor must be
able to add enough value to the deals to cover the extra costs of managing the
fund’s investment portfolio, including the return on the VC’s commitment of
effort (human capital). Beyond addressing these problems, the manager must
Venture Capital and Angel Investing 95
be able to commit the fund’s capital for long periods with little hard evidence
that value is being created for investors. The VC fund manager must be able to
expand, contract, and refocus efforts in response to changes in opportunities
and new information about prospects for success, and must be able to redeploy
the VC firm’s human capital when its ability to add value to a venture wanes.
The Limited Partnership Structure
To address these challenges in the context of the regulatory environment described earlier, the VC limited partnership has become the dominant organizational form for VC investing in the U.S. Figure 3.7 is a schematic representation of the limited partnership form of a VC fund. Unlike a corporation or
Fi g u r e 3 .7
Organizational
structure of VC funds
VC funds are usually
organized as limited
partnerships. The GP
manages the fund, while
LPs provide most of the
investment capital. The
fund invests in a portfolio
of new ventures.
•
•
•
•
•
Effort and
1% of
capital
General Partner
Generate deal flow
Screen opportunities
Negotiate deals
Monitor and advise
Harvest investments
Annual management
feeFee
2%–3%
2-
Carried interest
20%–30%
of gain
20 -
Investment
capital and
effort
Venture
Capital
Fund
Capital
appreciation
70%–80% of
gain
99% of
investment
capital
•
•
•
•
•
Financial
claims
Limited Partners
Pension plans
Life insurance companies
Endowments
Corporations
Individuals
Portfolio
Companies
• Value
creation
96
Chapter Three
open-end mutual fund, a VC limited partnership has a finite life span, typically 7–​10 years. The general partner (GP), usually a VC firm that is itself organized as a partnership, is the fund organizer and is responsible for raising
investment capital from the limited partners (LPs) and deploying the capital
by investing in portfolio companies. On the capital deployment side, the GP
screens opportunities based on quality and compatibility with the GP’s capabilities and with timing of the fund’s capital flows. When an attractive investment prospect is identified, the GP negotiates the terms for investing.
Committing the fund’s financial capital to a venture also commits some of
the GP’s human capital to ongoing involvement in monitoring and advising. The
intensity of these efforts varies across funds and across portfolio companies.
Philosophies differ, and some ventures warrant more active investor involvement than others. Finally, the GP is responsible for harvesting the investment.
Harvesting enables the fund to provide a return to the LPs and allows the GP
to redeploy human capital to other investments.
Raising the capital for a VC fund is a costly endeavor. The GP commits a
substantial amount of time to marketing the fund to prospective investors and to
managing relations with existing investors. Most institutional investors require
periodic (at least annual) reporting, including valuations and status reports on
the fund’s portfolio companies.
The GP’s primary contribution to the fund is in the form of effort. In addition, though specifics vary, the GP normally commits 1% of the fund’s capital;
LPs provide the other 99%. A small fraction of invested capital is used each
year to cover costs related to managing the operation of the fund. The balance
is invested in portfolio companies, in exchange for financial claims on the companies. If and when these investments are harvested, the returns are distributed
to LPs, first to repay their initial investments, with the balance (the capital gain)
being shared between the LPs and the GP. Normally, the LPs receive 70–80%
of the gain and the GP receives the balance as “carried interest.”
The Process of Investing in VC
Figure 3.8 illustrates the VC investment process and its relation to fund maturity. After the fund concept is developed, the GP makes concurrent efforts
to secure commitments from investors and to generate deal flow. The fund
closing and first capital call mark the point when the GP begins to build a
portfolio of investments.
Some VC performance statistics are reported based on funds grouped by
“vintage year.” The vintage year is the year of the first capital call or first investment, even though some activities commence months earlier. Most funds are
intended to last about 10 years.
Venture Capital and Angel Investing 97
Development of
Fund Concept
Fi g u r e 3 . 8
The VC investment
process
Secure
commitments
from investors
Closing of Fund
First capital call
Year 0
2–3 years
Generate deal
flow
Screen
business
plans
Evaluate and
conduct due
diligence
Negotiate
deals and
staging
Additional
capital calls
Invest funds
Value Creation and Monitoring
4–5 years
2–3 years
or more
Board service
Performance evaluation and review
Recruit management
Assist with external relationships
• Help arrange additional financing
•
•
•
•
Harvesting
Investment
• IPO
• Acquisition
• LBO
• Liquidation
Distributing
Proceeds
• Cash
• Public shares
• Other
10 years
plus extensions
The role of the GP changes over the life of the fund, from activities related
to investing capital to those related to managing and monitoring investments
and finally to those related to harvesting.
Normally, it takes 2 to 3 years before a fund is fully invested. During this
period, the GP is busy screening plans, conducting due diligence on prospective investments, and negotiating deals. Corresponding to these efforts, the GP
makes additional capital calls and seeks to place the entire commitment with
new ventures. While the subscription agreement makes reference to a “call-down
schedule,” in practice the timing of capital calls can be accelerated or extended
depending on how quickly appropriate deals are identified.
In addition to capital calls made to existing committed investors, a fund can
also have more than one “closing.” A fund closes when sufficient commitments of
capital have been made and sufficient investment opportunities have been found
to warrant going forward. A closing is a legal process in which the ­commitments
98
Chapter Three
are used to define an ownership group. During the fund-raising stage, the GP
may bring in new investors and oversee a second or third closing. A closing
defines a group of investors who are treated identically as the fund progresses.
Managing a fund with multiple closings gives rise to potential opportunism as it
enables investors to get into the fund at different times. Existing investors may
be concerned with the potential for opportunism by new investors who might
try to buy into existing successes at valuations that are too low. Conversely, new
investors may be concerned that existing investors will try to exploit information
advantages or get better deals for themselves. Thus, having a second closing
with a different investor group elevates the importance of accurate valuations
of portfolio companies and fund investments in them.
Fund managers can try to address potential opportunism by segregating
existing and new investments into separate funds. Of course, an investor can
also avoid opportunism by acquiring the right, ex ante, to participate in each
closing at the same level (e.g., 5% of each closing). However, this may increase
the investor’s financial commitment beyond what was desired.
Following investment in a portfolio company, the GP’s responsibility shifts to
value creation and monitoring. Service on boards of portfolio firms is routine,
as is continual performance evaluation. In addition, the GP often is involved
in recruiting the management team, building relations with trading partners,
and helping to arrange subsequent financing.
Most GPs hope to harvest their investments about 5 years after the investments are made. Ideally, the GP continues to work with a portfolio company
as long as the GP is able to add value and until a point when a liquidity event
is possible. Harvesting enables investors to realize the gains on their involvement, through receipt of either cash or publicly tradable shares. Distributions
are natural milestones for the GP in its efforts to establish another fund.
The harvesting phase is typically 2 to 3 years. The long window allows the
GP to time the exits in light of market conditions and company-specific factors.
Because portfolio companies progress at different rates, the periods of investment, value creation, and harvesting within a single VC fund overlap. Typically,
the LPs can agree to extend the life of the fund for several years to permit orderly
liquidation of investments.
The finite life of the fund limits and controls the GP’s behavior. A GP that
is successful in adding value will have little trouble generating commitments to
a new fund, whereas one that has not been successful is unlikely to attract new
investors. In practice, VC firms usually try to launch new funds more frequently
than every 10 years but with enough time in between so that investors can assess whether the fund seems to be on track for success. Here again, long-term
relationships and reputation are important. Unlike with the public equity mar-
Venture Capital and Angel Investing 99
ket, prospective investors are few and can easily communicate with each other.
Concern with reputational damage disciplines the GP and protects investors
from short-run opportunism.
Terms in a VC Limited Partnership
Private Placement Memorandum
The VC private placement memorandum (PPM) is similar to the prospectus of
a public company. It sets out conditions for investing, requirements for closing, distribution requirements, and the other terms such as industry or stage
focus for investments in portfolio companies. Table 3.1 contains a summary of
some of the more important terms in the PPM.
Tab le 3 .1 Summary of terms in a VC limited partnership private placement memorandum (PPM)
Purpose
General Partner
Limited Partnership Interests
General Partner’s Investment
Minimum for Closing
Payment of Subscriptions
Term
Allocation of Profit and Loss
Distributions
Management Fee
The “purpose” identifies the fund’s focus and investment objective, such as “to earn superior returns from early-stage investments in e-commerce ventures.” Provisions may
limit the amount invested in any one venture and/or restrict the types of investment vehicles the fund can use.
Normally the general partner is a partnership of individual general partners. Investors
in the fund may seek access to the contract between the limited partnership and the individual general partners to assess their incentives and perhaps seek revisions and limitations to the agreement.
The provision specifies the total size of the fund that is being raised, such as $250 million, and the minimum per investor, such as $10 million.
The usual contribution is 1%, and sometimes may be in the form of a promissory note.
The provision specifies the dollar commitment, such as $100 million, that must be received before an initial closing. If there is a subsequent closing, it may be required to occur within a fixed period, such as one year after the initial closing.
Investors must provide a specified amount of capital at the time of closing (such as
30% of their total commitment). Subsequent calls often are made on short notice to afford maximum flexibility for investing the funds. Penalties for late or missed calls will be
enumerated.
The term of the agreement usually is 10 years, with options to extend under certain conditions, which permits orderly liquidation of investments.
The provision specifies the allocation and distribution of returns from investment. The
most common allocation is 80% to limited partners and 20% to the general partner.
The provisions ensure prompt distribution of invested capital and gains as investments
in portfolio companies are harvested. Normally, distributions can be in the form of cash
and public securities. Terms may specify that the limited partner investors receive a full
return of their invested capital before the general partner receives any distributions. An
alternative is to distribute 20% of the gains to the general partner, but if there are losses
on some investments, then the agreement obligates the general partner to make up for
the overcompensation through a clawback provision.
Normally, the management fee is 2% of contributed and committed capital for established VCs managing large funds, and up to 3% for smaller funds with newer VCs. The
fee may be linked to expenses, may provide for specific offsets to be charged, and may be
set in light of other funds under management.
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In contrast to public capital markets, VC is a market where the LP’s reputation
is as important as the LP’s money. When a new fund is created, the GP seeks
capital commitments from investors at the same time that it assesses investment opportunities and negotiates deals. Each LP’s commitment is formalized
in a subscription agreement. Actual investments in new ventures are not made
until the fund’s closing. Each investor’s commitment is conditional on the fund
generating sufficient commitments from other investors to reach the minimum
total for closing. When the closing occurs, the GP can make an initial “capital
call” on the investors. Following a capital call, the investors have a short time
(such as 30 days) in which to deliver the funds.
To achieve the best performance, the GP makes capital calls only when there
are immediately attractive portfolio investment opportunities. The investors may
receive several capital calls during the first few years of the fund’s life. Because
each investor is expected to deliver capital when called upon, the investors’
reputations are important to fund operation. Failure to respond to a capital call
could cause the fund to miss investment opportunities and otherwise disrupt
fund operation. Because of this, penalties for missed capital calls are substantial.
Use of closing dates and capital calls enables a VC fund to operate with very
little internal liquidity. The need for liquidity does not disappear, however; it
is shifted instead to the investors. Consequently, the typical investors are those
whose ordinary levels of liquidity are sufficient to enable them to respond to
unpredictable capital calls.
The upshot is that GPs seek investors who can reliably commit their capital
for the entire life of the fund. What better place to look for such investors than
institutions such as pension funds, endowments, and life insurers, all of which
have predictable needs for liquidity that are small relative to the overall sizes of
their investment portfolios? By extension, the GP screens for individuals who
can reliably commit to maintaining their capital investments.
Why Limited Partnership?
The LP structure solves many of the managerial problems that arise in new
venture investing. Because investors can participate in multiple funds and can
diversify across a broad array of other investments, the GP is free to concentrate on the kinds of opportunities in which the partner’s expertise adds the
most value. The finite life of a VC fund subjects the GP to ongoing market
discipline. The LP structure also enables the pool of VC funds to expand or
contract depending on the opportunities perceived by the GP. Finally, the
structure makes efficient use of the liquidity that naturally accrues to large
institutional investors. The downside, compared to the closed-end fund struc-
Venture Capital and Angel Investing 101
ture, is that most individual investors in the U.S. are foreclosed by SEC regulations from investing directly in VC partnerships.
Venture Capital Contracts with Investors
Traditional corporate control mechanisms are not available to LPs. For example, there is no board of directors to represent the LPs’ interests and no market
for corporate control to discipline the GP. Thus, other mechanisms must align
GP and LP incentives. A VC limited partnership agreement (LPA) formalizes
the PPM and addresses concerns related to shirking and opportunistic behavior by the GP.
Table 3.2 describes some of the more common fund management covenants
associated with LP agreements. These are in addition to terms described in the
PPM (Table 3.1). With respect to overall management, investors may be concerned with the GP’s incentives to take on excessive risk, to favor existing funds
over new one, and to increase management fees by delaying disbursements. To
address the incentive to take excessive risk, the agreement may limit the investment in any single venture and restrict the ability of the GP to add leverage.
Another concern is that the GP may make investment decisions that favor its
existing funds. Consider a VC firm that manages several funds. Suppose an older
fund has an investment in a portfolio company that is seeking second-round
financing. The GP may take advantage of LPs in the new fund by using their
capital to make a follow-on investment in the venture on terms that benefit the
older fund. This is more of a concern if the VC is not well established and wants
to use the early-fund performance to attract capital. To obviate this concern, a
fund contract may give investors the right to review the GP’s decisions to make
follow-on investments in ventures held by its other funds or specify that it may
do so only as a co-investor along with another first-time investor in the venture.
To avoid fee manipulation, the agreement may restrict the GP’s ability to
reinvest fund assets. This limits the ability of the GP to increase its management
fees (which usually are calculated as a percentage of the value of assets under
management) by postponing distributions. In addition, as a means of controlling
potential shirking while collecting management fees, the agreement may limit
the investment the GP can make in financial instruments that offer low expected
returns and do not require significant commitments of VC effort.
Additional concerns regarding the activities of GPs relate to self-dealing
and dilution of effort. To address self-dealing, GPs’ partners and employees
may be precluded from buying or selling investments in a venture in advance
of transactions on behalf of the fund. To control shirking, the agreement may
limit the amount of outside investment a GP can make. It also may restrict the
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Chapter Three
Tab le 3 . 2 Key covenants in a VC limited partnership agreements (LPA)
Overall Fund Management
Key Person Provision
Investment in a Single Firm
Use of Debt
Co-Investment by Venture
Capitalist’s Other Funds
Reinvestment of Profits
The provision addresses what occurs if there are important changes in core investment
personnel. Limited partners may have the right to suspend investment or terminate the
fund.
As a way to restrict risk-taking by the venture capitalist, the covenant limits the amount
of the fund’s investment in a single firm.
The covenant limits borrowing by the fund and possibly debt maturity. It also may limit
borrowing by the general partner (venture capitalist) or guarantees of loans by the venture capitalist. The covenant addresses concerns with leveraging the fund and creating
conflicts of interest.
To limit opportunism and avoid incentive conflicts, the covenant provides for monitoring
of decisions to invest in any portfolio company that is in one of the venture capitalist’s
other funds. The covenant may require co-investment with other funds.
The covenant restricts or prohibits reinvestment of profits from a fund and concerns
possible conflicts of interest regarding the venture capitalist’s management fees.
Activities of General Partner
Personal Investing in Portfolio Companies
Sale of Interest by General
Partner
Fund-Raising
Outside Activities
Addition of General Partners
The covenant limits the ability of the members of the general partnership to invest
alongside of, or in advance of, the fund. The provision serves several purposes, including controlling incentives of the venture capitalist to overinvest efforts in those funds in
which it has a personal investment, and to avoid conflicts about when to harvest. Limits
on the timing of investment may prevent front running by the venture capitalist.
The general partner may not be permitted to sell its interest in a portfolio company separately from selling the limited partners’ interest. This provision protects the limited
partner from self-dealing.
The covenant may limit ability of the general partner to raise new funds until existing
funds are substantially committed (to avoid dilution of effort and to prevent efforts to
try to increase fees).
The provision ensures that the general partner devotes sufficient effort to portfolio companies and limits its outside activities or involvement with outside companies, especially
during the portfolio companies’ early stages.
The covenant limits additions of partners to ensure that the quality of effort to the fund
remains high.
Types of Investments
Restrictions on Asset Classes
The provision limits the ability of the general partner to invest in certain classes of assets, notably those that require less effort (such as public securities), other venture funds,
or portfolio companies that compete in industries where partners lack expertise. The
provision limits opportunistic behavior by the venture capitalist.
ability of the VC to raise capital for new funds. Further, it may limit the outside
activities of the partners, as well as the addition of new partners, and may set
expectations for intensity of monitoring.
As might be expected, the most commonly negotiated terms in a LPA are
management fees, the percentage split of the carried interest, clawbacks, and
key person provisions. The LPs have ultimate control of the fund through their
ability to withdraw from the partnership or terminate the agreement. As a
Venture Capital and Angel Investing 103
practical matter, however, withdrawal is unlikely because substantial sanctions
usually are contractually imposed for withdrawal and because early termination
usually is not in the interest of the partners.
3.3 Investment Returns and Compensation
As noted earlier, the gain on investment in a portfolio company is shared between the LPs and the GP according to the terms specified in the limited partnership agreement. While this sounds simple, implementation is challenging.
Beyond this, since the GP is itself usually a partnership with employees, returns to the GP must be allocated to the individuals who are involved.
Returns to Limited Partners in VC Funds
The returns to LPs are commonly reported either as internal rates of return
(IRRs) or “net multiples.” The IRR is computed over the period from when
the money is actually received by the fund from the LPs until it is returned to
them. A net multiple is essentially just the ratio of cash paid out to cash invested. So a return of $3 for each $1 invested is a net multiple of 3—the investors got their money back plus $2 more for each $1 invested.
Both measures provide useful information and help to discipline the GP to
act in the interest of the LPs. Since holding on to large amounts of cash before
it is needed for good projects will reduce the IRR, the measure discourages the
GP from making capital calls before they are actually needed and encourages
them to pay out cash quickly once it is no longer needed. The net multiple does
somewhat the opposite. It discourages the GP from harvesting too soon just to
be able to report a high IRR. The two measures work best together. The IRR
is sometimes maximized by harvesting good projects quickly because holding
longer would lower the return, even if the project would continue to earn a rate
higher than its cost of capital. The net multiple, conversely, encourages holding
too long, provided that the project is still generating cash, even at a low rate.
The actual returns to VC funds are subject to considerable volatility over
time. Figure 3.9 shows quarterly net returns to LPs. The GP’s carried interest
generally is equivalent to about one fourth of the return to the LPs. In many
periods in the figure the LPs’ returns are small or negative; in some years, however, the return is very high.
As can be seen in the figure, LP returns from VC are pro-cyclical. They were
quite high during the market run-up associated with the tech rally, but plunged
and became negative after mid-2000 when the high-tech market collapsed.
Chapter Three
104
80%
Quarterly percentage return
60%
40%
20%
2016 Q1
2015 Q1
2014 Q1
2013 Q1
2012 Q1
2011 Q1
2010 Q1
2009 Q1
2008 Q1
2007 Q1
2006 Q1
2005 Q1
2004 Q1
2003 Q1
2002 Q1
2001 Q1
2000 Q1
1999 Q1
1998 Q1
1997 Q1
1996 Q1
1995 Q1
1994 Q1
1993 Q1
1992 Q1
1991 Q1
1990 Q1
1989 Q1
1988 Q1
1987 Q1
1986 Q1
1985 Q1
1984 Q1
1983 Q1
1981 Q1
(20%)
1982 Q1
0%
Fi g u r e 3 . 9
Net returns by quarter to limited partners of VC funds: 1981–2016
sources:
Returns through Q1 2009 are from the National Venture Capital Association. Returns after that date are from Preqin.
­ oreover, the very high returns of the late 1990s were generated over unusuM
ally short holding periods, so they can overstate the investment return potential.
A similar but less dramatic episode surrounds the 2008 financial market collapse. In Figure 3.9, the simple quarterly average of the returns to VC investing
over the entire time period is 3.3%. However, the simple average overstates the
long-run rate of return an LP would have realized by investing at the beginning
and holding to the end. Converting to continuously compounded returns, the
quarterly average is 2.9%, and the compound annual IRR is 12.0%. We defer,
until later, the question of whether this level of return is sufficient to cover the
opportunity cost of taking money away from other investment opportunities.
Returns to General Partners
Waterfalls. Returns to LPs and the GP follow a progression that is sometimes
referred to as a “waterfall.” The progression is depicted in Figure 3.10.
Venture Capital and Angel Investing 105
Investment Return
Management fee
LP return of
principal
LP preferred
return
GP carry on
preferred return
LP 80% of
residual capital
gain
GP 20% carry on
residual capital
gain
Fi g u r e 3 .1 0
Depiction of a VC waterfall provision
In this illustration, the distribution of returns follows a pattern: payment of management fees, return of the LP’s initial capital investment,
payment of LP’s preferred return (if any), a catch-up payment of the GP’s carry on any preferred return to the LPs, distribution of 80% of
the residual capital gain to the LP and 20% of the residual capital gain to the GP.
To illustrate, consider a small fund with a $100 million commitment. The
GP comprises five partners and a number of other employees. For simplicity,
the fund pays a 2.5% fee on committed capital each year for the operating budget and there is no preferred return to the LPs. The GP is entitled to a carried
interest of 20%.
Suppose the fund has a 10-year life span and a 5-year average holding period
of investments, with 20% annual appreciation after fees. For simplicity, suppose
the fund invests $20 million per year in Years 1 through 5 and realizes uniform
returns of $49.8 million per year in Years 6 through 10 (a 20% per year return on
each investment). The management fee is $2.5 million per year, or $500,000 per
partner of the GP, but is used to pay for fund operations including base salaries.
The GP’s gross return due to capital appreciation is the 20% carried interest
[0.2 × ($49.8 million − $20 million)], or about $5.95 million annually, equivalent
to about $1.2 million per partner of the GP in each of years 6 through 10. These
returns, of course, are before partnership expenses. The LPs invested $20 million in each year, 1 through 5, and receive a $20 million return of principal in
each year 6 through 10. They also receive 80% of the $29.8 (i.e., $23.84) million
gain in each of the last 5 years.
Clawbacks. In the preceding illustration, the GP receives a portion of the
carried interest each time a venture is harvested. This works in the example
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Chapter Three
because each investment has a positive payout that is more than the amount
invested. The reality is more likely to be that good investments are harvested
relatively quickly and bad investments stay on the books until the end of the
fund life. Challenges arise because if payouts on good investments are made
early, the GP may receive more than 20% of the gain. In a strict application of
the waterfall, the GP would not realize any carried interest return until all of
the assets of the fund had been liquidated through sale, acquisition, or failure.
Normally, the GP does not want to wait 10 years or more until all of the returns
are realized.
Accordingly, the waterfall distributions are often handled asset by asset,
as in the preceding example. Thus, if the fund invests in a venture that has a
successful IPO early in the life of the fund, the carried interest fraction of the
proceeds may be distributed to the GP at the time. But what happens if the
later ventures do not do well, so that the LPs do not realize the overall return to
which they were entitled? In such cases, the investment agreement may include
a “clawback” provision that requires the GP to return “excess distributions” to
the partnership. A clawback represents the GP’s promise that it will not receive
a greater share of the distributions than was bargained for.
Consider a simple example of a fund that makes only two investments of
$10 each. The first investment is later harvested for $60. The LPs get $50 (the
$10 investment plus 80% of the gain). The GP gets $10 (20% of the gain). Now,
the second investment turns out to be a failure and is written off. In this case,
the fund invested $20 and the total payoff was $60, so the total gain is $40. The
GP is entitled to 20% of the gain, or $8. Since the GP has already received $10,
$2 would be clawed back. While it seems risky for LPs to allow the GP to be
rewarded before the entire fund is liquidated, the normal exposure is not large.
The amount that is clawed back in this simple example is small relative to the
total the GP had received.
VC Firm Internal Structure and Compensation
Because VC firms are small and private, data are sparse regarding their internal organization and compensation. Some data are available from Gompers,
Gornall, Kaplan, and Strebulaev (2016), who surveyed a large sample of VCs
and find the average firm employs 14 people, 5 of whom are senior partners.
Most VC firms have a few junior deal-making personnel as well. Others who
work at VC firms include entrepreneurs-in-residence, analysts (at larger firms),
and staff. VC firms that focus on early-stage investment tend to be smaller,
and those with a later-stage focus require more personnel for due diligence ef-
Venture Capital and Angel Investing 107
forts. Gompers et al. also find that in 60% of the funds, partners will specialize
in different activities, including fund-raising, deal making, deal sourcing, and
networking. The typical respondent in their sample (a senior partner) held five
board seats, and the data indicate that the single largest amount of time spent
by VCs is in working with portfolio companies.
Based on the survey, most VC firms compensate partners depending on their
individual success, although, in recognition of the value added by the organization, larger and more well-established VC firms are less likely to allocate compensation heavily based on individual success.11 The compensation structure
must create incentives for individual effort and for cooperation. The optimal
mix is likely to vary with the size, stage, and industry focus of the funds that
are being managed by the VC firm.
The typical compensation package for partners of a VC firm includes a salary,
a bonus, and a share of the carried interest. Not all VC firm partners receive
equal shares of the carry, particularly in early-stage VC firms. The compensation will vary with the size of the fund. An estimate by Ramsinghani (2014) is
that for larger well-known VC firms, the compensation for a managing GP of
one of its VC funds could consist of salary ($700,000), bonus ($350,000), and
carry ($101,000) for a total of $1.151 million. An analyst would receive a salary
and bonus of about $110,000 and would not participate in the carried interest.
An individual partner of the VC firm may manage assets for more than one
VC fund. Note, however, that these estimates are for successful funds, so there
is a selection bias in the numbers; if a fund performs poorly, the partner’s career
as a VC may be brief.
3.4 Impact of Compensation on Investment Selection
The compensation arrangement between the GP and LPs is an important determinant of investment attractiveness. The financial claim of the GP has the
characteristics of a call option: a limited downside and a significant upside.
If the fund does well, the GP realizes a significant return through its carried
interest. Conversely, if the investments perform poorly, most of the loss accrues to the LPs, who contribute the bulk of the financial capital; the GP continues to collect a management fee.
This structure can affect the kinds of ventures that attract the GP’s interest,
as well as the terms of financial contracts between the fund and its portfolio
companies. A venture with an attractive expected return but limited upside potential is unlikely to be a prospect for VC, even if downside risk is small. Other
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Chapter Three
things equal, such a venture does not offer the potential for the GP to benefit
significantly by sharing in the gain.
The attractiveness of an investment to the GP also depends on the terms of
the financial contract. A high-risk venture that is structured to create financial
claims that offer safe but reasonable returns to investors is unlikely to attract
VC funds. On the other hand, a low-risk venture can receive funding if the
financial claims are structured to offer substantial upside potential by shifting
downside risk to the investor.
The financial claims most commonly sought by VC investors are those that
align the interests of the GP with those of the LPs. Interests are aligned when
both types of partners participate in new venture success but are protected
against losses if the venture performs poorly. Convertible preferred stock, for
example, is often used in VC financing. Convertible preferred offers some downside protection (ahead of common stock) and still preserves the potential for
significant gain. Structures involving staging of investment or put options for
the investor can have similar effects. We discuss the economics underlying VC
contracts with portfolio companies in Chapter 4.
3.5 Aspects of the VC Industry Structure
Geographic Clustering
A notable feature of the VC industry is that entrepreneurial firms and VC
firms tend to cluster in geographic areas (Figure 2.3). California, Massachusetts, and New York attract the most VC investments, with these three states
accounting for 75% of U.S. VC dollars invested in 2016 and 52% of all investee
companies.
Geographic concentration suggests the presence of agglomeration economies.
Such economies arise when firms’ costs are reduced as a result of being proximate to their rivals and other market participants, such as suppliers and specialized labor. These characteristics fit the VC industry. Value is added through
close working relationships between VCs and portfolio firms. For portfolio
firms, proximity to rivals is likely to keep them competitive and better informed.
When new venture activity is concentrated, VC firms can more easily monitor
industry trends. In addition, VC firms and portfolio companies have specific
human capital requirements. It is no accident that the two largest pockets of VC
activity in the U.S. are located near large and talented university populations.
Consistent with this, a VC that decides to fund a firm outside its geographic
area may predicate funding on relocation of the venture.
Venture Capital and Angel Investing 109
Tian (2011) documents a relationship between the use of VC staging and geographic proximity of the venture and the VC. The author finds that VC investors
located farther away from an entrepreneurial firm tend to finance the firm with
a larger number of rounds of shorter duration between successive rounds, and
smaller amounts per round. Thus, when firms are not close to their investors and
monitoring costs are higher, staging and deal structure can, in part, substitute
for direct monitoring.
Syndication
Syndication occurs when VC funds co-invest in ventures. Typically, for any given
venture, one fund is the lead investor and the others are co-investors. The lead
normally is the fund with the highest level of direct involvement with the venture
and is the one most likely to serve on the venture’s board of directors. Syndication is a reciprocal, ongoing, informal relationship, in which VC funds tend to
collaborate by distributing the responsibility of serving as lead investors or coinvestors. The practice enables VC firms to pool their human capital resources,
to invest more economically in geographically dispersed markets, and to spread
the investments of their funds over larger and more diverse portfolios.12
Because the LP investors in VC are normally well diversified over many investments, diversification within a fund is normally of little direct value to
them. Fund-level diversification is mainly of direct value to those involved in
the VC firm. Fund-level diversification can indirectly benefit VC LPs because the
managers whose interests benefit from diversification can agree to lower levels
of compensation and can limit the effects of their personal concerns about
fund-level underdiversification on investment selection.13
In countries where VC investing is well established, syndication among reputable VCs is common. In emerging economies, the lack of both attractive investments and established VC firms is an important impediment to syndication and
industry growth.
There can be different reasons for later-round syndication than for earlyround. Based on a study of biotech investment rounds, Lerner (1994b) finds that
in first-round investments, established VC firms tend to syndicate with each
other and avoid less experienced firms. Later rounds are more likely to involve
syndicated investments by less well-established VC firms. Lerner interprets the
evidence as being consistent with “window dressing” in the syndication of laterround investments by less well-established VCs. Also, in cases where a less wellestablished VC happens upon an good early-stage investment that does well, better established VCs may join the syndicate later and provide additional funding,
greater access to harvesting opportunities, and additional expertise and contacts.
110
Chapter Three
Admati and Pfleiderer (1994) develop a rationale for the allocation of ownership in later-round syndication based on informational asymmetries. Because
the first (lead) investor may have an informational advantage over subsequent
investors, opportunism in later rounds can be avoided if the lead maintains a
constant share of the firm’s equity. If the venture needs to raise a large amount
of capital in a later round, this can imply that additional investors must provide
a portion of the later-round financing.
The lead investor sometimes is paid for assisting in the syndication. The
typical fee is 2% of the money raised from co-investors. This fee is sometimes
shared with the other members of the initial syndicate.
3.6
How Venture Capitalists Can Add Value
GPs charge substantial fees for fund management and share significantly in
the success of their investments. Although GP returns may seem excessive, VC
funds attract most of their financial resources from sophisticated investors.
The investors, either directly or through gatekeepers, continuously monitor
the actions and decisions of the GP. The sophistication of VC investors supports the inference that the compensation structures are justified. If so, the
fund managers must contribute significantly to performance; sophisticated investors do not reinvest with GPs in whom they do not have confidence.14
VC firms, if they are to survive, must be able to add value sufficient to cover
their compensation. Beyond the efforts to create value for an individual fund, the
GP seeks to create value for the VC firm. VC firms expect to realize the benefits
of information and other scope economies by operating multiple funds. They
also expect to maximize the value of the firm’s human capital by deploying experienced partners to work with multiple portfolio companies. When one fund is
being liquidated, another often is being formed, so that the firm’s human capital
is used efficiently over the course of the investment/monitoring/harvesting cycle.
Managing a VC fund is complicated because the fund supplies a joint product consisting of investment capital and consulting/monitoring services. The
gross return to the VC fund must cover both the opportunity cost of the LPs’
investment capital and the human capital services of the GP. VC firms seek to
accomplish this in a variety of ways.
Selecting Investments and Negotiating Deals
The ideal ventures to include in a portfolio are those in which the GP can add
significant value without an excessive commitment of time. Other things being
Venture Capital and Angel Investing 111
equal, an investment is more attractive if it appears to be well managed and
unlikely to require much assistance. Such a company, however, should also be
able to negotiate a favorable deal with the GP. Beyond this, a venture is more
attractive if its needs meld with the specific capabilities of the GP. Simple metrics of fit include industry focus, stage of development, and location.
A prospective venture is more attractive if the expected time commitment
for VC involvement corresponds to a period over which the market can come
to recognize the value of the venture so that exit is possible. If the market takes
too long to recognize value, the GP may be compelled to continue to devote
time to the venture. This involvement can extend beyond the point where the GP
is adding value, especially if it causes the GP to forgo work on other ventures.
Because LPs are concerned with earning returns that exceed what they can
earn from other investments, the GP does not want to raise capital before it is
needed. Similarly, the GP does not want to hold a portfolio company after the
market recognizes its value. Market conditions affect the decision calculus. Ideally, the manager seeks to create the fund during a period when opportunities
to invest in new ventures are abundant and seeks to harvest when the market is
receptive to public offerings.
There is some evidence that VCs can time distributions to correspond with
periods when market values are high. Lerner (1994c) finds that in choices between IPOs and private rounds, VC-backed biotech companies tend to select
IPO after run-ups in the market and before market declines. Ball, Chiu, and
Smith (2011) examine VC-backed exits by IPO or merger over a longer period
and more sectors. Consistent with Lerner, they find that IPOs are selected after
market or sector run-ups. However, they find no evidence that VC-backed firms
can spot opportunities to issue in anticipation of market declines.
Selecting Entrepreneurs
VC firms evaluate thousands of ventures each year. A primary component of
the evaluation is assessing the entrepreneur’s knowledge and skills. Gompers et al. (2016) survey the methods VC firms use to evaluate entrepreneurial
firms and the management team. On average, VC respondents report that they
spend 118 hours on due diligence per firm, but more on late-stage than earlystage firms. Respondents rank the team as the most important factor in selection, followed by business model and product. Kaplan, Sensoy, and Stromberg
(2009) find that in a sample of VC-backed firms, the business plans of the firms
are “remarkably stable” over time while management turnover is “substantial.” Based on observed behavior, they infer that investors in start-ups place
more weight on the business model than on the management team.
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Changing the Management Team
A study of Silicon Valley portfolio companies by Hellman and Puri (2002) indicates that professional managers replace more than half of founding entrepreneurs.15 In about 40% of these cases, the founder retains a position at the
company; in the remainder, the founder has no discernible tie with the company after being replaced.
In another study, based on survey data, Fiet, Busenitz, Moesel, and Barney
(1997) provide perspective on the types of “mistakes” that can lead to dismissal
and the contractual covenants that are effective in aligning managers’ incentives with the interests of the VC fund. They find that likelihood of dismissal is
reduced when manager salaries are low, contracts include earn-out provisions,
revenue per employee is growing, and VC control of the board is low.
These results do not establish causation. A VC that is concerned about the
capabilities of the entrepreneur is more likely to negotiate performance-based
covenants and require board representation. An entrepreneur who wants to
signal confidence may be willing to accept unfavorable board composition in
exchange for more funding or favorable terms.
Monitoring and Advising Portfolio Companies
VCs can seek to add value by selecting and monitoring portfolio companies
and by providing services to the companies, including introductions that facilitate the hiring of key employees and facilitate exit via an acquisition or IPO.
There is evidence that portfolio companies value association with reputable
VCs and that VC backing is associated with job creation, higher levels of patent
awards, and more citations to patents.16 VC backing also appears to play a certification role in the IPO process, as VC backing is associated with less severe
IPO underpricing, lower total cost of going public, and going public sooner.17
How much of the return to VC is attributable to successful screening of
ventures (as opposed to post-investment involvement)? Bernstein, Giroud, and
Townsend (2016) try to sort this out by adopting an identification strategy to hold
company selection fixed. They conclude that on-site involvement can increase
both innovation and the likelihood of a successful exit.
3.7 Luck Versus Skill: What Accounts for
Venture Capital Success?
Kaplan and Schoar (2005) find that the returns to VC persist over funds of the
same VC firm. That is, high LP returns for one VC fund imply that LP returns
Venture Capital and Angel Investing 113
of subsequent funds offered by the same VC firm will also be high. There is
significant evidence that more reputable VCs have better results in terms of returns to investors in the fund and better exit results for portfolio companies.18
These results raise the interesting question of whether persistence of fund
performance and portfolio outcomes is due to the skill of the VC firm or luck.
Conceivably, a VC firm that is lucky with an early fund can attract better deal
flow and raise capital from investors with less effort than a first-time fund or
one with a track record of poor performance. To the extent that search costs for
investors and ventures are reduced by prior success, even if the success was due
to luck, some of this gain can be expected to be shared with investors.
Scholars have tried to distinguish between luck as a plausible explanation with
the competing hypotheses that top VC firms have a unique skill that is difficult
for new entrants to develop. Sorensen (2007) finds evidence that luck plays an
important role but that the direct influences of the VC firm also matter. Smith,
Pedace, and Sathe (2011) find that both style persistence and sector experience
are positively related to fund performance, but they also find that agility, the
ability to move quickly into a new sector, contributes positively to returns.
Consistent with VC skill playing a role in venture success, Ewens and RhodesKropf (2015) find evidence of both skill and exit style differences among VC firms
and even among individual partners at the same VC firm. Their estimates suggest
that, in explaining performance, an individual partner’s human capital is 2 to 5
times as important as the VC firm’s organizational capital. Also, evidence from
Hsu (2004) suggests that VCs add value in that entrepreneurs “pay extra” to affiliate with VCs that are highly reputable. Hsu finds that reputable VCs acquire
start-up equity at a 14% discount. Entrepreneurs apparently believe that VCs provide more than financial capital and that the extra services are valuable to them.
3.8 The Role of Reputation in the Venture Capital Market
Reputation plays a role in the functioning of most markets; as we have seen,
the market for VC is no exception. Reputation is an important enforcement
mechanism for explicit contract terms, such as the LPs’ commitments to respond quickly to capital calls. The alternative is to insist that investors place
committed funds in escrow accounts until the GP needs them. But this alternative impedes the LPs’ abilities to invest the funds efficiently until they are
needed. Accordingly, there can be significant advantages to working with incomplete, flexible contracts, where reputation substitutes for explicit contract
terms. If the GP can depend on the LPs not to make opportunistic decisions,
then elaborate provisions to limit the choices of the LPs can be avoided. If the
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VC can trust the entrepreneur to act in the interest of the venture, then specific
provisions designed to shift risk to the entrepreneur are less important.
Contractual relationships that are based partly on the reputations of the parties can generate higher returns for both parties. Evidence of this implication
is manifested in several ways. First, the preference of VCs to raise funds from
institutions, whose reputations are more easily assessed, demonstrates that the
cost of dealing with individual investors is expected to be higher. Second, wellestablished VC firms can raise larger amounts of capital more quickly, reducing
the overall costs of fund-raising. Third, well-established VCs can command
higher fees and a larger carried interest, but normally do not. The most common
terms are a 2% fee and 20% carried interest. Finally, the ability of established
entrepreneurs to raise capital more easily than first-time entrepreneurs indicates
that investors rely on the entrepreneur’s experience and demonstrated commitment as an element of the negotiation.
Several studies support the view that reputation is important for understanding the functioning of the market for VC. Some of these studies find that IPOs
with VC backing are less underpriced than those without such backing.19 These
findings suggest that VC investors perform an important monitoring function
and a certification role.
One economic rationale for a VC firm’s investing in reputation is that, because
the firm’s human capital is specific to new ventures, the firm benefits by developing a reputation for not selling overpriced shares in IPOs. Thus, VC reputation
elicits more interest from IPO investors and also leads to lower underwriter
fees (Megginson and Weiss, 1991). Evidence indicates that more established
VC firms are better able to bring portfolio companies public at early stages of
development than are other VCs. This VC reputation becomes highly sought
after by investment banks, who sometimes even offer GPs personal incentives
to do business together (Loughran and Ritter, 2004). The data indicate that VCs
with established reputations seek to maintain their reputations by selling shares
in IPOs only if they expect the IPOs not to be overpriced. Established VCs are
also more likely to forgo selling shares when IPO shares are overpriced or are
fully priced. In effect, they are more inclined than less well-established VCs to
sacrifice immediate return for long-run gain.20
3.9 Angel Investing
Angel investors are affluent individuals who provide capital for start-ups in
exchange for either equity (convertible preferred stock) or convertible debt.
The latter is a type of loan that converts to equity at a trigger point, which
Venture Capital and Angel Investing 115
typically is a subsequent financing event where there is a valuation (such as
a VC investment). One of the advantages of convertible debt, as analyzed in
Chapter 4, is that it is simple because it allows the investor to wait for a thirdparty valuation, presumably at a point when some major sources of uncertainty about the early-stage venture have been resolved. In addition to providing capital, angels may also add value by providing expertise, introductions,
monitoring, and so on.
Angels may invest individually or in groups. While individual angel investors
have a long history, angel groups are a more recent phenomenon beginning in
the mid-1990s (Lamoreaux, Levenstein, and Sokoloff, 2004). Traditionally angel
groups have been local organizations comprised of around 10 to 150 members
(median size 45). The groups enable the angels to share research costs and due
diligence efforts and to pool their investment capital.
The number of angel groups has been increasing both worldwide and in the
U.S. Most estimates put the number of angel groups worldwide at around 500.
The Halo report estimates that there are about 300 angel groups in the U.S.
A recent survey of angel groups shows that while all major cities in the U.S.
have angel groups, California attracts the most investment. California is also
the location of 30% of the companies that are involved in angel-backed deals.21
The same report shows that the median funding round size of an angel group
is $950,000, which can include multiple angel group and VC investments. The
median angel group investment in these deals is $127,000.22 In terms of market
size, recent estimates suggest that U.S. angels pump around $20–$25 billion per
year into start-ups.23 While the focus of angel investments is usually early-stage,
some angels invest in expansion and late-stage as well.24
The formation of angel groups reflects an economic rationale for more efficient investment. The angel organization allows members to pool their capital
so that investments can be larger than they would be if made individually. The
members can also make smaller investments in more opportunities, thereby
diversifying. They can also economize on due diligence costs. Start-ups benefit
from the experience and expertise of the various group members who invest in
the firm.
Angel group business practices. Each angel group has its own set of
procedures, policies, and ethos. Common practices include regular meetings
to review business proposals and listen to pitches. Angels work together to
conduct due diligence. Many angel groups co-invest with other groups, individual angels, and VCs who make early-stage investments. Like VCs, angel
groups invest in a range of industries. Among the most common are software,
medical devices, telecom, and manufacturing. Most groups follow a procedure
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Chapter Three
of screening business plan submissions to eliminate applications that are not
appropriate. If selected, the founder is invited to an “investment meeting” to
make a presentation/pitch, followed by Q&A.25 If a sufficient number of angels
indicate they are interested in pursuing a venture, then a team of members will
conduct due diligence, and if the new venture survives due diligence and a final
screen, then the group or a subgroup will negotiate a term sheet and draft an
investment agreement.26
Evidence of angel impact on firm performance. Because angel investing is a private industry, data on financial performance is not readily available,
and when available, it is subject to a concern with selection bias. That is, investments that generate low or zero returns are unlikely to be reported. Instead of
relying on self-reported financial performance estimates, researchers look at objective measures of success such as venture survival, growth, successful exit, and
measures of innovation (patents) or impact of innovation (citations to patents).
Several studies suggest that angels are beneficial to the growth, performance,
and survival of start-ups.27 As with VCs, an important question is whether angels
add value primarily through superior deal selection or go beyond it and add
value through providing technical and managerial expertise and monitoring
and facilitating subsequent funding. Kerr, Lerner, and Schoar (2014) attempt to
distinguish between the two hypotheses by comparing ventures that receive angel
group funding to ventures that sought funding but were narrowly rejected. The
results show that while the selection metrics were similar, ventures that received
the funding performed better than those that did not. Hence, it appears based
on their sample that improved performance is attributable to the types of skills
and expertise that angels can bring to the table. Lerner, Schoar, Sokolinski, and
Wilson (2018) use a similar methodology (regression discontinuity) and generate
similar results using data from angel groups across multiple countries.
Frequently, for ventures that hold promise, an angel investment will be followed by VC. In other cases, ventures receive only angel funding and in others,
only VC funding. As noted, some VCs compete directly with angels in seed-stage
financing. Dutta and Folta (2016) try to exploit the variation to explore how VCs
and angel groups may be associated with different outcomes for ventures. Using a longitudinal sample of tech ventures, the authors find that VCs and angel
groups contribute equally to the innovation rates of ventures they finance, but
that VC-backed ventures produce more impactful innovation (measured by
patent citations) than angel-backed ventures and also faster commercialization.
VCs are also associated with more successful exit. They do not find marginal
benefits in rates of innovation to receiving VC funding if the venture has already
Venture Capital and Angel Investing 117
received angel group funding, which suggests that the impact of VC funding
may have been overstated in previous research.28
Angel Syndicates. In recent years, angel syndicate platforms such as AngelList and OurCrowd have altered the structure of the industry, which historically
has been fragmented by geography. The online investment platforms were created to enable diffuse groups of angel investors to aggregate investment capital
on a deal-by-deal basis. Through these platforms, accredited investors can invest
beyond their local angel groups. This enables them to gain some geographic
diversification and may give exposure to more opportunities.
An angel syndicate is a fund created to make a single investment and is led by
an individual who is an experienced technology investor or is another entity such
as a VC firm. The lead identifies an investment opportunity and elects to lead the
syndicate as a way to raise funds to pursue the opportunity. Once the syndicate
is established, accredited investors may seek to join, but their participation must
be approved by the lead. As compensation for the lead’s efforts to find the opportunity, conduct due diligence, and monitor the venture after the investment
is made, participants in the syndicate agree to pay the lead’s carried interest.
AngelList provides an example of how this works:29 Suppose a notable angel
investor decides to lead a syndicate to invest in a promising new venture. The
lead agrees to invest $250K in the venture. To do so she makes a personal commitment to invest $50,000, establishes a syndicate as an LLC, and offers the
remaining opportunity to prospective investors for the remainder of the deal
($200,000). The investors who join as members of the syndicate agree collectively
to invest $200,000 in the deal and to pay the lead 15% of any realized gain as a
carry. If the investment has a successful exit, the syndicate investors first receive
their $200,000, after which every dollar of the syndicate’s profit is split 80% to
the syndicate, 15% to the lead, and 5% to AngelList advisors.
There also will be out-of-pocket costs for each deal, such as fees paid to
regulatory agencies and accountants, which syndicate investors typically agree
to pay. Much like a VC fund, the syndicate investors do not invest directly in a
venture. Rather, the syndicate’s fund will invest by purchasing preferred shares,
convertible debt, or other instruments issued by the company. The syndicate
members invest in a special-purpose fund that is specifically created to invest
in companies selected by the syndicate lead, and the fund is formed as an LLC.
Both individuals and VC funds can form syndicates. By current regulation, each
syndicate can only accept up to 99 investors.
In contrast to the LPs of VC funds, syndicate investors can choose which
start-ups they want to invest in and can stop investing at any time, and they
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Chapter Three
also have much lower minimum investments. Syndicates generally do not charge
management fees and use the term deal carry (not fund carry). Also, in general,
the syndicate lead will put a larger portion of the total investment into the syndicate than the GP of a VC fund typically puts into a fund.
3.10 Summary
The modern VC firm has its roots in the U.S. in the midtwentieth century.
Limited partnership is the most common structure for VC investment. The
limited partners provide financial capital, while the general partner locates
and cultivates the ventures and arranges for harvesting the investments. Harvesting usually takes the form of arranging for the venture to go public via
an IPO, a private sale to another company, or a management buyout. LPs are
precluded from active participation in fund management.
The aggregate amount of capital invested in U.S. VC funds increased dramatically through 2000, largely as a result of institutional investors having more
flexibility in their investment decisions. As commitments of capital increased,
so did competition for deals. Intensified competition has heightened incentives
to improve valuation methods and to develop more sophisticated approaches
to generating and structuring deals, marketing them to investors, and designing exit strategies.
Following an investment in a portfolio company, the GP’s responsibility
shifts to creating value in the portfolio company. This means that a partner
of a VC firm may sit on the board of directors of the venture. The VC firm is
responsible for monitoring operations, recruiting management team members,
arranging additional financing, and generally providing expertise and industry
contacts. The VC fund has a limited life span and at some point, usually after
about 5 years, begins to harvest its investments. The chapter reinforces the role
of reputation in the VC market.
We considered the contracts that characterize the industry: contracts between
the LPs and the GP. The terms and provisions of partnership agreements reflect
issues regarding risk bearing and incentive alignment. LPs are passive investors who relinquish control of their investment to a GP that does not bear the
full cost of its decisions. Among the issues that arise in this setting are sorting
(selecting the portfolio companies from the deal flow) and controlling agency
costs and operating costs. Contractual compensation reflects the respective
functions of the two types of partners. If the fund does well, the GP realizes a
significant return through its carried interest; if the fund does poorly, almost
the entire loss accrues to the LPs. Hence, GP compensation is linked to value
creation in an asymmetric way.
Venture Capital and Angel Investing 119
The final section of the chapter deals with angel investing. Individual angels
can make individual investments in start-ups or they can invest through an
organized angel group or through an angel syndicate, which is a new and evolving investment platform. We document an increase in angel activity worldwide
and the business practices that angel groups and syndicates use when making
investments. Because the industry is highly fragmented, data on investment performance is sparse and relies on self-reported numbers. Recent studies suggest a
positive role for angels as measured by successful exits and innovative activity.
Review Questions
1. Referring to Figure 3.1, review the major developments that have affected the growth in the VC market in the U.S. Explain, for example, the
impact of the Investment Company Act, ERISA’s prudent investor rule,
the growth of institutional investing, and general stock market activity.
2. What types of deals are most appropriate for VC and why?
3. What are some possible explanations for the variation across countries
in the development of VC markets?
4. Describe the primary features of the LP structure of VC funds, including the roles of the GP and LPs, the length of the partnership, and the
compensation mechanisms for the partners.
5. How do VCs add value to new ventures? When can syndication add
value and why?
6. Describe the steps in the VC investment process (Figure 3. 8).
7. Explain how VCs distribute capital gains associated with a successful
exit of a portfolio company (the waterfall). How is a clawback provision
related to the overall distribution of returns to LPs and GPs?
8. Explain the economic rationale for key provisions in a limited partnership agreement, such as limitations on additions of general partners and
key person provisions.
9. Why is reputation so important in the VC market?
10. How do angel investors add value? What does the evidence show?
11. How do angel syndicates work?
Notes
1. See Gompers (1994) for more information about ARD.
2. Smith, Smith, and Williams (2001) provide a more detailed analysis of
the SEC’s interpretation of the ICA fair value standard.
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Chapter Three
3. Gompers and Lerner (1998b).
4. Based on Pitchbook, Q2 2017 European Venture Report. Based on
funds raised by European VC firms, Invest Europe, 2016 European Private Equity Activity, reports a lower total of €6.4 billion ($7.5 billion).
5. Invest Europe, 2016 European Private Equity Activity: Statistics on
Fundraising Investments & Divestments, https://​w ww​.investeurope​.eu/​research/​
activity​-data/​annual​-activity​-statistics/. The City of Dublin hosts the European
headquarters of a number of prominent firms, including Google, Facebook,
and eBay. Irish Venture Capital Association publications, http://​w ww​.ivca​.ie.
6. Data are from US Statistical Abstract, 2004. The government no longer collects this information.
7. Invest Europe, 2016 European Private Equity Activity: Statistics on
Fundraising Investments & Divestments, https://​w ww​.investeurope​.eu/​research/​
activity​
- data/​
annual​
-activity​
-statistics/. Government is by far the largest
source of funds for European VCs; the next largest is corporations. A fund-offunds is an investment strategy in which a fund invests in other types of funds
such as VC funds, stock funds, and bond funds instead of investing directly in
new ventures, bonds, stocks, and other types of securities.
8. Preqin Special Report, Sovereign Wealth Funds, August 2017.
9. R&D data are from the OECD Science and Technology and Industry Outlook 2015, https://​data​.oecd​.org/​rd/​g ross​- domestic​-spending​-on​-r​- d​
.htmwww​.iriweb​.org/​sites/​default/​fi les/​2016GlobalR​%26DFundingForecast.
Note that the definitions of R&D measured by Compustat and OECD are
different—for example, Compustat includes foreign expenditures of R&D by
U.S. multinationals, while OECD does not. See Gornall and Strebulaev (2015).
10. Gornall and Strebulaev (2015). Also see the National Venture Capital Association Yearbook (2017), https://​nvca​.org/​blog/​nvca​-2017​-yearbook​-go​
-resource​-venture​- ecosystem/.
11. Ewens and Rhodes-Kropf (2015) find evidence of individual style differences within a VC firm.
12. In their survey of VCs, Gompers et al. (2016) support these ideas,
finding that the important factors cited by VCs that lead to syndication are
complementary expertise, capital constraints (most important), risk sharing,
and future deals (least important).
13. See Ewens, Jones, and Rhodes-Kropf (2013). Chahine, Arthurs,
Filatotchev, and Hoskisson (2012) examine the effects of syndicate diversity
on earnings management. Cumming, Grilli, and Murtinu (2017) find that
European syndicates combining independent private firms and governmental VC investors achieve better exit performance than do government-backed
ventures.
Venture Capital and Angel Investing 121
14. Puri and Zarutskie (2012) find that cumulative failure rates of VCbacked firms are lower than those of non-VC-backed firms, and that most
of the difference arises shortly after VC involvement begins. Chemmanur,
Krishnan, and Nandy (2011) report that the overall efficiency of VC-backed
firms is higher than that of non-VC-backed firms and that total factor product increases after VC involvement begins. Guo and Jiang (2013) find that
VC-backed firms in China outperform non-VC-backed firms and that performance improves after VC involvement begins. Bernstein, Giroud, and
Townsend (2016) find that on-site involvement by VCs leads to increases in
innovation and the probability of successful exit.
15. Hellman and Puri (2002).
16. Hsu (2004); Kortum and Lerner (2000).
17. Megginson and Weiss (1991).
18. Studies include Sorensen (2006), who finds that companies funded
by more experienced (a proxy for reputation) VCs are more likely to go public, and Krishnan, Ivanov, Masulis, and Singh (2011), who find that VC firms
with high shares of prior IPO activity tend to have higher percentages of investments that result in an IPO and better performance after the IPO. Nahata (2008) finds that new ventures backed by VCs with superior reputations
are more likely to go public and tend to go public earlier. Smith, Pedace, and
Sathe (2011) find that fund reputation is positively related to fund IRR and the
net multiple return to investors in the fund.
19. See, for example, Barry, Muscarella, Peavy, and Vetsuypens (1990),
Megginson and Weiss (1991), and Brav and Gompers (1997).
20. Lin and Smith (1998) report that the initial returns average 11.6%
when VCs with established reputations are among the sellers, compared to
only 5.1% when they are not. That is, they appear to protect their reputations
by forgoing selling in IPOs that are more likely to be overpriced.
21. Halo report (Wilmington, NC: Angel Resource Institute, 2017).
22. Halo report, 2017.
23. See also Shane (2012).
24. Data from J. Sohl, “The Angel Investor Market,” reports from various years, Center for Venture Research, University of New Hampshire.
25. Kerr, Lerner, and Schoar (2014) describe the procedures followed by
two major U.S. angel groups: Tech Coast Angels and Common Angels. Sudek,
Mitteness, and Baucus (2008) include a template illustration of the TechCoast
Angels screening process.
26. Angel Capital Association, www​.angelcapitalassociation​.org. Other
sources include the Center for Venture Research and the Halo report.
27. See, for example, Lerner, Schoar, Sokolinski, and Wilson (2018).
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Chapter Three
28. See also Hellmann and Thiele (2015).
29. https://​angel​.co/​help/​dyndicates/​how​-syndicates​-work.
References and Additional Reading
Admati, A. R., and P. Pfleiderer. 1994. “Robust Financial Contracting and the
Role of the Venture Capitalist.” Journal of Finance 49: 371–​402.
Ball, E. R., H. Chiu, and R. L. Smith. 2011. “Can VCs Time the Market? An
Analysis of Exit Choice for Venture-Backed Firms.” Review of Financial
Studies 24: 3105–38.
Barry, C. B., C. J. Muscarella, J. W. Peavy III, and M. R. Vetsuypens. 1990.
“The Role of Venture Capital in the Creation of Public Companies.”
Journal of Financial Economics 27: 447–​71.
Bernstein, S., X. Giroud, and R. R. Townsend. 2016. “The Impact of Venture
Capital Monitoring.” Journal of Finance 71: 1591–1622.
Bernstein, S., A. Korteweg, and K. Laws. 2017. “Attracting Early-Stage Investors: Evidence from a Randomized Field Experiment.” Journal of Finance
72: 509–38.
Brander, J. A., R. Amit, and W. Antweiler. 2002. “Venture Capital Syndication: Improved Venture Selection Versus the Value-Added Hypothesis.”
Journal of Economics and Management Strategy 11: 423–​52.
Brav, A., and P. Gompers. 1997. “Myth or Reality? The Long-Run Performance of Initial Public Offerings: Evidence from Venture and Non-Venture Capital-baked Companies.” Journal of Finance 52: 1791–821.
Chahine, S., J. Arthurs, I. Filatotchev, and R. Hoskisson. 2012. “The Effects
of Venture Capital Syndicate Diversity on Earnings Management and
Performance of IPOs in the US and UK: An institutional Perspective.”
Journal of Corporate Finance 18: 179–92.
Chemmanur, T., K. Krishnan, and D. Nandy. 2011. “How Does Venture Capital Financing Improve Efficiency in Private Firms? A Look Beneath the
Surface.” Review of Financial Studies 24: 4037–90.
Chemmanur, T., E. Loutskina, and X. Tian. 2014. “Corporate Venture Capital, Value Creation, and Innovation.” Review of Financial Studies 27:
2434–66.
Chen, H., P. Gompers, A. Kovner, and J. Lerner. 2010. “Buy Local? The Geography of Successful and Unsuccessful Venture Capital Expansion.”
Journal of Urban Economics 57: 90–102.
Cumming, D., L. Grilli, and S. Murtinu. 2017. “Governmental and Independent Venture Capital Investments in Europe: A Firm-Level Performance
Analysis.” Journal of Corporate Finance 42: 439–59.
Venture Capital and Angel Investing 123
Dutta, S., and T. Folta. 2016. “A Comparison of the Effects of Angels and Venture Capitalists on Innovation and Value Creation.” Journal of Business
Venturing 33: 39–54.
Ewens, M., C. Jones, and M. Rhodes-Kropf. 2013. “The Price of Diversifiable
Risk in Venture Capital and Private Equity.” Review of Financial Studies
26: 1854–89.
Ewens, M., R. Nanda, and M. Rhodes-Kropf. 2017. “Cost of Experimentation
and the Evolution of Venture Capital.” Harvard Business School working
paper 15-070. Journal of Financial Economics, forthcoming.
Ewens, M., and M. Rhodes-Kropf. 2015. “Is a VC Partnership Greater than
the Sum of Its Partners?” Journal of Finance 70: 1081–113.
Fiet, J. O., L. W. Busenitz, D. D. Moesel, and J. B. Barney. 1997. “Complementary Theoretical Perspectives on the Dismissal of New Venture Team
Members.” Journal of Business Venturing 12: 347–​66.
Gilson, R. J., and D. Schizer. 2003. “Venture Capital Structure: A Tax Explanation for Convertible Preferred Stock.” Harvard Law Review 116:
875–​916.
Gompers, P. 1994. “The Rise of Venture Capital.” Business and Economic History 23: 1–​24.
Gompers, P., W. Gornall, S. N. Kaplan, and I. A. Strebulaev. 2016. “How Do
Venture Capitalists Make Decisions?” NBER working paper 2287.
Gompers, P. A. 1995. “Optimal Investment, Monitoring, and the Staging of
Venture Capital.” Journal of Finance 50: 1461–​89.
Gompers, P. A., and J. Lerner. 1996. “The Use of Covenants: An Empirical
Analysis of Venture Partnership Agreements.” Journal of Law and Economics 39: 463–​98.
———. 1998a. “Venture Capital Distributions: Short-Run and Long-Run Reactions.” Journal of Finance 53: 2161–​84.
———. 1998b. “What Drives Venture Capital Fundraising?” Brookings Papers on Economic Activity: Macroeconomics, 149–​204. Washington, DC:
Brookings Institution.
———. 1999. “An Analysis of Compensation in the U.S. Venture Capital Partnership.” Journal of Financial Economics 51: 3–​44.
Gornall, W., and I. Strebulaev. 2015. “The Economic Impact of Venture Capital: Evidence from Public Companies.” Stanford University Graduate
School of Business research paper 15-55. http://​dx​.doi​.org/​10​.2139/​ssrn​
.2681841.
Guo, D., and K. Jiang. 2013. “Venture Capital Investment and the Performance of Entrepreneurial Firms: Evidence from China.” Journal of Corporate Finance 22: 375–95.
124
Chapter Three
Hellmann, T. 1998. “The Allocation of Control Rights in Venture Capital
Contracts.” RAND Journal of Economics 29: 57–​76.
Hellmann, T., and M. Puri. 2002. “Venture Capital and the Professionalization
of Start-Up Firms: Empirical Evidence.” Journal of Finance 57: 169–​97.
Hellmann, T., and V. Thiele. 2015. “Friends or Foes? The Interrelationship Between Angel and Venture Capital Markets.” Journal of Financial Economics 115: 639–53.
Hochberg, Y., A. Ljungqvist, and Y. Lu. 2010. “Networking as a Barrier to Entry and the Competitive Supply of Venture Capital.” Journal of Finance
65: 829–​59.
Hsu, D. H. 2004. “What Do Entrepreneurs Pay for Venture Capital Affiliation?” Journal of Finance 59: 1805–44.
Kaplan, S. N., and A. Schoar. 2005. “Private Equity Performance: Returns,
Persistence and Capital Flows.” Journal of Finance 60: 1791–823.
Kaplan, S., B. Sensoy, and P. Stromberg. 2009. “Should Investors Bet on the
Jockey or the Horse? Evidence from the Evolution of Firms from Early
Business Plans to Public Companies.” Journal of Finance 64: 75–115.
Kaplan, S. N., and P. Stromberg. 2003. “Financial Contracting Theory Meets
the Real World: An Empirical Analysis of Venture Capital Contracts.”
Review of Economic Studies 70: 281–​316.
Kerr, W., J. Lerner, and A. Schoar. 2014. “The Consequences of Entrepreneurial Finance: Evidence from Angel Financings.” Review of Financial Studies 27: 1–19.
Kortum, S., and J. Lerner. 2000. “Assessing the Contribution of Venture Capital to Innovation.” RAND Journal of Economics 31: 674–​92.
Krishnan, C. N. V., V. Ivanov, R. W. Masulis, and A. K. Singh. 2011. “Venture Capital Reputation, Post-IPO Performance, and Corporate Governance.” Journal of Financial and Quantitative Analysis 46: 1295–333.
Lamoreaux, N., M. Levenstein, and K. Sokoloff. 2004. “Financing Invention
During the Second Industrial Revolution: Cleveland, Ohio 1870–1920.”
NBER Working Paper series 10923, National Bureau of Economic
Research.
Lerner, J. 1994a. “A Note on Private Equity Partnership Agreements.” Harvard Business School Case 294-084. Cambridge, MA: Harvard Business
Publishing.
———. 1994b. “The Syndication of Venture Capital Investments.” Financial
Management 23: 16–27.
———. 1994c. “Venture Capitalists and the Decision to Go Public.” Journal of
Financial Economics 35: 293–​316.
Venture Capital and Angel Investing 125
———. 1995. “Venture Capitalists and the Oversight of Private Firms.” Journal of Finance 50: 301–​18.
Lerner, J., A. Schoar, S. Sokolinski, and K. Wilson. 2018. “The Globalization
of Angel Investments: Evidence Across Countries.” Journal of Financial
Economics 127: 1–20.
Lin, T. H., and R. L. Smith. 1998. “Insider Reputation and Selling Decisions:
The Unwinding of Venture Capital Investments During Equity IPOs.”
Journal of Corporate Finance 4: 241–​63.
Loughran, T., and J. R. Ritter. 2004. “Why Has IPO Underpricing Changed
over Time?” Financial Management (Autumn): 5–37.
Megginson, W. L., and K. A. Weiss. 1991. “Venture Capitalist Certification in
Initial Public Offerings.” Journal of Finance 46: 879–​903.
Nahata, R. 2008. “Venture Capital Reputation and Investment Performance.”
Journal of Financial Economics 90: 127–​51.
Puri, M. and R. Zarutskie. 2012. “On the Life Cycle Dynamics of VentureCapital- and Non-Venture-Capital-Financed Firms.” Journal of Finance
67: 2247–93.
Ramsinghani, M. 2014. The Business of Venture Capital, 2nd ed. Hoboken, NJ:
Wiley.
Sahlman, W. A. 1990. “The Structure and Governance of Venture Capital Organizations.” Journal of Financial Economics 27: 473–​521.
Sedlacek, P. and V. Sterk. 2017. “The Growth Potential of Startups over the
Business Cycle.” American Economic Review 107: 3182–210.
Shane, S. 2012. “The Importance of Angel Investing in Financing the Growth
of Entrepreneurial Ventures.” Quarterly Journal of Finance 2: 1250009-54.
Smith, J. K., R. L. Smith, and K. Williams. 2001. “The SEC’s ‘Fair Value’
Standard for Mutual Fund Investment in Restricted and Other Illiquid
Securities.” Fordham Journal of Corporate and Financial Law 6: 421–​74.
Smith, R. L., R. Pedace, and V. Sathe. 2011. “Venture Capital Fund Performance: The Effects, of Exits, Abandonment, Persistence, Experience and
Reputation.” Financial Management (Winter): 1029–65.
Sorensen, M. 2007. “How Smart Is Smart Money? A Two-Sided Matching
Model of Venture Capital.” Journal of Finance 62: 2725–​62.
Sudek, R. C., C. Mitteness, and M. Baucus. 2008. “Betting on the Horse or the
Jockey? Impact of Expertise on Angel Investing.” Academy of Management Proceedings.
Tian, X. 2011. “The Causes and Consequences of Venture Capital Stage Financing.” Journal of Financial Economics 101: 132–59.
C h a p t e r Fo u r
Ve ntu r e D e al s
I n t h i s c h a p t e r we turn to the contractual relationship between the en-
trepreneur and the investor and explore how financial contracting can be used
to benefit both parties. We emphasize the considerations that bear on the choice
of contract terms, namely differences between the entrepreneur and the investor
in diversification, in the information they possess, in their expectations, and in
incentives.
Although dealing with investors can be stressful, time consuming, and inconvenient for the entrepreneur, investors can also be beneficial. Potential benefits
derive from three sources:
• Outside investment enables the entrepreneur to invest less and increase
diversification.
• Because well-diversified investors have lower required rates of return,
increasing outside investment can increase the present value of the
venture.
• An investor may contribute expertise, advice, and information that enhance value.
Of course, there are also potential problems—disagreements over direction,
more time devoted to maintaining the relationship, and conflicts of interest,
among others. While we recognize the potential problems, we focus on how
contract design can be used to address these concerns and how, on net, investors can add value.
In financial terms, contracts with investors have three effects: they allocate
risk, they allocate expected returns, and they change risk and expected returns.
Debt financing, for example, allocates most of the risk to the equity investor/
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entrepreneur, allocates expected returns between the equity holder and creditors,
and changes overall returns due to the incentive and tax effects.
4.1 The Economic Framework for Financial Contracting
Economics provides a useful framework for thinking about risk and return
with the objective of understanding, analyzing, and designing the basic elements of venture deals.
Information and Incentive Problems and
Financial Contracting
Information and incentive problems are at the core of negotiations between
entrepreneurs and investors. New venture markets are highly uncertain and
subject to being influenced by unanticipated events. The parties involved
may have different expectations about venture success and may have difficulty communicating their expectations. Outside parties often cannot know
whether venture performance is due to luck or to managerial capability and
effort. We refer to such problems as information problems.
Information problems affect start-ups in many ways. For example, a prospective entrepreneur may have an idea with significant commercial value but be
unable to protect it by patent or copyright. The entrepreneur cannot disclose the
idea to prospective investors without risking its appropriation. How do entrepreneurs deal with such dilemmas? How, conversely, can investors determine
the entrepreneur’s true expectations?
In addition to information problems, incentive conflicts can arise between
the investors and the entrepreneur and among different investors. An entrepreneur with a limited equity stake may not always act in the best interest of other
shareholders. If outside financing is in the form of debt, the entrepreneur may
want to take on more risk than creditors would like. How can incentive problems
such as these be overcome?
A Taxonomy of Information and Incentive Problems
A useful distinction exists between costs that arise before a contract is entered
and those that arise afterward. Before the agreement, the fundamental problem is information; each party is unsure about what the other knows. After an
agreement is reached, the problem is incentives for performance.
Consider the notion of a perfect contract. A perfect contract anticipates and
provides for every contingency. Its terms bind the parties, so that neither can try
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to take advantage of the other. The contract must contain sufficient detail so that
a third party can fully enforce its provisions. A perfect contract is efficient in
the sense that (1) each risk is allocated to the party who can bear it at least cost,
and (2) collectively, the contract terms exhaust the possibilities for mutual gain.
New venture financial contracts are far from perfect. They often include
extensive lists of representations and warranties and complex contingency structures. Such provisions are designed to deal with information and incentive problems. They tend to be complex but incomplete in that they do not address every
contingency. Contractual complexity arises from efforts to address information
and incentive problems through detailed contract terms.
Precontractual Information Costs
Information costs arise before any sunk (nonsalvageable) investment is made.
They arise because the future is complex and uncertain, because the parties
may have different information and expectations, and because each party has
an incentive to distort their true information or beliefs. Such problems are
known as “adverse selection” because they can cause good opportunities to be
forgone. The choice to buy health insurance is the classic example of adverse
selection. If all people are offered insurance on the same terms, those who expect to be healthy and not to use the insurance will opt not to take it, whereas
the unhealthy “adverse selectors” will enroll. Adverse selection is driven by
three major types of precontractual information costs: bounded rationality,
information asymmetry, and impacted information.1
Bounded rationality. The idea that individuals have limited capacity to
deal with complexity is referred to as bounded rationality.2 The parties to a
venture cannot anticipate every contingency the venture might face. Even if
delineating the contingencies were possible, negotiating provisions to deal with
each remote contingency would be too expensive to justify. Decision makers
weigh the costs and benefits and rationally stop short of explicitly contracting
over all contingencies.
To illustrate, consider an entrepreneur who is seeking funding. Product development time may be short or long. Quality of the resulting product and consumer
demand may be high or low. Rivals may be successful or unsuccessful in their
efforts to compete. Catastrophic events (death of the entrepreneur, war, etc.) may
intervene. The funding needs of the venture depend on all of these factors. In
some scenarios the investor will be eager to provide funding but not in others.
In some, the entrepreneur would prefer to abandon the venture. If contracting
costs were low, the parties could develop an elaborate list of contingencies and
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design a contract that would specify the response to each. Bounded rationality
explains why entrepreneurs and investors do not try to do this.
Information asymmetry. Information asymmetry means one party has
information that the other lacks and cannot easily acquire. Asymmetric information can prevent a bargain from being struck or cause risk and return to be
allocated less efficiently than if information were shared. Information asymmetry can be a problem for entrepreneurial ventures. An entrepreneur usually
cannot know why a prospective investor is interested in the venture. The investor
may only be seeking to assess the venture as a competitive threat. Entrepreneurs
claim that investors sometimes only get involved in a venture to keep it from
reaching the market. This could happen if the investor was involved with another venture that targeted the same market. Conversely, the investor may not
be able to accurately assess the expertise and commitment of the entrepreneur.
Impacted information. The problem is not just that the information is held
asymmetrically but also that each party has an incentive to distort what they
know. Consequently, each fears exploitation by the other and is reluctant to commit to the venture. Information is impacted when one party is uncertain about
what the other knows (information asymmetry) and the parties cannot easily
communicate what they know to each other. Impacted information raises the
cost of market exchange and contracting because prospective trading partners
fear being taken advantage of. To complete the exchange, one or both parties
must expend resources to overcome real or perceived information disadvantages.
For new ventures, the expenditures often take the form of due diligence investigations in advance of contracting or negotiations of contractual contingencies
that mitigate the value of information advantages.
Postcontractual Incentive Problems
Once a contract has been entered or a sunk investment has been made, incentives change. The parties may act in ways that are not consistent with their
original intentions. Incentive problems arise when contracts are incomplete
or when parties cannot monitor performance perfectly. Such problems are
known as “moral hazard” because behavior changes once the contract is entered. The classic example again comes from health insurance.3 A person who
is fully responsible for his or her medical bills makes value-maximizing tradeoffs in deciding whether to visit a doctor. Because insured individuals are not
required to pay the full cost of their medical treatments they will tend to overuse medical services in the sense that the benefit of a specific service to the
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patient is less than the cost of providing it, a factor that drives up the cost of
insurance for everyone. Anticipating moral hazard is a first step toward finding ways to use deal structure to minimize the costs associated with moral
hazard.
Financial contracts also can give rise to moral hazard problems. For example,
once an investor commits to a venture and shares in the benefits of its success,
the entrepreneur’s ownership share is reduced and the entrepreneur may devote
less effort to achieving that success. It makes sense that the entrepreneur would
devote less effort than if he were the exclusive beneficiary of his efforts. Replacing outside equity with debt financing changes the entrepreneur’s incentives, but
if the debt is risky, it does not eliminate the moral hazard.4
Moral hazard is driven by three major types of precontractual information
costs: bounded rationality, specific investment, and small numbers bargaining.
Bounded rationality. Just as with adverse selection, if complete contingent
claims contracting were costless, the parties could remove the potential for moral
hazard by completely specifying the contingencies. Costless complete contracting requires both the ability to specify the full range of contingencies and the
ability to costlessly monitor the performance of the entrepreneur, neither of
which is even remotely feasible.
Specific investment. Specific investments are investments that support
a given activity or relationship but have little value in alternative use. Once a
specific investment is made, it is, in effect, sunk. An example is an entrepreneur’s
investment of effort and resources in developing a working prototype of a circuit
board that is customized to the needs of a particular customer.5 Suppose, given
the planned rate of production, that the amortized quarterly economic cost
of effort and resources invested in developing the prototype is $4,000 and the
quarterly variable cost of circuit board production is $2,000. The circuit board
sales contract between the entrepreneur and the customer calls for the customer
to pay $7,000 quarterly. This means that the economic gain to the entrepreneur
is $1,000 per quarter. This gain is called an “economic rent.” Economic rent is
an ex ante concept and is measured as the excess of expected return over the
opportunity cost of resources committed. In deciding whether to make the investment to produce the circuit boards, the entrepreneur commits the resources
only if the economic rent is positive. Thus, the economic rent is what makes the
NPV of the investment positive.
Now consider a scenario in which the resource commitment to manufacture
the circuit boards has already been made. Economic rent is no longer relevant;
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instead, the concern is whether the return is sufficient to cover the alternative
use value of the resources (the variable cost). Suppose these resources have a
salvage value of $2,500 per quarter. The $7,000 quarterly revenue is more than
sufficient to justify continuing to make circuit boards. The $4,500 difference
between revenue and salvage value is called a “quasi rent.” Quasi rent is an ex
post concept. It measures the difference between revenue received and minimum
revenue necessary to justify keeping the equipment in its current use.
Quasi rents exist because of sunk investments. Moral hazard problems are
related to the existence of quasi rents. Because the entrepreneur will continue
to supply circuit boards as long as the quarterly return is at least $2,500, the
customer who is paying $7,000 has an incentive to threaten not to purchase unless the entrepreneur reduces the price. Such an attempt to appropriate the quasi
rent is an example of moral hazard that arises after a specific investment is made.
This example shows that the parties to a new venture must be sensitive to the
presence of specific investments. If the investment is not specific, competition can
prevent appropriation. If it is, then the party investing in the specific asset faces
the risk of appropriation, and the contract structure may need to address the risk.
Small numbers bargaining. Market exchange works best when large numbers of buyers and sellers compete. The market for initial outside financing can
be such a market. It is easy to see how the presence of large numbers of potential
investors facilitates exchange. Any investment the entrepreneur makes at an
early stage is likely to be specific to the venture and nonsalvageable. With a large
number of investors competing, the entrepreneur does not need to be concerned
about making value-enhancing investments before agreeing to a contract. If
the entrepreneur is right, competition among investors will result in terms that
more than compensate for the sunk investment. The difficulty arises when the
number of prospective investors is small; then the entrepreneur cannot rely on
competition to give him an opportunity to recover sunk investment. In fact,
sunk investment invites appropriation by an investor.
Having large numbers of competitors vying in the initial stage does not prevent opportunism in later-stage negotiations. Many ventures evolve from large
numbers of potential investors to small numbers as the venture progresses. The
investor in the first round may gain a first-mover advantage that would make
it difficult for rival investors to compete in later-stage negotiations. It is not
useful to characterize this practice of aggressive bargaining by the investor in
the second round as opportunistic, particularly if the pattern is characteristic
of the market so that the ability to bargain more aggressively in later rounds is
reflected in the terms for the initial investment.
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Still, in new venture financing the potential for opportunism is large. To
obtain initial financing, the entrepreneur may be asked to provide an abandonment option and possibly other rights, such as the right to acquire control or to
terminate the entrepreneur. Why do venture financing contracts give so much
control to investors, when it would appear that the potential for opportunism
could be limited by using a more complex contract that might give similar rights
but only under certain well-specified conditions?
Apparently, entrepreneurs in such arrangements do not fear opportunism by
investors—but why not? The answer is threefold: First, if the venture is successful, the entrepreneur can pursue other financing, even if the investor withdraws.
Second, the investor needs to preserve a good working relationship with the
entrepreneur. Third, any investor who wishes to be involved repeatedly in new
ventures must avoid gaining a reputation for behaving opportunistically.
4.2 Essentials of Contract Design
The term “contract” spans a spectrum of agreements or understandings between parties. At one end, “discrete contracts” are those that contain very explicit provisions. The transactions are impersonal, self-contained exchanges,
and performance is easily observable and measurable. The contracts are of
short duration, and any contingencies are anticipated and provided for explicitly. At the other end of the spectrum are “relational contracts.” These are
highly flexible, implicit contracts based on ongoing relationships between the
parties. A relational contract is like a constitution that describes what the parties are trying to accomplish and how, in general terms, they aspire to share
the benefits.
Discrete Contracting
An example of a discrete financial contract is one between an entrepreneur
and a bank. If the loan is collateralized, the contract is discrete. The bank does
not need to accept the entrepreneur’s sales projections or verify the venture’s
financial performance. The entrepreneur is not concerned that the lender will
act opportunistically as long as it is clear that the venture can meet its debt
obligations. If the lender does not renew the loan in spite of the entrepreneur’s
adequate collateral, the entrepreneur can turn to another lender.
Discrete contracting works best for simple exchanges, where the parties have
similar expectations, objectives are easy to specify, and performance is easy to
verify. If the parties have materially different expectations, it may be possible
Venture Deals 133
to design a more complex contract that is still discrete but circumvents the need
to have similar expectations.
If expectations are held symmetrically and incentives are not a concern,
then the better-diversified party should bear most of the risk of a new venture.
For entrepreneurial ventures, this implies that the investor (who is likely to be
well diversified) would bear most of the risk and the entrepreneur would have a
relatively low-risk claim on the venture’s cash flows. However, both information
asymmetry and incentive considerations are central to contracts involving investment in entrepreneurial ventures. The contractual solution will be different if
beliefs are asymmetrically held; in such cases, deal terms may shift risk toward
the better-informed or more optimistic party. Suppose, for example, that the
entrepreneur is confident he can achieve $200,000 in monthly sales within 18
months. The investor is less optimistic and projects monthly sales of $100,000. A
deal that provides for equity sharing can be based on the investor’s projections
but include a provision that if the entrepreneur’s revenue expectation is met, he
will receive a specified percentage increase in equity. Even though the diversified investor is better able to bear risk, the entrepreneur now accepts some of
the risk to address the parties’ different expectations.
Discrete contracting can also address incentive problems. Suppose an entrepreneur is concerned that a first-round investor may attempt to hold up the
entrepreneur when second-round funding is needed. In the first round, the entrepreneur can rely on competition among investors, but afterward the selected
investor may gain an information advantage over rivals and could exploit that
position. Consistent with earlier discussion, the incentive problem arises because
the entrepreneur will have made a specific investment in the venture that is appropriable by the investor. The concern can be addressed by discrete contracting:
the parties can agree in advance on the terms and conditions under which the
investor will provide second-round financing.
Discrete contracting, however, can also create incentive problems. It is usually difficult to state explicit conditions that do not distort incentives. If, for
example, the entrepreneur’s equity share is tied to sales in month 18, then the
entrepreneur may devote too much attention to sales and not enough to profitability. On balance, it is sometimes better to leave certain provisions general or
unstated and to rely more on reputation and the relationship.
Relational Contracting and Flexibility
Relational contracts rely on implicit mechanisms such as damage to reputation for enforcement of an understanding.6 The virtue of a relational contract is flexibility. Consider the alternatives of seeking financing from either a
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­ assive investor who has limited familiarity with the market or from a VC who
p
is involved in the industry and would be an active participant in the venture’s
management. The passive investor is more likely to seek a discrete contract.
Because of bounded rationality, the contract is likely to be designed around
crude proxies for states of the world, such as revenue or profit targets that are
only rough indicators of the success of the venture. Conditions for later-stage
investment, allocation of ownership, and other factors may hinge on whether
the venture meets those targets. Many unforeseen conditions could result in
failure to meet the targets even if the venture is worth pursuing. The problem
is exacerbated if the investor is passive and has no firsthand knowledge of why
a performance target was or was not met.
VC financing is likely to be more flexible. Essentially, the VC offers a relational contract that generally does not tie funding to specific performance but
rather provides continued support (and possibly funding) as long as doing so is
in its interest. Deal terms enable the VC to monitor the entrepreneur and even
to restrict the entrepreneur’s ability to act without VC consent. The net effect
is that if additional funds are needed, the VC is in a good position to evaluate
whether to advance the funds.
The entrepreneur typically does not have previous experience or reputation
for a managing business. One of the potential benefits for associating with a
reputable VC is that they have extensive networks they can rely on to provide
expertise when needed. The VC cannot know with certainty how much direct
involvement will be required. The result is that the contracts are not explicit
about the amount of oversight or monitoring the VC will provide.7 Such contracts
expose entrepreneurs to the risk of opportunism, but that risk is mitigated by
the reputation of the VC.
Discrete contracts and flexible relational contracts are different responses to
bounded rationality. Which response is better in any particular setting depends
on the identities of the parties, the costs and benefits of flexibility, and the ability
of the parties to agree on explicit terms.
Incomplete Contracts and Mechanisms for Resolving
Information and Incentive Problems
Relational contracts do not “solve” information and incentive problems. Because the contracts are incomplete, other mechanisms that address informational asymmetry and opportunism often accompany relational contracts.
The mechanisms that are adopted to complement relational contracts involve
signaling, screening, bonding, and monitoring. Technically, the terms “signaling” and “screening” apply to mechanisms that address adverse selection
Venture Deals 135
(informational asymmetry), whereas “bonding” and “monitoring” apply to
mechanisms that address moral hazard (opportunism).
Signaling. It can be difficult for an investor to distinguish high-quality ventures from the “lemons.”8 Part of the problem is that the entrepreneur’s claims of
success potential are costly or impossible to verify. Sometimes an entrepreneur
can convey positive private information simply by showing the information to
investors and leaving them to draw their own conclusions. But this is risky if
an investor could decide to appropriate the opportunity.
Sometimes, providing the information is not feasible for the entrepreneur.
How, for example, without elaborate documentation, can an entrepreneur establish that efforts to develop new voice-recognition software have progressed
much more rapidly than expected? Conversely, how can a VC firm show that it
is not involved with or considering any competing products that could influence
its attitude toward the entrepreneur’s product? The challenge is to do so in a way
that is convincing, yet still preserves the value of the information.
One solution is for the holder of the information to use a signal. A signal
is a credible demonstration that obviates the need to convey the information.
Sometimes, deal terms can serve as signals. Suppose an investor is concerned
that an entrepreneur’s financial projections are overly optimistic. By proposing
a contract that ties his return to performance targets, the entrepreneur “signals”
his positive beliefs.
Consider the following terms for a contract between an entrepreneur, Miles
Stone, and a VC firm, Limited Deals, Ltd., concerning financing for a virtual
amusement park venture. Upon signing the agreement, Limited is committed
to finance the venture up to $4.0 million for one year, by which time specific
benchmarks are to be met. If the benchmarks are met, Limited will invest an
additional $2.0 million; if the venture fails to meet one or more benchmarks,
Limited can decide not to invest in the second round. If Limited does not invest, then Miles has 60 days to find a new investor; if he is unable to do so, then
Limited has the right to force liquidation. Miles retains ownership of the amusement park idea and the related copyrights and trademarks. If the other assets
are sold, the proceeds are to be distributed in proportion to shares of invested
capital. Finally, if Limited invests the second-round $2.0 million even though the
venture does not meet the benchmarks, then Limited gains managerial control.
Willingness to abide by these terms signals several things about Miles. First,
the benchmarks are based on his projections. By structuring the deal around
them, Miles signals confidence that the projections are reasonable. Second,
his willingness to relinquish control if performance expectations are not met
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s­ ignals confidence in his managerial skills. The arrangement also affects Miles’s
incentives to devote effort to the venture. By working harder, he helps assure
that the benchmarks are met and that he maintains control. Thus, signaling
also addresses moral hazard.
Screening. Screening is much like signaling, except that the party without
the private information offers a menu of alternative terms so that the party
with information reveals the information through the act of choosing. In a
new venture setting, the investors can offer entrepreneurs alternative types of
financing. Given the choice, entrepreneurs with low expectations, for example,
are likely to prefer financing that does not include liquidation preference for
the entrepreneur or terms that reduce the entrepreneur’s own risk exposure.
In contrast, entrepreneurs who are confident in their expectations are more
likely to accept favorable liquidation preferences and more of the risk exposure,
believing that the venture will succeed and everyone will do well at exit. The
screening contract in effect elicits information about the entrepreneur’s beliefs
and expectations.
Bonding. In contract terms, posting a bond is one way to give credibility to
a commitment not to engage in opportunistic behavior. A bond is a penalty that
will be paid by the party who makes a promise in the event that the promise
is broken. The fundamental element of a bond is that the party who posts it is
made worse off if the commitment is violated. Thus, a bond provides an incentive to fulfill a commitment. The commitment can be explicit and specific (like
agreeing to resign if the investor is not satisfied with the entrepreneur’s effort)
or implicit (like tying compensation to the attained level of sales).
Investors may be concerned that an entrepreneur will not focus on the right
activities and problems. For example, the entrepreneur may be more interested
in working on technical problems than in managing a rapidly growing organization, or he may not recognize failure or may not be open to refocusing the
venture.
A bond can be provided in several different ways. Sometimes a set of specific
contract provisions can function as a bond, such as giving up equity if certain
conditions are not met: for example, signing a sales contract with a major customer that the entrepreneur promised he can deliver. Other means of implementing a performance bond would be to give the investor the right to terminate the
entrepreneur as CEO if the targets are not met, with the caveat that reliance on
specific conditions is not always ideal because random aspects of new venture
development may be beyond the control of the entrepreneur and unrelated to
Venture Deals 137
his abilities. Moreover, explicit conditions are subject to manipulation in ways
that are not in the parties’ interests.
It should be easy to see how the reputation of a VC, or of an entrepreneur
who has previous successes, can perform the same function as a bond. In the
economics literature, a party can be “trusted” when refraining from opportunistic behavior is the party’s higher-valued course of action. The literature
refers to trust in terms of reputation and defines “reputational capital” as a
nonsalvageable, intangible asset that is most valuable in its current use. Because
of its nonsalvageability, reputational capital functions as a bond. The owners of
a reputable VC firm do not want to lose their sunk investments in reputational
capital, as could result if the firm were to cheat by reneging on an implicit longterm contract.
Uncertainty is greater concerning the behavior of a new entrant to the VC
industry or a first-time entrepreneur. If the VC firm or the entrepreneur has not
made much of an investment in industry-specific nonsalvageable capital, then
others cannot rely on reputation to enforce the contract. Thus, entrepreneurs
and investors with significant industry-specific reputations can rely more heavily
on promises and trust.
What happens in relationships where only one party has reputational capital? A VC with an established reputation, for example, might require that the
contract contain explicit provisions regarding the entrepreneur’s performance,
whereas the first-time entrepreneur dealing with an established VC might settle
for more generally worded promises without explicit enforcement mechanisms.
There are indirect means of bonding performance as well. Certification by a
third party can fill the gap when reputation of the contracting parties is insufficient.9 For example, reliance on a sales forecast can be supported by tangible
evidence that a significant customer is interested in buying the product or has
placed a large order. In effect, the entrepreneur is “borrowing” the reputation
of one of its customers and using it to enhance the credibility of projections.
Similarly, a firm’s affiliation with a reputable VC can provide certification for
an untested entrepreneur.
Monitoring. The other way opportunistic behavior is controlled is by monitoring. In lieu of bonding, the parties can rely on observation. Monitoring
can be direct, as in the case of an investor who serves on the venture’s board,
or indirect, as in the case of requiring financial statements to be periodically
audited by a reputable accounting firm.
The relationships between VC firms and their portfolio companies involve
elements of both bonding and monitoring. The deal structures frequently involve
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bonding arrangements such as termination rights or warrants, but the investors
also closely monitor performance. Some monitoring involves formal triggers;
for example, hypothesis testing and staging give the investor specific, periodic
opportunities to evaluate whether to continue investing.
For ventures where borrowing is possible, the use of debt covenants is an
example of monitoring that employs a trigger. Debt covenants are contractual
terms used by lenders to place restrictions on borrowers. Debt covenants may
restrict the borrower from taking on additional debt or making capital investments beyond what originally was contemplated. They may also require the
borrower to adhere to certain constraints with respect to various financial ratios
and may limit the ability of the borrower to make distributions to other investors.
Use of convertible debt can be a way to allay the lender’s concern that the
entrepreneur may take on risks that do not benefit the lender. The debt is convertible at the lender’s option (or triggers specified in the contract) to a predetermined number of shares of common stock. In that way the lender is positioned to
benefit from the firm’s performance if it is especially strong and the entrepreneur
or firm manager has less incentive to take unwarranted risk. The conversion
option thus aligns the interests of the entrepreneur more closely with those of
the lender. It is common for high-risk new ventures to borrow using convertible debt or to issue convertible preferred stock.10 In the following sections, we
discuss both convertible debt and convertible preferred stock as two main types
of securities used in financing high-risk ventures.
4.3 Elements of VC Deal Structure
A central aspect of an entrepreneur’s attempt to raise capital is negotiating
“the deal” with potential investors.11 The most important and negotiable
aspects of the deal between an entrepreneur and a VC or angel investor are
stated in the “term sheet.” The term sheet defines the allocation of risk and
return as well as the rights and obligations of the entrepreneur and the investor. Because every new venture is different, arrangements between the entrepreneur and investors defy generalization. Nonetheless, certain elements and
documents are common.
First, it is common for the parties to reflect their mutual understanding in
the term sheet. In particular, a typical term sheet will include a statement of
covenants and undertakings that indicates what the parties are agreeing to seek
to accomplish and what they agree not to do. The statement serves to focus
the efforts of the entrepreneur on activities that are intended to advance the
Venture Deals 139
agreed-upon objective and constrains the entrepreneur from devoting resources
in other directions.
In addition, it is common for a term sheet to include a statement of representations and warranties. The statement may, for example, indicate that the investor
has no conflict of interest that would be contrary to pursuing the success of the
venture, that the entrepreneur’s educational background is as indicated in a
résumé, and that the entrepreneur owns the intellectual property that is critical
to venture success.
The term sheet typically reflects an agreed-upon valuation and sets out the
amount of investment that is to be made, as well as the ownership claims the
investor will receive.
In addition, the term sheet may identify some of the options, rights, and
responsibilities of each party. For example, the investor may have the right to
make appointments to the board of directors and, under some conditions, may
have the right to withdraw from the project or to terminate the entrepreneur.
The entrepreneur may have the right to call on the investor for additional funds
in the event that certain milestones are achieved. Often, holders of convertible
securities have the right to vote their ownership claims on an as-converted basis
and may have additional voting rights in certain situations.
Table 4.1 summarizes the basic provisions of a VC term sheet, where the investment is in the form of convertible preferred stock. The term sheet provides
the essential information about ownership and risk allocation that, along with
other provisions, is a step on the path to a formalized legally enforceable investment agreement. A well-structured deal that takes account of information and
incentive problems and of differences in expectations and tolerance for risk can
create value for both the entrepreneur and the investor. In the following discussion we consider the basic categories of terms and provisions contained in both
the term sheet and the investment agreement.
Investment Form
By far the most common type of security that VCs use to make the investment
is convertible preferred stock. In contrast, the entrepreneur holds straight equity and is the residual claimant on the cash flows of the firm. To attract investment, the entrepreneur can signal confidence by agreeing to compensate
the investor with preferred stock instead of common.
Holders of convertible securities generally have the right to convert at any
time, but there are also conditions when conversion is automatic, such as when
a company goes public. Preferred stock investors are often entitled to receive
dividends, and debt investors are entitled to receive interest. However, because
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Tab le 4 .1 Standard provisions of a VC term sheet for convertible preferred investment
Terms and Provisions
Description and Purpose
Amount Raised, Price per
Share, Pre-Money Valuation,
Capitalization
Describes the general parameters of the financing round. Pre-money valuation is
the value implied by the price per share before including the proceeds of the round.
Capitalization describes the VC structure, including preferred shares used in the
funding round.
Describes how dividends will accrue to preferred shareholders and timing and conditions under which dividends are paid.
Describes the order of payment of proceeds from liquidation, such as: first return
the proceeds invested by preferred shareholder; then pay preferred dividends; then
pay common dividends; then, if investor shares are participating, share the balance
either pro rata or according to terms of the agreement.
Requires the company to provide for an employee pool of common shares to aid in
recruitment and retention of employees.
Provides the right to avoid dilution of ownership stake in a subsequent funding round that results in lower value per share; weighted average ratchets are most
common.
Requires preferred investors to participate in down rounds or convert to common or
lose some preferred rights.
Describes the conditions of the right to convert preferred shares to common. Mandatory conversion specifies conditions under which a public offering of the venture
would force conversion of the preferred shares.
Provides a first right to purchase shares offered by the company and right to sell
when other owners sell.
Describes any special voting rights (on an as-converted basis) and the right to vote
separately to elect a specified number of directors.
Describes whether preferred shareholder gets a seat and vote on the board.
Describes rights of preferred investors, as a group, to demand redemption of investment from available funds. Describes conditions for registration of shares issued to
preferred investors, and the lockup provisions on trading.
When there is a public offering of shares, this provision provides for registration of
investor shares along with the shares of the company.
Provides rights to deal with decisions to sell or take the venture public when there
are majority and minority shareholders. For example, a majority who want to sell
can drag along the minority, compelling them to sell their shares. Conversely, in special cases where a potential buyer only cares about a majority of shares, then tagalong rights allow for the minority to sell shares on the same terms as the majority.
These may include noncompete clauses, employment contracts, key person insurance, information rights, board meeting requirements, and stock and stock option
vesting terms.
Dividends for Preferred Shares
Liquidation Preference and Participation (if any)
Establishment of Employee Pool
Anti-Dilution
Pay-to-Play
Conversion Rights
Rights of First Refusal and
Co-Sale
Preferred Stock Voting Rights
Board of Directors
Redemption and Registration
Piggyback Registration
Drag-along, Tag-along
Other Rights
entrepreneurial ventures are cash-constrained, dividends and interest payments
are normally accrued rather than being paid out. Accrued dividends and interest
are reflected as increases in the amount invested. In addition, investments in
the venture may be “sweetened” with warrants that would enable the investor
to acquire additional shares at a prespecified price. Also, the entrepreneur and
employees may be compensated partly in the form of stock options.
Venture Deals 141
Price and Valuation
In any investment round, the term sheet specifies the amount invested. In investment rounds that include common or preferred shares, the term sheet also
specifies the number of shares to be acquired in exchange for the investment.
The terms for investing implicitly or explicitly establish a price per share for
the investment round. This price per common or preferred share effectively
establishes a new valuation for the venture. It is customary and useful to state
the valuation on a “fully diluted” basis, where full dilution assumes the conversion of all preferred shares or other convertible securities to common and
the exercise of all outstanding stock warrants and options (including any that
are issued in conjunction with the financing round). For the purpose of valuation, the normal approach is to ignore any sweeteners of preferred shares that
make them more valuable than common shares.
Term sheets commonly specify the establishment of an employee pool of
shares and stock options. Often, employees in new ventures receive material
fractions of their compensation in the form of stock options or shares of stock
that may not vest until after several years of employment in the venture. The
larger the employee pool, the larger will be the fully diluted number of shares
and, therefore, other things being the same, the lower will be the value per share
to which the investor will agree.
Pre-money and post-money valuation. Two concepts of valuation are
frequently used in new venture deals—pre-money and post-money valuation.
Post-money value refers to the value of the venture that is implied by the negotiated share price multiplied by the total fully diluted number of shares that would
be outstanding after the investment round (i.e., post-money). Pre-money value
can be inferred either by subtracting the amount being invested from the postmoney value, or directly by multiplying the negotiated share price by the number
of fully diluted shares before the round. Both pre- and post-money values for a
given investment round are based on the negotiated share price in that round.
It is easy to confuse pre-money value with the post-money value from the
prior round. These are not the same. In fact, the difference between the current
pre-money value and the prior post-money value is a measure of the increase
or decrease in the total value of the existing shares that has transpired since
the prior round. If this change in value is positive, the current round is an “upround” in that the value per share has increased. If the change is negative, the
current round is a “down-round,” suggesting that the venture is not performing
up to the expectations that were reflected in the prior round.
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Consider a simple deal in which an investor contributes $1 million in exchange for 400,000 shares of common stock and the entrepreneur retains 600,000
shares. Effectively, the investor is acquiring the shares for a price of $2.50 per
share. The term post-money value refers to the total value of the venture that
is implied by multiplying $2.50 by the entire 1 million shares outstanding after
the new investment. In this case, the post-money value (sometimes referred to
as capitalization) is $2.5 million. Pre-money value is the implied value of the
venture before the investment. In this case, the pre-money value is $1.5 million
(i.e., the post-money value less the $1 million investment). The post-money value
is a measure of the value of the venture to the outside investor. The entrepreneur
may have different beliefs about value, and those beliefs may affect the negotiation, but they do not appear in the term sheet.
If, in the next round, an investor agrees to invest $2 million in exchange for
500,000 shares, the new implied value per share is $4.00. The post-money value
in the round is $6 million ($4 time 1.5 million shares), and the pre-money value
is $4 million, a $1.5 million increase over the prior post-money value.
A caveat on valuation. As noted previously, the post-money value does not
reflect the effects of any sweeteners that might have caused the investor to agree
to a higher price per share. Accordingly, when the investor’s claim is preferred
equity, post-money value is an upwardly biased measure of what the venture is
worth. Suppose, in the preceding example, the investor pays $2.50 per share,
but the shares are convertible preferred stock (the most common form of VC
investment). Because the owner of preferred shares has a prior claim to common
stockholders (i.e., the entrepreneur) in a liquidation event, post-money value as
calculated above overstates value relative to what the investor would have been
willing to pay for common shares. The more sweeteners the entrepreneur is
willing to surrender to the investor, the more the investor should be willing to
pay for the shares. The result is a higher valuation, both post- and pre-money,
but in reality value is not necessarily increased. Rather, it is transferred from
the entrepreneur to the investor.
The ultimate concern of the entrepreneur is not the post-money valuation
but the true value of the entrepreneur’s ownership interest. Many entrepreneurs
make the mistake of focusing on post-money value and ignore the value of the
“sweeteners” they have promised to the investor. Of course, an entrepreneur
who is optimistic about the future of the venture may not perceive it as very
costly to give a liquidation preference to a less optimistic investor. In fact, the
entrepreneur’s willingness to agree to such a sweetener is one way the entrepreneur can signal confidence in the venture.
Venture Deals 143
Liquidation Preference and Participation
Normally, preferred stock has a liquidation preference, where a liquidation
event is defined broadly to include such occurrences as bankruptcy, acquisition, sale of voting control to an outside party, or sale of substantially all of
the company assets. Under this definition, an IPO is not a liquidation event
but rather is another funding event. The IPO generally triggers the immediate
conversion of preferred shares to common stock.12
When a liquidation event occurs, the preferred stock investors will have preference to common equity and, hence, will receive distributions first. A typical
liquidation preference will specify that the preferred investor first gets a return
of some multiple of her investment—such as 1.5 times her investment or 3.0 times
her investment. (She may also receive accrued dividends that are paid out as
common shares.) If there is an IPO at a sufficient price per share, the convertible
preferred equity immediately converts to common equity. Conversion removes
the liquidation preference and any other preferences and thus facilitates the IPO.
The preferred stock liquidation preference creates a contingent payoff structure for the investor. If the company goes public, conversion is automatic and the
investor’s return comes from the increased value per share relative to the value
when the preferred stock was purchased. If a liquidation event occurs, the preferred investor can opt to accept the liquidation preference in lieu of converting
to common. The investor is not required to accept the liquidation preference. If
there is a high-value acquisition, for example, the preferred investor might do
better by converting to common than by accepting the liquidation preference.
In addition to the liquidation preference, the preferred shares may also be
entitled to participate in the proceeds from a liquidation event. These shares are
referred to as convertible participating preferred shares. Participation usually is
pro rata (proportional to ownership). In many cases it is common for participation to be capped. For example, participating preferred equity with a 1.0 times
liquidation preference and a 3.0 times cap on participation would return a total
maximum of $3 on each $1 invested.
Antidilution Rights
An antidilution right is another sweetener that works to the benefit of preferred
stock investors. Antidilution rights are designed to protect the investor from
loss of value and ownership share in the event that the next investment round
is a down-round. The most common protections are commonly referred to as
“ratchet” provisions. Such protections can be full or partial. Under a full ratchet,
if the valuation per share declines from what the investor paid, the e­ ntrepreneurial
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firm will issue the investor enough new shares for free (or for a nominal price) so
that the investor’s average cost per share is the same as the cost to a new investor. Since a ratchet provision either fully or partially protects the investor from
loss of value, an investor who has a ratchet can be expected to demand a smaller
fraction of ownership in exchange for a given level of investment. Inclusion of a
ratchet, however, can also make raising subsequent financing more difficult.
The most common antidilution protection is a “weighted average” ratchet.
This provision adjusts the investor’s average price per common share based
on (1) the amount of money raised and price per share in the prior round and
(2) the amount of money being raised and price per share in the current round.
Following a down-round, which is the only time a ratchet provision is activated,
this weighted average price is always lower than the original purchase price.
The weighted average formula for two financing rounds can be simply stated as
(Round 1 investment + Round 2 investment)
(Round 1 shares + Round 2 shares)
So, for example, if 1 million shares are issued in round 1 at a price of $2.00,
and 3 million shares are issued in round 2 at a price of $1.00, the total investment is $5 million (i.e., $2 million in round 1 and $3 million in round 2), and a
total of 4 million shares are issued. The weighted average formula will lower the
first-round investor’s average price to $1.25 (i.e., $5 million/4 million shares). To
lower the first-round investor’s average cost from $2 to $1.25, the investor will
need to receive an additional 600,000 shares for free.
It is important to recognize that the existence of an antidilution provision
from a prior round complicates the valuation in the next round. In the preceding
example, the second-round investor has to take account of how the additional
free shares going to the first-round investor will affect total shares outstanding.
Had there been no such provision, there would be 600,000 fewer shares outstanding and the second-round investor would end up with a larger fraction of the
total shares. Accordingly, the ratchet must reduce the amount the second-round
investor would be willing to pay per share. In the preceding example, the $1.00
price in the second round would have been higher if there had been no ratchet.
Pay-to-Play Provisions
Closely related to antidilution rights are pay-to-play provisions. Such a provision makes an investor’s ability to benefit from an antidilution right contingent
on the preferred stock investor’s full participation in the subsequent downround. That is, an investor who does not participate fully in the next round
does not receive any antidilution protection. In a more extreme form, the pay-
Venture Deals 145
to-play provision can specify that nonparticipation triggers conversion of the
preferred shares to common and the loss of all preferred stock preferences.
Pay-to-play provisions are important because it is difficult to attract new
investors to a down-round if existing investors are not participating. The provision is often waived in up-rounds, when attracting funding from new investors
may not be difficult. The exact definition of “full participation” can vary from
the expectation that investors in the prior round fully fund the next round to a
more limited case where they fully fund a percentage of the next round, which
percentage is determined by the board of directors.
Rights of First Refusal/Preemptive (Pro rata) Rights
Somewhat the converse of a pay-to-play provision is a right of first refusal or
preemptive right. Such rights give prior investors the right to maintain their
pro rata ownership percentages by participating in subsequent financing
rounds or the right to prevent sale to an outside party as long as the investors are willing to match the negotiated price. Among other things, preemptive rights enable existing investors to maintain their percentages of voting
control, and rights of first refusal enable existing investors to prevent bringing
in new investors who could be problematic to deal with later on. Rights such
as these are sometimes referred to as non-price-based antidilution provisions
since they enable the investor to avoid dilution of voting rights.
One potential problem with a right of first refusal is that it can impair the
entrepreneur’s ability to shop for better terms from a new investor. Deciding
whether to invest in a venture and on what terms is a costly undertaking for a
potential investor. If an existing investor has a right of first refusal, the potential
investor could be unwilling to undertake the evaluation since any deal the potential investor proposed could be preempted by existing investors via the exercise
of their right of first refusal. The net effect is that the round negotiation over
investment terms becomes more bilateral, weakening the entrepreneur’s ability
to rely on competition with potential investors to produce a good valuation. As
a precursor to effective negotiations with potential new investors, it can be helpful for the entrepreneur to seek a waiver of the right of first refusal from existing investors. On the other hand, partial participation in the round by existing
investors can signal to new investors that the terms for investing are attractive.
Board Representation, Protective Provisions, and
Information Rights
New venture term sheets generally include a provision that specifies the size of
the board of directors and gives the investor the right to select a given ­number
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Chapter Four
of the directors. They also can include protective provisions that require the
venture to gain approval of the investor before undertaking certain actions.
For example, investor approval may be required before the venture can increase the number of shares, issue shares or other claims to which the existing
preferred shares would be subordinate, declare a dividend, borrow money, or
make major purchases. Additionally, the term sheet normally indicates information rights the investor may have to budget projections, audited financial
statements, and other similar information.
Rights Related to Harvesting and Exit
A key concern of investors is that they have a clear path to harvesting their investment. Since investors commonly do not have majority control, if they do not
provide a mechanism that can be used to force redemption of their investment,
they can become locked into the venture and the entrepreneur may be satisfied
with simply continuing to operate the venture and collecting a salary. A number
of provisions can work together to assure the investor with a means of harvesting.
Redemption rights. Investors may have the right to demand of the entrepreneur that their shares be redeemed based on prespecified terms, such as the
initial investment plus accrued but unpaid dividends. This right can be valuable
if the venture is successful as a going concern but is not a good candidate for
IPO or high-valued acquisition, and particularly where the likely arm’s-length
value of the shares would be less than the price indicated by the formula.
Demand registration rights. Redemption rights are unlikely to be found
in the absence of registration rights. Demand registration rights give the investor the ability to demand that company stock be registered even if the company
does not need to raise capital and the entrepreneur does not want to take the
company public in an IPO. The main value of demand registration rights is that
they can force the entrepreneur to consider either taking the company public or
negotiating to buy out the investor. If the public market value is expected to be
high, an entrepreneur who wants to keep the company private may need to buy
out the investor at a value that is even higher than the public market value. If
the company is not doing well, the main benefit of demand registration rights is
to force the entrepreneur to negotiate a buyout. Redemption rights can function
as a floor on the buyout price.
Piggyback registration rights. These rights generally are triggered when
a company elects to go public and specify that the investor’s shares are to be
Venture Deals 147
registered along with the shares of the company. Normally, the full cost of registration is borne by the company. The investor may also agree that even though
her shares are registered, she will refrain from selling in the public market until
(normally) 180 days after the IPO. The main purpose of piggyback registration
rights is to enable the investor to gain a public market for her shares without
incurring the cost of registration.
Drag-along and tag-along rights. It can be difficult to negotiate the
sale of a venture unless all shareholders agree to the sale. This gives rise to a
potential holdup where a minority of shareholders refuses to sell unless they get
a disproportionately large fraction of the sale proceeds. Also, an entrepreneur
who holds a minority interest may prefer to continue managing the venture,
rather than selling out to an acquirer. Drag-along rights give the majority that
wants to sell the right to drag the minority along by selling their shares on the
same terms as the majority. In contrast, tag-along rights give the minority the
right to sell on the same terms as the majority. A tag-along right can be valuable
in the case where a potential buyer cares only about acquiring majority control
so that the minority potentially can be frozen out.
Capitalization Table
A term sheet commonly will include a capitalization table (“cap” table) that
describes who owns the company both before and after the financing round.
The table is a spreadsheet or table that shows capitalization, or ownership
stakes, in a company, including equity shares, preferred shares and options,
and the prices paid by stakeholders for these securities. Table 4.2 is an example
of how a simple cap table could display the ownership interests and changes
over the first two financing rounds after the founder’s original investment.
The founders collectively invest $100,000 and do so at a valuation of $0.01 per
share. Based on the investment amount and price per share, the founders receive a total of 10 million shares of common stock. At that point, since no
other investor is involved, the founders’ combined ownership share is 100%.
In the table, as shown, two things happen. First, a group of angels invests a
total of $500,000 in exchange for 2 million shares, implying a valuation of $0.25
per share. In addition, at this point, the investors collectively decide to establish an employee pool that can be used as partial compensation to employees.
Initially, this pool can be shares, stock options, or a combination. The pool will
be allocated to employees over time, so at any time there may be some shares
or options that are allocated to specific employees and others that are not. The
important point for the cap table is that at the time the pool is established, no
Tab le 4 . 2 Simple capitalization table
Initial Capitalization
Founders
First Round Investment
Investment
($000)
$ per
Share
Shares
(000)
Percent
Ownership
$100
$0.01
10,000
100.0%
Angel Investors
Investment
($000)
$500
Employee Pool
$ per
Share
$0.25
Second Round Investment
Shares
(000)
Percent
Ownership
10,000
Shares
(000)
Percent
Ownership
66.7%
10,000
58.8%
2,000
13.3%
2,000
11.8%
3,000
20.0%
3,000
17.6%
2,000
11.8%
17,000
100.0%
VC Series A
Preferred
Total
Investment
($000)
$4,000
10,000
100.0%
15,000
100.0%
$ per
Share
$2.00
A capitalization table describes who owns the company both before and after financing rounds. The founders begin with 100% of the company but after the angels and VCs invest, the
percentage ownership is 58.8%. Caveat: percentages shown do not take into account the nature of the equity investment (e.g., convertible preferred versus common) or sweeteners such as
liquidity preferences. Those distinctions are clearly documented in a full cap table.
Venture Deals 149
investment is made by employees. In the figure, the employee pool is established
to reserve 20% of outstanding shares for employees, or 3 million shares. Based
on the total shares outstanding at this point, and the $0.25 value per share from
the angel investment, assuming that the angels invest at fair value (a zero net
present value), the overall value of the venture is implied to be $3.75 million.
Notice that the cost of the employee pool is borne entirely by the founders. If
there were no employee pool, the venture would still be worth $3.75 million, of
which $500,000 would have been provided by the angels. The remaining $3.25
million would be the value of the founders’ shares, or $0.325 per share. Including
the employee pool reduces the per-share value to $0.25 and the resulting value
of the founders’ shares to $2.5 million. The $750,000 difference in the founders’ position reflects the implied value of the employee pool. Through the angel
investment and the establishment of the employee pool, the founders’ ownership
percentage is reduced to 66.7%.
In the second outside round, VC investors contribute $4 million in exchange
for 2 million shares, an implied value per common share of $2.00. In the cap
table the preferred shares are reported on an “as-converted” basis, and no adjustment is made for the value of any other sweeteners that could have affected
per-share value. With 17 million shares outstanding at this point, the implied
value of the venture is $34 million. The value of the VC shares is the negotiated
$4 million (i.e., a zero net present value investment). The angel shares also have
an apparent value of $4 million, and the apparent value of the employee pool is
$6 million. The residual value of the founder’s shares is implied to be $20 million and the founders’ ownership percentage is reduced to 58.8%. Because the
VC investors received preferred shares, the implied valuations of the holdings
of the other investors are likely to be somewhat overstated.
4.4 Analysis of Key Term Sheet Provisions
In negotiating the key provisions of a term sheet, it is important for both the
entrepreneur and the investor to consider a range of possible future valuations
and to understand how wealth allocation would be affected by the provisions.
Liquidation Preference
To begin, consider an example where a VC investor invests $1 million in convertible preferred stock that has a 3X liquidation preference or is convertible
to 20% of the common shares. Based on the investment of $1 million for 20%
of common shares, the implied post-money value of the venture at the time of
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Chapter Four
18.0
16.0
Value of ownership share
14.0
12.0
Investor conversion value
Investor preference value
Founder residual value
Founder share value
10.0
8.0
6.0
4.0
2.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
16.0
16.5
17.0
17.5
18.0
18.5
19.0
19.5
20.0
0.0
Venture value
Fi g u r e 4 .1
Liquidation preference: $1 MM investment with a 3X preference or convertible to 20% of common equity
The solid lines in the figure compares the preferred stock liquidation value with a 3X liquidation preference against the value of converting immediately to 20% of common equity. The solid black line shows liquidation value with a 3X preference. The solid gray line shows the
value of converting to 20% of common. As shown, when venture value exceeds $15 million, the holder of the convertible preferred equity
would convert to common equity and forgo the liquidation preference. Dashed lines show the values of the founders’ shares depending on
the choice made by the preferred stock investor.
investment is $5 million. At the time of a liquidation event, the investor must
choose either the liquidation preference or conversion to common. Figure 4.1
shows the liquidation values of the investor’s alternative claims (the 3X value
or the conversion value to common equity) as solid lines. The respective values
of the founders’ claims are shown as dashed lines. Note that if the liquidation
value is less than $3 million, the venture cannot fully meet the liquidation preference. Instead, the investor receives the entire value of the venture and the entrepreneur gets zero.
As shown by the solid lines in the figure, the investor’s liquidation preference
is more valuable than the conversion option as long as the exit valuation is below
$15 million. If the exit is above $15 million, it is better for the investor to convert
to common and forgo the liquidation preference. The investor can be expected
to select the alternative that results in highest value. For the founders, the effect
is the converse. Since the investor can be expected to choose the greater of the
liquidation preference or conversion, the entrepreneur (founder) will get the
lesser of the dashed line payoffs in the figure.
Negotiating Investment Terms
Figure 4.1 shows only the payoffs that would result from an agreed 3X liquidation preference that is convertible to 20% common stock. Suppose, however,
Venture Deals 151
6.0
Value of ownership share
5.0
Max of 3X or 20% common
25% common
4.0
3.0
2.0
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
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5.5
6.0
6.5
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7.5
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8.5
9.0
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19.5
20.0
1.0
Venture value
Fi g u r e 4 . 2
Liquidation preference: $1MM investment with a 3X preference or convertible to 20% of common stock, versus an
alternative contract of 25% of common stock
The figure shows the liquidation values of two different investor claims as the value of the venture increases. The comparison is between the
maximum value of a 3X preference convertible to 20% of common stock (the black line) versus 25% common (the gray line).
that this is only one of two alternative terms the investor is considering. The
other is that instead of the convertible preferred stock with liquidation preference, the investor could receive common stock representing a 25% interest in
the venture. Figure 4.2 compares the investor’s payoffs under these alternative
provisions. The black line in the figure shows the maximum values from Figure 4.1 of either taking the liquidation preference or converting to 20% common. The gray line shows the alternative value of 25% common stock (with no
preference). As you can see, the preferred stock offers a higher payoff for the
investor if liquidation value is below $12 million, but a lower payoff if liquidation value is above $12 million. The founders, in contrast, would do better by
agreeing to the liquidation preference if the liquidation value turned out to be
above $12 million.
It should be apparent now how the parties can use variations in investment
terms to work around their different perceptions of value. If the investor expects
a low liquidation value but the entrepreneur expects a high one, the parties would
probably both want the preferred stock with liquidation preference. The investor
sees the preferred payoff as attractive, whereas the more optimistic entrepreneur
does not see offering the preference as very costly (i.e., he expects a liquidation
value above $12 million). It should also be apparent that negotiating over terms
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such as these is a way for a party to “put their money where their mouth is.” If the
investor is truly pessimistic, she should be attracted by the preference, whereas
if the entrepreneur is truly optimistic he should want to offer the preference
rather than giving up a larger percentage of the common stock.
Convertible Participating Preferred
In addition to liquidation preference, preferred stock sometimes also participates in high-valued liquidations. Such participation may be unlimited or
capped at a certain level, usually specified as a multiple of the preferred investment. Figure 4.3 compares four alternative preference and participation
contracts from the perspective of the investor. The two solid lines in the figure
are the same as in the prior figure—solid black is the maximum value of a
3X liquidation preference or immediate conversion to 20% of common equity.
The solid gray line is an alternative provision where the investor would receive 25% of equity in exchange for the $1 million investment. As noted, between these two, an investor who expects a liquidation value below $12 million would prefer the 3X liquidation preference to a larger fraction of common
stock ownership.
6.0
Max of 3X or 20% common
5.0
1X preference with full participation
Liquidation value
2X preference with 4X cap
4.0
25% common
3.0
2.0
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
9.0
9.5
10.0
10.5
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
15.5
16.0
16.5
17.0
17.5
18.0
18.5
19.0
19.5
20.0
1.0
Exit valuation
Fi g u r e 4 . 3
Comparison of four alternative terms for $1MM investment: 3X liquidation preference or convertible to 20% of
common stock; 1X preference and full participation with 20% ownership; 2X liquidation preference with 20%
participation up to a 4X cap; and 25% of common stock
The figure shows the liquidation values of four different investor claims as the value of the venture increases. The comparison is between the
two former contracts shown in Figure 4.2 and two participating convertible contracts: 1X preference with full participation and 2X preference with a 4X cap on participation.
Venture Deals 153
The black dashed line in the figure shows an alternative contract term where
the investor has a 1X liquidation preference and participates fully (with 20%
ownership) in any excess of the exit value over the $1 million preference. The
gray dashed line shows the value of preferred stock with a 2X preference and
a cap on the full 20% participation of 4X. The cap is reached at an exit value
of $12 million. Given these four alternative contracts, the most conservative/
cautious investor should prefer the 3X preference with no participation, and a
somewhat less conservative investor would prefer the 2X preference with capped
participation at 4X. This contract is the best alternative for exit values between
$7 and $16 million. An optimistic investor who expected an exit value above $16
million would do best by forgoing preferred stock and selecting 25% common
stock. If all four alternatives are available, there is no valuation where the 1X
preference with full (20%) participation does best. But in pairwise comparisons,
it is better than 25% common equity at low valuations and better than both 3X
and 2X preferences at high exit valuations.
To be clear, we do not expect the entrepreneur to be offered such a full array
of options. Rather, the entrepreneur and investor should understand the basic
features of the feasible options and be able to evaluate them and then focus
negotiation on the structure each finds most appealing given his or her own
expectations for the performance of the venture.
Antidilution
Antidilution provisions—either full or partial ratchets—are complicated to
evaluate. Once a ratchet is in place, if there is a subsequent down-round, the
investor in the next round will need to recognize that her decision to invest
will cause the earlier investor to acquire additional shares, essentially at a zero
price. Since the total number of shares will increase because of the ratchet, the
amount the investor should be willing to pay per share should go down.
Many presentations of the effects of ratchet provisions ignore the dilutive
effect of the provision on the price the new investor is willing to pay. They
simply take the new round price as a given and use it to determine the relative
holdings. This is clearly misleading as there is simultaneity at work: the price
the new investor is willing to pay and the adjustment in the number of shares
that will be issued must be determined simultaneously.
To see the effect of a full ratchet provision, consider the following example.
An entrepreneur holds 4 million shares of the venture. Subsequently, in an A
round, a VC invests $4 million in exchange for 2 million shares that include a
full-ratchet antidilution provision. Based on the $2 per-share value in the A
round, the post-money value of the venture is $12 million. Subtracting the new
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money, the entrepreneur’s shares have an apparent value of $8 million (ignoring
the fact that the ratchet would have positively affected the price paid by the VC
in the A round).
Now, suppose that the venture has lost half of its value and needs another
$2 million. As shown in the first panel of Table 4.3, had there been no ratchet,
the B round investor would have been willing to pay $1 per share—a downround. If there were no ratchet provision, there would then be 8 million shares
outstanding at a value of $1, indicating a post-money value of $8 million. With
no ratchet, the entrepreneur’s shares would appear to be worth $4 million and
the A round VC’s shares would be worth $2 million. From the A round to the
B round, both would have lost 50% of their value.
Now, consider the effect of the ratchet. First, we can assume that the total
value of the company is still the same $8 million as computed above. We also
know that the Series A investor’s full ratchet will provide enough additional
shares to maintain the value at $4 million and that the new investor will acquire
shares worth $2 million. This leaves only $2 million as the residual value for
the entrepreneur. We also know that the entrepreneur has 4 million shares. To
achieve the $2 million valuation for the entrepreneur, the shares must be worth
only $0.50 each. Since the Series A investor has a $4 million position, she must
have a total of 8 million shares (6 million additional shares over the original 2
million). Likewise, for the Series B investor to invest $2 million on a zero-NPV
basis, the investor needs to receive 4 million shares. So after exercise of the
ratchet, the second panel of Table 4.3 shows that there will be a total of 16 million shares and the entrepreneur’s fractional ownership will have fallen from
66.7% after the A round to 25% after the B round—potentially a loss of control
for the entrepreneur.
There are two important points to make about the mechanics of ratchet
provisions. First, the new shares to fulfill the ratchet obligation come from the
venture and not from the entrepreneur. The entrepreneur’s shares remain constant relative to the prior round. Second, the entrepreneur bears the full cost
of the ratchet provision. That is, the increase in shares that go to the Series A
investor to maintain the value gives rise to a per-share value reduction whereby
the cost of maintaining the Series A value reduces the value of the entrepreneur’s
shares by an equivalent amount.
If post-money value is known, evaluating the effects of a full ratchet is
straightforward. Simply subtract the amount of the new investment and the
amount of the prior investment that is protected by the ratchet, and divide the
residual value by the entrepreneur’s total share holdings. This gives the round
price per share and from there it is easy to determine how many shares will be
held by each party.
Tab le 4 . 3 Comparison of anti-dilution provisions: full ratchet and weighted-average ratchet
No Ratchet Provision
Entrepreneur Shares
Entrepreneur Value
Percentage Ownership
A Round Shares
A Round Value
Percentage Ownership
Weighted Ratchet Provision
Founders
A Round
B Round
Founders
A Round
B Round
Founders
A Round
B Round
4,000,000
NA
100.0%
4,000,000
$8,000,000
66.7%
4,000,000
$4,000,000
50.0%
4,000,000
NA
100.0%
4,000,000
$8,000,000
66.7%
4,000,000
$2,000,000
25.0%
4,000,000
NA
100.0%
4,000,000
$8,000,000
66.7%
4,000,000
$2,666,667
33.3%
2,000,000
$4,000,000
33.3%
2,000,000
$2,000,000
25.0%
2,000,000
$4,000,000
33.3%
8,000,000
$4,000,000
50.0%
2,000,000
$4,000,000
33.3%
5,000,000
$3,333,333
41.7%
B Round Shares
B Round Value
Percentage Ownership
Total Shares
Value per Share
Total Value
Full Ratchet Provision
2,000,000
$2,000,000
25.0%
6,000,000
$2.00
$12,000,000
8,000,000
$1.00
$8,000,000
4,000,000
$2,000,000
25.0%
6,000,000
$2.00
$12,000,000
16,000,000
$0.50
$8,000,000
3,000,000
$2,000,000
25.0%
6,000,000
$2.00
$12,000,000
The figure shows the impact of two types of anti-dilution provisions when new rounds of equity are raised in a down-round: full ratchet and a weighted ratchet.
12,000,000
$0.667
$8,000,000
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Evaluating the effects of a weighted average ratchet is more difficult. The
standard equation for the weighting of a ratchet that was shown earlier in the
subsection on antidilution calculates the dilution price as a value-weighted average of the sum of the prior and new investments divided by the sum of the prior
and new shares. But unless the number of new shares is given, this equation
cannot be used to determine the dilution price for the protected investor. In the
third panel of Table 4.3, we use the information we do know to determine the
round price. We know that that the Series A investment was $4 million and that
the Series B investment is $2 million. We use these two amounts to compute the
weighted value of the Series A that takes account of the antidilution provision
(i.e., 2/3 × $4 million + 1/3 × $2 million = $3.33 million). We also know that the
post-money value is $8 million, that the Series B investor will have a 25% interest, and that the entrepreneur has four million shares. We can find the per-share
value for the entrepreneur by subtracting the new investment and the diluted
value of the A round investment from $8 million. The indicated value of the
entrepreneur’s shares is $2.67 million, resulting in a value per share of $0.667.
This is the value used to determine the number of shares in the B round—3.0
million shares. Then the number of shares for the A round investor is the diluted
value divided by the round price of $0.667, or 5.0 million shares. This is one
example of a weighting approach. Actual approaches can vary.
IPO ratchet. Ratchets can be negotiated in any round of investment, including a round right before an IPO. In recent years some notable late-stage rounds of
convertible preferred investments have included IPO ratchets. These provisions
protect the investor, and can provide a promised minimum return, by specifying that if the IPO is priced below a stated price per share (perhaps a price that
would be insufficient to generate an agreed-upon return on investment), then
the IPO conversion of those shares to common is adjusted so that the investor
receives enough additional shares such that the predetermined minimum return
on the investment is assured. In effect, it is IPO price protection. Square’s Series
E preferred stock investors, who invested right before the IPO, for example,
negotiated such protection.13
4.5
Deal Structures of Angel Investments
The preceding discussion is related primarily to investments by VC funds.
Angel investors often enter into investment agreements that are less complex. They may be as simple as providing a brief description of the venture
Venture Deals 157
and the parties’ aspirations for it, then specifying an amount of investment in
exchange for a percentage ownership interest. The agreement might call for
periodic reporting, board participation, or other access to information. Angel
deals often do not involve formal staging commitments, preferential forms of
investment, antidilution protection, or provisions designed to force a liquidity
event.
It may seem, given that uncertainty is probably greatest at the stages suitable
for angel investment, that the deal structures would be similarly complex. Angel
investors tend to rely on simple structures for several reasons: (1) the preexistence
of complex deal structures that would need to be unwound before VC investment
is possible may discourage VC investment; (2) in contrast to VCs, angels rely on
informal rather than contractual methods of screening and monitoring; and (3)
it is not cost effective to prepare elaborate contracts for small investments where
time until the next investment round is needed is likely to be short.
With the rise of organized angel investor groups, angel investment agreements have become somewhat more structured, moving more in the direction
of VC deals. Ibrahim (2008) ascribes the change to the greater professionalism
of angel groups, the higher transactions costs of group investing, and the larger
amounts that the groups are able to invest.14
Nonpriced Rounds
Often, angel investments are made at very early stages in the life of a venture.
In such cases, there is little basis upon which to establish a valuation. Moreover, the amount of investment may not justify spending effort to agree on a
valuation. Recently, in the face of these difficulties, some early-stage investors
have been using convertible debt instruments to circumvent the need for explicit valuation. Even more recently, investors have begun to use a similar, but
non-interest bearing instrument known as a SAFE (Simple Agreement for Future Equity). In fact, some angels now invest only with these types of financial
instrument. Financing rounds where such instruments are used are referred to
as “nonpriced rounds.”
The logic of this type of financing is that in the event of a good outcome, the
loan will convert to stock (usually convertible preferred stock) but will do so on
terms that are directly tied to the valuation in the subsequent “priced round,”
where the subsequent investors put a dollar valuation on the company. In the
event of a bad outcome, the convertible debt holder has a prior claim on the
value of the venture’s assets up to the value of the loan plus any accrued but
unpaid interest. Usually, when such an instrument is used, the loan agreement
will specify either a discounted conversion price relative to the price in the next
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round, a cap on the conversion price that does not depend on the next round
price, or both. The following are examples.
Convertible note with discount and cap. Initially, an entrepreneur has
10 million shares of common stock. In a seed round, the entrepreneur raises $1
million in a convertible note from an angel investor. Conversion is triggered if
there is a priced equity financing round of at least $2 million. The note has a 30%
discount and a $12.5 million cap on the pre-money value. The note will convert
at whichever is more favorable to the note holder. Pre-money at the time of the
angel investment refers only to the shares held by the entrepreneur.
A few months later, the entrepreneur receives a Series A term sheet from a VC
firm offering $5 million for 20% of the company. Because of this triggering event,
the note holder will convert the loan into preferred equity based on the implied
valuation of $25.0 million ($5 million/20%). Because the note has a discount
and a cap, the angel investor can choose whichever provision is more valuable.
Since the cap terms are prespecified, we can easily determine that the maximum share price under the cap is $1.25 (the $12.5 million pre-money value divided
by the entrepreneur’s 10 million shares). On this basis, the $1 million note would
convert to 800,000 shares. In total, the entrepreneur and the angel would hold
10.8 million shares, which would represent 80% of the shares. Accordingly, the
total number of shares would be 13.5 million and the VC investor would get 2.7
million shares. Given the $5 million investment, the implied value would be
$1.8518 per share. Under the cap, with the $25 million valuation, the angel would
own 5.93% of the outstanding equity and the entrepreneur would own 74.07%.
In either case, the cap or the discount, the VC would own 20%.
Pricing under the discount is more complicated since the dilutive effect of the
discount depends on the price that is negotiated in the VC round. That is, the
round price and total number of shares are determined simultaneously. We do
know that the VC is seeking a 20% ownership interest in exchange for a $5 million
investment. We also know that the entrepreneur has 10 million shares, that the
angel has invested $1 million convertible to shares at a 30% discount, and that
together the entrepreneur and the angel will hold 80% of the shares. Algebraically,
$5MM/Price = 0.2 × Shares
and
10MM + $1MM/(0.7 × Price) = 0.8 × Shares
Solving both equations for the number of shares gives
$25MM/Price = Shares
and
12.5MM + $1.785714MM/Price = Shares
Venture Deals 159
Equating these and solving for Price gives
Price = $1.87714
Then substitution this value for Price in one of the original equations gives
Shares = 13.46156MM
We can then find the conversion price for the note as
Price = 0.7 × $1.87714 = $1.30
and the allocations of ownership are as follow:
Entrepreneur:
10,000,000 shares
74.29%
Angel investor:
769,231 shares
5.71%
VC investor
2,692,328 shares
20.00%
In this case, with a valuation of $25 million, the cap is more favorable to the
angel investor than is the discount. The capped value is $1.25 per share and the
discounted value is $1.30. Under the cap, the angel investor would have 5.93% of
the equity and under the discount the ownership would be 5.71%. The additional
ownership fraction under the cap comes from the entrepreneur. At a post-money
valuation of $25 million, the advantage of the cap is slight. At higher valuations
the cap would be even more valuable. At lower valuations, the discount would
gain value relative to the cap.
One final point should be made about the use of caps and discounts. While
a discount is harder to evaluate than is a cap, a cap may be harder to negotiate
than a discount. While both are referred to as “nonpriced,” the cap implies that
there is at least a rough understanding about the range of possible valuations in
the next round. If the parties disagree too much about the possible values, it may
not be possible to agree on a cap. That is, the entrepreneur may believe that a
cap proposed by an angel (or VC) in the first round is too low and will give too
much ownership to the angel, whereas an angel may believe that one proposed
by the entrepreneur is too high and may actually be higher than the next round
valuation, making the cap nonbinding. Possibly for this reason, some angels
and VCs negotiate discount terms but avoid using caps.
4.6 Summary
The chapter develops a conceptual framework for evaluating contractual
features of new venture financing. Differences in attitudes toward risk and
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­ ifferences in expectations about value and potential success give rise to a
d
significant opportunity for an entrepreneur to contract with outside investors
in ways that both parties perceive to be beneficial. The chapter demonstrates
that thoughtful design of contracts between entrepreneurs and outside investors can enhance the value of projects and turn unacceptable projects into attractive ones.
Adverse selection arises when the parties enter a negotiation with asymmetric
information and expectations. Informational asymmetry is an impediment to
effective contracting between entrepreneurs and prospective investors, since
each may be concerned that the other knows more and is trying to take advantage. Solutions to adverse selection include signaling and screening. A signal
is a mechanism that a party with positive information can use to distinguish
herself from those with negative information. A screen is a mechanism that a
party without information can use to compel the party with information to
reveal whether the information is positive or negative. Screening and signaling
are similar in that parties with negative information find that it is not economical to imitate those with positive information.
Moral hazard arises after an agreement is entered. If a sunk investment has
been made and is specific to a relationship, a party to the agreement can attempt
to appropriate the value of the investment. Techniques for controlling moral
hazard problems include bonding and monitoring. Bonding involves making
a commitment or investment such that the party who has the opportunity to
appropriate would be worse off by exploiting the sunk investment than by not
doing so. Monitoring can be used by one party to limit the ability of the other
party to act opportunistically.
Given the venture and the entrepreneurial team, the financial contract
emerges as a potentially important determinant of success or failure. A welldesigned contract can contribute dramatically to the value of an idea and help
allay the concerns of investors about the capabilities and commitment of an
untested entrepreneur. A poorly designed contract, however, can just as easily
prevent a good idea or product from reaching the market. Information and incentive problems arising from adverse selection and moral hazard are important
determinants of financial contract terms and organizational structures.
The chapter shows how the provisions in venture capital contracts with entrepreneurs are tied to concerns with the many information asymmetries and
incentive issues that characterize start-ups. Use of convertible equity securities
(along with liquidation preferences and participation rights) shifts more of the
risk of failure to the entrepreneurs. Covenants also address investor concerns
with dilution of ownership that may arise in subsequent financing rounds; they
define governance issues that address monitoring concerns; and they address
Venture Deals 161
issues related to exit through provisions such as defining “qualified IPOs” and
redemption and registration rights.
The last part of the chapter focuses on a quantitative analysis of several key
features of VC contracts, namely liquidation preferences, participation rights,
and antidilution provisions (ratchets). You should now be able to recognize the
differences in types of convertible preferred securities and analyze the costs
and benefits of provisions from the perspectives of both the entrepreneur and
the investor.
The chapter concludes with an analysis of convertible note contracts that are
commonly used by angel investors and some VCs in seed-stage financing. Firms
at this stage are subject to so much uncertainty that investors may not want to
put much effort into valuation and hence prefer to invest by way of convertible
debt, which converts to equity at a later stage when the firm has received an
equity round (and hence a valuation). You should now be able to evaluate the
impact for the entrepreneur and the investor of the basic features of convertible
notes, in particular the discount and the valuation cap, and how they interact.
Review Questions
1. Explain the difference between moral hazard and adverse selection. Why
do these two concepts pose contracting problems for new ventures?
2. What is the difference between an information problem and an incentive
problem? Give an example of each in an entrepreneurial setting. How
do these two problems relate to moral hazard and adverse selection?
3. What is the difference between a screen and a signal? How can screens
and signals help to mitigate contracting problems?
4. What are the meanings of bonding and monitoring in the context of
new venture finance? What, from an economic standpoint, makes a
bond effective?
5. Why is convertible preferred equity the most common instrument used
by venture capitalists when investing in new ventures? What incentive
and/or information problem does this form of equity address compared
to straight equity?
6. Liquidation preferences and participation are both common features of
term sheets. How do these provisions work when there is a liquidation
event like a winding up of the company or an acquisition?
7. What problem does a ratchet (antidilution provision) try to address?
Why would a full ratchet be less appealing to the entrepreneur than is a
weighted average ratchet?
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Chapter Four
8. Why is a convertible note an attractive type of financing instrument for
seed-stage ventures?
9. What are the roles of a cap and a discount in a convertible note
instrument?
10. Identify some common provisions of a term sheet. What information
and incentive problems do these provisions anticipate, and how do they
help to mitigate the problems?
Notes
1 Williamson (1975, 1985) refers to these problems in his work on organizational choice and form.
2 Williamson’s (1975) discussion of bounded rationality in a contracting
context draws on work by fellow Nobel laureate Herbert Simon. Simon first
introduced the concept of bounded rationality in the context of employer-employee relations. See Simon (1957, 1961).
3 The example was developed by Akerlof (1970)
4 For the effects of moral hazard problems on firm value and financing
choices, see Jensen and Meckling (1976), Myers (1984), Darrough and Stoughton (1986), and Harris and Raviv (1991).
5 See Klein, Crawford, and Alchian (1978) for seminal work on the risk
of appropriation of sunk investments and the choice of contractual arrangement and organizational form.
6 For discussion and illustration of the distinction between discrete and
relational contracting, see Joskow (1985, 1987).
7 Gorman and Sahlman (1989) report that VCs regularly monitor their
portfolio firms but are not normally involved in day-to-day management. Also
see Tian (2011).
8 A well-known example of signaling is Michael Spence’s job market signaling model (1973, 2002) where potential employees send a signal about their
ability level to the employer by acquiring (costly) education credentials. The informational value arises from the employer’s belief that there is a positive correlation between education and greater ability given that it is more costly for a
lower-ability employee to acquire the education, and they must bear a greater
risk of losing the job when true ability is revealed. Leland and Pyle (1977) are
among the first to analyze the role of signals in a finance context, specifically
the IPO process. They show that “good companies” can send clear signals to
the market when going public by keeping control of a significant percentage of
the company. The signal must be too costly for “bad companies” to imitate. If
no signal is sent, the result can be adverse selection in the IPO market.
Venture Deals 163
9 For evidence on certification, see James and Wier (1990). Thakor (1982)
provides a model of third-party certification, and Millon and Thakor (1985)
discuss the role of information-gathering agencies.
10 See Kaplan and Stromberg (2002) and Gompers, Gornall, Kaplan,
and Strebulaev (2016), who provide evidence on the use of convertible securities in term sheets.
11 See Feld and Mendelson (2016) for elaboration on deal structure and
negotiation of terms.
12 This conversion may require the consent of a majority of the preferred
stockholders because conversion means that shareholders relinquish their
preferences. The term sheet may also identify the size requirement for a “qualified IPO,” so that an IPO is not used opportunistically as a means of removing liquidation preferences.
13 TechCrunch, “Square’s S-1: Of Ratchets and Unicorn Valuations”
https://​ t echcrunch ​ . com/​ 2 015/​ 11/​ 10/​ s quares ​ - s ​ -1​ - of​ - ratchets ​ - and ​ - unicorn​
-valuations/.
14 Ibrahim (2008); also see DeGennaro and Dobson (2017), who discuss
the interactions between VC and angel investing.
References and Additional Reading
Akerlof, G. 1970. “The Market for Lemons: Quality Uncertainty and the Market Mechanism.” Quarterly Journal of Economics 84: 488–​500.
Alchian, A., and H. Demsetz. 1972. “Production, Information Costs and Economic Organization.” American Economic Review 62: 777–​95.
Arrow, K. 1963. “Uncertainty and the Welfare Economics of Medical Care.”
American Economic Review 53: 941–​73.
Beatty, R. P., H. Bunsis, and J. R. M. Hand. 1998. “Indirect Economic Penalties in SEC Investigations of Underwriters.” Journal of Financial Economics 50: 151–​86.
Beatty, R. P., and J. R. Ritter. 1986. “Investment Banking, Reputation, and the
Underpricing of Initial Public Offerings.” Journal of Financial Economics
15: 213–​32.
Bernardo, A., and I. Welch. 2001. “On the Evolution of Overconfidence and Entrepreneurs.” Journal of Economics and Management Strategy 10: 301–​30.
Bhide, A., and H. H. Stevenson. 1990. “Why Be Honest If Honesty Doesn’t
Pay.” Harvard Business Review 68 (September–​October): 121–​29.
Bitler, M., T. Moskowitz, and A. Vissing-Jorgensen. 2005. “Testing Agency
Theory with Entrepreneur Effort and Wealth.” Journal of Finance 60:
539–​76.
164
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Booth, J. R., and R. L. Smith. 1986. “Capital Raising, Underwriting, and the
Certification Hypothesis.” Journal of Financial Economics 15: 261–​81.
Brennan, M., and A. Kraus. 1987. “Efficient Financing Under Asymmetric Information.” Journal of Finance 42: 1225–​43.
Coase, R. H. 1937. “The Nature of the Firm.” Economica 4: 386–​405.
———. 1960. “The Problem of Social Cost.” Journal of Law and Economics 3:
1–​44.
Coyle, J. F., and J. M. Green. 2014. “Contractual Innovation in Venture Capital.” Hastings Law Journal 66: 133–83.
Cumming, D. 2005. “Capital Structure in Venture Finance.” Journal of Corporate Finance 11: 550–​85.
———. 2008. “Contracts and Exits in Venture Capital Finance.” Review of
Financial Studies 21: 1947–​82.
Cumming, D., and S. Johan. 2008. “Information Asymmetries, Agency Costs,
and Venture Capital Exit Outcomes.” Venture Capital: An International
Journal of Entrepreneurial Finance 10: 197–​231.
Darrough, M. N., and N. M. Stoughton. 1986. “Moral Hazard and Adverse Selection: The Question of Financial Structure.” Journal of Finance 41: 501–​13.
DeGennaro, R., and E. Dobson. 2017. “The Future of Angel Finance.” In The
World Scientific Reference on Entrepreneurship, vol. 2, ed. D. Siegel and N.
Dai. Hackensack, NJ: World Scientific.
Dessein, W. 2005. “Information and Control in Ventures and Alliances.” Journal of Finance 60: 2513–​49.
Feld, B., and J. Mendelson. 2016. Venture Deals, 3rd ed. Hoboken, NJ: Wiley.
Gompers, P., W. Gornall, S. N. Kaplan, and I. A. Strebulaev. 2016. “How Do
Venture Capitalists Make Decisions?” NBER working paper 2287.
Gompers, P. A. 1995. “Optimal Investment, Monitoring, and the Staging of
Venture Capital.” Journal of Finance 50: 1461–​89.
Gompers, P. A., and J. Lerner. 1999. “Conflict of Interest in the Issuance of
Public Securities: Evidence from Venture Capital.” Journal of Law and
Economics 42: 1–​28.
Gorman, M., and W. A. Sahlman. 1989. “What Do Venture Capitalists Do?”
Journal of Business Venturing 4: 231–​48.
Grossman, S., and O. Hart. 1986. “The Costs and Benefits of Ownership: A
Theory of Vertical and Lateral Integration.” Journal of Political Economy
94: 691–​719.
Harris, M., and A. Raviv. 1991. “The Theory of Capital Structure.” Journal of
Finance 46: 297–​355.
Hart, O., and J. Moore. 1988. “Incomplete Contracts and Renegotiation.”
Econometrics 56: 755–​86.
Venture Deals 165
Hellmann, T. A. 2002. “Theory of Strategic Venture Investing.” Journal of Financial Economics 64: 285–​314.
Ibrahim, D. M. 2008. “The (Not So) Puzzling Behavior of Angel Investors.”
Vanderbilt Law Review 61: 1405.
James, C., and P. Wier. 1990. “Borrowing Relationships, Intermediation, and
the Cost of Issuing Public Securities.” Journal of Financial Economics 28:
149–​71.
Jensen, M. C., and W. H. Meckling. 1976. “Theory of the Firm: Managerial
Behavior, Agency Costs, and Ownership Structure.” Journal of Financial
Economics 3: 305–60.
Joskow, P. L. 1985. “Vertical Integration and Long-Term Contracts.” Journal
of Law, Economics, and Organization 1: 33–​80.
———. 1987. “Contract Duration and Transactions Specific Investment: Empirical Evidence from the Coal Market.” American Economic Review 77:
168–​83.
Kaplan, S. N., and P. Stromberg. 2000. “Venture Capitalists as Principals:
Contracting, Screening, and Monitoring.” American Economic Review
Papers and Proceedings 91: 426–​30.
———. 2002. “Characteristics, Contracts, and Actions: Evidence from Venture Capitalist Analyses.” Journal of Finance 59: 2177–​210.
———. 2003. “Financial Contracting Theory Meets the Real World: An Empirical Analysis of Venture Capital Contracts.” Review of Economic Studies 70: 281–​316.
Kerr, W. R., and R. Nanda. 2015. “Financing Innovation.” Annual Review of
Financial Economics 7: 445–62.
Klein, B., R. G. Crawford, and A. A. Alchian. 1978. “Vertical Integration, Appropriable Rents, and the Competitive Contracting Process.” Journal of
Law and Economics 21: 297–​326.
Klein, B., and K. B. Leffler. 1981. “The Role of Market Forces in Assuring
Contractual Performance.” Journal of Political Economy 89: 615–​41.
Krishnan, C. N. V., V. Ivanov, R. W. Masulis, and A. K. Singh. 2011. “Venture
Capital Reputation, Post-IPO Performance and Corporate Governance.”
Journal of Financial and Quantitative Analysis 46: 1295–333.
Landier, A., and D. Thesmar. 2009. “Financial Contracting with Optimistic
Entrepreneurs.” Review of Financial Studies 22: 117–​50.
Leland, H. E., and D. H. Pyle. 1977. “Informational Asymmetries, Financial
Structure, and Financial Intermediation.” Journal of Finance 32: 371–​87.
Millon, M. H., and A. V. Thakor. 1985. “Moral Hazard and Information Sharing: A Model of Financial Information Gathering Agencies.” Journal of
Finance 40: 1403–​22.
166
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Myers, S. C. 1984. “The Capital Structure Puzzle.” Journal of Finance 39:
575–​92.
Myers, S. C., and N. S. Majluf. 1984. “Corporate Financing and Investment
Decisions When Firms Have Information That Investors Do Not Have.”
Journal of Financial Economics 13: 187–​221.
Simon, H. 1957. Models of Man. New York: Wiley.
———. 1961. Administrative Behavior, 2nd ed. New York: Macmillan.
Spence, A. M. 1973. Market Signaling: Information Transfer in Hiring and Related Processes. Cambridge, MA: Harvard University Press.
———. 2002. “Signaling in Retrospect and the Informational Structure of
Markets.” American Economic Review 92: 434–59. (Also available as his
Nobel Prize lecture: http://​nobelprize​.org/​economics/​laureates/​2001/​
spence​-lecture​.pdf.)
Thakor, A. V. 1982. “An Exploration of Competitive Signaling Equilibria with
Third Party Information Production: The Case of Debt Insurance.” Journal of Finance 37: 717–39.
Tian, X. 2011.”The Causes and Consequences of Venture Capital Stage Financing.” Journal of Financial Economics 101: 132–59.
Williamson, O. 1975. Markets and Hierarchies. New York: Free Press.
———. 1983. “Credible Commitments: Using Hostages to Support Exchange.”
American Economic Review 73: 519–​40.
———. 1985. The Economic Institutions of Capitalism. New York: Free Press.
C h a p t e r Five
N e w Ve ntu r e Str ategy an d
R e al O p tio n s
Yo u m ay wo n d e r why, in a book on entrepreneurial finance, we would
devote this chapter and the next to strategic planning. There are three key reasons.
First, strategic planning is about choosing a course of action that is designed to
achieve a particular objective. In business settings, the overarching objective is
financial return. Even in not-for-profit settings where the primary objective may be
philanthropic, adequacy of financial return must be an intermediate goal. Second,
almost any strategic plan affords opportunities to change course after the initial
direction has been selected. In financial terms, these choices are described as real
options. Finance provides a means of valuing real options and taking account of
those values in the initial strategic choice. Third, for new ventures, the ability even
to pursue a particular strategy can depend on whether financing can be found.
This chapter establishes the basics of strategic planning in an entrepreneurial setting and develops a framework for evaluating alternative strategies.1 The
framework describes real options as decision trees and uses investment valuation
to evaluate alternative strategies. The framework begins with identifying the
objective and the strategic alternatives for achieving it. In general, we assume
that the objective is maximum NPV for the entrepreneur. The alternatives are
structures of real options that can be described and evaluated as the branches of
a decision tree. We begin by highlighting the interconnected nature of productmarket, financial, and organizational choices.
5.1 Product-Market, Financial, and Organizational Strategy
Especially for new ventures, financial strategic decisions must be determined
jointly with product-market and organizational strategic decisions. The
169
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Chapter Five
­ otential for success is greatest when the decisions are in harmony. Financial
p
strategy defines the type and timing of financing. Strategic financial decisions
include the amount of outside financing, target capital structure, staging of
cash infusions, and so on. Product-market strategy involves the targeted sales
growth rate, product price, product quality, product differentiation, whether
to produce multiple products, and the like. Organizational strategy concerns
the horizontal and vertical boundaries of the firm and in whom decision-making authority resides.
As an illustration, suppose an entrepreneur were to conclude that rapid sales
growth was the preferred strategy in the product market. The strategic choice
to grow rapidly would commit the firm to a limited menu of financing options.
Rapid growth usually requires external capital. The firm could choose to operate with high financial leverage, in which case it might sacrifice product-market
and organizational flexibility and the entrepreneur would realize a residual
return after debt service. Or, it might turn to equity capital, in which case the
entrepreneur might have to secure agreement of other shareholders before acting
and would have to share the total return with the outside investors.
It does not make sense to settle on a product-market strategy without considering how that choice restricts financing options. It also makes little sense
to place product-market strategy ahead of financing in the decision hierarchy.
While it may be possible to settle on a financing option that makes the choice
of product-market strategy viable, sequencing the two decisions can lead to
second-best outcomes. Even though rapid growth may appear to be attractive,
the entrepreneur might be better off if the firm were to grow more slowly. Similarly, there are interactions between financial and organizational strategy and
between product-market and organizational strategy.
Compared to new ventures, the interdependencies among product-market,
organizational, and financial decisions can be less significant in large, wellestablished organizations. If a large company contemplates a strategy of rapid
growth in a market that accounts for a small fraction of its total activity, that
decision does not necessarily commit the company to a highly leveraged capital
structure or the need to raise equity capital by selling stock. Thus, investors
may not be harmed if the investment decision and financing decision are treated
separately. In fact, this principle of separation is the hallmark of large public
corporations and an important difference from entrepreneurial firms. Our point
is that for new ventures, product-market, organizational, and financial decisions
need to be viewed simultaneously. Simultaneous consideration helps guarantee
that the first-best overall strategy is identified and selected.
Thinking about product-market, organizational, and financial strategies
as simultaneous rather than sequential choices takes us beyond the limits of
New Venture Strategy and Real Options 171
intuitive decision making. Later in the chapter, we use decision trees as devices
to capture the interplay between simultaneous and sequential decisions and to
identify and evaluate alternative strategies.
5.2 The Interdependence of Strategic Choices: An Example
To illustrate the interdependencies of strategic choices, consider the case of
C2FO​.com, one of several companies that have been established to arbitrage
well-known inefficiencies in the management of working capital. In many industries, trade credit terms are established as industry norms to which suppliers must normally adhere. For example, net 30 is the standard term in some
industries and means that customers have up to 30 days to pay for products
they have purchased and received.
Traditionally, as discussed in Chapter 2, a supplier that was short of cash but
had a significant balance of uncollected accounts receivable might be able to
use the receivables as collateral for a bank loan or might be able to sell the receivables to a factor. At the same time, customers that were offered net 30 terms
might be holding substantial cash balances that were invested at low rates of
return. This traditional structure could often result in markets with cash-poor
suppliers and cash-rich customers, suppliers resorting to expensive means of
borrowing, and customers settling for low returns on cash holdings. The rigidities of the trade credit, bank lending, and factoring markets adversely affected
the profitability of both customers and suppliers.
C2FO is one of the firms that saw this inefficiency as an opportunity that
could be addressed with new technology that would match cash-rich customers
with cash-poor suppliers and enable them to more efficiently negotiate with each
other to transfer liquidity from customers to suppliers. Costco, for example,
deals with multiple suppliers and has been a customer on the C2FO network
since 2011. Using the C2FO marketplace, Costco can post an available cash
balance. Suppliers to Costco, such as those that had sold on terms of net 30
and are in need of cash, can post offers of discounts on their receivables from
Costco in order to collect their receivables more quickly. Costco can then decide
which offers to accept.
The C2FO example illustrates how product-market, organizational, and financing strategies all work together. We can think of cash-short suppliers as
potential borrowers that are willing to pay for cash if the loan terms are right.
Rather than funding loans to these companies directly and raising its own capital
to do so, C2FO effectively intermediates a lending operation (an organizational
choice for C2FO that facilitated a new financing strategy for its customers). The
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Chapter Five
cash comes from purchasers of products supplied by the purchaser’s vendors.
C2FO earns a return by intermediating these exchanges in a way that is more
efficient and more adaptable to specific needs than what a bank or factor might
do. C2FO captures an intermediation spread related to transaction volume.2
5.3 What Makes a Plan or Decision Strategic?
It is useful to distinguish strategic decisions from other decisions along three
dimensions. First, strategic decisions are consequential. Unlike the decision
of which way to drive home from work, strategic decisions involve substantial
commitments of time and resources. They are of sufficient magnitude and importance that they are rare, and little precedent exists on which to base them.
Second, strategic decisions are both active and reactive. The decision is made
in a competitive setting, with regard to the possible actions and reactions of
others who have competing or complementary objectives. In selecting among
alternatives, the decision maker must consider the choices that may already have
been made by others whose objectives overlap and must recognize that others
may react to the decision. The decision of when, where, and how to locate a
retail store is strategic in this sense.
Third, strategic decisions are costly to reverse. If a decision maker selects a
wrong course of action, she cannot simply retract it. Investments made to pursue
the first course of action are, to some extent, sunk. Sunk investments limit flexibility because the full cost of changing direction must be compared to only the incremental cost of continuing in the same direction. Consequently, an initial wrong
strategic choice is one from which the decision maker may never fully recover.
5.4
Financial Strategy
Financial strategic choices have the same three elements. A financing choice
can limit future financing options in a variety of ways. For example, contractual provisions of a debt agreement may restrict the firm’s ability to redeem the
debt and replace it with equity. Existing debt financing may limit the financing
sources available for new projects. And debt service requirements may limit
the firm’s ability to undertake new projects that would generate negative cash
flows in the short run.
Competitive interdependencies are also present. Financing choices that are
costly to reverse or change involve sunk investments in arranging the financing. In such cases, a firm’s financing choices may credibly commit the firm to a
New Venture Strategy and Real Options 173
particular course of action that is observable by competitors, suppliers, customers, employees, and stockholders. Such credible commitments can influence the
actions of these groups. For example, securing project financing can discourage
others from moving ahead with plans of their own. Conversely, announcement
of intent to develop a new product, if not backed by a credible commitment,
could touch off a scramble among rivals to be first in the market.
For example, when Airbus announced in 2000 that it was proceeding with
the production of its super jumbo jet, the A380, Boeing immediately said that
it too would introduce a new super jumbo jet to replace the aging 747. It was
not until Airbus had secured financing and began investing in A380 production
capacity that Boeing abandoned its plans for a 747 replacement and pursued
the 787 Dreamliner.
The scope of financial strategy is quite broad. It goes beyond the simple
debt-versus-equity financing decision and includes such considerations as the
connections between financing choices and growth, flexibility, and control. In
addition, financial strategy includes such choices as the use of financial contracts
to address or overcome informational asymmetries between entrepreneurs and
investors and to better align the incentives of entrepreneurs and employees with
the interests of investors.
5.5 Deciding on the Objective
For many entrepreneurs, the decisions of whether to launch a new venture or
continue in current employment and how to develop the venture are made by
intuition. Yet these are not decisions where intuition is likely to lead to the
best outcome. Also, some entrepreneurs place weight on qualitative considerations. They may, for example, take pride in having created something new or
they may value self-employment.3
While subjective considerations can be important, our objective is to lay out
an analytical framework that can help entrepreneurs and investors make better
decisions. Here, we propose a two-step process: first, select the strategy that
yields the highest estimated NPV; second, make qualitative adjustments to the
NPV by assigning subjective values to the considerations that are important
to the prospective entrepreneur. Our emphasis is on the first step, maximizing
NPV. Generally, we examine NPV from the perspective of the entrepreneur by
structuring the terms for outside investment to yield an NPV of zero and keeping the full residual for the entrepreneur.
More formally, the first step in developing the analytical framework for strategic planning is to clearly specify the objective. Making rational choices is a
174
Chapter Five
forward-looking concept. Rationality does not mean that the choice is always
right, ex post, only that it is expected to be right given the information available at the time of the decision. A prospective entrepreneur considers a variety
of alternatives and selects the action that she expects will result in the highest level of satisfaction. A number of qualitative factors bear on the choice. A
risk-tolerant person is more willing to abandon secure employment to pursue
a venture than is a person who places a high value on security. A person who
enjoys work-related challenges is more willing to start a venture than is one
who values leisure time. These are the entrepreneur’s personal choices, and we
can do little in a formal way to assess such qualitative trade-offs. But we can
provide an analytical approach to help the entrepreneur evaluate the trade-offs.
Strategic planning reduces the importance of intuition in the decision process so that entrepreneurs and investors can make better-informed decisions.
To achieve this, we must begin with an objective that can be measured. We assume that the entrepreneur’s objective is to maximize the value of the venture to
herself. Thus, in purely financial terms, 40% ownership of a $5 million business
is more valuable to the entrepreneur than 15% of a $10 million business. The
value of the entrepreneur’s interest is what should influence the strategic choice.4
This does not mean that the entrepreneur should substitute value maximization for utility maximization as the ultimate determinant of the choice. Once
the quantitative value of the entrepreneur’s interest is determined, it is easier for
the entrepreneur to assess the qualitative trade-offs related to such factors as
control and security. To illustrate, suppose, as an entrepreneur, you are offered
two choices by a prospective investor. Under one you would retain control, and
under the other you would not. In exchange for relinquishing control, however,
you would receive additional compensation having a present value of $100,000.
You can then assess whether you would be willing to relinquish control in exchange for a $100,000 increase in the expected value of your interest.5
The entrepreneur is likely to be concerned with the financial return, and risk
is also an important consideration. Beyond this, entrepreneurial decisions are
likely to be influenced by, among other things, liquidity, diversification, and
flexibility; by transferability of ownership; and by the effects of the choice on
the entrepreneur’s control and accountability to others.
At a more fundamental level, the primary determinant of value is the tradeoff between risk and expected return. Factors such as liquidity, diversification,
transferability, and flexibility are taken into consideration through their effects
on expected return and risk. The value of control is an additional qualitative
factor that goes beyond the objective assessment of present value.
As a final note, value maximization does not mean that an entrepreneur is
greedy or that a venture is exploitative of either consumers or employees. A vi-
New Venture Strategy and Real Options 175
able venture must offer a product or service that is attractive enough to draw
consumers away from alternative purchases. Compensation packages offered
to employees must be sufficient to attract them away from other positions.
5.6 Strategic Planning for New Ventures
New ventures are different from established businesses because their plans are
unconstrained by previous decisions. Questions of financing, organizational
design, and product-market strategy are all open. The planning process needs
to reflect simultaneous consideration of all three components of the strategy.
The distinction between simultaneous and sequential consideration of strategic choices is fundamental. Suppose that on weekends while working in your
current position you have perfected a technology for making calorie-free ice
cream. You are trying to decide whether to resign and start a venture that
will employ the technology. Should you decide to proceed, you must make a
number of other decisions. To keep matters simple, in the product market you
must choose either a high-margin, slow-growth approach or a low-margin, highgrowth approach. With respect to organizational design, you must choose either
to enter only at the manufacturing level and contract for distribution or to enter
into both manufacturing and distribution.
Let’s suppose that the nonintegrated option involves selling through grocery
stores and the integrated approach involves creating a network of branded ice
cream stores. Figure 5.1 illustrates the implications of the various productmarket and organizational choices for the venture’s financing needs. If you enter
only manufacturing and pursue a slow-growth strategy, your own resources are
sufficient to fund the initial investment. The growth rate is determined by the
operating cash flows of the venture. With one-level entry and a plan of rapid
growth, you must rely on outside financing to supplement the financing available through operating cash flow. If you decide to enter both manufacturing
and distribution, even with slow growth the initial investment is too large for
you alone, and additional start-up financing is required. With both vertical
integration and rapid growth, outside financing is needed both for start-up and
to sustain growth.
Figure 5.1 displays the two product-market alternatives, along with the two
organizational structure alternatives. These choices have direct implications for
the available financing options. Each cell in the table represents a combination of
a product-market and organizational choices and describes the implied financing
choice. The amount shown in the cell along with the financing description is the
entrepreneur’s expected NPV associated with that combination of choices. The
176
Chapter Five
Product Market Choice
Slow growth
Rapid growth
One-level
entry
Organizational
Choice
Integrated
entry
Initially financed by
entrepreneur, growth
financed with
operating cash flows
Initially financed by
entrepreneur, growth
financed with
operating cash flows
and outside financing
NPV = $40
NPV = $120
Initial financing
includes outside
equity, growth
financed with
operating cash flows
Initial financing
includes outside
equity, growth
financed with
operating cash flows
and outside financing
NPV = ($20)
NPV = $70
Fi g u r e 5 .1
Financial implications of product-market and organizational strategic choices
Product-market and organizational strategic choices are interdependent with financing choices. One-level entry combined with slow growth
minimizes immediate and ongoing needs for external financing. Integrated entry and rapid growth normally require higher levels of immediate and ongoing external financing. NPV reflects the expected value to the entrepreneur.
most valuable is a strategy of entering only at the manufacturing level (relying
on contracting for distribution), pricing aggressively to foster rapid growth, and
supporting the enterprise initially with your own resources followed by operating cash flows and outside financing. This choice provides the entrepreneur with
an expected NPV of $120.
However, suppose you do not think through all of the alternatives and settle
first on the integrated organizational strategy. In that case, no matter what the
product-market strategy, you cannot expect to do better than $70. Moreover, if
you choose a high-price/slow-growth strategy, then the expected NPV is negative.
Perhaps you can go back to the investors and try to change the deal. But even
if the change is possible, it will require renegotiation, and your expected value
can never be as high as if you had studied the alternatives first and planned
accordingly. Even in this simple illustration, the product-market and organizational decisions limit the financing options. If entry is integrated, available
outside financing is likely to be equity. You may have to sacrifice voting control or give other control rights to providers of financing. With rapid growth,
financing needs to occur after the venture is established. In the early-growth
stages, equity-like claims may still be required, but the fraction of ownership
that must be relinquished per dollar raised is likely to be less than if funding is
required at start-up.
As the venture grows and begins to generate taxable income, debt financing
becomes a more realistic and attractive possibility. In addition, alternatives
New Venture Strategy and Real Options 177
such as franchising of distribution outlets and equipment leasing may have
advantages over either equity or debt financing. A well-thought-out strategic
plan takes such alternatives into consideration.
Of course, any commitment of resources to a particular strategy limits flexibility. If, for example, the entrepreneur builds the ice cream factory in California, it would be costly to change and decide that New York would be a better
location. But here we are concerned with something more—a loss of flexibility
that arises from reliance on outside financing. Almost any outside financing
that the entrepreneur can raise comes with limitations on how it can be used
and even on how the other resources of the venture can be used. Commonly,
such limitations are tied to the strategy that was described in the business plan
and was presented to the investors. Even if it were clear to the entrepreneur that
a change of plans would be good for the venture, the change often cannot be
made unless the investors are also convinced. Convincing them is likely to be
difficult, particularly if they have not been heavily involved in the operation or
if their interests conflict with those of the entrepreneur. The surest way to limit
this problem is to methodically plan the venture before presenting it to investors.
5.7 Recognizing Real Options
Strategic planning is not a one-shot exercise. With the passage of time, original targets will be exceeded or missed, and new developments will render the
initial plan obsolete. Rather than planning a single immutable course of action, it is more useful to select the strategy that offers the highest expected
value in light of the flexibility that the strategy affords for dealing with surprises. Opportunities to abandon a venture, expand it, or change direction
are real options. You can think of the focus of strategic planning as deciding
which real options to acquire, retain, and abandon at key decision points.6
Option Basics
An option is the right to make a decision in the future. In the stock market, for
example, a call option is a right to buy a share of stock at some future date for
a price that is negotiated today. The right to buy a share of Spacely​.com common stock anytime during the next 3 months at a price of $110 is a call option
with an exercise price of $110, where the underlying asset is a share of Spacely
common stock.
The value of the option depends on several factors. Most basically, a call
option gains value the more uncertainty there is about the eventual value of
178
Chapter Five
the underlying asset. It gains value if the market price of the underlying asset
rises, and it loses value if the price of the asset falls. Suppose Spacely is selling
at expiration for $118 per share. The $110 call option would yield an $8 payoff
($118 − $110). If the price of Spacely goes to $125, the value of the call option
increases—in this example the payoff almost doubles to $15 ($125 − $110). It
follows that call options with low exercise prices are more valuable than those
with high exercise prices. The right to buy Spacely for $100 per share obviously
is more valuable than the right to buy for $110.
Panel A of Figure 5.2 shows the payoffs for this call option with a $110 exercise
price. The figure shows clearly that options limit downside risk. Since option
risk is one-sided, the more volatile the underlying asset, the higher the value of
an option on the asset. Suppose, over the next three months, Spacely is equally
likely to increase in value to $140 or to decline in value to $100. If the price of
Spacely stock increases to $140, a call option at $110 will be in the money and
Call Option Value at Expiration
Panel A
(Exercise price = $110)
$120
Option value at expiration
Expiration date values
of call and put options
on Spacely​.com stock
$100
$80
$60
$40
$20
$0
$0
$50
$100
$150
$200
$250
$200
$250
Stock price at option expiration
Put Option Value at Expiration
Panel B
(Exercise price = $110)
$120
Option value at expiration
Fi g u r e 5 . 2
$100
$80
$60
$40
$20
$0
$0
$50
$100
$150
Stock price at option expiration
New Venture Strategy and Real Options 179
can be exercised to acquire the stock for a saving of $30 per share. If the price
of Spacely falls to $100, the call option is out of the money and will not be exercised. Thus, buying a call option limits the downside risk of investing in Spacely
but preserves the potential for gain. Because the exposure to risk is one-sided,
an option is more valuable the higher the risk of the underlying asset. Because
volatility increases with time to expiration, long-term options are more valuable.
Similarly, because new ventures are very risky and can take years to harvest,
real options on new ventures can be very valuable.
The time value of money affects option values because buying an option
works like borrowing. If you buy a call, you do not have to come up with the
money to exercise the option and purchase the shares until later, if and when
you exercise the option. This is different from buying the stock outright, where
you must pay the full price today. Accordingly, buying a call option is like borrowing the exercise price without having to pay interest. Since the value of not
having to pay interest is greater the higher the interest rate for borrowing, call
option value increases with increases in the cost of money.
A put option provides the right to sell an underlying asset during a specified
period at a specified exercise price. If the owner of a put decides to exercise, he
receives the exercise price. In contrast to calls, puts gain value when underlying asset values are low and when exercise prices are high. As can be seen in
Panel B of Figure 5.2, a put on Spacely at $110 is more valuable if Spacely is
selling for $80 than $90. Like call options, put options are more valuable when
the underlying asset is riskier. Because the owner of a put is effectively lending
the exercise price without charging interest, a put is less valuable if the cost of
money is high (i.e., more interest income is forgone).
Calls and puts can be bought or sold. The price of an option contract is called
the premium. The seller (or writer) of a call option is obligated to deliver the
underlying asset in exchange for the exercise price if the call is exercised. The
writer of a put is obligated to buy the underlying asset at the exercise price if
the put is exercised. For agreeing to this obligation, option sellers collect the
option premium. Puts and calls can be used to allocate the risk of investing in
the underlying asset. For example, an investor in Amazon common stock who
buys a put option has reallocated the downside risk to the writer of the option.
In this case, the stockholder is hedged against the downside risk, and the put
writer is acting as an insurer.
Comparisons Between Real and Financial Options
Puts and calls on shares of stock are financial options. The underlying asset is
a financial asset, and exercising or not exercising the option does not affect the
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Chapter Five
value of the underlying asset. In several respects, real options are similar. As
with financial options, the values of real options increase with the riskiness of
the underlying asset and time to expiration. The values of real options are also
affected similarly by the difference between the exercise price and the underlying
asset value.7
Yet real options differ from financial options in important ways. First, the
markets for financial options are often complete, meaning that calls and puts
on a stock are available with the same exercise price and expiration date, the
underlying asset is freely traded, and riskless borrowing is possible. If the market is complete, option value can be determined by appealing to the ability of
investors to arbitrage risklessly any pricing disparities. In contrast, real option
markets are not complete and simple arbitrages are not generally feasible.
Second, real options are often interdependent in ways that make the application of formal option pricing models inappropriate. Financial options can be
bought, sold, and exercised separately, and the value of a portfolio of financial
options is simply the sum of the values of the individual options. Real options,
in contrast, are often interdependent, and the decision to exercise one may have
implications for the values of others. Consequently, the value of a portfolio of
real options often cannot be determined by simply adding up the values of the
individual options. The term “real options” is used to stress their similarity to financial options while preserving the notion that there are important differences.
Returning to the calorie-free ice cream example, once you have developed
the technology to manufacture the ice cream, you have options to exploit the
technology in many ways. Some are reflected in Table 5.1. You also have the
option to do none of those things and instead continue working in your existing
position. In addition, you may have the option to delay and, for example, start
the venture next year (though in a changed environment). Once you begin the
venture, depending on contractual financing arrangements, you or the investor
may have the option to abandon it if things do not work out as well as you hoped.
We can also intentionally incorporate real options into strategic choices to
create additional flexibility. For example, the ice cream venture might acquire
options to lease facilities in both California and New York.
Effective strategic planning takes account of the values of important real
options. Because of the importance of real options to most new ventures, it
is useful to incorporate formal decision analysis techniques into the strategic
planning process.
The Real Option Premium
The cost of acquiring an option is called the option premium. Generally, financial options must be purchased from counterparties who consider the op-
New Venture Strategy and Real Options 181
Tab le 5 .1 Examples of real options
Category of Option
Description
Examples
Option to wait
If it is possible to postpone an action,
the decision maker has an option to
wait.
Decisions involve comparing the value
of acting now with the expected value
of waiting.
Learning options are similar to waiting
options except that the focus is on the
resolution of uncertainty. The option
holder can either make the best choice
in light of the uncertain future or wait
until some important uncertainty is
resolved.
An entrepreneur who has the ability
to increase the scale of a venture has
an option to expand. One who has the
ability to downsize has an option to
contract.
The owner of a forest can cut the trees today or wait until next year when the trees will
have grown larger and lumber prices may have
changed.
Option to learn
Options to expand or
contract
Options to switch inputs
or outputs
An entrepreneur has an option to
switch inputs or outputs when she can
alter the mix of a production process in
response to market prices.
Option to abandon
An abandonment option is a right to
discontinue an activity and redeploy the
real assets to some other use.
A person who hopes to receive job offers from
two different employers either could choose to
minimize the expected commute today by buying a house located between the two or could
wait for an offer and then locate near the ultimate
employer.
An entrepreneur might decide to acquire an expansion option by purchasing a facility that is
larger than the anticipated need. An entrepreneur
might acquire an option to contract by closing
down a production line if demand is less than anticipated. The option can be acquired by building
a flexible production process.
An entrepreneur who designs a facility to operate on either electricity or natural gas has the real
option to switch between these two inputs. A refiner who can switch between producing heating oil and gasoline has the real option to switch
outputs.
Abandonment options include the options to discontinue a research project, close a store, or resign
from current employment.
tion premium to be sufficient to compensate them for bearing the risk that the
option will end up being in the money.
One important difference between real and financial options is that the real
option premium bears no necessary relation to the value of the option. The value
of an abandonment option, for example, depends on the highest alternative-use
value of the assets. Automobiles, for example, have high option value since there
is a well-functioning market for used cars. If the original purchaser no longer
has a use for the car, it can be easily resold. Although the car has high option
value, the new-car market has many competitors, so that the price of the new
car is driven mainly by production and distribution costs. Because of the competitive new-car market and well-functioning used-car market, and because the
new-car seller is not the counterparty in the used-car market, the new-car seller
is not able to demand a price that reflects the option value to the buyer. Thus,
the cost of acquiring a real option may have little to do with the option value.
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Chapter Five
It follows that the value of a strategy can be dramatically improved by making
good choices about what real options to acquire and by using them effectively.
The terms of a new-car lease contract, in contrast, do reflect an important
financial option premium. When a car is leased, the lessee acquires a financial
option. At the end of the lease term, if the market value of the car is high, the
lessee can purchase the car by paying a residual value that was negotiated at the
time of the original lease transaction. Alternatively, if the market value is low,
the lessee can return the car to the lessor so that the lessor bears the downside
risk about the future value of the car. Although the underlying asset is real, the
car lease contract is a financial option. It is effectively a bet about the future
market value of the car, and not an instrument that redeploys the actual (real)
use of the asset.
5.8 Strategic Planning and Decision Trees
A decision tree is a useful way to conceptualize strategic alternatives that
involve real options. Constructing a decision tree imposes discipline on the
evaluation process and helps the entrepreneur identify relevant real options
and the points at which critical decisions must be made. It also enables the
entrepreneur to assess, in a structured way, the connections between decisions
made today and the value of the venture in the future.
Building and Pruning Decision Trees
A decision tree incorporates both decisions and uncertain events that affect
the value of the outcome. It identifies a sequence of decisions in which the
range of available choices is limited by previous decisions, and the best decision depends on which state of the world is realized. The decision maker is
uncertain about which state will be realized but knows or estimates the probabilities of the different states. In addition, the decision maker estimates the
NPV of a choice conditional on which future state attains.
A few simple techniques can ensure that a decision tree accurately reflects
the important choices and help to value them correctly.
• Focus on the most important choices. Because the number of branches expands geometrically, decision tree analyses can become intractable. Focusing on a few critical decisions and a few discrete choices is usually all
that is needed.
• Reason forward to construct the tree. Sequencing is chronological. The
tree should illustrate how each choice limits the options for subsequent
New Venture Strategy and Real Options 183
decisions. You can build the tree by first determining the important
choices available today. For each of these, determine the choices that
would be available at the next key decision point (node), and so on.
• Keep track of what is known and unknown at each node. If the decision
on optimal scale of entry depends on the level of future demand (a state
of the world that is uncertain today), you can only base the decision you
make today on expected future demand.
• Evaluate choices recursively. Start with the last decision point (the terminal node) and compare the values of the alternatives that emanate from
that node as if you had followed the branches up to that point. These are
the payoffs conditional on having made the choices that are represented
at the prior nodes.
• Prune the tree. Select the branch with the highest expected value, conditional on the earlier choices, and eliminate, or “prune,” the inferior
choices. For example, if conditional on high demand, you would choose
to expand, then you can eliminate the other choices. Move backward to
the next earlier decision point and evaluate the choices considering only
the highest-valued branch from the subsequent node.
• Select the branch of the tree with the highest expected value. This process
of backward induction, working from the best future decision conditional on choices made previously, leads to a set of valuations that reflect
the values of the embedded options in the decision process.
An Illustration
Suppose an entrepreneur is considering investing in a retail venture such as
a restaurant or a seller of Internet-of-Things (IoT) products. For simplicity,
she assumes that demand for the venture’s products could turn out to be any
one of three states of nature: high, moderate, or low. She is considering building a Large Facility, building a Small Facility, or not entering the business
at this time. The cost of the Large Facility is $750,000. The cost of the small
one is $600,000. The entrepreneur has $400,000 to invest and plans to bring
in an investor for the balance. The investor requires 1% of the equity for each
$10,000 invested in the venture, resulting in a 35% interest in the Large Facility or a 20% interest in the small one. The entrepreneur retains the balance of
ownership.
At this point, our focus is on strategic planning and our purpose is to demonstrate the use of decision trees for making strategic choices. To keep things
simple, we assume (for now) that the entrepreneur has already estimated the
present values (PVs) of the different facility sizes conditional on the different
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Chapter Five
states of nature and has already estimated the probabilities of the different
states. We will relax these assumptions in the next chapter.
Naturally, the Large Facility has the potential to generate more revenue, but
it also necessitates a larger investment and greater fixed operating expenses.
The entrepreneur’s estimates of the PVs in different states of the world reflect
these economies. If the high-demand state occurs, the limited capacity of the
Small Facility constrains its value. In the high-demand state, we assume that
the entrepreneur expects the PV of the Large Facility to be $1.5 million and the
PV of the small one to be $800,000. If the moderate-demand state occurs, the
PV of future cash flows of both the Large and the Small Facility is expected to
be $800,000. In the low-demand state, the PV of the Large Facility is $300,000
and that of the Small Facility is $400,000. The difference is due to the higher
fixed cost of operating the Large Facility.8
Evaluating the Venture as an Accept/Reject Decision
To establish a baseline for evaluating the real options, consider the project
as a simple accept/reject decision with mutually exclusive alternatives—the
conventional way of evaluating simple capital investment projects. Figure 5.3
represents the choices in the form of a decision tree. The square in the figure
represents the one decision point. The circles represent uncertain outcomes
that are beyond the entrepreneur’s control; they reflect states of the world with
respect to demand. You can think of “nature” as choosing a state of the world.
Each state has an associated probability. In Figure 5.3, the entrepreneur
has simplified the decision problem by thinking of it in terms of three states
of demand and has estimated the probability of each. The probability of high
demand is 30%, the probability of moderate demand is 50%, and the probability of low demand is 20%. In the accept/reject decision, the entrepreneur must
decide which, if any, facility to open before the uncertainty about demand is
resolved. Her investment is $400,000 regardless of which facility she chooses.
Because we are examining the choice from the entrepreneur’s perspective, the
difference between the Large and the Small Facilities is shown as the fraction
of the value that accrues to the entrepreneur.
The triangles in the figure are terminal nodes. Next to each is the NPV the
entrepreneur expects if she selects the corresponding branch and the specified
level of demand is realized. For example, if the Large Facility is built and the
high-demand state is realized, the entrepreneur will invest $400,000 in return
for 65% of a facility with a total PV of $1.5 million. The figure shows that
$975,000 is the entrepreneur’s 65% share of the PV, so that the resulting NPV of
the entrepreneur’s investment is $575,000. In our simple example, the entrepre-
New Venture Strategy and Real Options 185
neur decides how to invest by multiplying each state-contingent payoff by the
probability of that state’s occurrence and her fractional ownership interest (.65
or .80) and then subtracting her investment. The result of the calculation is the
NPV of that choice to the entrepreneur. Thus, if the Large Facility is selected,
her expected payoff is as follows:
Expected Payoff = .65 (.3 × $1,500,000 + .5 × $800,000 +
.2 × $300,000) = $591,500
As this amount exceeds the entrepreneur’s $400,000 investment by $191,500,
it has a positive NPV, which means that it is better than not investing at all. The
figure shows $191,500 as the NPV of selecting the Large Facility.
By a similar calculation, the entrepreneur’s payoff from investing in the Small
Facility is $576,000, an NPV of $176,000. Because both the Large and the Small
30.0%
High Demand
$975,000
= –$400,000 + 0.65 × $1,500,000
Chance
Large Facility
–$400,000
$191,500
50.0%
Moderate Demand
$520,000
20.0%
Low Demand
$195,000
30.0%
High Demand
$640,000
50.0%
$120,000
20.0%
-$205,000
0.0%
$240,000
= –$400,000 + 0.65 × $800,000
= –$400,000 + 0.65 × $300,000
= –$400,000 + 0.80 × $800,000
Chance
Small Facility
–$400,000
$176,000
50.0%
Moderate Demand
$640,000
20.0%
Low Demand
$320,000
Accept-Reject
30.0%
$575,000
0.0%
$240,000
0.0%
-$80,000
= –$400,000 + 0.80 × $800,000
= –$400,000 + 0.80 × $400,000
Decision
$191,500
High Demand
30.0%
$0
0.0%
$0
Chance
Do Not Enter
$0
$0
Moderate Demand
50.0%
$0
Low Demand
20.0%
$0
0.0%
$0
0.0%
$0
Fi g u r e 5 . 3
Decision tree for accept/reject decision to invest in the venture
Prepared using PrecisionTree™, Palisade Corporation.
With a one-time accept/reject decision, the entrepreneur cannot anticipate the level of demand that will be realized. The investment decision and choice of level of investment are made in light of existing uncertainty by maximizing expected NPV.
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Chapter Five
Facility have positive NPVs, either one is better than not investing. Because the
Large Facility offers the entrepreneur a higher NPV, that choice is better than
investing in the Small Facility. Thus, the entrepreneur would want to prune the
alternatives of investing in the Small Facility or not investing.
Ex post, it may turn out that selecting the Large Facility was the wrong decision. If demand turns out to be intermediate, the Small Facility would have been
a better choice. If demand turns out to be low, not entering would have been
better than either of the other choices. At the time of the decision, however, the
entrepreneur cannot know which state of the world will occur. Given what is
known and the estimated probabilities of the different states, the Large Facility
is the best alternative.
In Figure 5.3, the decision facing the entrepreneur is a one-shot choice. There
are no real options reflected in the figure. This is the “base case,” with an NPV
of $191,500. We now want to expand the range of possibilities by considering
three types of real options: (1) the option to wait and learn more about market
demand, (2) the option to expand if the Small Facility is selected initially, and
(3) the option to abandon.
The Learning Option
The ability to wait and learn more before acting is a call option that is open
to most prospective entrepreneurs. Rarely does an investment opportunity involve a now-or-never choice. Waiting can add value because uncertainty is reduced, or technology advances, or expenditures of resources are deferred until
they are more immediately needed. The offsetting cost is that waiting to invest
may encourage others to enter or market conditions may change.9
Returning to our example, suppose that by not investing immediately in the
venture, the entrepreneur can learn more about which state of the world is likely
to occur, but waiting increases the likelihood of entry by competitors, so that
the expected payoffs in the various states are reduced. For simplicity, assume
that by waiting the entrepreneur can learn market demand with certainty. Accordingly, in each case she will build the facility that maximizes her NPV. If
the good state of nature attains, the entrepreneur can respond by investing in a
facility that is optimally sized for high demand.
As with any call option, the value of the learning option is higher if the level of
uncertainty is high and if delay will materially reduce the uncertainty. Because
of the delay and likelihood of competitive entry, we assume that the PV of the
Large Facility declines (relative to the base case in Figure 5.3) to $1.3 million
and the PV of the Small Facility declines to $700,000.
To examine the value of the learning option, we compare the NPV with
delayed investment to the base case NPV. The difference is the value of the
New Venture Strategy and Real Options 187
learning option. Figure 5.4 modifies Figure 5.3 to reflect the learning option.
Because waiting only affects the “Do Not Enter” branch of Figure 5.3, we have
collapsed the two branches that involve investing today. For them, we show only
the entrepreneur’s NPV.
Under the learning option, conditional on the high-demand state, the entrepreneur’s best course of action is to invest in the Large Facility. Her NPV of
waiting and then investing in the Large Facility is $445,000 (net of the $400,000
investment). Because, in the event of high demand, the “Small Facility” and “Do
Not Enter” choices have lower NPVs ($160,000 and $0, respectively), we will be
able to prune those branches. Figure 5.4 shows that conditional on high demand,
the entrepreneur chooses the Large Facility, and the resulting NPV is $445,000.
Large facility
FALSE
–$400,000
Small facility
FALSE
–$400,000
Learning option
Chance
+
$191,500
+
$176,000
Chance
Decision
$213,500
Large facility
TRUE
$445,000
Small facility
FALSE
$160,000
High demand
30.0%
$0
TRUE
Chance
$0
$213,500
FALSE
0.0%
$0
$0
FALSE
$55,000
Small facility
TRUE
$160,000
50.0%
$0
FALSE
FALSE
Large facility
–$205,000
Small facility
FALSE
–$80,000
$0
0.0%
$55,000
= –$400,000 + 0.65 × $700,000
50.0%
$160,000
= –$400,000 + 0.80 × $700,000
$160,000
$0
20.0%
= –$400,000 + 0.80 × $700,000
Decision
Do not enter
Low demand
$160,000
$445,000
Large facility
Moderate demand
= –$400,000 + 0.65 × $1,300,000
0.0%
Decision
Do not enter
Wait
30.0%
$445,000
0.0%
$0
0.0%
–$205,000
= –$400,000 + 0.65 × $300,000
0.0%
–$80,000
= –$400,000 + 0.80 × $400,000
Decision
$0
Do not enter
TRUE
20.0%
$0
$0
Fi g u r e 5 . 4
Decision tree for investing in a venture, with the option to delay investing until uncertainty about market demand is
resolved
Prepared using PrecisionTree™, Palisade Corporation.
Not investing today may preserve an option to wait until more information is known about the true state of demand.
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Chapter Five
Similarly, the Small Facility is the best choice for the moderate-demand
state. The NPV of investing in the Small Facility if the moderate-demand state
occurs is $160,000. By waiting, the entrepreneur also avoids the potential mistake of investing only to learn that the low-demand state has been realized. If
low-demand is realized, not entering is the best choice. Because, at present, we
do not know which of the three states will be realized, we can only compare
the expected value that would result from learning against the most favorable
alternative available by choosing not to wait.
The immediate question facing the entrepreneur is whether to build now
and face the uncertainty about demand or to wait for more information about
demand. For this, we need to compare the NPV of investing now (and, as already established, building the Large Facility) against the NPV of waiting. The
expected NPV conditional on using the learning option is simply the probabilityweighted average of the three possible outcomes (.3 × $445,000 + .5 × $160,000
+ .2 × $0), or $213,500. Because this value is greater than the $191,500 base case
expected NPV of the best act-now choice, it is better to wait.
The learning option adds $22,000 to the value of the project; this is the value
of the real option. The value comes from two sources: the upside gain from
building the Large Facility when we know demand is high, and avoidance of the
negative NPV outcome that occurs if we were to build any facility in the lowdemand state. These two benefits outweigh the costs associated with waiting.
The Expansion Option
A second kind of option is the option to expand the venture after the initial investment has been made. Suppose that after an initial investment of $600,000
in the Small Facility (including $400,000 invested by the entrepreneur), and
after learning market demand, the facility can be expanded to the large size by
investing an additional $200,000. Assume that the $200,000 (if needed) comes
from the outside investor, but on more favorable terms. The terms are better
because the second investment is less risky given that uncertainty about demand is resolved. Specifically, the second $200,000 is raised in exchange for a
10% ownership share (1% of the equity for each $20,000 invested), bringing the
investor’s total to 30% in the event of expansion (rather than 35% in the nowaiting, Large-Facility scenario). Because the initial investment is sufficient
to establish a market presence, we assume that the PV of the Large Facility
is $1.4 million, higher than if no immediate investment were made but lower
than if the Large Facility were built today.
What is the value of the expansion option? Figure 5.5 modifies Figure 5.3
to reflect this option. Here, we have ignored the learning option and have col-
New Venture Strategy and Real Options 189
lapsed the branches where the expansion option does not apply. As the problem
is structured, if the Large Facility is chosen initially, the expansion option is
implicitly forgone.
If the Small Facility is built, the entrepreneur can either expand or not. Expanding, if the high-demand state is realized, increases the entrepreneur’s NPV
to $580,000 ($980,000 minus the entrepreneur’s original $400,000 investment).
Not expanding leaves the NPV at $240,000, as in Figure 5.3. As this is below
$580,000, we can prune the “High Demand/Do Not Expand” branch.
Figure 5.5 also compares the values of expanding to not expanding in the
moderate- and low-demand states of the world. Expansion is not a good idea
with moderate demand because investing reduces the entrepreneur’s ownership share without increasing the value of the venture. So we can ignore the
“Moderate Demand/Expand” branch. Not surprisingly, if demand is low, the
best choice is “Do Not Expand.”
Clearly, expansion is better if the high-demand state obtains. But is it a good
idea to invest in the Small Facility first and wait to see what happens to demand?
Large facility
FALSE
–$400,000
+
Chance
$191,500
Expand
High demand
30.0%
$0
TRUE
$980,000
$580,000
FALSE
$640,000
0.0%
$240,000
= –$400,000 + 0.80 × $800,000
Chance
TRUE
–$400,000
$278,000
Expand
FALSE
$560,000
Moderate demand
50.0%
$0
Expand
TRUE
FALSE
$210,000
20.0%
= –$400,000 + 0.70 × $800,000
$240,000
$640,000
Low demand
0.0%
$160,000
Decision
Do not expand
50.0%
$240,000
= –$400,000 + 0.80 × $800,000
0.0%
–$190,000
= –$400,000 + 0.70 × $300,000
Decision
–$80,000
Do not expand
TRUE
$320,000
Initial choice
= –$400,000 + 0.70 × $1,400,000
Decision
Do not expand
Small facility
30.0%
$580,000
20.0%
–$80,000
= –$400,000 + 0.80 × $400,000
Decision
$278,000
Do not enter
FALSE
$0
+
Chance
$0
Fi g u r e 5 . 5
Decision tree for investing in a facility, with the option to expand the initial investment
Prepared using PrecisionTree™, Palisade Corporation.
The option to expand if demand turns out to be high is an example of the flexibility associated with real options.
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Chapter Five
The answer in this case is yes. As shown in Figure 5.5, the expected NPV from
investing initially in the Small Facility and expanding only if the high-demand
state is realized is as follows:
NPV = .3 × $580,000 + .5 × $240,000 + .2 × ($80,000) = $278,000
where the parentheses indicate a negative value. Comparing this to the base
case $191,500 expected NPV makes the expansion option worth $86,500. This
is the value of the expansion option compared to the simple accept/reject
decision.
In this case, we can compare the expansion option strategy that has a NPV of
$278,000 with the $213,500 NPV of the learning option (Figure 5.4). According
to these calculations, the NPV from investing immediately in a Small Facility
(preserving the option to expand) is higher than from investing in a Large Facility ($278,000 > $191,500) and higher than from waiting to invest ($278,000 >
$213,500). The incremental value of the expansion option compared to waiting
is $64,500.
The key point of Figure 5.5 is that ignoring the option to expand would have
led the entrepreneur to select a less valuable strategy. In some cases, a project
may be passed up entirely because of the failure to recognize that initial investments sometimes create valuable options that can be exercised in the future if
the environment is right.
Here we see a difference between real and financial options that sometimes is
important: the waiting option and the expansion option are mutually exclusive
ways to use the real asset. The entrepreneur cannot exercise both. Normally,
financial options are independent of each other—they can be acquired separately and decisions to exercise them can be made independently. But because
real options relate to how an underlying real asset is used, they sometimes are
mutually exclusive. In this case, if the entrepreneur wants to acquire the expansion option, the investment must be made today, thus forgoing the learning
option. That is, unlike financial options, where exercise choices can be made
independently, sometimes the holder of a real option has to choose from available options—exercising one can preclude exercising others.
The Abandonment Option
The final type of option we consider is the option to abandon the venture if
things do not work out as well as expected. Suppose the facility has an alternative use as office space. If converted to office space (for a negligible net expenditure), the PV of the Large Facility would be $600,000 and the value of the
Small Facility would be $300,000.
New Venture Strategy and Real Options 191
You can see from the numbers that the option to abandon the Small Facility
is worthless. This is because a Small Facility, even in the low-demand state, has
a PV of $400,000, which is more than its PV as office space. But the option does
have value for the Large Facility, because $600,000 is more than the $300,000
present value as a facility in the low-demand state.
The best strategy up to this point is to build small and then expand if demand
turns out to be high. Is the value of the option to abandon the Large Facility
enough to tip the balance in favor of building the Large Facility immediately?
The “Large Facility/Low Demand/Abandon” path would mean a $390,000 payoff to the entrepreneur (.65 × $600,000). The entrepreneur’s resulting NPV from
investing immediately in the Large Facility with the option to abandon is as
follows:
NPV = −$400,000 + .3 × $975,000 + .5 × $520,000 +
.2 × $390,000 = $230,500
This is less than the $278,000 NPV of initially investing in the Small Facility
with the option to expand but higher than any other alternative. If the option
to expand did not exist, then investing in the Large Facility with the option to
abandon would be the preferred strategy. It would exceed the NPV of the next
best alternative—waiting until the state of the world is realized—by $17,000
($230,500 − $213,500).
The option to abandon a venture that does not work out as well as expected
can be critical to value and thus to the decision to launch the venture in the first
place. It can also impact the willingness of investors to fund the venture. Test
pilots of new aircraft normally take along parachutes in case something goes
wrong; otherwise, they would be much less interested in testing new designs.
5.9 Decision Trees and Contract Negotiation
In the preceding example, we have assumed that the entrepreneur has found
an investor who will agree to invest on the indicated terms (i.e., 1% of equity
for each $10,000 invested in most cases). More realistically, the entrepreneur
will need to negotiate and the terms will need to be mutually beneficial. In this
illustration, we do not know whether the discount rate that would be used by
the investor would be the same as that used by the entrepreneur. We also do
not know whether the investor agrees about the probabilities of the different
outcomes or the expected cash flows conditional on an outcome or real option
choice. It could also be that the investor’s equity claims have a preference relative to the entrepreneur’s shares.
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If we assume that the investor has the same expectations as the entrepreneur
and uses the same discount rate, we can use the decision tree information to
determine that the best option for the entrepreneur, build the Small Facility and
expand if demand is high, would not be attractive to the investor. The investor’s
NPV from this strategy would be $38,000.
NPVInvestor = [0.3 × (–400 + 0.3 × 1,400) + 0.5 ×
(–200,000 + 0.2 × 800) + 0.2 × (–200 + 0.2 × 400)] × $1,000
NPVInvestor = $6,000 – $20,000 – $24,000 = –$38,000
The NPV is positive if demand is high but negative otherwise. The willingness
of the investor to accept the terms in the example indicates that the investor
has a different perspective on the venture, such as a lower discount rate, lower
perceived risk, or a higher probability that expansion would be warranted.
For example, as discussed in Chapter 1, the entrepreneur’s valuations could
be based on a high discount rate due to necessary underdiversification. A welldiversified investor could appropriately use a lower discount rate. If doing so
were to double the present values of the cash flows under the expansion option
strategy, the investor’s NPV would be positive $184,000 and would be positive
in both the high- and moderate-demand outcomes.
While the details of discounting cash flows to present value is beyond the
scope of this chapter, the point here is that the entrepreneur needs to think
about the venture terms from both perspectives and seek to develop a financing
strategy that would appeal to both parties. Examining the decision tree from
both perspectives can enable the entrepreneur to assess this.
5.10 Rival Reactions and Game Trees
Decision trees do not explicitly incorporate the reactions of rivals or counterparties. For example, consider a venture that offers an innovative line of 3D-printed
athletic shoes. Its entry strategy—of price, advertising expenditures, geographic
scope, and so on—may depend critically on how it expects incumbent firms to
react. The firm may decide to enter with an aggressive pricing strategy, assuming that incumbents will keep their prices constant. But if incumbents react by
pricing aggressively, then the effectiveness of the new entrant’s pricing strategy
will be reduced and perhaps a toehold entry strategy would discourage rival
reaction and be better for the entrepreneur in the long run.
Similarly, the terms of an agreement that is negotiated between a VC and an
entrepreneur can have game-theoretic implications. For example, a VC might
New Venture Strategy and Real Options 193
be concerned that committing too much capital at an early stage might lead the
entrepreneur to work less diligently on achieving the next important milestone,
whereas investing a smaller amount might elevate the entrepreneur’s concern
about the difficulty of negotiating a fair valuation in the next round.
Rival (and counterparty) reactions are not an issue in perfectly competitive
markets because other firms will not react specifically to the entry of a new rival and competition will mitigate the potential for holdup by an early investor
(counterparty). For a small venture entering a large market or an entrepreneur
who can deal with many potential investors, it may make sense to think of the
market as nearly perfectly competitive. Furthermore, rival reactions are not an
issue for a venture that offers a unique product and anticipates no entry, and
therefore has market power. But planning for rival reactions can be important
in settings where there are only a few actual or potential competitors. In such
situations, the decisions of the competing firms and counterparties are highly
interdependent.
One way to evaluate decision choices is to assign probabilities to rival reactions and use a decision tree to evaluate the choices. This approach is not likely
to yield reliable results, however, because the probabilities effectively yield a
weighted average reaction from among the various possibilities, whereas the
actual reaction will be one choice or another.
There is a better way to think about the decision when rival reactions are
important. The alternative is to explicitly assess what reaction would be in the
best interest of each rival. The underlying reasoning is that a firm’s actions and
its rivals’ are interdependent. Each firm is assumed to behave rationally by trying to select the strategy that maximizes value given what it believes its rival
will do. Strategic interactions can be modeled by relying on the contributions
of game theory. Game theory is concerned with analysis of optimal decision
making when decision makers are aware that their actions affect each other’s
behavior and take these interactions into account.
To illustrate the uses of game theory in formulating strategy, we need to
introduce some new terminology. A game consists of (1) a set of players, (2) an
order of play, (3) the information set available to the players, (4) the set of actions available to each player, and (5) the payoff schedule that results from the
actions. The players are simply the decision makers, such as an entrepreneur, a
firm manager, a VC, or a rival.
The players make decisions at various points in a game (decision nodes). In a
decision tree, the decisions are based on alternative states of the world, which are
not strategic. In a game-theoretic setting, however, player actions are strategic
and driven by rationality. The sequence in which decisions are made is the order
of play. If all players make their decisions one at a time in a sequence, then the
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game is a sequential-move game. If all decision makers act at the same time,
then the game is a simultaneous-move game.
For an entrepreneur, it usually makes sense to think of the game as sequential,
with the entrepreneur as the first mover. A sequential-move game can be analyzed with a game tree.10 A game tree is a joint decision tree for the players and
is composed of nodes and branches like a decision tree. Each node represents a
decision point for one of the players. Each branch represents a possible action
for a player at that point.
An Illustration
Consider a common problem of a prospective entrepreneur. Ethan would like
to quit his bartending job and open his own establishment, Ethan’s Bar. His
options are (1) to enter the market with a large bar, (2) to enter with a small
bar, and (3) to wait to see if the town’s economy will support another bar. He
has estimated the various costs and revenues associated with the two establishment sizes. His biggest concern is a rumor that a national chain, Naomi’s
Pub, is considering opening a bar in the same town. Naomi has two options:
(1) enter the market or (2) do not enter. Ethan’s decision depends on how
Naomi might respond to Ethan’s decision.
The decisions are illustrated as a game tree in Figure 5.6. The tree represents Ethan’s perspective; both players’ payoffs, shown in the two columns on
the right, are expressed in terms of NPV. By acting quickly, Ethan can make
the first move. Thus, the first decision node (working left to right) belongs to
Ethan. The middle 3 decision nodes belong to Naomi. If Ethan chooses largescale entry, then Naomi’s Pub would make a loss of $100,000 if Naomi elected
to enter and $0 if Naomi elected to stay out. In this case, the rational choice for
Naomi is to stay out.
As with decision trees, we can ignore (or prune) the branches that represent
inferior choices for the decision makers. Thus, we should assume that if Ethan
opens a large bar, Naomi will choose not to enter. If Ethan chooses small-scale
entry, then Naomi’s Pub would earn a $200,000 NPV if Naomi were to enter
and $0 if she decided to stay out. The rational decision for Naomi in this case
is to enter, and we can prune the decision tree accordingly.
Finally, if Ethan decides to wait for the market to develop further, Naomi
must decide between entering and staying out without knowing what Ethan is
going to do. If Naomi enters, she would earn $100,000, $210,000, or $300,000,
depending on Ethan’s decision. If Ethan has decided to wait, then clearly Naomi
would prefer to enter because all the choices associated with an entry strategy
generate positive payoffs for Naomi’s Pub. Hence, Ethan can prune the bottom
branch of the game tree, “Naomi stays out.” If Ethan decides to wait, he knows
New Venture Strategy and Real Options 195
Ethan’s
Payoff
Naomi enters
Large bar
Naomi’s
Payoff
$380,000
$380,000
$380,000 –$100,000
$425,000
$425,000
$425,000
$0
$250,000
$250,000
$250,000
$200,000
$400,000
$0
$300,000
$300,000
$100,000
$190,000
$190,000
$210,000
$0
$300,000
$370,000
$370,000
$0
$350,000
$350,000
$0
$0
$0
Logic
$425,000
Naomi stays out
Ethan’s bar
Decision
$425,000
Naomi enters
Small bar
Logic
$250,000
Naomi stays out
$400,000
$400,000
Ethan enters with large
TRUE
$300,000
Ethan enters with small
FALSE
$190,000
Naomi enters
TRUE
$0
Decision
$300,000
Ethan stays out
FALSE
$0
Wait
$0
Logic
$300,000
Ethan enters with large
TRUE
$370,000
Ethan enters with small
FALSE
$350,000
Naomi stays out
FALSE
$0
Decision
$370,000
Ethan stays out
FALSE
0
$0
0
Fi g u r e 5 . 6
Entry decision game tree
Prepared using PrecisionTree™, Palisade Corporation.
In this sequential-move game, Ethan is the first mover. Ethan assumes Naomi will react rationally to Ethan’s investment decision. Ethan
can select the choice that maximizes value for him in light of Naomi’s expected reaction.
Naomi will enter. Given that, the figure shows that Ethan’s best response is to
open the large bar, which gives Ethan a $300,000 NPV and leaves Naomi with
$100,000. We can ignore the other branches, “Ethan enters with small” and
“Ethan stays out,” of this subtree.
Examining the remaining branches, we can adopt the same technique we used
with the decision tree: backward induction. Start at the terminal decision nodes
that display the NPVs of the various alternatives for the two parties. Beginning at
the top, if Ethan enters with a large bar, Naomi will stay out (avoiding the $100,000
loss) and Ethan’s payoff is $425,000. Moving down, if Ethan enters with a small
bar, Naomi will also enter, making Ethan’s payoff $250,000. Finally, if Ethan
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waits, we know Naomi will enter. Ethan will then respond with a large bar, giving
himself a $300,000 payoff. Since Ethan has the first move, and because he knows
how Naomi will react, his optimal strategy is to enter immediately with a large bar.
Nash Equilibrium
The preceding example is a noncooperative game. In a noncooperative game,
the players cannot enter into binding, enforceable agreements with each other.
Any solution of the game must be a “Nash equilibrium.”11 A Nash equilibrium is a set of strategies such that each player’s strategy is optimal given the
strategy of the other player(s). The Nash equilibrium in our example is the
pair of strategies such that (1) Ethan’s strategy maximizes his payoff, given
the strategy of Naomi’s Pub, and (2) Naomi’s strategy maximizes her payoff,
given Ethan’s strategy.
In the entry-decision game, the Nash equilibrium is (Ethan: “Enter with
large”; Naomi: “Stay out”). That is, Ethan does not wait to see whether Naomi’s
Pub opens; instead, he enters first at a scale that makes it unprofitable for Naomi
to open. Both parties maximize their profits given the other player’s action. If
each expects the other to choose its Nash equilibrium strategy, then both will.
In other words, in equilibrium, expected and actual behaviors converge.
Games Entrepreneurs Play
Strategic “games” include a large class of activities in which a decision maker
takes into account the actions and reactions of others. Strategic games commonly played by entrepreneurs include the following:
• The business plan. An entrepreneur must decide how much optimism to
build into the projections that are included in the plan. Overoptimism
can be dangerous. The investor may counter with a proposed deal that
ties the entrepreneur’s return to achieving the projections.
• Strategic partnering. An entrepreneur must decide whether to bring in a
vertically integrated company as a distributor and strategic partner or
risk the possibility that, if not invited to partner, the corporation will independently develop a competing product.
• Control. An entrepreneur must decide how much control to forsake in
exchange for securing funding. If the entrepreneur is unwilling to give up
control, a prospective investor might decide to forgo the opportunity.
• Contract negotiation. New venture contracting with investors is not a
onetime event. Rather, it can involve several rounds with varying levels
New Venture Strategy and Real Options 197
of information and changing values. The dynamic nature of contract negotiation makes the process game theoretic.
• Information disclosure. A new venture’s management must decide
whether to patent an idea now and risk copycat entry of rivals or maintain the idea as a trade secret.
Strategic Flexibility Versus Strategic Commitment
Game theory forces the entrepreneur to think about a venture from the perspective of competitors, customers, suppliers, and investors. Decision trees
and game trees are useful for assessing trade-offs between the value of maintaining flexibility (real options) and the value of committing to a more limited
course of action. As in Figure 5.6, even though the waiting option has value,
committing first to a large-scale bar may have an even greater NPV. In the figure, strategic commitment is the best choice, whereas in Figure 5.5, strategic
flexibility (preserving the expansion option) was the best choice.
It is important for the founder of any new venture (and the investor) to systematically consider the values of the important embedded real options that it
may have. However, early commitment to a course of action can preempt or
limit rival reactions in the best interest of the new venture.
5.11 Real Options with Continuous Distributions
In the main example of this chapter, a retail facility, we limited uncertainty
about demand to only three possible states of the world—high, moderate, and
low demand. This, of course is artificial. In reality there are an infinite number
of possibilities and the actual range is much broader than is reflected in the
three that are examined. Artificially limiting the possibilities to a few discrete
outcomes enabled us to use decision trees in a simple way to evaluate the strategic alternatives.
As long as the strategic choices are limited to a few discrete alternatives, we can
still use decision trees to evaluate real options that involve continuous distributions
of random outcomes (such as about demand). However, doing so requires more
comprehensive descriptions of the uncertainty and the use of simulation software
to evaluate the strategic choices. That is, in order to evaluate the strategic alternatives, we need to simulate the payoffs over the full range of possible outcomes.
The use of simulation to evaluate real options is the focus of Chapter 6. In
this chapter, we use a few discrete scenarios to approximate the value we would
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find by simulating a continuous distribution of scenarios. Doing so in a reliable
way requires careful consideration of the strategic choices, including the use of
simulation to identify the boundary conditions under which an option should
be exercised.
To illustrate the use of discrete scenarios to evaluate real options involving
continuous distributions, suppose we are considering a venture similar to the one
described in Figure 5.5 and would like to evaluate the inclusion of expansion and
abandonment options. At this point, suppose that based on an examination of
similar ventures, instead of describing uncertainty about first-year demand as a
single discrete outcome, we have determined that demand uncertainty can be approximated by a lognormal distribution with a mean of 1,000 units and standard
deviation of 600 units. Based on the cost of expansion, we have also determined
that if first-year demand turns out to be more than 1,500 units, it would be beneficial to expand. Conversely, if first-year demand is below 500 units, it would
be beneficial to abandon the venture and liquidate the assets. Based on the parameters of the distribution, by simulating demand, we find that there is about a
15.7% probability that expansion will be warranted and a 16.5% probability that
it would be best to abandon. This leaves 67.8% as the probability of continuing
the venture without expanding.
Now that we have estimates of the probabilities of the three choices, we can
use conditional expected values to evaluate the project including accounting
for the real options. To do this, we will need to estimate the expected demand
associated each of the three strategies. Simulation is a simple way to estimate
the average demand associated with each. Using the simulation results, we estimate that the average demand from trials resulting in demand greater than
1,500 units is 2,073.4 units, the average for those with first-year demand below
500 units is 382.9, and the average over the trials where the venture would be
continued without expansion is 901.6 units.
If we would like to value the project using a simple decision tree similar to
the one in Figure 5.5, we would assign a 15.7% probability to expansion and an
expected first-year unit sales volume of 2,073.4 units under expansion. Similarly,
we would assign 16.5% as the probability of abandonment and an average firstyear unit sales volume of 382.9.
The full analysis would require additional information, such as the contribution margin on each unit of sales, a model of how first-year sales relate to
ultimate sales volume over time, and the level of fixed cost that is associated
with each strategy. The main point here is that condensing the simulation data
in this way makes it possible to evaluate the venture using a small number of
discrete scenarios.
New Venture Strategy and Real Options 199
5.12 Summary
Strategic decisions involve major commitments that limit the range of future
actions. Comprehensive strategic planning involves product-market and organizational choices that are highly interrelated with financing choices. By considering all 3 simultaneously, the entrepreneur can be assured of identifying
the alternative that yields the highest expected value.
A new venture can be thought of as a portfolio of real options, including,
among others, the options to delay investing, to expand the size of the investment, and to abandon the investment. Strategic planning is a process of identifying these real options and comparing the values of alternative combinations
of real options. Decision tree analysis provides a framework for identifying and
describing strategic alternatives and for identifying and managing the real options
that are embedded in any new venture. The different branches of the tree describe
the interplay between alternative strategic choices and uncertain states of the
world. By starting at the ends of the branches, determining the highest-valued
choice at each stage, and eliminating the other branches from further consideration, it is possible to identify the strategic alternative today that is expected to
result in the highest overall expected value of the project.
Game tree analysis is a useful extension of decision trees when the reactions
of other parties to specific strategic choices are important. Game theory forces
the entrepreneur to think about a decision from the perspectives of others who
will be affected by it. In the context of a competitive game, it is important to
consider the benefits of strategic flexibility relative to the benefits of strategic
commitment.
Review Questions
1. How are product-market, organizational, and financial strategies interdependent? Why, for new ventures, is it important to consider them simultaneously rather than sequentially? Give some examples.
2. What are the three aspects of a decision that make it strategic?
3. Why is it important for strategic planning to begin with a clear sense of
the objective?
4. How might the objective for an entrepreneurial venture be different
from that of a project pursued by a publicly held corporation?
5. How do embedded real options affect the values of investment opportunities? Why, when assessing investment alternatives, is it important to
consider the embedded real options?
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6. What are three important differences between real options and financial
options?
7. Describe an investment opportunity that includes at least one real option. On what does the value of the option depend?
8. Explain how you would construct a decision tree that includes the real
option(s) and uncertainty. How would you use the tree to decide on a
course of action?
9. How are game trees different from decision trees? For what kinds of decisions would you want to use game trees instead of decision trees?
10. How is the business plan related to strategic planning?
Notes
1. Background on the topic of strategy is provided by Porter (1998).
2. A more conventional example of these strategic interdependencies is
the early growth of Ford Motor Company. Ford’s product-market strategy
emphasized high-volume, low-product-price marketing. Its complementary
organizational strategy was to use assembly lines to build automobiles almost
entirely from parts supplied by others, and to sell through non-integrated
franchisee dealers. By focusing on final assembly, Ford reduced its need for
early-stage capital. It could take advantage of vendor financing (trade credit)
for the parts it purchased. By selling to dealers for cash, it shifted the burden
of carrying finished-goods inventory to the dealers. It is unlikely that Ford’s
product-market strategy could have been achieved without the complementary financial and organizational strategies.
3. Moskowitz and Vissing-Jorgensen (2002) estimate returns to the private
equity held by entrepreneurs and find a large public equity premium, and pose the
following puzzle: why would entrepreneurs willingly invest in a single privately
held firm with seemingly far worse returns than investing in the stock market? As
noted in the text, qualitative considerations such as valuing being one’s own boss
could explain this. In an update to their study, Kartashova (2014) finds that the
public equity premium puzzle does not survive in some subsets of the data. The
difference between private and public equity returns is positive between 1999 and
2007, whereas in the 2008–2010 period, the returns are very similar.
4. Hammond, Keeney, and Raiffa (1998) offer a survey of the pitfalls of
decision making that is guided by intuition or misapplication of more systematic approaches.
5. Bhide (1994) notes that a variety of factors in addition to NPV can influence the suitability of an investment.
New Venture Strategy and Real Options 201
6. Amram and Kulatilaka (1999) illustrate the application of real options approaches to new venture investing and other decisions. See Chen,
Kensinger, and Conover (1998); Childs, Ott, and Triantis (1998); and Loch and
Bode-Greuel (2001) for specific applications. Dixit and Pindyck (1995) provide
perspective on valuing investment decisions as options. Luehrman (1998a) discusses strategic decision making in terms of real options.
7. Luehrman (1998b) describes how simple investment opportunities can
be valued as real options using financial option valuation methods.
8. These values are from the perspective of the entrepreneur and are not
necessarily the same as values to an investor. Because the focus in this chapter
is on learning to recognize and compare real options, we are abstracting from
other aspects of the strategic analysis.
9. As an aspect of a theory of entrepreneurship, Baumol (1993) studies
the optimal rate of innovation using the trade-off between delaying introduction of new products as a means of improving their quality and the risk that a
competitor will enter first.
10. Simultaneous-move games are usually analyzed using payoff matrices that display the players’ outcomes for the simultaneous choices.
11. The Nash equilibrium concept is named in honor of John Nash, a
Princeton mathematician who was a pioneer of game theory.
References and Additional Reading
Amram, M., and N. Kulatilaka. 1999. Real Options: Managing Strategic Investment in an Uncertain World. Boston: Harvard Business School Press.
Baumol, W. J. 1993. “Formal Entrepreneurship Theory in Economics: Existence and Bounds.” Journal of Business Venturing 8: 197–210.
Besanko, D., D. Dranove, M. Shanley, and S. Schaefer. 2016. Economics of
Strategy, 7th ed. New York: Wiley.
Bhide, A. 1994. “How Entrepreneurs Craft Strategies That Work.” Harvard
Business Review 72: 150–61.
Chen, A. H., J. W. Kensinger, and J. A. Conover. 1998. “Valuing Flexible Manufacturing Facilities as Options.” Quarterly Review of Economics and Finance 38: 651–74.
Childs, P. D., S. H. Ott, and A. J. Triantis. 1998. “Capital Budgeting for Interrelated Projects: A Real Options Approach.” Journal of Financial and
Quantitative Analysis 33: 305–34.
Dixit, A., and B. Nalebuff. 1993. Thinking Strategically. New York: Norton.
Dixit, A., and R. Pindyck. 1995. “The Options Approach to Capital Investment.” Harvard Business Review 28 (4): 105–15.
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Dixit A., S. Skeath, and D. Reiley. 2015. Games of Strategy, 4th ed. New York:
Norton.
Ghemawat, P. E. 2009. Strategy and the Business Landscape, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall.
Hammond, J. S., R. L. Keeney, and H. Raiffa. 1998. “The Hidden Traps in
Decision Making.” Harvard Business Review 76 (5): 47–58.
Kartashova, Katya. 2014. “Private Equity Premium Puzzle Revisited.” American Economic Review 104: 3297–334.
Kim, Y. J., and G. L. Sanders. 2002. “Strategic Actions in Information Technology Investment Based on Real Option Theory.” Decision Support Systems 33 (1): 1–11.
Klein, B., and K. B. Leffler. 1981. “The Role of Market Forces in Assuring
Contractual Performance.” Journal of Political Economy 89: 615–41.
Kreps, D. M., and R. Wilson. 1982. “Reputation and Imperfect Information.”
Journal of Economic Theory 27: 253–79.
Loch, C. H., and K. Bode-Greuel. 2001. “Evaluating Growth Options as
Sources of Value from Pharmaceutical Research Projects.” R&D Management 31 (2): 231–48.
Luehrman, T. A. 1998a. “Investment Opportunities as Real Options: Getting
Started with the Numbers.” Harvard Business Review 76 (4): 51–67.
———. 1998b. “Strategy as a Portfolio of Real Options.” Harvard Business
Review 76 (5): 89–99.
Magee, J. 1964. “How to Use Decision Trees in Capital Investment.” Harvard
Business Review 42 (September–October): 79–96.
Milgrom, P., and J. Roberts. 1992. Economics, Organization and Management.
Englewood Cliffs, NJ: Prentice-Hall.
Mintzberg, H. 1994. “The Fall and Rise of Strategic Planning.” Harvard Business Review 72 (1): 107–14.
Moskowitz, T. J., and A. Vissing-Jorgensen. 2002. “The Returns to Entrepreneurial Investment: A Private Equity Premium Puzzle?” American Economic Review 92: 745–78.
Norton, S. 1997. “Information and Competitive Advantage: The Rise of General Motors.” Journal of Law and Economics 40 (1): 245–60.
Porter, M. 1998. Competitive Strategy. New York: Free Press.
Rappaport, A. 1991. “Selecting Strategies That Create Shareholder Value.” In
Strategy, Seeking and Securing Competitive Advantage, ed. C. A. Montgomery and M. E. Porter, 379–99. Cambridge, MA: Harvard University
Press.
Spulber, D. 1992. “Economic Analysis and Management Strategy: A Survey.”
Journal of Economics and Management Strategy 1 (3): 535–74.
New Venture Strategy and Real Options 203
———. 1994. “Economic Analysis and Management Strategy: A Survey Continued.” Journal of Economics and Management Strategy 3 (2): 355–406.
Trigeorgis, L. 1996. Real Options: Managerial Flexibility and Strategy in Resource Allocation. Cambridge, MA: MIT Press.
C h a p t e r S ix
D e ve lo pi n g Ve ntu r e
Str ategy Us i n g Si m u l atio n
D ec i s i o n t r e e s a r e used to identify strategic alternatives and to exam-
ine the sensitivity of expected value to discrete changes in individual variables,
one at a time. In Chapter 5, we simplified the analysis of strategic choice by
assuming there were only a few possible states of the world. However, decision
tree analysis based on discrete possibilities has limitations because the future
is being described by a few discrete outcomes and related probabilities. To
deal with the limitations of discrete scenarios, we now introduce simulation
and demonstrate how it can be used to evaluate strategic choices that include
real options. We begin with a few simple examples and then return to the main
example from Chapter 5 and use simulation to conduct a more robust analysis.
In later chapters, we illustrate the broad use of simulation to evaluate staging
options for investments in new ventures and for valuation.
A simulation model is a representation of the behavior of a complex system
through the use of another system. For our purposes, the complex system is the
future performance of a new venture. Simulation can take into consideration
uncertainty about the environment, the venture itself, and possibly even the
reactions of rivals.
The normal way to represent uncertainty in a simulation model is to describe each key element of uncertainty as a statistical distribution, for example,
a normal distribution with a given mean and standard deviation or a uniform
distribution over a given range. To simulate the future of a venture, we must
first build a spreadsheet model of the venture and identify the key decision
variables. We can then introduce mathematical expressions that describe the
important uncertainties that bear on value, cash needs, or other important factors. By using the computer to simulate the model, we can produce thousands
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Developing Venture Strategy Using Simulation 205
of possible trials (outcomes), where each trial is based on making a random draw
from each statistical distribution and computing the combined effect for the venture.
For example, you might wish to project the level of net income for a venture
at the end of 5 years, given four sources of uncertainty: the economy, the market’s reaction to the product, cost of production, and development lead time.
If you simulate the future of the venture one time, the result is a prediction of
net income in 5 years, given a specific outcome for each source of uncertainty.
The prediction from a single trial is not likely to be very helpful. However,
with a computer it is possible to run thousands of iterations of the model and
to aggregate the data from the iterations. Not only does averaging the results
improve the accuracy of the prediction, but the dispersion of outcomes serves
as a measure of the aggregate effect of all the various sources of uncertainty
that are built into the model. Moreover, the trial data can be analyzed for the
purpose of refining key decisions such as how much cash to invest at the outset
or to estimate the value of a particular real option.
As a management tool, simulation has been around for a long time. David B.
Hertz first advocated using simulation for investment decision making in 1968.
The technique was slow to catch on for a variety of reasons. Among the early
impediments were confusion about the correct way to apply simulation to investment decisions, lack of low-cost (fast) computational capacity, lack of data
useful for calibrating uncertainty, and lack of user-friendly software. In spite
of the difficulties, companies like Merck have been using simulation to analyze
investment decisions for many years.1
6.1 Use of Simulation in Business Planning: An Example
In the U.S., a new chemical with potential therapeutic value must undergo several stages of expensive clinical testing that can require many years to complete. Because of the nature and expense of the drug development process,
simulation can be a valuable aid to decision making. Given this, it is not surprising that the pharmaceutical industry was one of the first commercial applications of simulation. The industry is complex and subject to uncertainty
arising from many sources, including health care reform, the generic drug
market, insurance practices, and tort litigation. Such uncertainties are particularly acute in developing, testing, and marketing new drugs.
Investing in the development of new drugs has much in common with entrepreneurial investment in new ventures: uncertainty is very high, a new drug
goes through several identifiable stages of development, and the firm faces many
206
Chapter Six
opportunities to abandon or modify its development efforts as it learns more
about the drug over time.
Even during the 1990s, rather than relying on single-point estimates of the
future for allocating its R&D budget, Merck was developing simulation models
that incorporated probability distributions for key variables. Under the direction
of then CFO Judy Lewent, Merck developed its Research Planning Model, an approach that integrated principles of economics, finance, statistics, and computer
science to produce quantitative analyses of the specific strategic decisions that
Merck faced. Using simulation, the model synthesized probability distributions
for key variables such as revenues, cash flow, and NPV.
Much like the approach we will use, the output of Merck’s model was a frequency distribution that showed the probability that a project’s NPV would exceed
a certain level. Merck could use the distribution information to compute summary
statistics, such as standard deviation, which it could then use in other analyses,
such as to price an option to delay investing. Merck’s Research Planning Model
would simulate risk and return project-by-project (prior to commitment of funds).
Then, by allocating the research budget across the projects, Merck could simulate
the contribution of R&D to the financial performance of the entire corporation.
Consider how the model can be used to evaluate a drug research project.
Lewent describes it this way:
We may know at the beginning of a project that there is a market for a specific treatment that includes many thousands of people, and once we reach
a certain point in the process, we may know that a certain compound may
be effective. But we still aren’t 100% certain that the compound will prove so
safe and effective that it can be turned into a drug. So we have to ask ourselves, “Do we continue to invest?” Those are the kinds of decisions we face
every day. And these aren’t investments that easily lend themselves to traditional financial analysis. Remember that we need to make huge investments
now and may not see a profit for 10 to 15 years. In that kind of situation,
a traditional analysis that factors in the time value of money may not fully
capture the strategic value of an investment in research, because the positive
cash flows are severely discounted when they are analyzed over a very long
time frame. As a result, the volatility or risk isn’t properly valued.
Option analysis, like the kind used to value stock options, provides a
more flexible approach to valuation of our research investments . . . because it allows us to evaluate those investments at successive stages of a
project.2
Merck would take a systematic approach to modeling the risks associated
with R&D, manufacturing, and marketing. For example, in considering a new
Developing Venture Strategy Using Simulation 207
drug’s market potential, one of the constraints on development would be the
expected time required for FDA approval. Merck could model the range of possible time frames for approval. By drawing from the range and simulating cash
flows based on alternative assumptions, Merck could synthesize probability
distributions for output variables. The output variables of interest are, of course,
the standard measures of financial performance: revenue, cash flow, and NPV.3
6.2
Who Relies on Simulation?
Each of the early impediments to using simulation to evaluate important strategic decisions either has been or is being removed. Appropriate ways to use
simulation for making investment decisions have been developed, computational capacity is inexpensive and fast, appropriate software is inexpensive
and user friendly, and data are increasingly available. Furthermore, the growing recognition of the value of viewing investments as portfolios of options
points to greater reliance on simulation over time.
Most major companies use at least one of the commonly available simulation
packages in some parts of their business. Moreover, many universities have site
licenses to standard simulation software.
Several different software packages are available for running simulations on
personal computers. Some are freestanding decision analysis programs. Others
function as add-ins to Excel. Some employ random sampling to generate the
trial. Others use Monte Carlo techniques in an effort to generate more accurate
predictions of expected outcomes and distributions of outcomes using fewer
iterations. As computational speed has increased, the cost- and time-saving
rationales for Monte Carlo simulation have diminished in importance.
Whenever we use simulation in this book, our modeling will use the @RISK®
simulation software provided by Palisade. The software is similar to other Excelbased programs such as Crystal Ball®. When we show you the Excel syntax for
a simulated cell in a spreadsheet, we use the @RISK syntax. If you are a user
of the other most commonly used commercial package, Crystal Ball®, you can
study the parallel syntax by opening the appropriate files on this book’s website.4
6.3
Simulation in New Venture Finance
Nowhere is the case for using simulation more compelling than for decision
making about new ventures. Consider the following examples of where simulation can lead to better decisions:
208
Chapter Six
• Strategy formulation. An entrepreneur is considering a risky opportunity
to develop an enterprise and knows that building in options to abandon
or change the nature of the venture can reduce risk and make the project
more valuable. Simulation can be used to study the effects of different
option structures on risk and value.
• Deal structures. An entrepreneur and an investor are negotiating investment terms. The investor is willing to accept common stock but wants a
large share of total equity in exchange. The entrepreneur would like to
add sweeteners to the investor’s claims so that the investor’s ownership
fraction can be reduced. Simulation can be used to evaluate the effects
of alternative deal structures on the values of both parties’ claims.
• Risk allocation. An entrepreneur and an investor have different tolerances for bearing the risk of a new venture to build snow shovels
equipped with cardiac monitors. The entrepreneur is more risk averse
than the investor. Simulation can be used to design a deal structure that
shifts more to the investor, raising the overall value of the opportunity.
• Contingent claims. An investor is not convinced by the financial projections of an entrepreneur who wants to produce and market golf balls
equipped with location sensors. The entrepreneur is willing to accept financial claims that adjust the entrepreneur’s ownership share contingent
on success. Simulation can be used to design a deal structure that is attractive to both parties.
• Cash needs. An entrepreneur is trying to determine the total amount of
financing that is needed for a prospective new venture to sell a subscription-based service. The service will upload a daily podcast to your phone
that contains morning news content that is customized to your interests
and commuting time. If the venture performs worse than expected, the
need for financing will be greater. Simulation can be used to examine the
relation between attained performance and total cash needs.
• Staging investments. A VC wishes to invest in a project but would like
to stage the investment so that progress can be evaluated at critical
milestones. There is uncertainty about when the next milestone can be
achieved and about the cost of achieving it. Simulation can help the VC
decide on an amount to invest such that the probability of reaching the
milestone is reasonable and the potential for overinvesting in a project
that will never succeed is limited.
• Valuation. An investor is trying to value an opportunity to participate
in a venture and knows that the value of the investment depends not just
on the expected return but also on the riskiness of the return. Simulation
Developing Venture Strategy Using Simulation 209
can be used to determine the expected return, the riskiness of the return,
and the value of the investment.
In this chapter, we apply simulation to the problem of designing a new venture strategy. Later in the book we apply simulation to cash needs assessment,
valuation, and contract design.
6.4
Simulation: An Illustration
Suppose, to capitalize on demand created by e-commerce, you are considering “hit-and-run” entry into a local home delivery service. That is, you expect
that the venture would eventually be displaced by a much larger national or
multinational networked delivery organization. Before proceeding with the
venture, you have decided to do a simple NPV analysis, based on your projections of the required investment, expected revenue, fixed cost, and variable
cost percentage. Using information you have been able to find online and
your own judgment, you expect that the required investment in the venture
will be $120,000. For the first year of operation, you expect that revenue will
be $90,000, which you expect will grow at a rate of 5% per year, and that the
venture will have a 50% variable cost percentage. Your projections are in real
terms and you project that annual fixed cost will be $35,000. Revenue and all
expenses are in cash so that net income and net cash flow are the same. Further, you estimate that your opportunity cost of capital is 10% per year. You
expect that the venture could serve a niche market for 10 years, after which the
venture would have no liquidation value.
To assess the merits of the opportunity, you have constructed an Excel spreadsheet based on your expected projections for each item. The analysis is shown
in Figure 6.1. To your dismay, although the computed cash flow is positive every
year, the positive amounts, when present valued, are not enough to cover the
expected investment. It appears that the NPV of the venture would be slightly
negative.5
Before deciding to abandon the idea, it is worth recognizing that if you were
to take account of the uncertainties surrounding the different expected projections you might reach a different conclusion. In the “Parameters of distributions” panel of Figure 6.2, we have added distribution information that reflects
estimates of uncertainty about the key variables. The parameters appear in the
columns on the right. The parameter values are estimates you have been able
to develop from your research into the opportunity and your judgment. You
have determined that investment cost, which has an expected value of $120,000
(from Figure 6.1) could be as low as $90,000 and as high as $150,000 and that
Known inputs
Discount rate
Uncertain inputs
Investment cost
Year 1 revenue
Annual fixed cost
Annual revenue growth rate
Annual variable cost percentage
Financial projections
Year
Investment cost
Revenue
Fixed cost
Variable cost
Cash flow
DCF calculations
Outputs
Discounted cash flows
NPV
10%
Expected
$120,000
$90,000
$35,000
5.0%
50.0%
0
($120,000)
1
2
3
4
$ 9 0 ,0 0 0
$ 3 5 ,0 0 0
$45,000
($120,000) $10,000
$94,500
$35,000
$47,250
$12,250
$99,225 $104,186 $109,396 $114,865 $120,609 $126,639 $132,971 $139,620
$35,000 $35,000 $35,000 $35,000 $35,000 $35,000 $35,000 $35,000
$49,613 $52,093 $54,698 $57,433 $60,304 $63,320 $66,485 $69,810
$14,613 $17,093 $19,698 $22,433 $25,304 $28,320 $31,485 $34,810
($120,000)
($268)
$9,091
$10,124
$10,979
$11,675
5
$12,231
6
$12,663
7
$12,985
8
$13,211
9
$13,353
10
$13,421
Fi g u r e 6 .1
Venture NPV estimated based on the expected value of each input
The figure shows the computed NPV of the venture that is determined by using the expected value of each item (investment, revenue, fixed cost, variable cost percentage, and
growth rate) to project and value future cash flows.
Developing Venture Strategy Using Simulation 211
the most likely amount would be toward the lower end of the range. Because
actual investment is projected to fall within this range and the distribution is
not symmetrical, you have decided to model investment cost as a triangular
distribution with the stated minimum and maximum and a most likely value of
$100,000. Note that while the value in Figure 6.1 ($120,000) is halfway between
the high and low, the mean (i.e., expected value in the statistical sense) is lower
because of the skewness of the distribution.
The first numerical column in the panel is the simulated outcome of one
random draw from each distribution. The cell formulas in this column of cells
contain the @RISK functions that generate the simulation results. If you launch
@RISK and open the Excel file for Figure 6.2, you can see how the formulas
are linked to the parameters, and by pressing the F9 (recalculate) key on your
computer, you can generate new draws from the distribution. When you run
the simulation, @RISK will make hundreds or thousands of draws from these
distributions and will keep track of the results that you specify.6
Returning to the substance of the model, you also have concluded that the
uncertainty of the Year 1 revenue projection is best modeled as a triangular
distribution with a mode of $95,000 (somewhat above the mean in Figure 6.1),
a minimum of $75,000, and a maximum of $105,000. Here again, the mean of
this distribution is below the $90,000 value in Figure 6.1 because of skewness.
You have concluded that annual fixed cost is best modeled as a symmetrical
triangular distribution, with the mode (and mean) equal to the expected value
of $35,000 as in Figure 6.1 and a range of $32,000 to $38,000.
In @RISK, the general Excel formula for modeling a triangular distribution
is RiskTriang(minimum, mode, maximum). So, for example, the distribution
of investment cost can be coded as “=RiskTriang(90,000,100000,150000).”
As in Excel, generally, the parameters in this function can be replaced with
cell references that contain the values or with expressions that use numbers,
cell references, or a combination. The best way to understand this is to launch
@RISK and then open the Figure 6.2 spreadsheet in Excel.7
So far, we have only modeled revenue in Year 1. We have yet to address revenue growth and the variable cost percentage. In Figure 6.1, the assumed growth
rate was constant at 5%. There was no uncertainty about the growth rate and no
provision for the growth rate to be different in different years. Figure 6.2 deals
with the first of these limitations by modeling uncertainty about the growth rate
as a normal distribution with a mean of 5% and a standard deviation of 8%.
Note that with such a large standard deviation compared to the mean, there is
a pretty good chance that the realized growth rate in a trial will be negative.
In modeling revenue growth, it is important to think about the underlying nature of the process. In Figure 6.2, in any given trial, the growth rate is
Known inputs
Discount rate
10%
Uncertain inputs
Investment cost
Year 1 revenue
Annual fixed cost
Annual revenue growth rate
Annual variable cost percentage
Financial projections
Year
Investment cost
Revenue
Fixed cost
Variable cost
Cash flow
$118,543
$100,427
$33,195
1.5%
49.9%
0
($118,543)
($118,543)
DCF calculations
Outputs
Discounted cash flow
NPV
($118,543)
$86,896
4.0
Values × 10–6
Parameters of distributions
Distribution Parameter 1 Parameter 2 Parameter 3
Triangular
90000
100000
150000
Triangular
75000
95000
105000
Triangular
32000
35000
38000
Normal
5%
8%
Normal
50%
2%
1
2
3
4
5
6
7
8
9
10
$100,427
$33,195
$50,132
$17,100
$101,896
$33,195
$50,865
$17,836
$103,386
$33,195
$51,609
$18,583
$104,899
$33,195
$52,364
$19,340
$106,433
$33,195
$53,130
$20,109
$107,990
$33,195
$53,907
$20,888
$109,570
$33,195
$54,696
$21,680
$111,173
$33,195
$55,496
$22,482
$112,799
$33,195
$56,308
$23,297
$114,450
$33,195
$57,132
$24,123
$17,100
$17,836
$18,583
$19,340
$20,109
$22,482
$23,297
$24,123
$20,888 $21,680
Summary Statistics from Simulation (10,000 trials)
NPV
–0.125
0.265
5.0%
90.0%
5.0%
Statistics
Minimum
($213,118)
Percentile
1.0%
($162,385)
Maximum
$1,048,138
2.5%
($142,617)
$34,207
5.0%
($124,502)
$124,912
10.0%
($100,446)
3.5
Mean
3.0
Std Dev
2.5
Variance
15603125230
20.0%
($67,364)
Skewness
1.277764548
25.0%
($54,037)
Kurtosis
6.134157776
50.0%
$12,170
$12,170
75.0%
$98,451
Mode
($40,561)
80.0%
$123,221
Left X
($124,502)
90.0%
$197,229
Left P
5%
95.0%
$265,279
Right X
$265,279
97.5%
$341,065
Right P
95%
99.0%
$426,457
2.0
1.5
Median
1.0
0.5
Values in millions ($)
1.20
1.00
0.80
0.60
0.40
0.20
–0.00
–0.20
–0.40
0.0
Fi g u r e 6 . 2
Venture NPV estimated by simulation using the distribution of each input
The figure shows the parameters of each statistical distribution and the result of one random draw from each distribution. The financial model is constructed and the NPV is computed based on the random draws for this trial. The histogram at the bottom of the figure shows the distribution of NPV results from a simulation run of 10,000 trials. The panel to
the right of the histogram shows summary statistics for the sampling distribution of NPVs.
Developing Venture Strategy Using Simulation 213
­ rojected to be constant over time. The growth rate can be higher or lower than
p
the expected rate of 5%, but in this model the rate will not change over time.
For new ventures, it is worthwhile to think about how you might model a
growth rate that starts out with a random draw, as this one does, but declines
over time as the venture matures. Some durable goods have high initial growth
rates, but as the market becomes saturated, the growth rate can decline and
may become negative. Some products might have growth rates that are meanreverting, so, for example, the expected growth rate is 5% but each year there
is a new draw from a distribution with a mean of 5%. These are just a few examples, but they can yield much different results. So it is important to think
carefully about the nature of the process and how to represent it in a statistical
distribution function.
The final element of uncertainty in Figure 6.2 is the variable cost percentage.
Here, again, we assume a normal distribution. We might get a low-cost trial as
a percentage of revenue, or a high-cost trial, but as the model is constructed,
there is no year-to-year variation in the variable cost margin.
The “Financial projections” panel of Figure 6.2 has the same structure as in
Figure 6.1. The only difference is that the randomly generated trials values are
used instead of the fixed values from Figure 6.1. Figure 6.2 shows one randomly
generated trial. The DCF calculations are also generated in the same way as in
Figure 6.1. The reported NPV is the result from the random trial that is shown
in the panels above.
To assess the NPV of the opportunity, we generated 10,000 trials of the model
and retained the NPV result from each trial. The histogram at the bottom of
Figure 6.2 shows the results of the 10,000 trials graphically, and the chart to the
right shows summary statistics from the sampling distribution. The first thing
to note about the distribution is that it is positively skewed, meaning that it has
a long right tail with some very high NPV results, whereas the negative side is
more compact. We can see this graphically in the histogram, and the summary
statistics indicate that the venture has a potential maximum NPV greater than
$1 million, whereas the worst outcome is only about negative $200,000.
More importantly, the sample mean is $34,207. This is quite a bit higher than
the negative $268 that we found when we used the entrepreneur’s projected values
of each input instead of the distribution information. Based on the simulation,
the project has a positive NPV and should be pursued. So one benefit of simulation is that it helps us consider how uncertainty about the assumptions can
affect the decision to invest.
You might wonder if we should modify the NPV in light of the skewness and
great uncertainty of the outcomes. After all, there seems to be a lot of risk; the
most likely outcome is quite negative (the sample mode is negative $40,561 and
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Chapter Six
the sample median of $12,170 is quite a bit lower than the mean). The answer,
if you are confident that the discount rate is correct, is that the NPV you have
calculated already takes account of these considerations so that the accept/
reject decision should be based on the statistical mean result. In this case, it is
positive so the investment should be made.
6.5
Simulating the Value of a Financial Option
Because real options are important to the value of a new venture and the values of the financial claims that make up the deal structure, it is instructive to
first see how simulation can be used to value financial options, and then to
consider under what conditions the approach is applicable to real options. To
begin, consider how options can be used in financial markets. Suppose a share
of stock currently sells for $118 and that calls and puts that expire in one year
are available with an exercise price of $125. To keep the illustration simple,
suppose that securities trade monthly. We assume, initially, that the expected
return for investing in the stock is the risk-free rate of interest of 0.3% per
month (3.66% per year) but that in any given month there is a 0.3 probability
that the return is 4% lower and a 0.3 probability that it is 4% higher.
Figure 6.3 shows the simulation model. The share price begins at $118 in cell
B3. Then an @RISK function samples from the discrete probability-weighted
returns and combines the resultant draw with the base return of 0.3% per month.
The callout in the figure shows the syntax for cell C3, which generates the first
monthly return and calculates the new share price. Similar calculations are made
for the remaining 11 months, each looking back at the result for the prior month.
In this illustration of a single trial, the stock price does better than expected,
reaching $126.80 by year end for an annual return of 7.5%. The resulting ending
value of a call option with an exercise price of $125 is $1.80 ($126.80 − $125).
Because the stock price ends above $125, the put option is out of the money and
is worth zero at expiration.
Figure 6.4 shows the results of simulating this 12-month stock price model 20
times, starting from an initial price of $118. Each line in the figure is a random
draw from the possible price paths of the share of stock.8
We used the simulation model to estimate the expected value of the stock at
the end of 12 months and, more importantly, the standard deviation of annual
returns for investing in the stock. Given that the risk-free rate is 3.66% per year,
the true (theoretically correct) expected (mean) value of the stock at the end of
one year is $122.32. When we simulated the value with 10,000 iterations of the
model, the estimate of expected value (the sample average) turned out to be
Developing Venture Strategy Using Simulation 215
Fi g u r e 6 . 3
Simulation model
of stock price and
put and call option
expiration values
The figure shows the key
inputs, assumptions,
and cell formulas, along
with the outcome of
one random trial for a
12-period model of stock
price performance. The
resulting expiration-date
values of puts and calls
with an exercise price of
$125 are shown, as well as
their Month 0 PVs, which
are found by discounting
the payoff back at the riskfree rate.
Fi g u r e 6 . 4
$160
Simulation of
12 months of stock
price performance
$150
$140
Price per share
The figure shows the
results of 20 random
sequences of returns for a
stock that trades monthly.
The Excel spreadsheet that
generated these results
also generates the values
of puts and calls on the underlying stock and can be
used to simulate the effects
of different assumptions.
$130
$120
$110
$100
$90
$80
1
2
3
4
5
6
7
8
9
10
11
12
13
Month
$122.37, very close to the true expected value. The estimated standard deviation
of ending stock prices from the simulation was $13.00, or 11.02% of the initial
stock price, also very close to the theoretically correct value.
A call option has value at expiration if the price of the underlying asset is
above the exercise price. The value of the call, at expiration, is the excess of the
stock price over the exercise price. In the simulation, the call was in the money
42.3% of the time. Over all 10,000 iterations, the average ending value of the call
was $4.06. Discounting this by the risk-free rate of interest yields $3.91 as the
estimated PV of the call option. Based on the simulation, this is the expected
216
Chapter Six
market value (the expected option premium at time 0) of the 12-month call with
an exercise price of $125.
A put option has value at expiration if the price of the underlying asset is
below the exercise price. The value of the put, at expiration, is the excess of the
exercise price over the price of the stock. In the simulation, the put was in the
money 57.7% of the time, with an average ending value of $6.68 and a PV of $6.45.
How do these simulated values compare to the values that can be derived
from option theory using the Black-Scholes Option Pricing Model (OPM)? Using the true expected return and the estimated standard deviation in the OPM,
the call is worth about $3.95 and the put is worth about $6.45. The simulated
values are close to the theoretical values. The main reason the differences are
more than trivial is that the distribution of possible stock prices in the simulation is not quite a normal distribution, which is the assumed distribution of the
OPM. The normal distribution has a slightly lower standard deviation than the
simulated distribution.
If the OPM and simulation yield such similar values, why do we bother with
simulation? Why not just use the OPM for valuing real options? In part, we do
so because the decision tree approach that we use provides the information
required for using a conditional discounted cash flow approach. In part, the
answer is that it is often not realistically possible to identify an underlying asset
(like a publicly traded stock) that is key to use of the OPM. Beyond that, the
other assumptions of the OPM generally are not satisfied when real options
are being valued. Simulation has advantages over theoretical option pricing
because financial markets related to new ventures are incomplete and because
many real options are interdependent. We examine these concerns more fully
in Chapter 10.
6.6
Describing Risk
We have already illustrated two simple ways to describe risk. In the discounted
cash flow (DCF) example, we assumed that the probability distributions of
some inputs were triangular and others were normal. In the stock option example, we modeled risk as a sequence over time of discrete outcomes where
the probabilities of different outcomes vary. Clearly, this is not very realistic,
and there are better ways to describe the risk of a one-month investment in a
share of stock.
Perhaps a better example would be VC. Based on historical averages, about
10% of a VC fund’s investments go public in an IPO, about 20% are successfully
sold to an acquirer, and the rest are total losses or close to total losses.9 With a
Developing Venture Strategy Using Simulation 217
discrete distribution we could use these percentages and the typical number of
investments made by a VC fund (approximately 20) to estimate the distribution
of fund outcomes in terms of the percentages of IPOs, acquisitions, and failed
investments.
Figure 6.5 illustrates several other distributions that are often used in simulation models. Perhaps the most familiar way to characterize risk is as a normal
distribution. Many kinds of uncertainty can be reasonably described as normal
distributions. For example, a normal distribution could have been a better way
to describe the evolution of stock prices over time. The discrete distribution
information in Figure 6.3 yields a monthly standard deviation of 3.3% (not
shown in the figure). Recall that we assumed a mean risk-free return of 0.3%
monthly. We could have simulated the first month’s stock price by multiplying
the initial price of $118 by a draw from a normal distribution with a mean of
1.003 (i.e., 0.3% expected return) and a standard deviation of 0.033. The @RISK
syntax for this normal distribution is = RiskNormal(1.003, 0.033).
Sometimes normal distributions can be problematic. In our model and in
reality, a share price can never fall below zero. Similarly, sales revenue in a period can never be less than zero. Because normal distributions are unbounded,
it is possible for a simulation of the share price or a revenue forecast to produce
a negative value, especially after several months of repeated draws and if the
periodic standard deviation is large. You can avoid this by adding a constraint
that prevents the simulated share price from falling below zero; however, this
may result in unintended biases in your results. A better alternative, for some
processes, could be to choose a different distribution that does not yield negative prices or to simulate the returns from one period to the next, as we do in
Figure 6.3 (doing so will not result in negative prices).
Another solution to the problem of negative values is to use a lognormal
distribution and apply it to a starting value that is positive. The second panel of
Figure 6.5 shows the result of simulating a lognormal distribution with a mean
of 0.1 and a standard deviation of 0.2. The mean is an expected growth rate, so a
mean of 0.1 indicates that the price is expected to increase by 10%; the standard
deviation of 0.2, or 20%, describes the uncertainty of the growth rate [entered in
Excel as =RiskLognorm(0.1, 0.2)]. As is evident from the second panel of Figure
6.5, the lognormal distribution never yields a negative value. Thus, a lognormal
distribution could work well for simulating a stock price and for many other
risky processes where values can never be negative.
A triangular distribution is a convenient way to prevent the occurrence of
extreme outliers. By choosing the minimum, most likely, and maximum values
to approximate what you believe is the true distribution, you can describe risk
distributions that are more likely to yield low values [such as the one shown in
Chapter Six
Normal Distribution
(10,000 trials, mean = 0, standard deviation = 1)
Lognormal
(10,000 trials, mean = 0.1, standard deviation = 0.2)
700
800
600
700
600
500
400
Frequency
300
200
400
300
200
100
100
8.81
22.55
2.25
8.45
21.65
2.17
2.02
8.08
20.76
2.10
1.95
7.71
19.86
1.87
7.35
18.96
1.80
1.72
6.98
1.65
1.57
1.50
1.42
Exponential Distribution
(10,000 trials, mean = 1, standard deviation = 1)
450
2000
400
1800
350
1600
1400
300
250
Frequency
200
150
1200
1000
800
600
100
400
50
200
Value
1400
1200
1200
1000
1000
Value
6.61
6.24
5.88
5.51
17.16
16.27
15.37
14.47
13.57
12.67
11.78
9.98
10.88
9.08
8.18
7.29
6.39
5.49
4.59
25.49
24.47
23.45
22.43
21.41
20.39
19.37
18.35
17.33
16.31
15.29
14.27
13.24
12.22
11.20
10.18
9.16
8.14
0
7.12
200
0
6.10
200
5.08
400
3.69
600
400
4.06
5.14
800
2.80
600
1.90
800
1.00
Frequency
1600
1400
3.04
4.77
Binomial Distribution
(10,000 trials, 100 draws, success probability = 10%)
1600
2.02
4.41
Value
Poisson Distribution
(10,000 trials, lambda (arrival rate) = 10)
1.00
4.04
3.67
3.31
2.94
2.57
2.20
1.84
1.47
1.10
0.00
0.73
0
19.41
18.60
17.80
16.99
16.19
15.38
14.58
13.77
12.97
12.16
11.36
10.55
9.75
8.94
8.13
7.33
6.52
5.72
4.91
4.11
3.30
2.50
1.69
0.89
0.08
0
0.37
Frequency
1.35
Value
Triangular Distribution
(10,000 trials, min = 0, most likely = 5, max = 20)
Frequency
18.06
Value
1.27
1.20
1.12
1.05
0.97
0.90
0.82
0.75
0.45
0.60
0
3.38
3.09
2.79
2.49
2.20
1.90
1.60
1.31
1.01
0.71
0.41
0.12
–0.18
–0.48
–0.77
–1.07
–1.37
–1.66
–1.96
–2.26
–2.55
–2.85
–3.15
–3.45
–3.74
0
0.53
Frequency
500
0.67
218
Value
Fi g u r e 6 . 5
Illustrations of @Risk statistical distributions
Examples of the results of simulating some of the statistical distributions available from the @Risk software.
the third panel of Figure 6.5, entered as =RiskTriang(0,5,20)] or high values.
You can prevent negative draws with a triangular distribution by selecting the
minimum to be zero or a positive value.
An exponential distribution is appropriate when outcomes at one end of the
distribution are very high but unlikely to occur and those at the other end are
Developing Venture Strategy Using Simulation 219
bounded (such as not to be less than zero) and are more likely to occur. Often,
time-related uncertainty is described well as an exponential distribution. For example, the length of time a product might last before breaking can be described
as an exponential distribution. The example in the fourth panel of Figure 6.5 has
a mean of 1.0, such as an expected life of one year [entered as =RiskExpon(1)].
The last two distributions in the figure look similar to each other. The fifth
panel of Figure 6.5 is a Poisson distribution with an expected arrival rate of 10
[entered as =RiskPoisson(10)] and the sixth panel is a binomial distribution of
100 draws with a success rate of 10% [entered as =RiskBinomial(100, 0.1)]. For
large numbers of draws, the binomial distribution is approximately the same
as the Poisson. In both cases, the outcomes are discrete nonnegative numbers.
Poisson distributions are often used to simulate such things as how much inventory you need to have on hand if, on average, a certain number of customers per
unit of time (such as 10 per day) each want to buy a unit of the product. You can
see from the figure that, if your beginning inventory is 20 units, you will almost
always have enough to fully supply the demand.
Binomial distributions are convenient for yes/no kinds of processes. For example, if you know that the probability of innovating during a given period of
time is 10%, you can simulate how long it might take to achieve a successful
innovation.
The menu of distribution functions is different in different packages, but with
a little creativity, those we have discussed are sufficient to describe most risks
that an entrepreneur may face.
6.7 Using Simulation to Evaluate a Strategy
Now that we have seen some simple illustrations of simulation and some examples of distribution functions, let’s reexamine the retailing venture from
Chapter 5. Our purpose is to see how simulation can improve the evaluation of
strategic alternatives that include real options.
Simulation involves six distinct steps:
1. Identify the strategies to be evaluated.
2. Establish the criteria for evaluating the alternatives.
3. Model the strategies to which simulation is applied.
4. Specify the assumptions and uncertainties that influence value.
5. Run the simulation.
6. Analyze the results.
220
Chapter Six
Step 1: Identifying Strategic Alternatives
When simulation is used to evaluate strategic alternatives, the normal practice
is to compare simulated results that are generated from different models of the
venture, where each model incorporates a particular set of strategic choices.
We might, for example, develop separate models of the Large Facility and the
Small Facility and compare the simulated values of the two strategies to see
which is better under what circumstances.
Limiting the choices to a few discrete possibilities (e.g., a Large Facility or a
small one) maintains the tractability of the analysis. Although a retail facility can
be any size, it should be possible to answer the important questions by looking
at only a few possibilities, such as large or small. The effects of including real
options (like waiting, abandoning, and expanding) can be studied by making
minor modifications to the basic models.
Here, as we did in Chapter 5, we would like to consider:
• The Large Facility, without and with an abandonment option
• The Small Facility, without and with either the abandonment or the expansion option
• Waiting to see market demand, then deciding on the response (a learning option)
Step 2: Choosing Evaluation Criteria
The choice of evaluation criteria depends on the nature of the business and
the focus of the simulation. For a public corporation, maximizing shareholder
value is the most sensible overall objective. A marketing department that does
not have responsibility for pricing might focus more narrowly on market share.
For an entrepreneur (or outside investor), it makes sense to focus on maximizing the NPV of the entrepreneur’s (or investor’s) interest in the venture.
A simulation model must be designed to produce information relevant to the
evaluation criteria. Accordingly, for the entrepreneur’s decisions, the simulation
model must generate information about the NPVs of the various alternatives
so the entrepreneur can use the results to make the best decision. Later in the
book, we use simulation to generate information about the cash flows the entrepreneur or investor will receive and the related risk. We use this information
to compute the PV of the cash flows in a separate step that does not require
simulation. In this chapter, we set aside the extra level of complexity and focus
on NPV directly.
Developing Venture Strategy Using Simulation 221
Step 3: Modeling the Problem
We use the Large Facility to illustrate the design and use of a new venture
simulation model. As before, we assume that the appropriate discount rate is
already determined and reflected in the results so that the values of the different outcomes are PVs. For now, we also assume that prices and costs are expressed in such a way that it is unnecessary to deal explicitly with time value.
The model must specify mathematically how the entrepreneur’s decisions
contribute to the PV of cash flows. We first determine the PV of the facility as if
it were owned entirely by the entrepreneur. We then adjust that value downward
to reflect the fractional ownership interest retained by the entrepreneur. The
PV of the facility can be stated in terms of present valued streams of cash flows:
PV(Cash Flow) = PV[(Rev − Cash Exp – Depr) ×
(1 − Tax Rate)] + PV(Depr)
Because the business will be privately held, the entrepreneur should be able
to avoid most corporate taxes. Hence, we apply a corporate tax rate of zero to
simplify the preceding expression to
PV(Cash Flow) = PV(Rev) − PV(Cash Exp)
To model the retailing venture, we need to specify the underlying determinants of revenues and cash expenses. Revenue is a function of price and unit
sales. Unit sales are the lesser of the quantity demanded or facility capacity.
The demand side of unit sales can be described as market size multiplied by the
facility’s market share. On the supply side, we model unit sales to be limited by
a capacity constraint based on facility size. If demand exceeds capacity, then
the constrained quantity is what is sold. Otherwise, unit sales volume depends
on market demand. Thus,
PV(Rev) = PV(Unit Price × Unit Sales)
Unit Sales = Lesser of Demand Quant or Capacity
Demand Quant = Mkt Size × Potential Mkt Share
Capacity = An assumed maximum value
For simplicity, we aggregate market size and market share over the expected
life of the venture. Unit sales is calculated based on market size and market
share over the life of the venture. We model the PV of cash expenses in a similar
fashion but include both a fixed and a variable component, where the variable
component depends on unit variable cost and unit sales. Thus,
222
Chapter Six
PV(Cash Exp) = PV(Unit Cost × Unit Sales) + PV(Fixed Costs)
The preceding structure determines the PV of the venture as if it were owned
entirely by the entrepreneur. But the entrepreneur is willing to commit only part
of the required capital; the balance must be raised from an outside source. To
determine value to the entrepreneur, we need to know what fractional share of
ownership the entrepreneur retains. This depends on how much the investor
contributes and how much equity the investor receives for the contribution. The
PV of the entrepreneur’s interest can be specified as the residual:
PV(Entrep Interest) = PV(Cash Flow) − PV(Investor Interest)
where all of the PVs are expressed from the perspective of the entrepreneur.
The value of the investor’s interest can be expressed as
PV(Investor Interest) = PV(Cash Flow) × (Total Invest −
Entrep Invstmt) × Pct Equity per Dollar Invested
Finally, the NPV of the entrepreneur’s investment is
NPV(Entrep Interest) = PV(Entrep Interest − Entrep Invstmt)
This completes the model of the entrepreneur’s interest. Clearly, a more complex model could be developed by specifying the determinants, in equation form,
of some of the terms in the preceding equations; however, the returns from
adding complexity diminish rapidly. Although it is useful to think about the
complex relationships that drive success or failure, a parsimonious model that
is focused on key relationships is likely to yield results that are just as useful.
Step 4: Specifying the Assumptions and Describing
the Uncertainties
For the simulation to work, each variable in the model must be specified as an
assumed value, mathematical expression, or statistical process that will generate a value. No matter how carefully you model the venture, the result will only
be as good as its assumptions. Assumptions should be based on data, experience, and/or careful reasoning. If the model is to be shared with outside parties, each assumption must be defensible. As forecasting is the subject of the
next two chapters, we will defer discussion of the bases for our assumptions.
Figure 6.6 shows our assumptions for the model of the Large Facility.10 These
assumptions parallel the more simplified assumptions used in Chapter 5 for
the decision tree analysis. For example, we allow the average price and cost of
a transaction to be subject to uncertainty so that the actual average price can
be different from the expected value. The expected price is $10 per transaction,
Developing Venture Strategy Using Simulation 223
Fi g u r e 6 . 6
Assumptions and
statistical processes
of the large-facility
model
Variable
Assumption
PV unit price of a meal
Normal distribution (μ = $10, σ = $1)
PV unit cost of a meal
Normal distribution ( μ = $5, σ = $0.6)
Market size estimate (after first year)
Triangular distribution (6, 2.6, 1 million units)
Market size
Normal distribution (μ = estimate, σ = 100,000)
Market share estimate (after first year)
Normal distribution ( μ = 10%, σ = 1%)
Market share
Normal distribution ( μ = estimate, σ = 0.3%)
Capacity
500,000 meals
PV fixed costs
Normal distribution (μ = $500,000, σ = $50,000)
Total investment
Normal distribution (μ = $750,000, σ = $25,000)
Entrepreneur investment
$400,000
Percent equity per dollar invested
1% per $10,000 of outside investment
μ = mean or average, σ = standard deviation.
and we assume that the uncertainty can be characterized as a normal distribution with a standard deviation of $1. Similarly, the expected cost is $5, with a
standard deviation of $0.60.
For reasons that will be important later, we use a two-step process to determine market size. During the first year, the entrepreneur receives a preliminary
estimate of the actual market size. To characterize market size in the simulation
model, we use a triangular distribution with a maximum of 6.0 million transactions (over the life of the facility), a minimum of 1.0 million, and a most likely
size of 2.6 million.11 The entrepreneur then receives an update of the market size
estimate. Specifically, we assume that the actual size is learned and is equal to
the initial estimate plus a random error. We assume the error to be normally
distributed with a mean of zero and a standard deviation of 100,000 transactions.
We determine market share using a similar two-step process. Demand equals
the product of market size and market share. If simulated demand exceeds capacity, then sales volume is equal to capacity. Otherwise, demand determines unit
sales volume. In the model for the Large Facility, capacity is 500,000 transactions over the life of the facility.
We allow uncertainty about both the level of fixed costs and the size of the
total investment that is required to construct the facility. Because the entrepreneur’s investment is $400,000, the amount of outside investment is uncertain.
The investor receives 1.0% of the equity for each $10,000 of capital invested.
You probably can think of other ways of setting up the model and may question some of our assumptions. For the model to be useful, it is important to give
careful thought to the assumptions. If they are specified arbitrarily, no one will
have confidence in the results. The entrepreneur can use a variety of information
Chapter Six
224
sources to improve the quality of assumptions about uncertainty. We review
information sources relevant to new venture forecasting in subsequent chapters.
In addition, breaking assumptions into finer components is sometimes useful.
Doing so can allow you to substitute variables that are easier to estimate for
those that are difficult to estimate directly. For example, it may be easier to estimate population growth of an area and per capita transactions than to estimate
market size directly. You can then derive expected market size, together with
the uncertainty of market size, as the product of the two underlying variables.
Step 5: Running the Simulation
With the model complete and the assumptions specified, the simulation is
ready to run. To illustrate the usefulness of simulation, we selected five variables in the model, which are retained by @RISK for each trial and stored in an
Excel file: market size, unit sales, PV of the venture, the entrepreneur’s ownership share, and NPV to the entrepreneur. Figure 6.7 is based on the @RISK results and shows the simulation statistics for these five variables based on running 5,000 iterations of the model.12 For each iteration, the computer makes a
random draw from each of the statistical distributions that describe the uncertainty in the model. Thus, simulation improves upon using discrete scenarios
Panel A
Percentages
Average
Median
Standard
Deviation
Skewness
3,212,694
316,821
1,079,180
0.649
299,515
3,065,070
301,949
986,546
0.649
238,803
1,054,064
104,730
647,450
0.026
419,340
0.303
0.189
0.557
0.061
0.555
Output
Market size
Unit sales (life time)
Total present value
Entrepreneur's ownership share
Entrepreneur's NPV
Panel B
Panel C
NPV to Entrepreneur
(Estimated based on 5,000 trials)
100%
300
90%
250
2,415,842
235,401
577,137
0.630
(23,065)
50%
3,065,070
301,949
986,546
0.649
238,803
75%
4,045,972
401,659
1,498,539
0.666
567,702
Maximum
5,977,817
500,000
3,415,528
0.740
1,908,014
Distribution of Entrepreneur’s NPV
(Based on 5,000 trials)
200
70%
Frequency
Cumulative Percentage
80%
60%
50%
150
100
40%
50
30%
20%
0
10%
0%
($1,000,000) ($500,000)
908,109
78,866
(469,267)
0.562
(710,893)
25%
$0
$500,000
Value
$1,000,000
$1,500,000 $2,000,000
($710,893)
($603,999)
($497,104)
($390,210)
($283,316)
($176,422)
($69,528)
$37,366
$144,261
$251,155
$358,049
$464,943
$571,837
$678,731
$785,625
$892,520
$999,414
$1,106,308
$1,213,202
$1,320,096
$1,426,990
$1,533,884
$1,640,779
$1,747,673
$1,854,567
1
2
3
4
5
Minimum
Value
Fi g u r e 6 .7
Unconditional simulation results
Panel A shows the results generated from @Risk. Results are compiled using the model for the large facility. Panel B shows the cumulative
distribution of the entrepreneur’s NPV. Panel C is a histogram showing the dispersion of the entrepreneur’s NPV estimates from the trials.
Developing Venture Strategy Using Simulation 225
for sensitivity analysis by allowing us to examine the impact of changing a
number of variables at the same time.
We begin by considering the total PV of the facility, shown in row 3 of Figure
6.7, Panel A. This is calculated for each trial as
Total PV = PV(Cash Flow) = PV(Rev) − PV(Cash Exp)
The average is $1,079,180, with a standard deviation of $647,450. The minimum value is negative $469,267. As all of the cash invested in the venture went
into building the facility, nothing is left over to fund this shortfall.
A loss of more than the initial investment is possible only if the entrepreneur
makes subsequent investments or makes personal guarantees to investors or
suppliers beyond the $400,000 investment. Otherwise, the loss would accrue to
others who have provided resources to the venture before being paid, such as
investors and creditors.
The entrepreneur’s NPV is shown in row 5 of Figure 6.7, Panel A. The average
value from 5,000 trials is $299,515, demonstrating that even as an accept/reject
decision (i.e., before building in real options), the venture is worth pursuing. The
summary table of the @RISK results shows wide variability in the entrepreneur’s
NPV, including negative values for at least 25% of the trials. The cumulative
distribution in Figure 6.7, Panel B shows about a 27% chance that the entrepreneur’s NPV will be negative. Figure 6.7, Panel C shows the full distribution as a
histogram. The distribution is skewed toward the right, indicating the potential
for some very high-valued outcomes, whereas the downside is more limited.
Figure 6.7, Panel A shows that the entrepreneur’s expected ownership share
is 65%, with a range of 56% to 74%. Thus, despite the uncertainty about the
initial investment, the entrepreneur would always end up with a controlling
(majority) interest.
Recall that we use a two-step process to simulate market size. First, we use
a triangular distribution to generate a preliminary estimate. Then we add a
normally distributed random error (mean = 0, standard deviation = 100,000
transactions), which allows us to find the true size of the market. Figures 6.8,
Panel A and Panel B are histograms that illustrate the net effect of the two-step
process. The basic shape of the distribution is still triangular and not symmetrical, so that the peak is below the mean. In Panel A, we ran only 300 iterations of
the model, and the resulting shape of the sample distribution is quite irregular.
Panel B shows the result of running 5,000 iterations. The interplay of the triangular and normal distributions is clearer in this panel.
As we discussed earlier, the realized number of transactions is the lesser
of demand (market size × market share) or the capacity constraint of 500,000
transactions. Figure 6.9 shows the distribution of transactions and illustrates
Panel A
12%
Percentage of trials
10%
8%
6%
4%
2%
5,827,144
5,639,090
5,451,036
5,262,982
5,074,928
4,886,874
4,698,820
4,510,766
4,322,712
4,134,658
3,946,604
3,758,550
3,570,496
3,382,442
3,194,388
3,006,334
2,818,280
2,630,226
2,442,172
2,254,118
2,066,064
1,878,010
1,689,956
1,501,902
0%
Market size
Panel B
12%
Percentage of trials
10%
8%
6%
4%
2%
6,004,275
5,602,740
5,201,204
4,799,668
4,398,133
3,996,597
3,595,061
3,193,525
2,791,990
2,390,454
1,988,918
1,587,383
1,185,847
0%
Market size
Fi g u r e 6 . 8
Distribution of market size estimates generated by simulation
Panel A shows the results of 300 iterations of the simulation of market size. Panel B shows the effect of increasing the number of iterations
to 5,000. In both panels, the solid vertical line represents the expected value from the sampling distribution.
Developing Venture Strategy Using Simulation 227
Fi g u r e 6 . 9
450
Histogram of unit
sales simulation
results
400
350
300
Frequency
The figure illustrates the
effect of the capacity constraint at 500,000 transactions on total unit sales of
the larger facility. Results
are based on a simulation
of 5,000 trials.
250
200
150
100
491,405
474,216
457,027
439,838
422,649
405,460
388,271
371,082
353,892
336,703
319,514
302,325
285,136
267,947
250,758
233,568
216,379
199,190
182,001
164,812
147,623
130,434
113,245
96,055
0
78,866
50
Transactions
Fi g u r e 6 .1 0
$450,000
Convergence of
the entrepreneur’s
average NPV
$400,000
$350,000
$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
1
137
273
409
545
681
817
953
1089
1225
1361
1497
1633
1769
1905
2041
2177
2313
2449
2585
2721
2857
2993
3129
3265
3401
3537
3673
3809
3945
4081
4217
4353
4489
4625
4761
4897
The figure shows the rate
of convergence of the
entrepreneur’s NPV of
the large facility, based
on 5,000 trials. After
about 500 iterations, the
simulated estimate of NPV
is quite stable, even though
individual iterations are
subject to considerable
uncertainty.
Iterations
how capacity constrains total unit sales. In about 8% of the trials (about 400
of 5,000), the constraint is binding and only 500,000 transactions can occur.
How many iterations of the model are needed to provide a reliable basis for an
investment decision? One way to find out is to look at a graph of the rate of convergence of a variable of interest. Figure 6.10 shows the convergence of estimates
228
Chapter Six
of the entrepreneur’s NPV. Convergence is illustrated in the figure by plotting
the average value of the variable for all the iterations up to a given number. The
point on the far left reflects only the first trial from the model, while the point
on the right reflects the average of all 5,000 trials. After about 500 iterations, the
average entrepreneur’s NPV does not change very much. Thus, in this instance,
even a fairly small number of trials yields a reliable estimate of projected NPV.
If the entrepreneur’s choice is either to invest in the Large Facility now or to
do nothing, then 500 iterations seem sufficient to determine that the venture is
worth pursuing. But if the entrepreneur is trying to compare different alternatives, such as choosing between the Large Facility and the Small Facility, or is
particularly interested in the tails of the distribution, more trials may be needed.
Using the standard deviation and number of trials information from Figure 6.7, we compute that the standard error of the estimate for the NPV of the
entrepreneur’s investment is about $5,900.13 This means there is about a 65%
probability that the true mean NPV is between roughly $293,600 and $305,400
(i.e., $299,500 ± $5,900), and about a 95% probability that the true mean is in the
range of $287,700 to $311,300 (i.e., $299,500 ± 2 × $5,900).14 Adding more trials
reduces the standard error of the estimate of the mean, so that better decisions
can be made even when the differences between values of alternative strategies
are small. Had we limited the simulation to 500 trials, the standard error would
have been about $18,600 instead of $5,900.
Step 6: Analyzing the Results
The final step is to use the simulation results as a basis for making a decision. If the choice were simply between building the Large Facility and doing
nothing, the positive NPV would be sufficient to conclude that the investment
should be made. Most real decisions, however, are more complicated. They
involve comparing several different alternatives. Many require drawing inferences about alternative scenarios that have not been formally analyzed (such
as an intermediate-size facility). For such decisions, it may be necessary to develop several simulation models with alternative assumptions and to compare
the results. This is the focus of the next section.
6.8 Valuing Real Options and Comparing Strategic Choices
We turn now to our primary objective—use of simulation to compare strategic alternatives and to examine the values of real options. To do so, we use the
same set of alternatives for establishing the IoT retailing venture as in Chapter
Developing Venture Strategy Using Simulation 229
5. The branches of the decision trees from that chapter represent alternative
scenarios concerning the entrepreneur’s decisions. To recap, we consider the
following possibilities:
• Build a Large Facility immediately.
• Build a Small Facility immediately.
• Wait for more information on demand and build whichever size is best.
• Build the Small Facility now and expand if demand is sufficient.
• Build the Large Facility now and abandon if demand is insufficient.
• Build the Small Facility now and abandon if demand is insufficient.
To this list, we can add more complex alternative scenarios that combine the
options to wait, expand, and/or abandon.
We begin by comparing the Large Facility to the simple alternative of investing in the Small Facility. The Small Facility is modeled easily by making a few
modifications to the Large-Facility model.15 Specifically, first we reduce the
expected PV of fixed costs to $400,000 and the standard deviation to $40,000.
Second, we reduce the capacity constraint to 260,000. Third, we reduce the
expected cost of acquiring the facility to $600,000 and the standard deviation
to $20,000.
Simulating the Small Facility, we found that the expected NPV of the entrepreneur’s investment is $249,606. This is materially less than the $299,515 value
of the Large Facility. Based on NPV, the entrepreneur should select the Large
Facility. Anticipating the examination of real options to come, for the Small
Facility the entrepreneur’s NPV is negative 17.3% of the time and the facility is
capacity constrained 67.5% of the time. The comparable numbers for the Large
Facility are 27.0% and 8.0%. Finally, the entrepreneur has a larger stake in the
Small Facility, 80% on average versus 65% for the Large Facility.
Are there any considerations that would shift the balance in favor of the
Small Facility? One possibility is that the entrepreneur does not want to accept the downside risk of the large project. Although the initial investment is
$400,000, in the event of a loss the entrepreneur may be compelled to draw on
resources beyond those originally committed. For the Large Facility, the simulation showed that additional investment would be needed to cover operating
losses in about 1.0% of the trials; for the Small Facility, this drops slightly, to
about 0.7%.
To gain additional insight about relative risk exposure, the standard deviations of the NPV of the two facilities can be compared. The Small Facility has
a smaller standard deviation of the entrepreneur’s NPV. But, as its expected
return is also lower, this may not make the small project any more appealing.
230
Chapter Six
A desire to minimize the downside would favor the Small Facility but is offset
on the high end, where the Large Facility does substantially better.
Financial theory implies that the choice should be made based on NPV (assuming you have correctly valued all of the cash flows and real options). So
comparing the fractiles in the distributions is not defensible unless you think
there is something that the NPV calculations do not take into account.
Here is where qualitative considerations may influence the decision. For example, maybe the entrepreneur cares about the potential differences in control
that are implied by the two models. The expected ownership share favors the
Small Facility, as does the worst case of fractional ownership: 73% for the small
versus 56% for the large. Conceivably, the difference in expected ownership
share is enough to lead the entrepreneur to favor the Small Facility, despite its
lower NPV.
Offsetting the smaller share of ownership, the Large Facility generates more
cash for the entrepreneur. Looking back at the fractiles, however, we see that
the times when control is likely to be most important to the entrepreneur are the
scenarios in which the venture does not do as well as expected. The Small Facility tends to have a higher NPV than the large one in these underperformance
scenarios. Thus, the higher share and higher cash flow of the Small Facility in
bad states of nature could be something the entrepreneur would want to consider
as mitigating the NPV difference.
How much is the Small Facility’s larger ownership stake worth to the entrepreneur? Simple NPV comparisons cannot directly address qualitative considerations such as these.
The Option to Abandon
The analysis thus far has examined two strategic scenarios that do not incorporate any real options: a onetime investment in either a Large Facility or a
Small Facility. No real-world venture is that simple. It is usually possible, for
example, to abandon a venture if results are discouraging enough.
How does the abandonment option change the values of the two facilities?
In the case of the large one, we assume that the building has an alternative-use
value of $600,000.16 In the terminology of finance, the owners of the facility
have a put option with an exercise value of $600,000. As the simulation model
is constructed, the exercise date is the date when true demand becomes known.
The simulation model can be used to calculate the expected PV of continuing to
operate the venture, conditional on the true state of demand. If this turns out
to be less than $600,000, then an entrepreneur seeking to maximize the value
of the investment will exercise the abandonment option.
Developing Venture Strategy Using Simulation 231
To estimate the value of the abandonment option, we run 5,000 iterations
of the Large-Facility model, modified to include the option. For each trial we
compare the venture’s PV to the abandonment value of $600,000. If the PV is
lower, we exercise the option, convert to office space, and realize $600,000, which
is shared between the entrepreneur and the investor based on their ownership
fractions. The resulting estimate of NPV to the entrepreneur is $331,455. Comparing this to the earlier value of $299,515 for the Large Facility, it appears that
the option is worth about $31,940 to the entrepreneur.17
When we evaluate the abandonment option of the Small Facility, with an
assumed abandonment value of $300,000, we find that it is worth about $5,738
to the entrepreneur, increasing the entrepreneur’s expected NPV from $249,606
to $255,344. The Small Facility’s abandonment option has a relatively low value
for two reasons: first, the probability of a state of nature being sufficiently bad
to put the option in the money is lower; second, the alternative-use value of the
Small Facility is low.
Based on the simulation results, the abandonment option should not alter the
initial decision to invest in the Large Facility. In fact, it reinforces the relative
value of the Large Facility. However, investment in either size is more attractive
when it includes an abandonment option.
As the problem is structured, the abandonment option is costless to the entrepreneur. But what if it were not? Suppose some locations have high values as
office space but others do not. Locations that afford valuable alternative uses
are likely to sell for more because even real options are usually not costless to
acquire. To see how much the entrepreneur should be willing to pay (in terms of
a location premium) for the option to abandon, we would need to run the model
again. The answer as to what the entrepreneur should be willing to pay is not
obvious, since the entrepreneur’s contribution is capped at $400,000 regardless
of the location choice. Thus, the investor would contribute the full location
premium and yet would have to split the increase in the venture’s value with the
entrepreneur based on fractional ownership. Also, because the option raises the
expected value of the venture, shouldn’t the entrepreneur be able to convince
the investor to take a smaller equity position per dollar of capital contributed?
If your intuition does not lead quickly to the answers to these questions, then
you should begin to recognize the value of simulation.
The model can be used to value options that are more complex than the simple
onetime option to abandon. In reality, the facility owner never knows demand
with certainty. Each year is different from the one before. The abandonment
option does not disappear just because it is not exercised at the end of the first
year. With a more elaborate simulation model that explicitly covers several years,
we could, in principle, estimate the value of a complex abandonment option that
232
Chapter Six
would give the entrepreneur the option to abandon at the end of each year.18 If
the option is exercised, the process ends; if not, the option for that year expires,
but options to abandon in the future continue to contribute positively to value.
Let’s look in more detail at the values of the Small Facility and the Large
Facility with options to abandon. Figure 6.11, Panel A plots the individual outcomes of 600 iterations of the model for the Small Facility. The horizontal axis
represents the number of transactions over the life of the venture. You can see
that the outcomes are dispersed around the upward-sloping line drawn in the
figure and that there is a “floor” on the entrepreneur’s NPV at around negative
$175,000. This floor reflects the downside protection against losses provided by
the abandonment option.
To show the option quality of the abandonment strategy more clearly, in Figure 6.11, Panel B we remove the uncertainty about prices and costs by using their
expected values. The only random variable is the level of demand. In addition,
we model demand so that all of the uncertainty is resolved after the first year.
The pattern in the figure can be represented as a combination of three securities: (1) an underlying asset (the entrepreneur’s claim on the venture), (2) a put
option to abandon the venture for $300,000 that is exercised if demand is low,
and (3) a call option the entrepreneur has “sold” by not building a facility large
enough to handle high demand. The upward-sloping portion of the value function in Figure 6.11, Panel B shows that by building the facility, the entrepreneur
acquires a long position in the market demand for the venture’s products. The
floor reflects the abandonment (put) option and the ceiling represents the call
option that is implicit in the capacity constraint.
Figure 6.11 shows that the entrepreneur is effectively hedged against low demand by acquiring an abandonment (put) option, which is valuable at demand
levels below about 140,000 transactions. By building a facility that is too small
to serve high levels of demand, the entrepreneur has effectively sold a call option
on demand in excess of what the facility can serve. The implicit proceeds from
the sale of the call option on high demand are reflected in the figure as a lower
facility cost compared with the alternative of building a much larger facility
that can meet the highest conceivable level of demand.
To assess the differences between the Small Facility and the Large Facility, in
Figure 6.12 we overlay the NPV functions based only on variations in demand. It
may surprise you to see that when demand is low, the Large Facility is more valuable to the entrepreneur than the small one (when demand is less than about 175,000
total transactions). This occurs because the Large Facility requires $150,000 more
in outside investment but increases abandonment value by $300,000.19
Although the investor pays the entire incremental cost, most of the increase in
abandonment value accrues to the entrepreneur, who has a majority ownership
Developing Venture Strategy Using Simulation 233
Panel A shows the
sampling distribution of
the entrepreneur’s NPV
from 600 iterations of the
small-facility model with
abandonment option.
The effect of the option
is reflected by the lower
bound, or “floor,” of negative NPVs. Panel B shows
the combined effects of
capacity constraints and
the abandonment option
for the small facility, leaving out the other sources of
uncertainty.
Panel A
Small-facility NPV to entrepreneur
$1,400,000
$1,200,000
$1,000,000
NPV to entrepreneur
Small facility: NPV to
entrepreneur
$800,000
$600,000
$400,000
$200,000
$0
–$200,000
–$400,000
Total demand (transactions)
Panel B
Small-facility NPV to entrepreneur
$1,000,000
$800,000
NPV to entrepreneur
Fi g u r e 6 .11
$600,000
Capacity
$400,000
$200,000
$0
–$200,000
–$400,000
Total demand (transactions)
stake. Over the demand range from 175,000 to about 300,000 transactions, the
Small Facility is more valuable for the entrepreneur. Generally, over that range
the Small Facility can meet nearly all of the demand but at lower cost than the
large one; this is due to its lower fixed costs. Beyond demand of 300,000 total
transactions, the extra capacity of the Large Facility makes it more valuable
than the small one.
Chapter Six
Fi g u r e 6 .12
$1,000,000
Large facility overlaid
with small: NPV to
entrepreneur
The figure shows the combined effects of capacity
constraints and abandonment options for the large
and small facilities over a
range of market demand.
The figure removes
uncertainty by using the
expected value for all variables except those related
to demand quantity.
$800,000
Large facility
NPV to entrepreneur
234
$600,000
Small facility
$400,000
$200,000
$0
0
100,000
200,000
300,000
400,000
500,000
600,000
–$200,000
Number of transactions
Figure 6.12 makes the choice appear simple. As long as we know true demand
and can strip away the uncertainties about other factors such as prices and costs,
it is obvious which of the two facilities should be built. Unfortunately, we cannot
simply remove those uncertainties, nor can we ever be certain about the level of
demand. We can, however, use simulation to help determine which of the two
facilities has the higher expected NPV.
The Learning Option
Another choice that is available to the entrepreneur is to wait to build either
size facility until the preliminary estimate of market size is learned. We assume that delaying investment invites competitive entry, so that the expected
market share for the entrepreneur’s venture would be reduced. Further, we assume that putting off the investment for even longer—until demand is known
with certainty—would result in loss of the opportunity to invest.
Because the entrepreneur invests only $400,000 but can do so in a facility of
either size, the entrepreneur has a complex call option involving mutually exclusive facility size choices on an uncertain share of the value of a venture. The
option is a call on the expected value to the entrepreneur of either size venture,
whichever value is greater. The exercise price is $400,000, and the option expires
shortly after the preliminary estimate of demand is revealed. If the entrepreneur
decides to invest, she also acquires an option to abandon the venture once the
true level of demand becomes clear. She will exercise the abandonment (put)
option if the expected value of the venture is less than the value of the facility
in alternative use.
Developing Venture Strategy Using Simulation 235
The learning option is not costless. Delaying entry reduces the PV of future
cash inflows. Moreover, the delay increases the chance that competitors will
enter and reduce expected market share. To reflect these costs, we assume that
potential demand is 10% less than if entry were not delayed.
Figure 6.13 is the branch of the decision tree (i.e., the subtree) that the entrepreneur faces if she decides to wait and learn before investing. With simulation,
we no longer describe the states of nature (demand) as a few discrete possibilities
(low, moderate, high). Instead, as in the figure, we describe the decision rule
the entrepreneur will use to respond to nature’s choice, whatever it may be. The
three-pronged choice after an estimate of demand is received reflects a complex
call option on either a Large Facility or a Small Facility. The binary choices,
once true demand is learned, reflect the abandonment (put) options.
To simulate this complex structure, we modified the new venture model. After
the estimate of demand is received, the simulation uses the result to estimate the
entrepreneur’s NPV for both the Large Facility and the Small Facility. If both
NPVs are negative, the entrepreneur will not invest in a facility of either size; if
both are positive, the entrepreneur selects whichever size has the higher expected
NPV. Then the entrepreneur learns actual demand and recalculates PV. Based
on the actual demand, she compares the PV of the selected facility size against
the PV of the abandonment option and decides whether to continue or abandon.
Wait
Large facility
Continue
Actual demand
Abandon
Expected demand
Small facility
Continue
Actual demand
Abandon
Do not invest
Receive Estimate of Demand:
Estimate NPV of entrepreneur’s interest.
(1) If resulting expected NPV > $0 and >
NPV of small facility, invest in large facility,
(2) If resulting expected NPV > $0 and >
NPV of large facility, invest in small facility,
(3) If resulting expected NPV < $0, do not
invest.
Learn Actual Demand and Cost:
Calculate PV of entrepreneur’s interest.
(1) Large facility, Continue if PV >
$600,000, otherwise abandon.
(2) Small facility, Continue if PV >
$300,000, otherwise abandon.
Fi g u r e 6 .13
Subtree for venture learning option with simulated uncertainty
Prepared using PrecisionTree™, Palisade Corporation.
The entrepreneur’s decisions are represented by squares; information received by the entrepreneur is represented by circles.
236
Chapter Six
At the starting point, the entrepreneur cannot know which, if any, of the options should be exercised. The only decision is whether waiting is more valuable
than the highest-NPV immediate alternative (i.e., more valuable than building
the Large Facility with option to abandon). When we simulated the model represented by the decision tree in Figure 6.13, the resulting expected NPV for the
entrepreneur was $306,409. Thus, the learning option’s expected value is $25,046
lower than the expected value of investing today in the Large Facility with an
abandonment option (NPV = $331,455). Hence, in this case the learning option
has no value—it is better to proceed now.
It may seem counterintuitive that one strategy containing the same and in
fact more options than another strategy could have a lower NPV. The main
reason the option to wait does not add value relative to the Large Facility/abandon alternative is that waiting encourages competitive entry, which reduces the
entrepreneur’s expected market share conditional on entering with the Large
Facility. As mentioned, the expected loss of market share can be thought of as
the cost of acquiring the option to delay investing. For the Large Facility, the
cost of acquiring the option, measured in terms of the expected loss of future
business, is more than the increase in value that results from learning more
about actual demand.
As it turns out, the abandonment option is not very valuable in conjunction
with the option to delay. This is because most of the uncertainty about future
sales is resolved when the initial estimate is received. Accordingly, the option
to abandon is almost never exercised when the option to wait is employed.
Generally, when combinations of real options are used, they involve mutually
exclusive or interdependent choices so that their values are not additive. This is
an important difference compared to financial options, where exercise decisions
can generally be made independently of each other.
Techniques such as simulation or other numerical evaluation methods are
extremely valuable for assessing structures involving mutually exclusive choices.
Because the values of individual nonindependent real options diminish as more
choices are added, we do not need to employ overly complicated models or to
completely describe and model the strategic alternatives. A parsimonious model
that captures the main strategic choices can generate a reliable estimate of the
expected value of a venture.
The Expansion Option
Finally, if the Small Facility is built immediately, the entrepreneur acquires an
option to expand in the event that realized demand justifies the Large Facility.
Initially, the Small Facility is built, and afterward the entrepreneur receives an
Developing Venture Strategy Using Simulation 237
estimate of demand; based on the estimate, the entrepreneur decides whether
to expand. The option to expand is a call option on additional capacity, with a
$200,000 exercise price (the cost to increase capacity).
As we have styled this example, the entrepreneur does not need to invest anything further to exercise the option; all of the $200,000 comes from the investor.
However, the expansion does reduce the entrepreneur’s ownership share. This
structure, where additional financing is provided by an investor in exchange for
an ownership percentage that reduces the entrepreneur’s share, is typical of VC
financing arrangements. Finally, the model is structured to reflect the realistic
expectation that the cost of building the Large Facility in stages ($600,000 +
$200,000) is higher than that of building it at once ($750,000).
When demand is allowed to vary continuously and the initial estimate of
demand is uncertain, the best course of action (Large Facility or Small Facility)
is not clear. Simulation can help evaluate the range of expected demand levels
over which exercising the option to expand would add value.
Figure 6.14 shows the subtree facing the entrepreneur who initially invests in
the Small Facility. The option to expand is evaluated by comparing the expected
Small facility
Expand
Continue
Actual demand
Abandon
Branch #2
Do Not expand
0
Continue
Actual demand
Receive Estimate of Demand:
Learn actual cost and ownership share.
Estimate present value of entrepreneur’s
interest.
(1) Expand if expected PV of
entrepreneur’s share > critical value
(2) Do not expand if expected PV of
entrepreneur’s share < critical value
Learn Actual Demand:
Calculate PV of entrepreneur’s interest.
Abandon
(1) Large facility: Continue if PV >
$600,000, otherwise abandon.
(2) Small facility: Continue if PV >
$300,000, otherwise abandon.
Fi g u r e 6 .14
Subtree for venture expansion option with simulated uncertainty
Prepared using PrecisionTree™, Palisade Corporation.
The entrepreneur’s decisions are represented by squares; information received by the entrepreneur is represented by circles.
238
Chapter Six
PV of the expanded facility with the expected PV of the small one over a range
of critical values for expected unit sales. Waiting to expand until more information about market demand is obtained reduces the risk of the outside investment that is required for expansion. Because the risk of investing at this point
is lower, we assume that the investor receives relatively less equity compared
to the earlier investment round: 1.0% of the equity for each $20,000 invested in
expansion. Again, the entrepreneur also has an option to abandon once true
demand becomes known.
Our purpose at this point is to determine the value of a strategy of investing
in a Small Facility and waiting to see the market response before a larger investment is made. When we evaluated the option to delay investing in Chapter 5, we
set up the analysis to always select the choice with the highest expected value.
This time we search for the best decision by examining different critical values
for the decision to expand and comparing the values for this strategy to the
alternative of investing immediately in the Large Facility.
Using the simulation model, we evaluated options to expand at critical values
of expected demand ranging from 200,000 units to 500,000 units, in 20,000-unit
increments. Exercising the option to expand benefits the entrepreneur over the
entire range when compared to keeping the facility small. However, the benefit
to the entrepreneur from expanding when expected demand is at the low end of
the range comes at the expense of the investor. This is because at low levels of
sales it is actually better to abandon the venture. Bringing in an investor under
such conditions effectively subsidizes part of the entrepreneur’s losses. An investor who recognizes this conflict is unlikely to enter into such an arrangement.
Accordingly, we limit the option to expand to the range of expected demand
levels where the investor derives a positive expected NPV from the project.
With this constraint, the option can only be exercised if expected demand
is at least 300,000 units. At this level, the option to expand increases the value
of the entrepreneur’s position to about $432,000, almost $100,000 higher than
building the Large Facility initially. Using the model, we found that expected
NPV is roughly constant up to a critical value of about 340,000 transactions.
Accordingly, we settled on a strategy of exercising the option only if expected
demand exceeds 340,000 transactions.
Why does the option to expand create so much value for the entrepreneur even
though it increases the overall cost of getting a Large Facility? There are two
reasons: First, the expansion option allows the entrepreneur to avoid the higher
costs of having a Large Facility when demand turns out to be low. Second, the
option reduces the uncertainty of the return on the second-stage investment for
the outside investor, prompting the investor to accept a smaller equity stake in
exchange for contributed capital.
Developing Venture Strategy Using Simulation 239
This is an important lesson and one that will be explored in greater detail
later. By staging the needs for outside capital, the entrepreneur can offer the
investor a safer bet and can retain a larger share of the venture as a result.
6.9
Summary
The objective of strategic planning for a new venture is to develop a framework for maximizing value. While we examine strategic choices from the perspective of the entrepreneur, a similar analysis could be done from the perspective of the investor. By the value-maximization criterion, success depends
on making good assessments of the risks and uncertainties and on developing
a strategy that anticipates the need to adapt to new information as it arrives.
An effective framework is one that helps the entrepreneur decide whether to
undertake the venture, promotes effective negotiation with providers of financing, and encourages value-maximizing decisions.
Simulation is a powerful tool for evaluating the critical decisions that an
entrepreneur faces. It is especially valuable for new venture strategies, which
typically involve numerous real options. It can add substantial value to the
venture and to the entrepreneur’s ownership stake.
There are six steps to implementing a simulation for strategic purposes. First,
identify the important strategic alternatives, possibly by representing them in a
decision tree. Second, decide on the criteria for evaluating the choices, such as
the NPV of the entrepreneur’s investment. Third, for each strategy, develop a
model that can be used to evaluate the various options facing the entrepreneur,
and specify mathematically how the decisions of the entrepreneur contribute
to the evaluation criteria. Fourth, specify the assumptions of the model and
describe the uncertainties. Fifth, run the simulation. Sixth, interpret the results.
The best way to appreciate the value of simulation is to work through specific
examples.
Review Questions
1. What value did Merck see in using simulation in its R&D process?
2. What are some ways that simulation can foster better decision making for new ventures? In each case, explain how simulation could be
beneficial.
3. What are the steps involved in using simulation to evaluate a strategy? Explain each using a specific example of your own or use the DCF
240
Chapter Six
i­llustration in Section 6.4 to identify the steps and explain how they are
useful in evaluating the key decisions in your example.
4. What potential problems do you see with using normal distributions to
project revenues or stock prices in simulation modeling? What are some
ways of addressing these problems?
5. If you were trying to simulate the value of a call option, why would you
first simulate the value of the underlying stock rather than simulating
the option risk directly?
6. Give some examples from new venture development that fit the distributions depicted in Figure 6.5 (e.g., arrival probabilities on a website could
be modeled as a binomial distribution).
7. Describe how you could use simulation to evaluate an expansion option.
How could you determine the value of the option?
8. Describe how you could use simulation to evaluate an abandonment option. How could you determine the value of the option?
9. If you simulate distributions of NPVs for two different strategies, why
would it usually be inappropriate to compare the distributions and
choose the one that is less risky?
10. When evaluating risk, why is it better to simulate the distribution of
possible NPVs rather than taking the expected value of each risky factor and calculating the expected NPV based on the expected values?
Notes
1. See Nichols (1994). Merck has continued to use simulation in their
R&D budget allocation and continues to post jobs with simulation analysis
in the titles. Many Fortune 500 companies make extensive use of simulation
packages such as @Risk and Oracle Crystal Ball.
2. Available at http://​hbr​.org/​1994/​01/​scientific​-management​-at​-merck​-an​
-interview​-with​- cfo​-judy​-lewent/​ar/​1.
3. Drawn from Nichols (1994).
4. The @Risk introduction tutorial provides a number of simulation
examples, including discounted cash flow, portfolio construction, and real
options.
5. While Excel has a menu of financial functions for computing such
things as NPV, use of these functions can obscure the logic of the calculations. Accordingly, in general, we rely on the fundamental mathematics of
time value rather than the Excel functions. In Figure 6.1, we compute the NPV
mathematically.
Developing Venture Strategy Using Simulation 241
6. If you have access to @Risk, we recommend that as you read the rest
of this section, you open Excel, launch @Risk, and open the Figure 6.2 file
that is available on the companion website.
7. Other Excel-based simulation software packages such as Oracle Crystal Ball have different syntax, but the general logic is the same.
8. Figure 6.4 is part of an Excel file (available on the companion website)
that also contains the model used for the simulation and the summary output
table from @Risk. The Figure 6.4 and Model tabs require @Risk to open
correctly. The other worksheets in the file do not.
9. Based on a sample of companies in the U.S. that received VC funding
between 1987 and 2008, Hall and Woodward (2010) find 2,015 (13.2%) IPO exits, 5,625 (37.0%) M&A, and 7,572 (49.8%) that either were confirmed to have
failed or had no record of successful exit after 5 or more years. In more recent
years, as VCs have moved more into angel-stage deals and IPO exits have declined relative to M&A, these percentages have declined. In PitchBook data
from the 2017 PitchBook-NVCA Venture Monitor, the average annual number of companies receiving VC funding in 2006 through 2011 was 4,447. During the subsequent 5 years (2012 through 2016), the average annual number
of IPOs was 78 (1.8% of the 4,447 average), and the average number of M&A
exits was 845 (19.0%), suggesting that around 79.2% had not achieved successful exits.
10. The companion website contains a copy of the Excel simulation
model of the venture incorporating the assumptions described here. Separate
simulation models are constructed to reflect the various real option structures
for the Large Facility and the Small Facility. All can be studied on the website.
11. It is up to the user to select and calibrate a distribution that provides
an accurate representation of future uncertainty. We use a triangular distribution to describe market size partly because it is an easy way to provide for
a high degree of uncertainty but avoid the possibility of negative simulated
values.
12. The NPV estimate converges quickly to a stable value. We use a large
number of trials because we also want good information on the rest of the distribution. In a model that includes real options that are likely to be exercised
only infrequently, a large number of trials will give more reliable information
on the options.
13. The standard error equals the standard deviation of the entrepreneur’s NPV (from Figure 6.7) divided by the square root of the number of
iterations.
14. Confidence intervals are derived using the properties of a normal
distribution: approximately 65% of the distribution is within one standard
242
Chapter Six
­ eviation of the mean and about 95% is within two standard deviations. A
d
more accurate approach to estimating confidence intervals would rely directly
on the trials data.
15. You can open the Excel file on the companion website to review the
modifications.
16. We do not allow for any uncertainty about this value, but with simulation we easily could do so.
17. Because the option changes the riskiness of the project, it could also
affect the discount rate that is appropriate for valuing cash flows. At this early
stage, we have not taken account of this in the simulation assumptions. However, conceptually we can think of the facility with the option as a two-asset
portfolio consisting of the underlying Large Facility (that is valued at the original discount rate), and a put option (valued at the risk-free rate).
18. This is comparable to a model for new drug development, where the
pharmaceutical company has the option to abandon the effort at numerous
points in the clinical testing and FDA approval stages.
19. If the investor is astute, the terms of the deal will be different for the
Large Facility than for the small one, in part because the entrepreneur gets
more benefit from the abandonment option of the Large Facility.
References and Additional Reading
Bell, D., and A. Schleifer Jr. 1995. Decision Making Under Uncertainty. Cambridge, MA: Course Technology.
Copeland, T., and V. Antikarov. 2003. Real Options, Revised Edition: A Practitioner’s Guide. New York: Texere.
Hall, R. E., and S. E. Woodward. 2010. “The Burden of Nondiversifiable Risk
of Entrepreneurship.” American Economic Review 100: 1163–94.
Hertz, D. B. 1968. “Investment Policies That Pay Off.” Harvard Business Review 46 (1): 96–​108.
———. 1979. “Risk Analysis in Capital Investment.” Harvard Business Review
57 (5): 169–​81.
Mun, J. 2016. Real Options Analysis: Tools and Techniques for Valuing Strategic Investments and Decisions with Integrated Risk Management and Advanced Quantitative Decision Analytics, 3rd ed. CreateSpace.
Nichols, N. A. 1994. “Scientific Management at Merck: An Interview with
CFO Judy Lewent.” Harvard Business Review 72 (1): 89–​99.
Stevenson, H. H., D. F. Muzyka, and J. A. Timmons. 1987. “Venture Capital
in Transition: A Monte Carlo Simulation of Changes in Investment Patterns.” Journal of Business Venturing 2: 103–​21.
C h a p t e r S e ven
R e ve n u e Fo r eca sti n g
F i n a n c i a l f o r ec a s t i n g i s a critical element of planning for a new
venture. The principal benefits of a good forecast include:
• Forecasting provides a basis for estimating value so entrepreneurs and
investors can make objective comparisons between pursuing the venture
and other opportunities.
• Forecasting is a disciplined way to evaluate how much cash the venture is likely to require and how much it might need if it develops more
quickly or slowly than expected.
• Forecasting helps entrepreneurs and investors compare strategic alternatives and select the one with the highest expected value.
• Forecasting helps the entrepreneur and investors understand the
strengths and weaknesses of the venture.
• A forecast provides a benchmark against which to compare actual performance, thereby providing a means to test hypotheses about the venture and to provide early warning of potential problems.
We use the first part of this chapter to introduce the basics of financial forecasting, beginning with forecasting revenue and extending to integrated financial
statement forecasting. Revenue forecasting is important because revenue is a
key driver of value and for determining how much to invest in the inputs that
are essential for responding to demand as it develops.
To develop the revenue forecast, we begin by specifying the assumptions that
drive revenue and revenue growth—assumptions about such things as market
size, market share, and price. Clear, explicit, and well-supported assumptions
245
246
Chapter Seven
make the forecast more than just an exercise and make it credible to investors. In
this chapter, we present a variety of forecasting techniques and review information sources that can serve as foundation for key assumptions. We then use the
forecasting methods to design and construct forward-looking pro forma financial
statements that allow us to forecast venture performance in an integrated way.
In subsequent chapters, we use the integrated pro forma financial statements as
a basis for valuation and to assess the cash needs of the venture through time.
7.1
Principles of Financial Forecasting
It is useful to begin with an overview of the principles that guide the forecasting process.
• Build and support a schedule of assumptions. The assumptions may come
from fundamental analysis of the opportunity, information about comparable firms, or expert judgment. These assumptions guide construction of a financial model of the venture.
• Begin with a forecast of revenue. It is usually easiest to start with a revenue forecast that is developed in light of the planned scale, scope, and
nature of the selling effort. Most other aspects of the model are linked to
revenue. For example, the targeted level of sales or rate of sales growth
can determine the investment in production capacity and the necessary
level of inventory. It can be useful to develop an aggregate revenue projection by combining projections for appropriate segments of targeted
customer/user groups (e.g., age, region, incomes, propensity to adopt
new products) and to separately estimate the numbers and growth rate
of each segment.
• Decide whether to develop the forecast in real or nominal terms. Nominal
forecasts should include an explicit forecast of inflation, whereas realterm forecasts are in constant dollars. If you expect selling prices and input costs to track the inflation rate, then forecasting in real terms can
simplify the model. If prices and costs are unrelated to inflation, it can
be better to forecast in nominal terms and make explicit adjustments for
price changes. Interest rates on debt and discount rates used in DCF valuation are usually quoted in nominal terms.
• Integrate the financial statements. Using formulas to integrate the pro
forma balance sheet, income statement, and cash flow statement is essential for (1) testing sensitivity to assumptions, (2) performing scenario
analysis, and (3) simulating the uncertainty of future performance.
Revenue Forecasting 247
• Choose an appropriate time span for the forecast. The span covered by
the forecast depends on the purpose. If the forecast is to be used for valuation, the period must be long enough to carry the venture to a point
where harvesting opportunities are likely. If it is to be used to determine
cash needs, it should extend to the point where the venture would be in a
position to attract follow-on financing based on its track record.
• Choose an appropriate forecasting interval. The appropriate interval depends on the planning period of the venture. For assessing financial
needs of an early-stage venture, intervals of one year are too long. To arrange financing on a timely basis, the entrepreneur and investors must
project cash needs over much shorter intervals. If the forecast is to be
used for control, the important performance milestones are unlikely to
be annual. On the other hand, daily or even weekly forecasts are unlikely
to be of much value. Departures from projected results over very short
intervals are largely random. Generally, for an early-stage venture an interval of about a month to a quarter provides a sensible balance of timeliness and reliability for use in cash needs assessment.
• Assess the reasonableness of the model. Think through the relations
among assumptions and line items within and across statements. Do
they make sense? Are they internally consistent? Try a basic “what if”
analysis to see if the results are internally consistent and conduct stress
tests to make sure the model is robust to extreme outcomes.
7.2
Forecasting Revenue
For valuation and to anticipate financing requirements, we need to link
product-market performance to financing needs. The revenue forecast is the
customary link. This is because financing supports the start-up investments
that are necessary before revenue generation can begin. Once the venture is
producing and selling a viable product, sales growth is the primary driver of
financing needs. For any given forecast of future revenue, we can work back to
estimate the free cash flows from operations that are expected to be available
or the additional financing needed to support the growth.
Forecasting the Revenue of an Established Business
A reliable revenue forecast for an established business can sometimes be based
on prior experience. For example, we might project that revenue will grow
at the average rate of the previous five years. A more sophisticated approach
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Chapter Seven
could take into consideration the trend in the rate of revenue growth (as opposed to the average), or the forecast could be tied to changes in underlying
economic and demographic factors.
One simple approach to forecasting revenue is to extrapolate the average
historical nominal growth rate. A possible way to improve the revenue forecast
may be to make it in real (inflation-adjusted) rather than nominal terms. Another
way to improve forecast accuracy can be to weight historical data so that the
more recent experience receives greater weight. Weighting is based on the idea
that the future will probably be more like the recent past than the more distant
past. With enough historical data and appropriate software, one could determine
the weights that yield the (statistically) best estimates of sales in later years.
Exponential smoothing is a weighting scheme that is easy to apply and can
work well when historical data are limited. The simplest form of the exponential
smoothing model is
ForecastT+1 = α × ActualT + (1 − α) × ForecastT
(7.1)
where α is a weighting factor between zero and one. For example, suppose that
the revenue forecast for period T was 100 and the actual was 110. If α is set to
0.2, the forecast for T + 1 is
0.2 × 110 + 0.8 × 100 = 102
whereas, if α is set to 0.8, the T + 1 forecast is
0.8 × 110 + 0.2 × 100 = 108
Although the equation does not refer to periods before T, they are reflected
in the forecast implicitly. Because the period T forecast is estimated in the same
way as the T + 1 forecast, actual results of earlier periods are implicit in the T
+ 1 forecast, through the previous forecast.
The term “exponential smoothing” signifies that when Eq. (7.1) is used, the
weights applied to earlier results decrease exponentially. The relative importance
of each year’s data to the forecast is determined by the weight factor, α. A high
value of α is used if recent results are believed to be an important predictor of
future results. A lower value increases the weight on older results. If α is high,
the forecast adjusts quickly to new results. In the extreme, if α is equal to 1.0,
then the next period’s forecast is equal to this period’s actual result. If α is low,
the forecast adjusts gradually.
The approaches to revenue forecasting that we have discussed, including
exponential smoothing, are examples of “naïve forecasting” methods. They
extrapolate existing trends without considering underlying economic forces that
drive demand and revenue. More elaborate naïve models are available to deal
Revenue Forecasting 249
with factors like seasonality or with a growth rate that is expected to decline
systematically over time. In some cases, it may be more reliable to forecast
growth rate percentages instead of revenue levels. Beyond that, we could consider underlying economic forces that affect the level of sales or the growth rate.
These forces might be macroeconomic variables, such as the growth rate of GDP,
or demographic factors, such as the population growth rate or the average age
of the population.1 Finally, they could be industry-specific factors, such as the
revenue growth rate for the industry, emergence of new competitors, or product
innovations. We caution, however, that it is not helpful to identify relationships
between revenue and other factors that are themselves difficult to forecast.
Forecasting Revenue of a New Venture
Developing a revenue forecast for a venture with no track record is more difficult, and the result is likely to be less certain. How can we forecast revenue
for a product that does not yet exist, where the full scope of applications and
customers is not yet known, and where actions and reactions of competitors
are yet to be seen? Rather than allowing these concerns to overwhelm us, it is
important to search for simplicity. We consider two approaches: (1) yardsticks
and (2) fundamental analysis.
Yardsticks. An approach that is sometimes useful is to identify reasonable
“yardstick” companies for which public (and possibly nonpublic) data are available. A yardstick is a firm that is comparable to the venture on some dimensions
that are important to the forecast. Actual comparability may not need to be very
close. Depending on the kind of information we wish to forecast, it is not even
necessary that a yardstick firm be producing the same product. Comparability
can be based on factors such as the expected market, distribution channels,
adoption rates, usage rates, repurchase rates, uniqueness of the product, or
technology.
Data availability is one advantage of the yardstick approach. Many small
companies go public every year. In the process, they often supply a great deal of
information about the period before the company was public. Companies that
go public are ideal candidates for assessing revenue growth potential. However,
revenue estimates based on public companies are by nature optimistic for a new
venture. Public firms are success stories and have already survived for longer
than the typical new venture. Moreover, issuing shares publicly suggests that
the firms have grown rapidly enough that outside equity financing has become
important for growth. By studying the experiences of yardstick companies,
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the entrepreneur can obtain information on the various stages of new venture
growth and their changing financing needs.
Sometimes the IPO prospectus contains enough historical data to measure
revenue growth over a number of years. In some cases, there is enough historical
information to infer the length of the development period before the company
was able to market a product successfully and to infer the costs involved in the
process.
Financial information from yardsticks has value beyond forecasting. The
prospectus also contains information on how those companies met their financing needs before going public. Each company can serve as a case study,
providing insight into the financing choices the entrepreneur faces. In addition, the companies’ financial statements can aid in formulating the assumptions the ­entrepreneur needs for projecting the financial statements of the new
venture.
IPO prospectuses are available from various sources, including the issuing
company and the underwriter. The SEC maintains an electronic database of
prospectuses and other corporate submissions on its EDGAR website.2
How can we use the sales information from yardsticks to generate a revenue
forecast? The techniques are fundamentally the same as for an established business using its own historical revenue. For an existing business, its own historical
experience is simply one yardstick that is likely to be particularly good. For a new
venture, this convenient sales record is not available, but the task is the same—to
use historical sales experience (in this case, of other firms) to generate a forecast.
A simple example. Suppose you are considering opening a coffee shop,
Morebucks, that would be similar to, and compete with, other retail coffee shops.
As a means of estimating revenue, as shown in Table 7.1, you have collected data
from the SEC reports of several publicly held businesses that operate companyowned coffee shops. From the public filings, you have collected information on
revenue per company-owned store.
The data come from a variety of reports required by the SEC. Forms SB-2,
SB-2A, and 10SB12G/A are original and amended securities registration forms
that are required of publicly held small businesses; F-1 is a registration form
used by foreign firms with securities that are traded on U.S. exchanges; S-1, S1A,
and 10-12G are original and amended registration forms with publicly traded
securities; and a 10-K is an annual report for a firm with publicly traded shares.3
From these reports, to minimize the effects of economies of scale and revenues
from nonretail store operations, we use the earliest information where data on
both revenue and number of shops are available.
Revenue Forecasting 251
Tab le 7.1
Yardstick company data
Company
Coffee People, Inc.
David’s Tea, Inc
Diedrich Coffee,
Inc.
Einstein Noah
Restaurant Group
Java Detour, Inc.
Peabody’s Coffee,
Inc.
Peet’s Coffee &
Tea, Inc.
Starbucks, Inc.
Tully’s Coffee
Corp.
SEC Filing
SB-2A
9/27/1996
F-1
5/26/2015
S-1A
8/12/1996
S-1
5/18/2007
SB-2
5/2/2007
10SB12G/A
3/15/2000
S-1A
1/23/2001
10-K
9/29/1996
10-12G
7/27/1999
Data Fiscal
Year
Number
of Shops
Owned
Revenue
($ Millions)
Revenue/Shop ($)
2016 Adjusted
Revenue/Shop ($)
1993
6
5.466
911,000
1,596,100
2012
105
73.058
695,791
731,660
1995
7
7.591
1,084,429
1,707,812
2006
416
363.699
874,276
1,040,835
2006
14
6.318
451,282
537,256
1999
26
1.795
69,032
99,449
1995
22
33.252
1,511,455
2,380,313
1994
403
248.500
616,625
998,613
1999
59
20.207
342,495
493,404
Notes: The following companies reported substantial negative net income and/or operating losses in and around the data year: David’s Tea, Java
Detour, Peabody’s Coffee, and Tully’s Coffee. Peabody’s was operating only kiosks and Tully’s was operating a mix of stores and kiosks.
Of these nine companies, Peabody’s and Tully’s are poor yardsticks for the
proposed coffee shop since many or all of their venues are kiosks so that revenue
per shop is low. Moreover, all of the companies that are unprofitable in and
around the data measurement period all have revenue per shop that is materially
below $1 million in 2016 dollars. The average and median revenues of profitable
coffee shops in the table are both somewhat above $1.5 million. Based on the
data in the table, it appears that revenue at this level would likely be sufficient
for profitable operation.
Based on the revenue-per-shop information for the yardstick companies, it
seems unlikely that Morebucks, as a new coffee shop with a single store, could
expect to do better than the median company. It seems reasonable to infer that
$1.5 million would be an optimistic estimate of expected annual revenue for
Morebucks when it becomes established as a single coffee shop.
You should be able to improve on this estimate in several ways. For example, if
you were to explore franchising opportunities with the companies shown in Table
7.1, you could ask the franchiser to provide information on average revenues
from franchise outlets and on the range of performance for franchise outlets, as
well as revenue by age of outlet. You could also contact other companies that
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operate privately owned coffee shops and might be able to convince the owners
to share some information with you. Many industries have trade associations
that provide representative financial and operating data on member firms.
Notice that the table does not include information about the growth rates
of store revenue. This is because, except for the first year or two, there is little
reason to expect that same-store revenues will grow at a rate very different from
the inflation rate, or perhaps inflation plus population growth in the immediate
market area. For this kind of retail venture, if you want to grow significantly,
you need to add stores. The corporate reports to the SEC include data on growth
in numbers of shops over time.
Fundamental analysis. Fundamental analysis is an alternative to reliance
on yardsticks. Approaches to fundamental analysis can vary. For a venture such
as a coffee shop that is similar to others already in operation, the fundamental
approach might be mainly empirical, such as observing the traffic at other coffee
shops, analyzing their product offerings and pricing, and talking to customers.
Consider Morebucks. Our yardstick estimate of $1.5 million of annual revenue depends on numerous factors. Total revenue is a function of days and hours
of operation, customers per hour, and average transaction size—all factors
driven by product mix, advertising, competition, and location. For example,
suppose you have researched two different coffee shops—one in a neighborhood
that is active from early morning until late in the evening, including weekends,
and one in a location such that it primarily serves employees of nearby businesses that are open standard hours. Your estimates of revenue are as follow:
Comparable Type
Days per year
Business/Entertainment Center
Business Only
360
300
Hours per day Customers per hour
18
12
40
45
Revenue per
customer
Annual revenue
$6.50
$8.00
$1,684,800
$1,296,000
As the evidence indicates, revenue is sensitive to location. While the business
and entertainment location generates significantly more revenue, it is also likely
to incur higher rent and entails longer hours of operation, resulting in higher
expenses. By comparing different locations and constructing a complete financial model for each, the entrepreneur can refine the location decision.
Demand and Supply Considerations
Revenue estimates for a venture can be generated from either the demand side
or the supply side. The demand-side approach assesses consumer willingness
Revenue Forecasting 253
and ability to buy the product, assuming that the venture has adequate capacity to supply all that is demanded. The approach begins with an estimate of
the venture’s market share that depends on such demand-related factors as
number of competitors, pricing, location, and intensity of marketing efforts.
For unique products, initial market share is easy to estimate—100%—but the
size of the market is difficult to judge, and the rate of market share erosion due
to competitive entry depends on defensibility of the entrepreneur’s position.
For a more traditional venture, the potential market size may be easy to estimate (published estimates may exist), but market share is more uncertain and
the reactions of competitors may be important.
In contrast, the supply-side approach seeks to determine how fast the venture
can grow given managerial, financial, and other resource constraints. Possible
supply-side constraints include limits on access to raw materials, financing,
and technology; there also may be constraints on the ability to hire and train
employees. In other words, even if demand increases rapidly, the venture’s
growth rate may be limited on the supply side. Supply shortages are common
in product markets where new models are regularly introduced. When Samsung
introduced its Galaxy S6 Edge, for example, it was unable to keep up with
demand partly because of the technical challenges of producing the phone’s
curved screen. In 2013, Tesla was unable to keep up with demand for its recently introduced Model S because of a shortage of batteries. Commenting on
the shortage, CEO Elon Musk stated, “We really are production-constrained,
not demand-constrained,” and indicated that the supply shortage was likely
to continue into 2014.
Combining the supply- and demand-side approaches, a venture’s expected rate
of growth is whichever is lower. Slow-growth scenarios normally are constrained
by the limits of market demand, whereas rapid-growth scenarios normally are
constrained by the organization’s ability to manage growth. One advantage of
the yardstick approach is that it uses the actual experiences of other firms and
therefore implicitly considers both supply- and demand-side factors.
Whether a forecast is based on yardsticks, fundamental analysis, or a combination of the two, it is important that projections be realistic and credible.
Fundamental analysis is subject to the greatest potential for wild speculation.
Consider the sales forecast of a proposed fast-food chain, the Bunny Hutch:
“Assume each person in an area with a population of 10 million eats one bunny
burger one night per week, at $1.00 per bunny burger. That’s $10 million per
week.”4 Projections made by individuals with established reputations and industry experience or by objective third parties are more likely to be realistic
and credible than estimates made by an entrepreneur who is enamored of the
idea in the first place. The best substitute for relying on independent expert
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projections is to base the analysis on solid reasoning and well-supported and
well-documented assumptions.
7.3 Estimating Uncertainty
For a new or early-stage venture, efforts to forecast revenue and other results
may seem to be of little value. After all, the probability that actual performance will turn out to be much like the forecast is quite low. Nonetheless,
forecasting is probably more important for a business with an uncertain future than for one that has experienced steady growth.
For a venture with an uncertain future, the forecast of expected performance
is simply an anchor for estimating uncertainty. For many purposes, the forecast
of uncertainty is far more important. For example, the financial needs of a
venture depend heavily on uncertainty. Failure to allow for possibilities such as
development delays, lower-than-expected profitability, or higher-than-expected
demand can result in critical financing errors. In this section, we introduce some
simple approaches for estimating uncertainty.
Assessing Risk Using Historical Data
The approaches discussed previously for revenue forecasting can also be used
to estimate uncertainty. One simple approach for an established company is to
generate a baseline historical trend for a key variable, such as sales or the sales
growth rate, and then estimate uncertainty as the historical standard deviation of differences between actual and expected values.
For a venture without a track record, uncertainty is greater and more difficult
to estimate. One approach is to base the estimate on the experiences of other
companies that are similar in important respects. For a new venture, such as the
proposed Morebucks coffee shop, we could use the dispersion of revenue across
yardstick companies and over time for those companies to estimate the standard
deviation. Another is to envision alternative realistic scenarios for the venture
and develop projections consistent with each. An estimate of uncertainty can
be developed by applying probabilities to the different scenarios.
Sensitivity Analysis
In sensitivity analysis, we vary the assumptions of the model one at a time or a
few at a time and observe the impact on the forecast. Used effectively, sensitivity analysis can clarify which parameters are most important in the forecast.
Revenue Forecasting 255
Once we identify the critical input parameters, we need to develop reasonable
descriptions of their uncertainty. History—from the firm’s experience or the
experiences of comparable firms—can provide guidance, but always with the
caveat that the past may not be predictive of the future.
Another shortcoming of sensitivity analysis is that varying individual model
inputs over predetermined ranges ignores possible interdependencies among
variables. Finally, the forecaster’s choice of maximum and minimum values for
a given input may be subjective and result in a biased forecast.
Developing Alternative Scenarios
Some of the limitations of sensitivity analysis can be overcome by considering specific scenarios. Scenarios allow several assumptions to be evaluated
simultaneously and can incorporate correlations among variables. Prospectuses of public companies and other public data sometimes can help you make
a reasonable forecast of a success scenario. The success scenario may be the
one that is most important for estimating how much financing the venture will
need. For other purposes, it is important to develop a more comprehensive
forecast of uncertainty. Value, for example, depends on expected performance
and uncertainty, not just on performance in a success scenario.
How can scenarios be developed that reasonably represent the uncertainty
of a new venture? For simple businesses like retail shops and restaurants, the
realistic range of performance may be narrow. For those, it may be possible to
rely on information from public sources, some of which we describe shortly.
But how can scenarios be developed for a business with tremendous potential
and great uncertainty?
Consider, for example, the difficulties of forecasting the value of a speculative,
but promising and potentially important, new medical treatment for cancer.
The answer depends partly on expected future revenues and the uncertainty
of those revenues.
Defining a success scenario for the venture’s revenue forecast is not difficult.
Public information is sufficient to accurately forecast the number of potential
candidates for treatment. Using the techniques discussed earlier, we can forecast
market share and sales volumes. Data on prevailing prices for proprietary treatments for other life-threatening ailments can be used to forecast revenues. However,
it is hard to imagine a project that is subject to more uncertainty. How likely is it
that the development efforts will succeed and the patent holder can sell the product
for a price unconstrained by government intervention? Moreover, how many competing research projects are under way, and how likely are they to succeed? What
effect would innovation by a competitor have on market share and selling price?
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Chapter Seven
One way to come to terms with the uncertainty is to define a small number
of realistic scenarios in addition to the success scenario, such as the following:
• Development efforts are successful and the product faces weak competition from other successful efforts.
• Successful development efforts are offset by strong competing products.
• Development efforts are not successful and the project is abandoned.
The entrepreneur’s challenge is to construct alternative scenarios with realistic assumptions about their effects on product price and quantity and realistic
assessments of their relative probabilities. Defining a small number of realistic
scenarios helps focus the research and reveals the kinds of information that
will be most useful.
Incorporating Uncertainty with Simulation
Simulation is the final technique we consider for incorporating uncertainty
into a revenue forecast. We first identify the assumptions behind the forecast.
Then we assign a probability distribution to each key assumption and estimate correlations among the variables. These assumptions can be developed
using historical data, evidence drawn from other companies, and/or fundamental evaluation of the market and potential demand for the product. We already have seen that IPO prospectuses can contain much useful information.
Reports from Wall Street equity analysts often include descriptions of target
customers and estimates of market size, selling prices, and time to market.
Agencies like the Food and Drug Administration (FDA) provide voluminous
data on drug and device approvals, clinical trial timetables and results, and
similar information. All such sources can be used to approximate probability
distributions for key variables and to establish relationships and estimate correlations among variables.
7.4 Building a New Venture Revenue Forecast:
An Illustration
Consider a start-up medical device venture called NewCo. We have done the
background research described earlier. Based on our research, we have generated the assumptions shown in Figure 7.1. We first use these assumptions to
forecast NewCo’s expected revenue and then will introduce uncertainty into
the forecast. In Chapter 8, we will build an integrated financial model based
Revenue Forecasting 257
Fi g u r e 7.1
NewCo revenue
assumptions
1.
Development will require six quarters, during which period no sales will be made.
2.
Initial quarterly sales of 300 units at a price of $200 beginning at the start of
Quarter 7.
3.
From Quarter 7, unit sales will grow 25% per quarter for three years (through
Quarter 19) and then remain constant.
4.
The sales price will increase each quarter at the inflation rate.
5.
Inflation at 4% per year (modeled as 1.0% per quarter).
on this revenue forecast but extended to include a complete set of forecasted
financial statements.
Because NewCo is a new venture, we decided to use a forecasting interval
of one quarter. Based on a (hypothetical) study of similar ventures, we assume
that the first six quarters are expected to be required for product development
and testing. The venture will initiate sales at the start of Quarter 7. Based on the
study and the characteristics of the market for NewCo’s product, if development
happens when anticipated, we expect initial quarterly sales of 300 units with a
selling price of $200 per unit (total revenue in Quarter 7 of $60,000). Following initiation of sales in Quarter 7, we expect unit volume to grow at 25% per
quarter for three years (through Quarter 19). After Quarter 19, unit sales will
remain constant.
In addition to growth in volume, revenue will also increase with price inflation. We estimate that inflationary price increases will average 1.0% per quarter.
Note that in this example, by using unit volume (real growth) and inflationadjusted selling prices, we are forecasting in nominal terms. In Chapter 8, we
will add expense and other assumptions, some that are fixed in nominal terms
and others fixed in real terms. Thus, we cannot escape dealing with inflation
in some way.
Figure 7.2 shows the expected pattern of NewCo’s revenue for the 6-quarter
development stage, initiation of sales in Quarter 7, 3 years of rapid growth,
and 2 years of inflationary growth. The total forecast window is 26 quarters,
or 6.5 years.
As shown at the top of the figure, the revenue forecast in Quarter 7 is simply
the 300-unit quantity times the $200 price, or $60,000 in total revenue. In each
of the next 12 quarters, unit sales increases by 25% over the prior quarter and
price increases by 1.0% over the prior quarter. Total revenue in each quarter is
the product of price and quantity. The rapid-growth period ends in Quarter 19,
after which the level of unit sales is constant but price continues to increase at
0
Quarte r
Development Success
Sales (units)
Selling Price/unit
Revenue
Unit Growth per Quarter
Inflation per Quarter
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
$0
$0
$0
$0
$0
$0
300
$200
$60,000
375
$202
$75,750
25%
1%
469
$204
$95,685
25%
1%
586
$206
$120,751
25%
1%
733
$208
$152,553
25%
1%
916
$210
$192,545
25%
1%
1145
$212
$243,088
25%
1%
1431
$214
$306,845
25%
1%
1789
$217
$387,446
25%
1%
2236
$219
$489,096
25%
1%
2795
$221
$617,484
25%
1%
3494
$223
$779,629
25%
1%
4368
$225
$984,394
25%
1%
4368
$228
$994,238
0%
1%
4368
$230
$1,004,181
0%
1%
4368
$232
$1,014,222
0%
1%
4368
$235
$1,024,365
0%
1%
4368
$237
$1,034,608
0%
1%
4368
$239
$1,044,954
0%
1%
4368
$242
$1,055,404
0%
1%
NewCo revenue forecast
$1,200,000
Expected Revenue for the Quarter
$1,000,000
$800,000
$600,000
$400,000
$200,000
$0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Quarter (0 = quarter of launch)
Fi g u r e 7. 2
NewCo revenue forecast
The chart shows the revenue trajectory for NewCo using the “base case” assumptions in Figure 7.1. Quarter 0 is the date of initial investment; sales begin in Quarter 7 after development is completed. The rapid-growth period for sales runs for three years (through Quarter 18). After that, through Quarter 26, unit sales growth is zero and the subsequent increases in revenue are due to the
impact of inflation on selling price.
Revenue Forecasting 259
the inflation rate. The bottom of the figure is a plot of quarterly revenue over
all 26 quarters.
The expected rapid-growth rate of unit sales means NewCo must be prepared to supply about 4,400 units per quarter by Quarter 19. To achieve this,
the venture must quickly develop the capacity to manufacture, distribute, and
support this level of sales.
7.5 Introducing Uncertainty to the Forecast:
Continuing the Illustration
The forecast of expected sales is only the beginning of the exercise. The forecast in Figure 7.2 is just one possible outcome from a distribution that includes
phenomenal success, complete failure, and many possibilities in between. At
the outset, it is unclear whether NewCo’s development efforts will be successful, or if they are, when success will be achieved. It is also not clear how successful the product will be, how rapidly demand will grow, or how quickly
the growth rate will slow to a steady state. The financing needs of the venture
can be sensitive to outcomes that are different from expected. Factors such as
development timing, rate of sales growth, and duration of the rapid-growth
period are difficult to predict with confidence but are likely to be among the
most important determinants of financing need and value. They depend on
unknowable factors such as competition, customer reaction, and future macroeconomic conditions.
Understanding and planning for the range of possible outcomes is essential
to good decision making about the venture. Some outcomes that will require
more financing than in the expected scenario are still worth pursuing, whereas
others are not. Effective financial planning requires awareness of the margins
of performance where it would make sense to continue and those where it would
make sense to abandon or modify the strategy.
How can we incorporate uncertainty into the forecast and use the uncertainty
results to assess financing needs and strategic choices? In this section, we examine ways of incorporating uncertainty into a revenue forecast. Subsequently,
we extend the uncertainty forecast to the full financial model of the venture.
Sensitivity Analysis
In sensitivity analysis, we vary key assumptions across a range of values. Impacts of uncertainty are examined by altering one variable at a time or a few
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at the same time. The NewCo revenue model, though simple, raises a number of issues. The key assumptions include (1) initial unit sales, (2) initial selling price, (3) length of the development phase, (4) length of the rapid-growth
phase, (5) rate of unit sales growth during rapid-growth, (6) rate of unit sales
growth at maturity, and (7) rate of price inflation.
To determine reasonable ranges to use in sensitivity analysis, we can use
historical data, publicly available forecasts, data from comparable firms, and/
or informed judgment. For example, inflation estimates are available from numerous sources and often include not only averages but also medians and the
range of forecasts.5 Suppose, based on such data, it appears that the inflation
rate over the venture’s forecast period could range from 0.5% to 2.0% per quarter around our base estimate of 1.0%. The following table shows the impact of
varying the rate of inflation on Quarter 26 revenue, the average revenue per
quarter for Quarters 7 through 26, and the cumulative revenue for the entire
26-quarter forecast period.
Inflation scenario
Low
Expected
High
Annual inflation
Quarterly inflation
Revenue Quarter 26
Average revenue
per quarter
Cumulative
revenue
2.0%
4.0%
8.0%
0.5%
1.0%
2.0%
$960,435
$1,055,404
$1,272,670
$546,784
$583,862
$665,970
$10,935,688
$11,677,240
$13,319,394
The table holds growth in unit sales and all other assumptions constant at
expected levels. By the end of the forecast period, even these modest changes in
the inflation rate can result in Quarter 26 revenue that is about 9% lower or 21%
higher than expected revenue. Such differences can be important if, for example,
the venture is financed with debt at a fixed nominal interest rate.
For growth in unit sales, another key variable, we might analyze adoption
rates for similar products or study the sales histories of comparable firms. Sensitivity analysis can be used to assess the impact of a more modest but realistic
growth rate during the rapid-growth phase, say 10% per quarter, or a more
successful scenario, such as 40% growth per quarter. The following table shows
the impact of varying the growth rate of quarterly unit sales while holding the
other variables constant at their expected levels.
Growth
scenario
Low
Expected
High
Unit growth
(quarterly)
10%
25%
40%
Quarter 26 units
941
4,368
16,999
Average units per
quarter
697
2,567
8,887
Cumulative units Cumulative revenue
13,937
51,213
137,742
$3,126,823
$11,677,240
$40,851,396
Revenue Forecasting 261
Assuming that the selected ranges for inflation and unit sales both reasonably reflect the true uncertainty, revenue is much more sensitive to variation in
unit sales growth than to variations in the inflation rate. Moreover, variations
associated with inflation are important only to the extent that product price
increases are different from inflation rates. In contrast, variations in revenue
due to unit sales growth are real. If the growth rate is too slow, the venture
can fail to achieve a level of sales that is sufficient for viability. Even if it does
eventually begin to generate positive free cash flow, if growth is slow then the
venture may need more financing to cover its operations until that point is
reached. Unit sales growth is also important because it has implications for
infrastructure investment.
In the preceding table, we assume that the variations in unit sales growth
rates are symmetrical around the expected growth rate (25% per quarter ± 15%).
However, the resulting differences in unit sales per quarter are not symmetrical
around the expected level. Under the low-growth assumption, ultimate unit sales
is 78% below the expected number, whereas under the high-growth assumption,
ultimate unit sales is 289% above the expected number. Given the asymmetry,
even if the venture would not be profitable at the expected growth rate, the
upside might be sufficiently attractive that it would make sense to pursue the
opportunity. Many VC investments are like this—the expected outcome is not
sufficient to justify continuing the venture, but some possible outcomes are so
attractive that accepting the high probability of failure is warranted.
Finally, we can allow both inflation and unit sales growth to vary at the same
time and observe the impact on a particular variable, such as cumulative revenue. In the following table, differences in each column are due to the inflation
rate assumption. Those in each row are due to the unit growth rate assumption.
Cumulative revenue over forecast period
Unit sales growth rate
10%
Inflation rate
25%
40%
0.5%
$2,951,662
$10,935,688
$38,107,969
1.0%
$3,126,823
$11,677,240
$40,851,396
2.0%
$3,512,841
$13,319,394
$46,942,340
Sensitivity analysis is simple to implement and can provide valuable information, but it has some shortcomings. First, there is little guidance as to what
constitutes an appropriate range for any given variable. Second, it is difficult
to test sensitivity to two or more variables at once. Third, even in this simple
example, we have not considered the effects of changing the development period,
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Chapter Seven
the rapid-growth period, or growth during the harvest period. Finally, variables
are often correlated, and sensitivity analysis does not readily accommodate the
correlations.
Scenario Analysis
Scenario analysis is one way to address the limitations of sensitivity analysis.
The analyst develops scenarios that incorporate reasonable values for the important variables and recognize the relationships that may exist among them.
For NewCo, consider the following two scenarios:
• Scenario 1. Product development proceeds more quickly than expected.
The venture’s sales start at 300 units in Quarter 5 rather than Quarter
7. The new product does very well in the market and NewCo is able to
patent important aspects of the technology. This keeps competitors at
bay and enables NewCo to increase the initial selling price to $220. Unit
sales grow at 35% each quarter for two years and then 30% quarterly for
one year. For the balance of the forecast period, quarterly unit sales are
assumed to be constant so that revenue grows at the 1.0% per quarter inflation rate.
• Scenario 2. Product development hits numerous roadblocks and a competitor beats NewCo to the market. When NewCo finally begins to sell
(in Quarter 9), the market only supports a $180 price. Unit sales start at
300 per quarter and grow at 15% each quarter for six quarters and then
10% for one year before falling to zero. Expected inflation is 1.0% per
quarter.
The following table shows revenue and unit sales numbers for both scenarios:
Revenue
Scenario 1
Scenario 2
Quarter 26
Quarterly average
$2,563,227
$217,438
$1,565,980
$139,769
Unit sales
Cumulative
$31,319,590
$2,795,373
Quarter 26
9,454
1,020
Quarterly average
6,146
779
Cumulative
122,913
14,022
These scenarios yield widely disparate forecasts, which is not surprising given
their very different assumptions. They also provide a rough picture of the uncertainty about NewCo’s future. Although scenario analysis overcomes some
of the limitations of sensitivity analysis, it has its own shortcomings. Practical considerations limit the number of scenarios we can develop and ana-
Revenue Forecasting 263
lyze. Moreover, for scenario analysis to be valuable, we need accompanying
assumptions about the probability of each scenario. Moreover, no matter how
carefully we construct each scenario, the subsequent reality will inevitably
differ considerably.
Simulation
Simulation overcomes some of the difficulties of sensitivity and scenario
analysis. As discussed in the previous chapter, simulation requires that uncertainty be described in terms of probabilities or statistical distributions.
When the simulation model is run, a random draw is made from each distribution and the results are used to construct a “trial.” Simulation can be used to
quickly generate thousands of trials based on the underlying uncertainty assumptions. In essence, each trial is a scenario, and because they are generated
from statistical distributions, each trial is equally likely to occur. By analyzing
the trials data, we can make inferences about the probabilities of good or bad
outcomes and can evaluate choices related to the exercise of real and financial
options.
In the NewCo example, the most important risk factors are development
timing, the length of the rapid-growth period, and the rate of growth during the
Fi g u r e 7. 3
NewCo revenue
simulation
assumptions
1.
The earliest that successful development can occur is Quarter 3. Starting in
Quarter 4, the probability of development success is exponentially distributed with
a mean of 6 quarters. However, if development is not completed within 15
quarters, then it is clear that successful development of a valuable product is no
longer feasible.
2.
If development is successful, the rapid-growth stage is expected to end around
Quarter 20, after which it is expected that unit sales growth will fall to zero. The
uncertainty about when the rapid-growth stage will end is normally distributed
with a mean of 20 and a standard deviation of 1.5 quarters.
3.
Sales begin the quarter when development is successful. The initial sales level is
expected to be 300 units if development occurs by Quarter 6, and to decline by
10 units for each quarter that development is delayed after Quarter 6.
4.
The initial selling price is subject to uncertainty depending on the quality of the
development result and competitive factors. This uncertainty is normally
distributed with a mean of $200 and a standard deviation of $20. After the first
quarter of sales, the selling price increases at the rate of inflation each quarter.
5.
During the rapid-growth period, quarterly unit sales growth is normally distributed
with a mean of 25% and a standard deviation of 6%.
6.
Inflation is forecast to be 1.0% per quarter.
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Chapter Seven
Fi g u r e 7. 4
Discrete probability approach to simulating development timing
The figure shows the simulation structure and a randomly generated outcome.
rapid-growth period. Figure 7.3 shows how we have modified the static NewCo
assumptions to reflect uncertainty and accommodate simulation.
The first assumption in the figure captures uncertainty related to development
timing and allows for the possibility that NewCo never succeeds in creating a
valuable product. The second assumption captures uncertainty related to the
length of the rapid-growth period. Assumptions 3 and 4 describe the starting
unit sales and selling price in the quarter after development is successful. Assumption 5 describes the uncertainty of demand growth.
There are many ways to build timing uncertainty into a simulation model.
We could assign a specific probability to each quarter, so that the probabilities
(including the probability of failure) sum to 100%; then, for each trial, we could
make one draw from the distribution to determine whether and in which quarter
development is successful. For example, suppose that the timing for an event
of interest (completion of a prototype, launch of a website, etc.) for a venture
could occur in any of three months (Month 1, 2, or 3) and that the respective
probabilities are 20%, 40%, and 30%. Because these sum to 90%, there is a 10%
chance of failure. Figure 7.4 is a screen shot that illustrates use of a discrete
probability distribution in @RISK to simulate a success month.
Revenue Forecasting 265
In the NewCo example, we model a more complex timing structure. Because we expect that development will take at least three quarters and that the
probability of success will be highest in Quarter 4 and taper off thereafter, we
use the exponential distribution to model development success. To reflect the
potential for increasing intensity of competition if development is delayed, we
reduce the starting unit sales volume by 10 per quarter for each quarter after
Quarter 6. We also assume that if success is not achieved by Quarter 15, then
it is not worth continuing to pursue the venture. Failure may occur because a
competitor beats us to the market or because of failure of our R&D efforts to
achieve a breakthrough.
Based on Assumptions 1 and 2 in Figure 7.3, the cumulative probability of
successful development is about 50% by Quarter 7 (start-up in Quarter 8) and
about 86.5% by Quarter 15 (the last quarter when development would be successful). This leaves a residual probability of failure of about 13.5%. The @RISK
cell formula to generate the development quarter outcome for each trial is as
follows:
=INT(4+RiskExpon(6))
where 4 is the first quarter when sales could begin and 6 is the mean of the exponential distribution after that point. As discussed in Chapter 6, the function
RiskExpon(μ) is the @RISK macro for drawing from an exponential distribution with a mean of μ, and INT is an Excel function that truncates the simulation result to an integer. For example, trial outcomes of 10.1 and 10.9 both
return a value of Quarter 10. In a separate step, we test whether the result is
greater than 15 and if it is, we assume that development fails.
Figure 7.5 shows the @RISK histogram from a simulation of 100,000 trials of
the first quarter of revenue generation (the quarter after development success).
If revenue generation starts after Quarter 15 in the simulation, the effort is classified as a failure, and for convenience in the modeling, we reassign Quarter 27
as the start of revenue generation. Quarter 27 is beyond the time range of the
simulation model so that effectively the 13.5% of observations that are shown
in Quarter 27 represent unsuccessful development efforts. Keep in mind that
successful development is only the first stage along the way to commercialization. Some of the success trials could still prove to be unprofitable.
At this point, we assume that the venture has enough cash to fund the development effort for as long as needed. Later, we will change that assumption to
allow for the possibility that the entrepreneur would need to raise additional
capital to continue the venture.
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Chapter Seven
86.5%
16%
13.5%
14%
Percentage of trials
12%
10%
8%
6%
4%
2%
0%
0
5
10
15
20
25
30
First quarter of revenue generation
Fi g u r e 7. 5
NewCo simulated distribution of the start of revenue generation
The figure shows the simulated distribution of development timing based on 100,000 trials of the revenue model. The simulation is based on
an exponential distribution with a mean of six quarters after the initial three (during which development success is assumed not to be
feasible). The model also reflects the assumption that revenue generation must begin within 15 quarters or the venture fails. Failure
percentage is shown in Quarter 27.
Figure 7.6 incorporates the other revenue projection assumptions from Figure 7.3 and shows plots of randomly generated revenue forecasts from five simulated trials. The figure illustrates a wide range of possible paths for NewCo
revenue. Trial 1, a moderately successful outcome, has development completion
in Quarter 7, with revenue generation beginning in Quarter 8, but a relatively
low rate of growth through Quarter 18. Trial 2, the most successful result in the
figure, has development success at the same time but a higher growth rate, and a
longer time before growth ends. Trial 3 has the earliest development success and
a high rate of growth, but the rapid growth period ends early. Trial 4 has very
late development success, low initial revenue, and a low rate of revenue growth.
Even though the rapid growth period ends the latest of the trials shown, ultimate
revenue is quite low. Trial 5 is a failure scenario where development never occurs.
The results shown in Figure 7.6 represent only five of an infinite number of
paths NewCo could take based on our starting variables and assumptions about
probabilities and statistical distributions. The simulation results incorporate
many more possibilities than either sensitivity or scenario analysis and align
more closely with what a new venture’s path might look like. They also dramatically reinforce the reality that the future of any new venture is highly uncertain.
Revenue Forecasting 267
Fi g u r e 7. 6
$1,400,000
NewCo revenue
forecast: Sample trial
results
Revenue (Trial 2)
Revenue (Trial 3)
$1,000,000
Quarterly revenue
The figure shows five
iterations of the NewCo
revenue forecasting model.
The model incorporates
uncertainty with simulation, using the assumptions outlined in Figure
7.3. Each trial shows when/
if development is successful, how rapidly revenue
grows, and how long the
rapid-growth period lasts.
Quarter 0 is date of initial
investment.
Revenue (Trial 1)
$1,200,000
Revenue (Trial 4)
Revenue (Trial 5)
$800,000
$600,000
$400,000
$200,000
$0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Quarter
7.6 Calibrating the Development Timing Assumption:
An Example
When product development efforts are expensive and timing of development
success is uncertain, the financial needs of a venture depend critically on development timing. A venture that runs out of cash before reaching a significant
milestone in the development process (completion of a prototype, completion
of preliminary testing, etc.) is likely to face great difficulty lining up additional
financing. The difficulty arises because failure to achieve the milestone may
suggest to investors that the entrepreneur’s projections are overly optimistic in
other ways as well.
While there is no general rule as to how long development of a new product
takes, it is possible to estimate expected time and timing uncertainty. In some
cases, engineering studies or regulatory reports are helpful, as are experiences
of comparable ventures. Innovation in new drugs is an example of products with
protracted, variable, and unpredictable development horizons, due, in part, to
technological and regulatory hurdles.
New drugs make a good example because the FDA keeps records of all new
drug applications. Since passage of the 1992 Prescription Drug User Fee Act,
development times for new drugs (the combined time for clinical trials and
regulatory approval) have fallen significantly. This trend is valuable information for anyone who is attempting to forecast financial information for a new
268
Chapter Seven
pharmaceutical venture. However, the general trend reveals nothing about the
uncertainty of the development process, which is critical for forecasting cash
flow and assessing cash needs. The FDA, however, does report, on a case-by-case
basis, information regarding the times required for completion of each stage
of testing prior to approval. The breakdown of development timing sometimes
includes long response times from the drug developer. The detailed data allow us
to better understand the approval process and can help us estimate the expected
development time and the variation, or standard deviation, of that estimate.6
7.7 Summary
Selling a product or service is at the core of any company’s business model.
Although estimating sales is especially challenging for a new venture, financial forecasting disciplines the way an entrepreneur thinks about the venture.
Not only can forecasting be used to develop and test hypotheses about the
opportunity and help determine cash needs and the timing of those needs,
it can also affect the value of the venture. A forecast can be an important
­fund-raising tool if it convinces prospective investors of the merits of the project and provides some specific performance benchmarks.
The starting point for any financial forecast is an estimate of revenue. The
venture’s strategy for getting customers to purchase its goods or services is of
critical importance for any business plan. If the forecast is for an existing business, we can use historical data as the basis for projections. Numerous statistical
techniques are available to improve forecast accuracy using historical data.
Trade­offs of selection criteria include data availability, model complexity, and
uncertainty.
For a new venture, yardsticks and fundamental analysis can help overcome
the lack of an historical track record. Yardsticks are comparable firms—either
public or private—that have a product and strategy reasonably close to that of
the venture. Data on yardsticks can come from IPO prospectuses, 10-K filings
and annual reports, and other public sources.
Even if good yardsticks are available, it is useful to develop a sales forecast
based on fundamental analysis. With fundamental analysis, the entrepreneur
collects data on customer demographics, macroeconomic factors, substitute products, and so on and then uses the data to develop a set of assumptions for forecasting revenue. The forecast model is most useful if it reflects a solid understanding
of the product and industry and if the forecast links expectations for performance
in the product market to implications for financial performance. The assumptions
are most useful if they are defensible and backed by research and objective data.
Revenue Forecasting 269
Any financial forecast must reflect the uncertainty implicit in the key assumptions. Uncertainty can be analyzed using sensitivity analysis, scenarios,
and simulation. With sensitivity analysis, we vary key assumptions over some
reasonable range to see how revenue or some other financial metric is impacted.
Sensitivity analysis is limited, as we can vary only one or two variables at a time
and correlations between variables are not recognized. Scenario analysis involves developing several stories about how the venture might progress and then
matching assumptions to each story. Using scenarios overcomes the limitations
of sensitivity analysis, but we are still limited by the number of scenarios that can
reasonably be evaluated and the need to assign probabilities to each outcome.
With simulation, an infinite number of possible paths can be generated and
analyzed. This method allows for the most realistic assessment of the venture’s
future, but reliable results are dependent on the validity of the assumptions.
The new venture revenue forecast forms the basis for forecasting the rest of
the financial statements, with an ultimate goal of estimating the venture’s cash
flows and valuing the opportunity.
Review Questions
1. In what ways can forecasting help entrepreneurs?
2. Why is forecasting revenue the logical starting point for preparing a new
venture’s financial statements?
3. What factors are important in establishing the appropriate forecast period and forecasting interval?
4. How can naïve forecasting methods be used to develop a revenue forecast for an existing business where management believes that more recent historical years are more predictive of future performance?
5. What are some of the considerations when deciding whether to forecast
in real or nominal terms?
6. What are the strengths and shortcomings of sensitivity analysis and of
scenario analysis?
7. Why is simulation particularly suited to forecasting revenue of new
ventures?
8. What techniques are available to introduce uncertainty into a revenue
forecast? Describe the pros and cons of each approach.
9. How might you use publicly available information to calibrate an assumption about the development time needed to build a drone delivery
service?
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Chapter Seven
10. What can you learn from a prospectus? Are the data in an IPO prospectus relevant to a new venture just getting off the ground?
Notes
1. The Federal Reserve Economic Database (FRED) is a freely available
source of U.S. and international macroeconomic data that can be useful for
developing new venture projections. Time series data on macroeconomic variables are available at https://​fred​.stlouisfed​.org/.
2. The EDGAR website can be accessed at http://​sec​.gov. Prospectuses of
new issues normally are posted to the site within a few days of filing.
3. Company filings are available from the SEC’s Edgar database at
https://​w ww​.sec​.gov/​edgar​.shtml.
4. Paraphrased from Silver (1994), p. 20.
5. The Philadelphia Federal Reserve Bank conducts a quarterly survey of professional forecasters and provides mean and median economic estimates for key macroeconomic indicators but no detail on individual forecasts, at http://​w ww​.phil​.frb​.org/​research​-and​- data/​real​-time​- center/​survey​-of​
-professional​-forecasters.
6. See https://​www​.fda​.gov/​downloads/​AboutFDA/​Reports​Manuals​Forms/​
Reports/​UserFeeReports/​PerformanceReports/​UCM548128​.pdf.
References and Additional Reading
Copeland, T., T. Koller, and J. Murrin. 2015. Valuation: Measuring and Managing the Value of Companies, 6th ed. New York: Wiley.
Doll, D., and M. Heesen. 2009. “NVCA 4-Pillar Plan to Restore Liquidity in
the U.S. Venture Capital Industry.” Presentation at the annual meeting
of the National Venture Capital Association. http://​w ww​.slideshare​.net/​
NVCA/​nvca​- 4pillar​-plan​-to ​-restore ​-liquidity​-in​-the ​-us ​-venture ​- capital​
-industry​-1360905.
Drucker, P. F. 1985. Innovation and Entrepreneurship. New York:
HarperCollins.
Hitchner, James R. 2017. Financial Valuation: Applications and Models, 4th ed.
New York: Wiley.
Shane, S. A. 2008. The Illusions of Entrepreneurship. New Haven, CT: Yale
University Press.
Silver, D. A. 1994. The Venture Capital Sourcebook. Chicago: Probus.
C h a p t e r Ei g ht
Fi nan cial M o d e li n g
O n c e a r e v e n u e forecast is completed, it can be used as a baseline for
developing pro forma financial statements. Ultimately, we are concerned with
forecasting cash flows from operations. Operating cash flow is a key factor in
determining a venture’s financing needs. Cash flow available to investors is central to valuation. To project cash flows, we first forecast the income statement
and balance sheet and then use those statements (and period-to-period changes
in the statements) to develop the pro forma statement of cash flows.
For a new venture, we confront the same issue here as we did in forecasting
revenue: that of estimating income statement and balance sheet relationships
for a company that has no operating history. Here again, it is useful to rely on
data from comparable public and/or private companies. However, the yardsticks
and dimensions of comparability that are most useful for projecting income
statement and balance sheet relationships are not necessarily those that are
most useful for projecting revenue.
Our main objective in this chapter is to illustrate how to build and integrate
financial statements into a model that can be used in cash needs assessment and
valuation. There are two aspects to this; the first is developing the assumptions
that drive the financial model, and the second is the process of building and
integrating the financial statements, including the interactions among them.
Mastering the chapter should enable you to build your own integrated model.
If the financial statements in a model are properly integrated, the full impact
of changing any assumption will be reflected automatically and consistently
in all of the statements and all of the forecast periods. This makes it easier to
perform sensitivity analysis and is necessary for incorporating uncertainty with
simulation.
271
272
Chapter Eight
We begin with an overview of the three main financial statements and how
they are linked. With this background, we develop the notion of statement
integration, beginning with an overview of the cash conversion process. Next,
we provide an example of how working capital policy choices act in an integrated fashion to affect cash needs. We then describe several standard sources
of information and demonstrate their use for developing forecast assumptions.
Finally, we work through the construction of two integrated financial models,
the first for Morebucks and the second for NewCo.
8.1 An Overview of Financial Statements
Pro forma financial statements used for business forecasting of a new venture
can be simple, but they require enough detail to capture the important activities of the enterprise. They are focused on the assumptions and financial
statement items most important to the venture. The model for a new gaming
software venture might incorporate development time and cost of bringing a
viable product to market. For a coffee retailer, cost of goods sold and inventory may be the most important factors to model carefully.
The first step in building an integrated model is to identify the critical assumptions. This list determines the accounts and line items that need to be
incorporated into the model. In an integrated model, changes to any assumption
will immediately update all three financial statements—the income statement,
balance sheet, and cash flow statement—through the formulas and links in the
spreadsheet.
The income statement (also called a profit and loss [P&L] statement or statement of operations) describes revenues and expenses over a period of time, such
as a quarter or a fiscal year. It answers the question of whether the business is
profitable. The balance sheet (also called the statement of financial position)
presents a picture at a point in time of what the firm owns (its assets), how much
it owes (its liabilities), and what is left for shareholders (equity, owner’s equity, or
net worth). Once we have created the pro forma income statement and balance
sheet, the cash flow statement is a simple derivation.
The Income Statement
For the income statement, a general form sufficient for many purposes is
shown in Table 8.1. The line items in the pro forma income statement depend
on the type of business. In this simplified statement, cost of goods sold (COGS)
includes the costs of raw materials and expenses associated with manufactur-
Financial Modeling 273
Tab le 8 .1 Income statement (year ended 12/31/2018)
Revenue
− Cost of goods sold (COGS)
Gross profit
− Operating expenses (cash)
Earnings before interest, taxes, and noncash expenses (EBITDA)
− Operating expenses (noncash)
Operating profit (EBIT)
− Interest expense (net of any interest income)
Earnings before tax (EBT)
− Income tax
Net income (NI)
note: Operating expenses includes both cash and noncash expenses like depreciation
and amortization. Normally, these are not reported separately. They are separated here
to show the distinction between EBIT and EBITDA.
ing the product. A retailer or wholesaler would show COGS as the acquisition
cost of the products they resell. For a service company, COGS is the labor and
machinery cost associated with providing the service.
Operating expenses are other expenses related to the productive activities
of the venture. These can include selling, general, and administrative (SG&A)
expenses, R&D, overhead expenses, lease expenses, depreciation expenses, and
similar items. Sometimes it is useful to include more detail on specific operating expenses that are central to success, especially if their inclusion makes
forecasting easier, more reliable, and more convincing. In particular, noncash
expenses, such as depreciation, are useful to break out because explicit listing
can facilitate construction of the cash flow statement. Earnings before interest,
taxes, and noncash expenses like depreciation and amortization (EBITDA) is
not generally separately reported in the income statement. EBITDA is a measure of the short-run cash flow that would be available for such things as debt
service, taxes, and new investment. Depreciation and amortization expenses
depend on asset investment decisions that were made in earlier periods and are
reflected in the balance sheet.
Operating profit (EBIT) is a core measure of performance, as it is calculated
before interest and tax expenses, two items that are usually outside the control
of operating managers. For a business that is in steady state, it is also a measure
of cash flow available to all investors and for taxes.
The next deduction, interest expense, is a result of the financing decision
that is reflected in the balance sheet. Sometimes, in developing the pro forma
forecast, it is convenient to build the model so that surplus cash is assumed to
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Chapter Eight
be retained in the venture. If so, it is appropriate to assume that the surplus
cash is invested to earn a risk-free rate of interest and is reported as interest
income. Income tax expense is determined by the profitability of the venture
and prevailing tax rules.
When developing the pro forma income statement, it is important to consider
the impact of fixed and variable costs. Fixed expenses will not change with increases or decreases in revenue (over some range), which introduces the concept
of operating leverage. Operating leverage describes the relationship between
percentage revenue growth and percentage profitability growth. Firms with a
larger proportion of fixed expenses have higher operating leverage and will see
profits increase (decrease) faster as revenue goes up (down).
However, overly simplistic classification of expenses as either fixed or variable
can yield incorrect results. It is a good idea to study actual expense levels of businesses of different sizes. You may find that your intuition about fixed and variable
expenses is not supported by the evidence. Few expenses are truly fixed, and others
may vary more than proportionately with revenue. Consequently, assuming that
variable expenses will change in proportion to revenue and that fixed expenses will
not change at all can overstate the profit potential associated with revenue growth.
The Balance Sheet
The balance sheet depicts the venture’s financial position at a point in time,
such as at the end of a fiscal year. The left side reports what the company
owns—its assets. The right side reflects how the ownership is financed—­
liabilities and equity. In the modeling, it will be more convenient for us to
stack the balance sheet information vertically—assets first, then liabilities,
then equity. This will enable us to keep all of the accounting numbers for a
given period in a single spreadsheet column.
A general form of a balance sheet sufficient for many purposes is shown in
Table 8.2. Asset accounts are presented in order of declining liquidity, starting
with cash. Accounts receivable (AR) and inventory are both current assets and
components of working capital. The fixed asset account represents the property,
plant, and equipment (PP&E) of the venture and is the basis for computing depreciation expense on the income statement. For a capital-intensive manufacturing
business, PP&E might be the largest balance sheet line item.
Liabilities, or what the firm owes, are ordered by the closest date of maturity.
Current liabilities, such as accounts payable (AP) and wages payable, are considered working capital items and are tied to the venture’s day-to-day operations.
Other liabilities are incurred by raising debt capital (notes payable, long-term
debt). Many technology ventures receive annual payments for services they are
Financial Modeling 275
Tab le 8 . 2 Balance sheet (year ended 12/31/2018)
Assets
Liabilities and Equity
Current assets
Current liabilities
Cash and cash equivalents
Accounts payable (AP)
Accounts receivable (AR)
Wages payable
Inventory
Deferred revenue
Other current assets
Notes payable
Total current assets
Other current liabilities
Total current liabilities
Fixed assets (PP&E)
Equity
Gross fixed assets
Preferred stock
Less: accumulated depreciation
Common stock
Net fixed assets
Retained earnings
Intangible assets
Total equity
Total assets
Total liabilities and equity
note: Other current liabilities include current portion of long-term debt and capital leases.
Long-term debt includes debt convertible to equity.
expected to deliver in the future over time, giving rise to a deferred revenue account, which is another current liability. Explicit borrowing or leasing can also
create a current liability such as a note payable or the current portion of longterm debt. An important distinction is that liabilities that arise from financing
activities usually carry explicit interest payments while other working capital
liabilities usually do not. Long-term debt and other funded debt such as notes
payable drive the interest expense calculation in the income statement. For many
entrepreneurial firms, long-term debt may consist of debt that is convertible to
equity, as discussed in Chapter 4.
Equity represents the shareholders’ position and is made up of two or three
main components. As discussed in Chapter 4, entrepreneurial ventures often
issue preferred stock to early investors, which stock is convertible to common
under certain conditions. Preferred stock plus common stock is the cumulative
amount investors paid for the venture’s stock when it was sold by the company.
The other component of equity is retained earnings; the retained earnings balance is the accumulated profit (or loss) of the venture from its inception to the
date of the balance sheet, less any dividends the firm has paid. The retained
earnings account does not indicate money available to shareholders. Over any
given period, retained earnings will increase by the net income for the period
and be reduced by dividends paid to shareholders. The result of the income
statement is net income, or the “bottom line.”
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Chapter Eight
The Cash Flow Statement
Net income is not cash flow. To determine financing needs and as a basis for
valuation, we need to calculate cash flow. The cash flow statement connects
the income statement and balance sheet changes but also takes account of
noncash expenses, new investments, and new financing transactions. We use
the “indirect” method to prepare the pro forma cash flow statement. This approach is mechanically straightforward and requires only the income statement and the beginning and ending balance sheets for the period. A general
format for the cash flow statement is shown in Table 8.3.
The cash flow statement is organized into three categories: operating cash
flow, investing cash flow, and financing cash flow. The entry “(Increase) decrease”
denotes whether the change in the balance sheet account represents a negative or
positive cash flow. Increases in noncash assets like A/R and investments in fixed
assets are uses of cash (outflows), while decreases are sources of cash (inflows).
For liabilities and equity, increases are sources of cash, while decreases are uses.
For example, because inventory must be purchased, an increase in inventory is
Tab le 8 . 3 Cash flow statement (year ended 12/31/2018)
Net income
+
Depreciation and amortization
+
(Increase) decrease in AR
+
(Increase) decrease in inventory
+
Increase (decrease) in AP, wages payable, etc.
+
Increase (decrease) in deferred revenue
Operating cash flow
+
Proceeds from sale of fixed assets
–
Gross investments in fixed assets
Cash flow from operations and investing
+
Increase (decrease) in notes payable
+
Increase (decrease) in long-term debt
+
Increase (decrease) in common stock
–
Dividends paid
∙
∙
∙
Operating cash flow
Investing cash flow
Financing cash flow
Net cash flow
+
Beginning cash and cash equivalents
Ending cash and cash equivalents
note: Free cash flow (FCF) is a non-GAAP financial performance measure, calculated as
operating cash flow minus capital expenditures. FCF represents the cash that a company is able to
generate from operations after making investments required to maintain or expand its asset base.
If FCF is negative, the company must obtain additional financing to pursue desired investments. If
FCF is positive, funds can be used to redeem debt early or can be distributed to equity investors.
Financial Modeling 277
a use of cash. Analogously, when subscriptions are paid in advance, an increase
in deferred revenue is a source of cash.
The total of operating cash flow, investing cash flow, and financing cash flow
is net cash flow for the period. In forecasting, adding net cash flow to the beginning cash balance tells us how much cash (or cash and equivalents) the firm is
expected to have on hand at period end.
8.2 Working Capital, Growth, and Financial Needs
To introduce the concept of financial statement integration, we begin with how
the working capital policies of a growing venture affect its financial needs.
The most important components of working capital usually are inventory (raw
materials and finished goods), A/R, A/P, and cash. Normally, because of the
cash flow cycle, a relationship exists between sales and the levels of the various working capital accounts. A business that sells from inventory must have
enough on hand to fill orders as they arrive. Because timing of demand is not
perfectly predictable and there is often an inventory “pipeline,” the business
normally will carry enough inventory to supply expected demand for several
days, weeks, or even months.
Purchases of inventory, either finished goods for resale or raw materials, are
often made on credit in the form of A/P. Purchasing on credit reduces the need
for cash, since payment is deferred. Conversely, if the business sells on credit,
the balance of A/R will be driven by the terms it offers to credit customers,
the extent of credit sales, and how quickly receivables are collected. Offering
customers the option to delay payment requires additional cash; the business
is deferring collection of payment but has already incurred the cost of goods.
Offering credit also means facing the reality of customer defaults.
Some working capital transactions generate financing that is referred to as
“spontaneous” in the sense that it arises naturally through the normal business practices of the venture. Since its inception, Amazon​.com, an example we
have previously mentioned, has taken advantage of the unique working capital
characteristics inherent in its business model. At year end 2000, when Amazon
was still focused on book distribution, its A/R balance was equivalent to 3.9
days of sales and its inventory was equivalent to 30.3 days of cost of goods sold.
The company’s combined investment in those two accounts was $204.2 million,
which had to be financed in some way.
Amazon was also taking advantage of the long payment terms that normally are offered by book publishers. These terms were originally offered to
278
Chapter Eight
encourage brick-and-mortar book sellers to stock large and diverse inventories
for customers to peruse and were an industry convention long before Amazon​
.com existed. At the end of 2000, Amazon’s A/P balance stood at 84.1 days of
cost of sales, or $485.4 million. This was far more than was needed to finance
the A/R and inventory balances. The contribution of these accounts to net
working capital was negative $281.2 million. This negative contribution to net
working capital functions as spontaneous financing that Amazon could use to
fund its operations.
Spontaneous financing is financing that comes about through the ongoing
operations and growth of the firm and supplants the need for formally raising
cash flows through explicit financing activities. We can define the level of spontaneous financing as the excess of current nonfinancial liabilities over current
nonfinancial assets other than the level of cash required for operating the enterprise. Many highly successful ventures have, like Amazon, relied heavily on
spontaneous financing. For example, at the time of its IPO, salesforce​.com had
nonfinancial current liabilities totaling $73.3 million and noncash current assets
totaling $14.0 million, resulting in spontaneous financing of $59.4 million. The
main contributor to this total was $52.3 million in deferred revenue. Salesforce
is a customer relationship management (CRM) cloud-based platform that offers
subscription-based applications for sales, service, and marketing. These prepaid
subscriptions are a current liability—deferred revenue—and hence are a source
of funds similarly to A/P. Other examples of companies with important deferred
revenue include Zendesk, Dropbox, and Box.
The extent of spontaneous financing can be affected in deliberate ways by
changing working capital practices. A business can deliberately change its reliance on accounts and wages payable as financing sources by changing the rate
at which it pays for its inventory and wages. In a competitive industry, however,
normal practices are guided by competitive pressure, and it can be difficult to
gain a sustainable advantage by manipulating working capital practices.
Working Capital Financing
Net working capital is the difference between the sum of the current asset categories of working capital and the spontaneous current liabilities.1 If the balance is positive (current assets exceed current liabilities), the difference constitutes a net investment in working capital and the resulting cash deficit must
be financed in some way. If the balance is negative, as in the salesforce​.com
example, then not only are the firm’s operations self-financing but they also
generate cash that is available to finance other investments or cover other expenses. For most businesses, however, the net working capital balance is posi-
Financial Modeling 279
tive and additional financing is required. The larger and faster the firm grows,
the more financing it requires for net working capital.
Figure 8.1 is a portion of an integrated set of financial statements that is designed to illustrate how working capital policy choices contribute to the funding
required for net working capital. The policy choices are represented in the dark
shaded text boxes in columns 1 and 4 of the figure. The unshaded text boxes in
columns 2 and 3 are directly related to the income statement. The light shaded
text boxes represent the current asset (column 5) and current liability (column 6)
accounts from the balance sheet. Shaded numerical boxes are those that can be
changed to evaluate alternative working capital policy choices. Dollar amounts
shown in columns 5 and 6 and as Income Contribution are the financial results
of the policy choices. The amount of net working capital (for which financing
is required) is shown at the bottom right side of the figure.
The numbers included in the figure as an illustration are representative of a
manufacturing venture that carries significant inventories, offers trade credit,
purchases raw material inventory on terms, and pays employees one week in
Fi g u r e 8 .1
Working capital policy
template
This figure is a template
for assessing the impact of
working capital policies
on financial needs. It can
be used to examine how
working capital policies
interact to determine
the balances of current
asset and current liability
accounts. Net working
capital is the excess of
current assets over current
liabilities and represents
a funding need (or source)
for the venture.
Column:
1
2
3
4
5
Pricing Policy
Price/unit
$10.00
Quantity
per day
100
Revenue/
day
$1,000
Credit
Policy
Days 45
Accounts
receivable
$45,000
Inventory
Policy
Days 12
Materials
inventory
$4,800
Purchasing
Policy
Materials
cost/unit
$4.00
Quantity
per day
Materials
cost/day
100
$400
Payables
Policy
Days 10
Inventory
Policy
Wage Policy
Labor
cost/unit
$2.50
Days 5
Quantity
per day
Labor
cost/day
100
$250
Accounts
payable
$4,000
Finished
goods
inventory
$3,250
Payroll
Policy
Days 7
Wages
payable
$1,750
Current
assets
Income Contribution
$350
6
$53,050
Current
liabilities
$5,750
Net
working
capital
$47,300
280
Chapter Eight
arrears. The result of the specific assumptions is a positive financing need of
$47,300, or approximately 47 days of sales. For other types of businesses, the
shaded numerical fields in the template can be modified to incorporate such
things as deferred revenue.
Working Capital Policy
As Figure 8.1 shows, a company’s working capital position is the result of several policy choices. Some are easily recognizable as aspects of working capital
policy, whereas others are not. Furthermore, as we explain, the effects of individual policy choices are interdependent in ways that are not emphasized in
the figure.
Pricing policy. Pricing policy is a generic descriptor of decisions related to
product positioning, pricing, and marketing. Collectively, these decisions determine the expected sales volume. We emphasize pricing in the figure because the
effective price (along with quantity of sales) is influenced by credit policy. If, for
example, a venture sells on terms that include a discount for prompt payment,
then the price used in the calculation should be the average expected price net
of anticipated trade discounts and collection losses. With cash sales, the price is
not discounted and there is no risk that a buyer will default. On the other hand,
the quantity of sales is likely to be higher if the venture offers credit.
Credit policy. Given that credit policy influences the effective average price
and quantity of sales, how does a business establish an appropriate policy?
Because selling on credit gives rise to A/R, Figure 8.1 suggests that a policy
of selling only on cash terms would reduce the amount required to finance net
working capital. But since selling only for cash could result in lower sales, simply
minimizing the need for financing is usually not consistent with maximizing
value. It could make sense to evaluate the effect of some alternative credit policies on profitability and value: If you were to sell only for cash, what would be
the likely effect on sales? If you were to accept credit cards, how much would
unit sales be expected to increase and what would be the expected effect on
average net price? Credit card sales are quickly converted to cash, so the A/R
period would be very short. The credit provider also discounts the payments to
the vendor as a fee for providing credit.
As a practical matter, new and small businesses often have limited ability to
determine their own credit policies. Smaller and less well-established businesses
are more likely than others to offer terms that include delayed payment. Custom-
Financial Modeling 281
ers may demand opportunities to verify the quality of the seller’s product before
they pay. Also, although trade credit terms vary greatly across industries, they
tend to be similar within an industry. Thus, in an industry where offering credit
is common, a new or small business generally must offer terms consistent with
the general practice. In an industry where cash payment is common, a new or
small business still may find it important to offer credit as a means of initially
attracting customers and signaling product quality.2
Purchasing and inventory policies. Purchasing policy relates to choices
that affect cost of materials. Choices of materials and negotiations with suppliers affect the cost of materials per unit produced. Inventory policy relates to
the average number of days of inventory the business seeks to maintain in raw
materials and in finished goods. Inventory policy affects average cost of goods
indirectly, because maintaining a large average inventory may enable the business to take advantage of purchase quantity discounts. This benefit is offset by
the costs of holding inventory, including storage, spoilage, and obsolescence.
Purchasing and inventory policies interact with sales quantity to determine the
cost of goods and represent another link between the balance sheet and income
statement.
Payables policy. Payables policy concerns the ability to defer payment for
materials as a means of financing. Vendors generally require cash payment,
especially for a new business with no track record of paying suppliers, or offer
credit terms either with or without a discount for prompt payment. Normally,
a purchaser would choose to delay payment, provided that doing so did not
affect the effective price. The policy decision is whether to take advantage of
discounts for prompt payment. Doing so reduces the effective cost of materials,
as suggested by the feedback loop in Figure 8.1, but also reduces the A/P balance,
giving rise to a need for additional financing. Often, discounts for prompt payment are large enough that a business with access to other sources of financing
would routinely choose to pay in time to get the discount.3
Wage and payroll policies. Wage policy relates to decisions such as
whether to offer a higher wage rate to motivate employees and limit turnover
or pay a lower wage. Depending on the importance of experience on the job,
motivation, availability of new employees, and similar considerations, average
labor cost per unit of sales could be lower by either approach. The average labor
and materials costs per unit of finished goods make up the direct cost of a unit.
These costs interact with finished goods inventory policy to determine the value
of finished goods inventory, which appears on the balance sheet.
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Chapter Eight
Payroll policy relates to the frequency and timing of payroll, relative to the
timing of the actual work done by the employees. The balance of wages payable as a source of spontaneous financing is increased by paying in arrears and
paying less frequently (e.g., biweekly instead of weekly). Payroll policy affects
wages indirectly. Wage payments that are substantially in arrears may necessitate higher wage rates. Wage and payroll policies interact with sales quantity
to determine the balance of wages payable.
Evaluating Working Capital Policy Choices
For many ventures, the need for working capital places a significant demand
on cash. It is important to understand the working capital needs and to factor them into the pro forma financials and estimates of financial needs. As
shown in Figure 8.1, the elements of working capital policy are interrelated
and impact many aspects of venture operations, making even small changes
important.
Working capital policy choices can be evaluated in the same way as any other
investment or financing decision. A policy change that increases net working
capital requires additional investment. Assuming that the change is expected to
be permanent, Figure 8.1 can be used to determine the daily increase in profitability from the change. Annualizing the daily increase and dividing by the
increase in net working capital is effectively an internal rate of return that can
be compared to a reasonable estimate of cost of capital to assess the merits of
the possible policy change. For example, a change that increases profit per day
by $5 will increase it by $1,825 per year (in perpetuity or as long as the policy
remains in place). If the change would increase net working capital by $20,000,
the annual return on that additional investment would be 9.125%. The change
is profitable in an accounting sense, but the IRR is lower than many reasonable estimates of cost of capital, so the change might represent a negative NPV
investment in additional working capital.
Conversely, a reduction in net working capital is effectively a financing choice
since it reduces the necessary investment to support the level of working capital.
If the action would reduce accounting profitability per day, that reduction can
be annualized and compared to cost of capital to evaluate whether the change
would be beneficial based on economic profit. If the cost of the reduction is below
cost of capital, the change is effectively a low-cost financing action.
Working capital is only one example of why the integration of income statement, balance sheet, and cash flows is important. Other important interdependencies are related to fixed assets and depreciation and to debt financing and
interest and debt service.
Financial Modeling 283
8.3 Developing Assumptions for the Financial Model
A new venture financial model is only as good as its underlying assumptions.
Assumptions that are evidence based and well-reasoned are important for reliable forecasting and for convincing investors of the validity of the projections.
There are many good places to search for data useful for projecting revenue,
expenses, working capital, fixed asset intensity, and cash flow. In this section,
we introduce some of them and illustrate their use for making the assumptions
we will use for building the Morebucks financial model.
Information Sources
One valuable source of information about small, revenue-generating, nonpublic companies is the Risk Management Association (RMA) publication Annual Statement Studies. RMA compiles financial statistics from information
supplied by credit customers of banks.4 Reporting companies are classified by
North American Industry Classification System (NAICS) code so that, for a
given venture, it may be possible to locate one or more industry groupings
as benchmarks. An attractive aspect of the RMA data is that the sample includes many small, nonpublic companies, which data are otherwise difficult
to obtain. Although tax minimization strategies of closely held businesses can
distort some of the income statement relationships, much of the information
is quite useful.
Other sources with similar information include the Almanac of Business and
Industrial Financial Ratios (CCH, Inc.); Industry Norms and Key Business Ratios
(Dun & Bradstreet, Inc.); the annual corporate income and unincorporated
income publications of the Internal Revenue Service (available at http://​w ww​
.irs​.gov/​taxstats/); and the Quarterly Financial Report for Manufacturing, Mining, and Trade Corporations (U.S. Department of Commerce). These sources
contain a myriad of financial information summarized at the industry level
and sometimes broken out by firm size. PrivCo provides business and financial
data on major nonpublicly traded companies, family firms, and private equity.
Standard & Poor’s Compustat and Capital IQ are comprehensive databases of
financial and market information for public firms worldwide.
In addition, trade associations and investment services such as the Value
Line Investment Survey and the Standard & Poor’s Analysts’ Handbook publish
industry-level data. Moody’s and Standard & Poor’s report data for individual
public companies. As discussed in Chapter 7, financial reports of individual companies are available on the SEC’s EDGAR database and through online services
such as Yahoo! Finance, Hoover’s, and LexisNexis. Press releases, ­conference
284
Chapter Eight
call transcripts related to periodic reports of public companies, and other information is available on the Factiva database. Many industries have one or
more specialized publications that contain valuable information and may be
available online. To locate other sources, consult the Gale Cengage Learning
Encyclopedia of Business Information Sources.
SEC filings of comparable firms may include financial data from before the
firm went public. If comparable firms can be identified, their income statement
relationships can be used to calibrate the assumptions underlying a financial
model. If the income statement benchmark ratios (such as gross margin) are
consistent, even with only a few comparable firms, then we can be more confident
when using them in a forecasting model. If there is substantial variation across
the comparable firms, it is important to try to understand why the financial
ratios for apparently similar companies differ.
Using Industry Data and SEC Filings to
Develop Assumptions
How might we use industry data and information on comparable companies
to develop the assumptions for Morebucks, the coffee shop venture we introduced in Chapter 7? To illustrate, we use information compiled by Dun &
Bradstreet (D&B) and reported in its key business ratios statistics. Table 8.4
shows the ratios reported by D&B for eating and drinking establishments. The
last three columns are data for the subset of smaller eating establishments,
those with $500,000 to $1 million in total assets ($560,000 to $1.112 million in
2015 inflation-adjusted dollars). Consistent with what we would expect of a
coffee shop that sells for cash or by credit card, D&B reports A/R collection
periods of only a few days—the medians range from 1.8 to 4.8 days’ sales.5 The
financial ratio reported by D&B for measuring inventory is the ratio of sales
to inventory, also known as inventory turnover. Median values range from
60.1 to 87.4 times per year.6 These numbers imply that eating and drinking
establishments typically maintain inventories sufficient for only 4 to 6 days.
This is not surprising given that their inventories are highly perishable.
Table 8.4 also reports information on other ratios, including A/P to sales,
some measures of financial solvency, and some measures of profitability. The
A/P to sales percentage is in the 2.0–3.0% range, which implies an A/P balance
between 7 and 11 days. The short payables period suggests that the ventures are
probably paying cash for some supplies and getting short credit terms for others.
We do not use the profitability measures because the D&B data include private
companies, where the owner may be seeking to reduce taxes by doing such things
as paying high salaries to family members. We do not use the solvency measures
because we intend to decide separately on financing.
Financial Modeling 285
Tab le 8 . 4 Key business ratios for eating and drinking establishments
SIC Code
Line of Business
Asset Size
5813
5812
5812
Drinking Places
Eating Places
Eating Places
All Asset Ranges
All Asset Ranges
$500,000 to $1,000,000
Sample Size
Statement Sampling: 12
Statement Sampling: 202
Statement Sampling: 42
Solvency
Upper
Median
Lower
Upper
Median
Lower
Upper
Median
Lower
Quick Ratio (times)
Current Ratio (times)
Current Liabilities/Net Worth (%)
Current Liabilities/Inventory (%)
Total Liabilities/Net Worth (%)
Fixed Assets/Net Worth (%)
3.1
6.0
6.7
86.4
40.3
80.7
0.9
1.3
31.1
415.0
127.6
107.4
0.4
0.7
90.3
777.3
260.9
193.2
1.0
2.0
23.0
326.9
38.5
63.4
0.5
0.9
49.2
768.8
101.7
112.9
0.2
0.6
103.5
999.9
275.2
194.9
1.7
2.2
19.7
263.2
24.7
29.4
0.7
1.4
36.9
506.5
49.9
75.9
0.3
0.7
99.7
929.6
157.8
117.2
Efficiency
Collection Period (days)
Sales/Inventory (times)
Assets/Sales (%)
Sales/Net Working Capital (times)
Accounts Payable/Sales (%)
2.6
96.8
23.5
144.6
1.3
3.5
60.1
70.4
24.1
1.8
6.2
46.1
112.2
10.4
2.5
1.5
128.4
24.5
29.4
1.8
4.8
87.4
45.7
14.4
3.0
11.7
47.0
71.4
7.8
4.1
0.7
125.9
21.2
28.6
2.2
1.8
70.6
28.5
20.7
3.0
7.0
36.2
46.7
6.8
3.7
8.2
19.3
19.5
3.8
5.1
11.6
–3.0
–4.1
–35.9
5.6
13.0
28.3
2.1
4.9
12.0
–0.6
–1.0
–0.3
6.8
19.2
38.7
3.3
10.5
20.6
0.8
2.3
5.3
Profitability
Return on Sales (%)
Return on Assets (%)
Return on Net Worth (%)
http://​kbr​.dnb​.com/​K BR ​_ Main ​.asp
What can you learn from a prospectus or annual report? Prospectuses of
young companies often contain historical information on early-stage financing, revenue growth, development time, margins, and relationships with suppliers and customers. In Chapter 7, we collected data from comparable public
companies to use as yardsticks for estimating the revenue of Morebucks. Given
its size and orientation at the time of IPO, we consider Coffee People to be the
most useful yardstick. For confirmation, we also use Peet’s Coffee but do not
report the statistics. Consideration of multiple yardsticks can be more informative and reliable.
The prospectus of a yardstick company such as Coffee People contains both
quantitative and qualitative information that can be useful. Coffee People filed
its prospectus (Form SB-2/A) with the SEC in September 1996 and went public
shortly thereafter. It was acquired by Diedrich Coffee in 1999, which was itself
acquired by Green Mountain Coffee Roasters in 2009. The prospectus describes
Coffee People as a seller of coffee beverages and other food products through
company-owned retail stores. At the time of the IPO, the company sold through
17 company-owned stores, up from 7 stores 5 years earlier.
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Chapter Eight
The prospectus includes the income statement and balance sheet information
shown in Table 8.5, along with data on the number of stores. The statements for
the most recent two years before the offering are provided in more detail than
those for earlier years. For example, instead of a detailed breakout of current
asset and liability accounts, the early-year information shows only a total for
net working capital.
We use the financial data to compute the common size income statements
and balance sheets shown in Table 8.6. In a common size income statement,
each revenue or expense line item is stated as a percentage of revenue. In a common size balance sheet, each asset or liability account is stated as a percentage
of total assets. Common size analysis allows us to compare firms of different
size and also makes it easier to spot trends. For example, the gross margin of
Coffee People varies somewhat between 1991 and 1995, but with no discernible
trend, which could indicate that there are no important economies of scale in
purchasing or production as the company adds stores.
Over the same period, store operating expenses increase somewhat more
slowly than revenue, as do other expenses that reduce operating profit. Overall,
the operating profit margin does not vary systematically with firm size. This
suggests that profitability is not very sensitive to scale over the range of firm
sizes represented by Coffee People.
Table 8.7 presents some standard financial ratios calculated using the data
from Coffee People. In contrast to the common size statements, financial ratios
often combine information from the income statement and balance sheet. For
most measures, we have data for only the last two years before the IPO. The
decline in the asset turnover ratio in the table in later years is likely to reflect
the company’s expansion in the number of stores.
When using yardstick data, it is important to be cognizant of differences in
the business models. For example, Coffee People, which is most similar to Morebucks, has very high A/R turnover (less than one day of sales) and less than 20
days of sales in inventory. Peet’s, in contrast, is more vertically integrated and
has about four days of sales in receivables and around 100 days in inventory.
Peet’s is not simply a multistore coffee shop business; rather, its business model
evolved over a number of years. Peet’s started with stores that sold only roasted
coffee beans. Just before its IPO, only about half of its store revenue was from
beverages and pastries, about 20% was from online sales and through specialty
grocers, and the rest was sales of other items.
In addition to quantitative information, the Coffee People prospectus contains qualitative information that could be relevant to the Morebucks financial
model. For example, the risk factors described in the prospectus include such
things as risk associated with the company’s growth strategy, uncertainty about
Tab le 8 . 5 Coffee People, Inc. financials prior to IPO
Fiscal Year ($000)
1991
1992
1993
1994
1995
Income Statement
Total revenue
3,512
4,498
5,466
7,708
11,257
Cost of sales
1,797
2,154
2,499
3,788
5,388
Gross profit
1,715
2,344
2,967
3,920
5,869
Store operating expenses
1,173
1,418
1,733
2,314
3,451
Other operating expenses
General and administrative expenses
Depreciation and amortization
Total operating costs and expenses
Income (loss) from operations
Interest expense
Other income
Income (loss) before income taxes
Income tax provision (benefit)
Net income (loss)
0
10
25
40
63
291
775
799
1,210
1,550
60
75
119
175
391
3,321
4,432
5,175
7,527
10,843
191
66
291
181
414
10
8
43
88
134
2
1
0
39
43
183
59
248
132
323
0
17
0
16
112
183
42
248
116
211
86
157
253
472
260
Balance Sheet
Assets
Current assets:
Cash and cash equivalents
Accounts receivable
12
9
Inventories
204
264
Other current assets
115
125
Total current assets
803
658
(517)
(517)
1,613
2,155
Net working capital
42
(24)
(217)
Fixed and intangible assets:
Property and equipment, net
Intangible and other assets, net
96
23
2,512
2,836
Accounts payable
941
775
Accrued liabilities
180
196
Other current liabilities
101
55
Short-term borrowings
152
322
Total current liabilities
1,374
1,348
Total assets
565
664
888
Liabilities and Shareholders’ Equity
Current liabilities:
Long-term liabilities:
Long-term borrowings, less current portion
469
633
298
355
1,086
1,843
1,981
669
855
Total shareholders’ equity
267
309
(198)
669
855
Total liabilities and shareholders’ equity
565
664
888
2,512
2,836
Total liabilities
Shareholders’ equity:
Common stock issued an outstanding
Number of Stores in Operation
Beginning of year
7
8
9
12
17
Store openings
1
1
3
5
0
End of year
8
9
12
17
17
Tab le 8 . 6 Coffee People, Inc. common size statements
Fiscal Year ($000)
1995
1996
1997
1998
1999
100.0%
100.0%
100.0%
100.0%
100.0%
51.2%
48.8%
33.4%
0.0%
8.3%
1.7%
94.6%
5.4%
0.3%
0.1%
5.2%
0.0%
5.2%
47.9%
52.1%
31.5%
0.2%
17.2%
1.7%
98.5%
1.5%
0.2%
0.0%
1.3%
0.4%
0.9%
45.7%
54.3%
31.7%
0.5%
14.6%
2.2%
94.7%
5.3%
0.8%
0.0%
4.5%
0.0%
4.5%
49.1%
50.9%
30.0%
0.5%
15.7%
2.3%
97.7%
2.3%
1.1%
0.5%
1.7%
0.2%
1.5%
47.9%
52.1%
30.7%
0.6%
13.8%
3.5%
96.3%
3.7%
1.2%
0.4%
2.9%
1.0%
1.9%
15.2%
23.6%
28.5%
7.4%
–3.6%
–24.4%
18.8%
0.5%
8.1%
4.6%
32.0%
–20.6%
9.2%
0.3%
9.3%
4.4%
23.2%
–18.2%
100.0%
64.2%
3.8%
100.0%
76.0%
0.8%
100.0%
37.5%
7.2%
4.0%
6.1%
54.7%
27.3%
6.9%
1.9%
11.4%
47.5%
Income Statement
Total revenue
Operating expenses:
Cost of sales
Gross profit
Store operating expenses
Other operating expenses
General and administrative expenses
Depreciation and amortization
Total operating costs and expenses
Income (loss) from operations
Interest expense
Other income
Income (loss) before income taxes
Income tax provision (benefit)
Net income (loss)
Balance Sheet Data
Assets
Current assets:
Cash and cash equivalents
Accounts receivable
Inventories
Other current assets
Total current assets
Net working capital
Fixed and intangible assets:
Property and equipment, net
Intangible and other assets, net
Total assets
100.0%
100.0%
Liabilities and Shareholders’ Equity
Current liabilities:
Accounts payable
Accrued liabilities
Other current liabilities
Short-term borrowings
Total current liabilities
Long term liabilities:
Long term borrowings, less current portion
Total liabilities
Shareholders’ equity:
Common stock issued an outstanding
Total shareholders’ equity.
Total liabilities and shareholders’ equity
52.7%
53.5%
122.3%
18.7%
73.4%
22.3%
69.9%
47.3%
46.5%
–22.3%
26.6%
26.6%
30.1%
30.1%
100.0%
100.0%
100.0%
100.0%
100.0%
Financial Modeling 289
Tab le 8 .7 Coffee People, Inc. financial ratios
Fiscal Year
Financial Ratio
Asset Turnover
Fixed Asset Turnover
Accounts Receivable Turnover
Days’ Sales in Accounts Receivable
Inventory Turnover
Days’ Cost of Sales in Inventory
Sales/Inventory
Accounts Payable/Cost of Sales
Days’ Cost of Sales in Accounts Payable
Compensation and Other Payable/Cost of Sales
Cash/Revenue
Days’ Revenue in Cash
1995
1996
1997
1998
1999
6.22
6.77
6.16
3.49%
12.74
4.63%
16.89
3.07
4.78
642.3
0.57
18.57
19.7
37.78
24.8%
90.7
4.8%
6.12%
22.35
3.97
5.22
1250.8
0.29
20.41
17.9
42.64
14.4%
52.5
3.6%
2.31%
8.43
2.45%
8.94
Definitions
Asset Turnover = Sales/Assets
Fixed Asset Turnover = Sales/Net Fixed Assets
Accounts Receivable Turnover = Sales/Accounts Receivable
Days’ Sales in Accounts Receivable = Accounts Receivable/(Sales/365)
Inventory Turnover = Cost of Sales/Inventory
Days’ Cost of Sales in Inventory = Inventory/(Cost of Sales/365)
Days’ Cost of Sales in Accounts Payable = Accounts Payable/(Cost of Sales/365)
Days’ Revenue in Cash = Cash/(Revenue/365)
market acceptance, and dependence on a single supplier of coffee beans. Some of
the risk factors provide important insights into specific risks facing the company.
How can we evaluate whether Coffee People is a reasonable yardstick company? One test is whether the ratios of A/R and inventory to sales are similar
to those from the D&B data for eating and drinking establishments. From
Tables 8.4 and 8.6, we can see that the A/R and inventory days are within the
ranges of the D&B data for small eating places. No matter what firms we select
as yardsticks, we may need to make appropriate adjustments for differences in
scale and scope of the venture.
Developing Assumptions Based on Fundamental Analysis
Industry data and yardsticks can be valuable, particularly when underlying
business models are similar to those of the subject venture. But for in-depth
planning, fundamental analysis is useful as a way to test the merits of assumptions that are based on yardsticks. Sometimes fundamental analysis is the only
290
Chapter Eight
reasonable choice for developing a specific assumption. For example, information for estimating operating leverage or the proportions of fixed and variable
expenses are rarely available from yardstick data.
As an example of the importance of fundamental analysis, consider the assumptions used to forecast fixed asset investment. If we were to use data from
Coffee People, we might divide the 1995 property and equipment balance of
$2.16 million by the number of stores (17 during 1995), producing an estimate
of about $127,000 in fixed assets per coffee shop ($142,000 in 2016 dollars). But
can we really trust this to be accurate for Morebucks? The investment in fixed
assets will be a function of the store location and depends on what leasehold
improvements the venture will require. Moreover, the yardstick information is
for net fixed assets (which are partially depreciated) and could be impacted by
whether stores are purchased or leased. For reasons such as these, there is more
variability in balance sheet estimates developed using yardsticks than for items
on the income statement.
For a venture like Morebucks, fundamental analysis can yield a more reliable forecast of the fixed asset investment and related depreciation expenses
than can a yardstick approach. When Morebucks opens, all of its furniture
and equipment will be newly acquired. Suppose the space will be leased but
that leasehold improvements, furniture, and equipment are estimated to cost
$150,000, all of which can be depreciated. If we assume five-year, straight-line
depreciation, the annual depreciation expense will be about $30,000. Because
the furniture and equipment are fully paid for at the start, there is no cash flow
directly tied to depreciation. However, depreciation expense reduces pretax
income and the tax the firm pays each year. At a 35% tax rate, the $30,000
depreciation expense would reduce income tax by $10,500 each year, which
does impact cash flow.
We could, of course, do more with fundamental analysis. The data from Coffee People show only a single line for store operating expenses, which includes
store salary and benefits. However, the operating expenses line also includes
other items. We could make a better forecast by developing salary forecasts for
Morebucks based on a specific staffing plan. We could collect information on
local wages to help estimate the prevailing level of wages and benefits. We could
develop a specific marketing plan—which we would need in any case—that
would enable us to estimate how much will be spent on advertising and promotion. And we could estimate raw materials costs based on local market prices,
the anticipated product mix, and volumes implicit in the revenue forecast.
Fundamental analysis is always useful when preparing pro forma financials.
The questions that drive the process are a standard part of any business planning exercise. Answering these questions forces the entrepreneur to undertake
Financial Modeling 291
significant due diligence and better understand the industry, the market, and
the new venture strategy. Yardsticks can then be used to validate forecast assumptions that are developed by fundamental analysis.
8.4 Building a Financial Model of the Venture
We can now develop the integrated financial statements for Morebucks. We
base the statement assumptions on the information and methods discussed in
the previous section. By building the model in stages, we demonstrate how the
financial statements interact and how the cash flow statement can be derived
from the pro forma balance sheet and income statement.
Of course, Morebucks will not achieve full capacity instantly; for this reason
the financial model needs to incorporate the anticipated growth trajectory. To
reflect this reality in the dynamic relationships among the financial statements,
we introduce a time dimension into the forecast. As a basis for this, we might
do further research using yardstick and industry data, perform additional fundamental analysis, and meet with local coffee shop owners about their start-up
experiences.
Recall from Chapter 7 that the estimate of annual revenue for viable coffee
shops was approximately $1.5 million. Suppose our new research and analysis
supports an assumption that, as a new venture with no established brand name
or reputation, Morebucks revenue will reach one third of the $1.5 million during the first year, two thirds during the second year, and the full estimate in the
third year. After the third year, annual revenue is expected to stay constant in
real terms at $1.5 million.
Table 8.8 shows a partially completed template of the three financial statements we will construct. It combines elements of the basic statements discussed
in Section 8.1 as well as line items from the Coffee People’s (CP) financials.
Because revenue is expected to take three years to reach steady state, we have
structured the pro forma statements to cover Time 0 and four years of operation. This allows us to illustrate the impact of start-up events on the financial
statements and to cover two years of steady-state operation. In Table 8.8, we
introduced only income statement data and have not yet incorporated depreciation expense in the income statement.
The table is constructed using Excel and includes the cell formulas needed
to link the assumptions to the financial statements and the financial statements
to each other. The columns to the right summarize the numerical assumptions
and describe from where they originate. Where possible, we link the assumptions directly to the spreadsheet so the effects of changing an assumption will
Tab le 8 . 8
Step 1 of pro forma financial model for Morebucks: income statement assumptions
Pro Forma Income Statement
Time 0
Year 1
Year 2
Year 3
Year 4
Net revenue
Cost of sales
0
500,000
242,000
1,000,000
484,000
1,500,000
726,000
1,500,000
726,000
Gross profit
Operating expenses
General, administrative, and other expenses
Depreciation and amortization expenses
0
258,000
472,500
69,500
0
516,000
472,500
139,000
0
774,000
472,500
208,500
0
774,000
472,500
208,500
0
Income from operations
Interest income (expense), net
0
(284,000)
(95,500)
93,000
93,000
Income before income taxes
Income tax provision
0
(284,000)
(99,400)
(95,500)
(33,425)
93,000
32,550
93,000
32,550
Net income
0
(184,600)
(62,075)
60,450
60,450
Time 0
Year 1
Pro Forma Balance Sheet
Year 2
Year 3
Assumption
Year 4
From revenue forecast
48.4% From CP common size statement (average)
31.5% From CP common size statement (at steady state)
13.9% From CP common size statement (at steady state)
5 Years, straight line—on fixed assets, gross
35% Effective rate—applies to all income
Assumption
Assets
Current Assets
Required cash
Surplus cash
Accounts receivable
Inventory
Total current assets
Fixed assets, gross
Less: Accumulated depreciation
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Net fixed assets
0
0
0
0
0
Total assets
0
0
0
0
0
Liabilities
Current Liabilities
Accounts payable
Wages and other payables
0.0%
0.0%
Total current liabilities
Long-term debt
0
0
0
0
0
Total liabilities
Equity
Common stock
Retained earnings
0
0
0
0
0
0
0
(184,600)
0
(246,675)
0
(186,225)
0
(125,775)
Total equity
Total liabilities and equity
0
0
(184,600)
(184,600)
(246,675)
(246,675)
(186,225)
(186,225)
(125,775)
(125,775)
Basis for Assumption
Basis for Assumption
Pro Forma Cash Flow Statement
Operating Cash Flow
Net income
Plus: Depreciation
(Increase) decrease in accounts receivable
(Increase) decrease in inventory
Increase (decrease) in accounts payable
Increase (decrease) in wages payable
Operating cash flow
Investing Cash Flow
(Increase) decrease in gross fixed assets
Time 0
Year 1
Year 2
Year 3
Year 4
0
0
(184,600)
0
(62,075)
0
60,450
0
60,450
0
0
(184,600)
(62,075)
60,450
60,450
Investing cash flow
Financing Cash Flow
Increase (decrease) in debt
Increase (decrease) in common stock
Dividend paid
Financing cash flow
0
0
0
Net cash flow
Beginning cash
0
0
(184,600)
0
0
(62,075)
0
0
60,450
0
60,450
0
Ending cash
0
(184,600)
(62,075)
60,450
60,450
294
Chapter Eight
be automatically reflected in all of the financial statements. This is standard
practice for any spreadsheet model we might want to build.
Modeling the Income Statement
The revenue forecast is taken from Chapter 7 and reflects the growth trajectory discussed above. The expense ratio assumptions—expressed as percentages of revenue—are based on data from the Coffee People prospectus. We
decided to use an average expense ratio from the 1991–19​95 data, which represents a five-year period when Coffee People was relatively small and before
it approached the point of going public. Coffee People does not provide information on the breakdown between fixed and variable expenses in its income statement. In our base case model, we assume that expenses other than
depreciation are fixed percentages of revenue for a single shop. When we add
uncertainty to the model, we will relax this assumption.
The final assumption required in the income statement is the effective combined state and federal tax rate, which we have assumed to be 35%. There are
various ways to model the effect of taxes in years when the venture has a net loss.
The simplest, the one we apply in this model, is to assume that the entrepreneur
has other earnings against which losses can be offset so that the tax benefit of
a loss is realized in the year of the loss. Alternatively, we could accumulate the
losses and apply them against future positive earnings. In light of the added
complexity, we do not use that approach here.
Because depreciation expense is driven by the amount invested in facilities
and improvements, we defer making this assumption until we construct the
balance sheet. The tax and net income totals will not be correct until the depreciation expense line is complete. Note, however, that the net income line
also now appears as the first item on the cash flow statement, where it is one of
the determinants of operating cash flow. This is one of many links between the
financial statements.
Modeling the Balance Sheet
In Table 8.9, we turn to the balance sheet, beginning with the current asset
and current liability accounts. Again, the numerical assumptions and their
sources are in the right-hand columns. In some cases, the assumptions come
directly from the Coffee People prospectus. When the data from Coffee People differ substantially from industry norms or the conclusions of our fundamental analysis, we have established the assumption based on judgment and
the evidence.
Financial Modeling 295
At this point, the only initial investment shown in the Time 0 column is the
$150,000 investment in fixed assets, which is assumed to be made with equity
capital provided by the entrepreneur. Note that the balance sheet in Table 8.9 is
in balance each year, that is, total assets equal total liabilities and equity each
year. To accomplish this, the balance sheet includes a line item we call “Surplus
Cash.” This is a modeling choice we made for this example. To force the statements to balance, we allow surplus cash to show negative balances. We use the
surplus cash account as a device for assessing financial need. If the balance is
negative in any period, the company has less resources than it needs to fund the
operation and additional financing is needed.
There are other ways to address this in an integrated financial model. One is
to constrain surplus cash to be nonnegative but allow the balance sheet to be out
of balance and then make subsequent adjustments. For example, suppose the
model’s assumptions produce assets greater than liabilities plus equity. Balance
can be achieved either by reducing assets or by increasing liabilities or equity
(or some combination of the two). For example, if in the Morebucks model we
were to assume an initial purchase of $150,000 of fixed assets but only $50,000
of equity investment, the balance sheet would be out of balance by $100,000.
Balance could be achieved by reducing the fixed asset assumption to $50,000,
increasing the equity investment to $150,000, adjusting both accounts, or increasing some other source of financing.
Generally, it is easier to force the statement to be in balance and keep track
of cash shortages or surpluses, as we have done here. In this approach, the
surplus cash “plug” becomes a line item on the asset side. In Table 8.9, the Year
1 balance sheet is balanced only because the surplus cash balance is extremely
negative. This is attributable primarily to the negative financial performance
in the first year, with no commensurate increase in liabilities or equity (i.e.,
financing). This funding shortfall causes surplus cash to be negative. This approach shows the financing shortage or surplus in the surplus cash line, which
represents an allocation of the total cash balance or an indication of a need for
additional funding.
Another good alternative is to shift the shortage or surplus to the liabilities
and equity side of the statement. Were we to do this here, we would first establish a minimum cash level on the balance sheet and force the cash balance to
equal the cash required. The model already has assumptions about all other
asset line items, so this would effectively determine the asset (left) side of the
balance sheet and produce a total assets number. We can then add a “plug” item
to the right side of the balance sheet, calling it “additional funding needed” or
“additional financing.” This makes the right side of the balance sheet a column
of numbers, including the unknown plug, that sums to a known number (total
Tab le 8 . 9
Step 2 of pro forma financial model for Morebucks: Balance sheet assumptions
Pro Forma Income Statement
Net revenue
Cost of sales
Gross profit
Operating expenses
General, administrative, and other expenses
Depreciation and amortization expenses
Time 0
0
0
Year 1
Year 2
Year 3
Year 4
500,000 1,000,000 1,500,000 1,500,000
242,000
484,000
726,000
726,000
258,000
516,000
774,000
774,000
472,500
472,500
472,500
472,500
69,500
139,000
208,500
208,500
30,000
30,000
30,000
30,000
Income from operations
Interest income (expense), net
Income before income taxes
Income tax provision
0
(314,000) (125,500)
63,000
63,000
0
(314,000) (125,500)
(109,900) (43,925)
63,000
22,050
63,000
22,050
Net income
0
(204,100)
40,950
40,950
Pro Forma Balance Sheet
Assets
Current Assets
Required cash
Surplus cash
Accounts receivable
Inventory
Total current assets
Fixed assets, gross
Less: Accumulated depreciation
Net fixed assets
Total assets
Time 0
0
0
0
0
0
150,000
150,000
150,000
Year 1
(81,575)
Year 2
Year 3
Year 4
19,000
38,000
57,000
57,000
(163,611) (204,697) (123,257) (52,307)
2,466
4,932
7,397
7,397
25,641
51,282
76,923
76,923
(116,504) (110,483)
18,063
89,013
150,000
150,000
150,000
150,000
(30,000) (60,000) (90,000) (120,000)
120,000
90,000
60,000
30,000
3,496
(20,483)
78,063
119,013
Liabilities
Current Liabilities
Accounts payable
Wages and other payables
0
0
47,432
10,164
94,864
20,328
142,296
30,492
142,296
30,492
Total current liabilities
Long-term debt
0
0
57,596
0
115,192
0
172,788
0
172,788
0
Total liabilities
Equity
Common stock
Retained earnings
Total equity
Total liabilities and equity
0
57,596
115,192
172,788
172,788
150,000
150,000
150,000
150,000
(204,100)
(54,100)
3,496
150,000 150,000
150,000
(285,675) (244,725) (203,775)
(135,675) (94,725) (53,775)
(20,483)
78,063
119,013
Assumption
Basis for Assumption
From revenue forecast
48.4% From CP common size statement (average)
31.5% From CP common size statement (at steady state)
13.9% From CP common size statement (at steady state)
5 Years, straight line—on fixed assets, gross
35% Effective rate—applies to all income
Assumption
Basis for Assumption
3.80% Based on CP cash/revenue ratio (average)
2 Days in A/R based on industry A/R turnover ratio
19.5 Based on CP sales/inventory ratio (average)
150,000 Based on fundamental analysis
19.6% Based on CP AP/cost of sales ratio (average)
4.20% Based on CP wages and other payables/cost of sale
150,000 Selected to cover start-up investments
Pro Forma Cash Flow Statement
Operating Cash Flow
Net income
Plus: Depreciation
(Increase) decrease in accounts receivable
(Increase) decrease in inventory
Increase (decrease) in accounts payable
Increase (decrease) in wages payable
Operating cash flow
Investing Cash Flow
(Increase) decrease in gross fixed assets
Investing cash flow
Financing Cash Flow
Increase (decrease) in debt
Increase (decrease) in common stock
Dividend paid
Financing cash flow
Net cash flow
Beginning cash
Ending cash
Time 0
0
0
0
0
0
0
0
Year 1
Year 2
(204,100)
30,000
(2,466)
(25,641)
47,432
10,164
(144,611)
(81,575)
30,000
(2,466)
(25,641)
47,432
10,164
(22,086)
Year 3
Year 4
40,950
30,000
(2,466)
(25,641)
47,432
10,164
100,439
40,950
30,000
0
0
0
0
70,950
(150,000)
(150,000)
0
0
0
0
0
0
0
0
0
150,000
0
0
0
0
150,000
0
0
0
0
0
(144,611)
0
(144,611)
0
0
0
0
(22,086) 100,439
(144,611) (166,697)
(166,697) (66,257)
0
0
70,950
(66,257)
4,693
298
Chapter Eight
assets). The spreadsheet would be designed so that the plug figure causes assets
to equal liabilities plus equity. If the model indicates that the enterprise requires
additional funding, the plug will be positive; if the venture has more resources
than needed, the plug will be negative. Note that the balance sheet in Table 8.9
is in balance each year; that is, total assets equal total liabilities plus equity.
The venture might be able to distribute cash to investors or pay down a loan.
A negative balance of additional funding needed serves the same purpose as a
positive surplus cash balance in the first approach.
A more elegant approach is to combine these last two alternatives. This third
approach recognizes the reality that a negative balance in either the external
funding or the surplus cash line items does not make sense. If, before forcing the
balance, assets exceed liabilities, then the firm needs more investment capital
and the Excel model can be structured so that additional financing is the plug;
if assets are less than liabilities, then the firm has more capital than it needs
and the Excel model can make surplus cash the plug—so there are only positive
plugs. This approach provides greater flexibility than the others. For example,
surplus cash might be credited with earning the risk-free rate of interest and
additional financing required might be assumed to be in the form of debt, with
a risky interest rate, or in the form of equity.
With the balance sheet completed, the changes in the various current account
balances from one year to the next appear in the cash flow statement in the
operating cash flow section. When the venture reaches steady state after Year
3, the net working capital balance does not change. As a result, the Year 4 cash
flows from changes in current assets and liabilities are zero.
Table 8.9 also shows a $150,000 investment in fixed assets, which is based
on fundamental analysis. This investment is depreciated over five years, which
means we can calculate the depreciation expense as $30,000 for each year. This
completes the income statement, with the possible exception of any interest
expense associated with financing. Accumulated depreciation on the balance
sheet is linked to the income statement and adds the depreciation expense each
year to the prior year’s accumulated depreciation balance. This has the effect
of reducing the net fixed assets balance over time. Because depreciation is a
noncash expense, it appears in the cash flow statement, where it is added back
to net income as part of operating cash flow.
The negative cash surplus shown in Table 8.9 implies that additional financing is needed. This can be resolved by adding an assumption that the venture
will be entirely equity financed and structuring the model to automatically add
the needed new equity. Table 8.10 shows the resulting financial statements. To
cover the cost of the initial investment in fixed assets and working capital, we
start with an estimate of $375,000 of equity, which appears on the Time 0 bal-
Financial Modeling 299
ance sheet as $375,000 in common stock that is divided between the investment
in fixed assets and surplus cash. The equity investment also shows up on the
statement of cash flows as a positive financing cash flow.
Based on our initial assumptions, the coffee shop is expected to incur significant losses in Years 1 and 2, due to start-up levels of revenue, and to be profitable each year thereafter. With no distributions or additional equity infusions,
Table 8.10 shows the accumulation of net income (or loss) from each year moving
to the retained earnings balance in the equity section of the balance sheet. The
retained earnings balance at the end of Year 4 reflects the projected accumulated
loss from Morebucks’ first four years of operation.
The balance sheet in Table 8.10 shows a positive surplus cash balance each
year, with the Year 2 ending balance being the lowest, and increasing thereafter.
Rather than assuming that the entrepreneur withdraws the cash as it accumulates, we have allowed it to remain in the venture. The surplus cash could earn
a return (by being held in an interest­bearing account such as a money market
fund), but to keep the model simple we ignore this for now.
The positive surplus cash balances raise the question of how the assumption
of a $375,000 initial investment was determined. Does the positive projected
cash surplus in Year 2 mean that the entrepreneur has put too much cash into
the venture up front? The projections seem to suggest that it does. However, we
should not draw this conclusion without further analysis. The current model
does not capture important timing issues during the first few years and does
not allow for uncertainty of performance. To accurately estimate cash needs,
we might want to prepare pro forma statements on a monthly or quarterly basis
and also make an assessment of uncertainty of performance.
Deriving the Cash Flow Statement
The cash flow statement allows us to see the venture’s sources and uses of cash.
This information may appear to be readily available from the income statement, but that is not the case. First, income statement items such as depreciation do not translate directly to cash flow; second, many cash transactions
don’t appear on the income statement. In addition, the income statement and
balance sheet provide little information about the venture’s ability to generate
cash to fund growth.
The cash flow statement translates the income statement and balance sheet
information into cash flows and separates them into three categories: operating,
investing, and financing. The coffee shop cash flow statement in Table 8.10 is
straightforward. When the venture begins operation and is growing, operating cash flow reflects increases in working capital accounts. When the venture
Tab le 8 .1 0
Step 3 of pro forma financial model for Morebucks: investment assumption
Pro Forma Income Statement
Net revenue
Cost of sales
Gross profit
Operating expenses
General, administrative, and other expenses
Depreciation and amortization expenses
Income from operations
Interest income (expense), net
Income before income taxes
Income tax provision
Net income
Pro Forma Balance Sheet
Time 0
0
0
0
0
0
Year 1
Year 2
Year 3
500,000 1,000,000 1,500,000
242,000
484,000
726,000
258,000
516,000
774,000
472,500
472,500
472,500
69,500
139,000
208,500
30,000
30,000
30,000
(314,000) (125,500)
63,000
(314,000)
(109,900)
(204,100)
(125,500)
(43,925)
(81,575)
63,000
22,050
40,950
Year 4
1,500,000
726,000
774,000
472,500
208,500
30,000
63,000
63,000
22,050
40,950
Time 0
Year 1
Year 2
Year 3
Year 4
0
225,000
0
0
225,000
150,000
19,000
61,389
2,466
25,641
108,496
150,000
(30,000)
120,000
228,496
38,000
20,303
4,932
51,282
114,517
150,000
(60,000)
90,000
204,517
57,000
101,743
7,397
76,923
243,063
150,000
(90,000)
60,000
303,063
57,000
172,693
7,397
76,923
314,013
150,000
(120,000)
30,000
344,013
0
0
47,432
10,164
94,864
20,328
142,296
30,492
142,296
30,492
0
0
0
57,596
0
57,596
115,192
0
115,192
172,788
0
172,788
172,788
0
172,788
375,000
(204,100)
170,900
228,496
375,000
(285,675)
89,325
204,517
375,000
(244,725)
130,275
303,063
375,000
(203,775)
171,225
344,013
Assumption
Basis for Assumption
From revenue forecast
48.4% From CP common size statement (average)
31.5% From CP common size statement (at steady state)
13.9% From CP common size statement (at steady state)
5 Years, straight line—on fixed assets, gross
35% Effective rate—applies to all income
Assumption
Basis for Assumption
Assets
Current Assets
Required cash
Surplus cash
Accounts receivable
Inventory
Total current assets
Fixed assets, gross
Less: Accumulated depreciation
Net fixed assets
Total assets
Liabilities
Current Liabilities
Accounts payable
Wages and other payables
Total current liabilities
Long-term debt
Total liabilities
Equity
Common stock
Retained earnings
Total equity
Total liabilities and equity
150,000
375,000
375,000
375,000
375,000
3.80% Based on CP cash/revenue ratio (average)
2 Days in A/R based on industry A/R turnover ratio
19.5 Based on CP sales/inventory ratio (average)
150,000 Based on fundamental analysis
19.6% Based on CP AP/cost of sales ratio (average)
4.20% Based on CP wages and other payables/cost of sales
150,000 Selected to cover start-up investments
Pro Forma Cash Flow Statement
Operating Cash Flow
Net income
Plus: Depreciation
(Increase) decrease in accounts receivable
(Increase) decrease in inventory
Increase (decrease) in accounts payable
Increase (decrease) in wages payable
Operating cash flow
Investing Cash Flow
(Increase) decrease in gross fixed assets
Investing cash flow
Financing Cash Flow
Increase (decrease) in debt
Increase (decrease) in common stock
Dividend paid
Financing cash flow
Net cash flow
Beginning cash
Ending cash
Time 0
0
0
0
0
0
0
0
Year 1
Year 2
(204,100)
30,000
(2,466)
(25,641)
47,432
10,164
(144,611)
Year 3
(81,575)
30,000
(2,466)
(25,641)
47,432
10,164
(22,086)
Year 4
40,950
30,000
(2,466)
(25,641)
47,432
10,164
100,439
40,950
30,000
0
0
0
0
70,950
(150,000)
(150,000)
0
0
0
0
0
0
0
0
0
375,000
0
0
0
0
0
0
100,439
58,303
158,743
0
0
70,950
158,743
229,693
375,000
225,000
0
225,000
0
0
(144,611)
225,000
80,389
0
0
(22,086)
80,389
58,303
302
Chapter Eight
reaches steady state, between Years 2 and 3, operating cash flow includes only
net income and the add-back for (noncash) depreciation expense. Because the
working capital account balances are constant, they have no impact on cash flow.
The statement next provides for changes in cash due to investing. The gross
fixed assets balance represents the cumulative original cost of the firm’s PP&E.
The change in gross fixed assets from the balance sheet is shown as an investment cash flow on the cash flow statement. When fixed assets are purchased,
the increase appears as a negative cash flow. If fixed assets are sold, the sale
produces a cash inflow.
The last section of the cash flow statement covers financing activities. The
only event in this example is the equity investment of $375,000 at Time 0. With
all of the financing provided at Time 0, there are no changes in debt or equity
for Years 1 through 4. Return of the surplus cash to the equity investor can
be treated in two ways. One way is to pay a dividend, which would appear
as a negative financing cash flow and would reduce retained earnings on the
balance sheet. Another is to repurchase shares, which would be a negative
financing cash flow and would reduce common stock outstanding on the balance sheet.
Finally, at the foot of the cash flow statement, we use the net cash flow plus
the cash balance from the prior period to compute the ending cash balance.
Both beginning and ending cash balances include required and surplus cash.
This information is used in the balance sheet to fill in the surplus cash amount.
An Illustration of Financial Statement Integration
Table 8.10 is a fully integrated Excel file of the Morebucks financial model. It
contains all important assumptions, along with source documentation. The
financial statements are linked to the assumptions, to one another, and also
over time. This allows us to immediately see the impact of changing any key
assumptions. For example, suppose it may be possible to acquire the needed
fixed assets for $320,000 rather than the original estimate of $350,000 and that
Year 1 revenue might only be $300,000. These changes can be made quickly.
The lower revenue causes net income to turn more negative. This is partially
offset by the reduction in depreciation expense arising from the smaller starting balance in gross fixed assets. Operating cash flow goes down, reflecting
both the lower net income and reduced investment in working capital. Surplus
cash is also lower and becomes negative in Year 2. Because the model is fully
integrated, the two changes to the assumptions are immediately reflected in
affected line items on all three statements. This allows the entrepreneur or an
investor to focus on the operational implications of the changes.
Financial Modeling 303
8.5 Adding Uncertainty to the Financial Model
The Morebucks financial model incorporates the static assumptions shown in
Table 8.10. The resultant pro forma financial statements are useful but do not
reflect the uncertainty inherent in any forecast. To introduce uncertainty, we
modify the Morebucks model in two ways. Further research into the performance of new coffee shops would reveal a high variability in first-year revenue
and many different three-year trajectories of revenue growth. For example,
10-K data from yardstick companies contain cross-sectional information on
per-store revenue. Fundamental analysis and discussions with other coffee
shop owners would provide additional data that can be used to estimate the
variability of revenue.
To incorporate uncertainty, we modify the model by replacing the static
revenue assumption each year with statistical distributions. We also introduce
uncertainty about operating expense and the size of the initial fixed investment.
The specific changes are shown in the following table. The revenue assumptions
are designed to model an uncertain initial revenue and rate of revenue growth
over the first two years, and to reach a specific expected ultimate revenue level
by Year 3.
Variable
Distribution assumption
Year 1 revenue
Year 2 revenue
Triangular with a mode of $500,000, a minimum of $400,000, and a maximum of $700,000
Triangular with a mode of 2 times the Year 1 result, a minimum that is $50,000 lower, and a
maximum that is $50,000 higher
Normally distributed with a mean of $1.5 million and a standard deviation of $100,000
The same as Year 3 revenue
Normally distributed with mean of 31.5% of revenue and standard deviation of 2%
Normally distributed with a mean of $150,000 and standard deviation of $5,000
Year 3 revenue
Year 4 revenue
Operating expense
Fixed asset investment
We used @Risk to track the initial assumptions and each year’s net income, and cash flow. Table 8.11 shows one trial from a simulation model that
incorporates these elements of uncertainty into the pro forma statements. The
cells modified to reflect these uncertainty assumptions are shaded and can be
reviewed in the Excel file. Table 8.11 is identical to Table 8.10 except for the effects of random draws from the assumed distributions.
Table 8.12 shows a summary of some of the results from simulating the model
over 1,000 trials. The Inputs section of the table shows that in some trials, the rate
of revenue growth is slower than expected and in some it is faster. The ultimate
level of revenue is reached in Year 3 and ranges from about $1.2 to $1.8 million.
The table also shows distribution results for the operating expense percentage
Tab le 8 .11 Pro forma financial model for Morebucks with simulation
Pro Forma Income Statement
Net revenue
Cost of sales
Gross profit
Operating expenses
General, administrative, and other expenses
Depreciation and amortization expenses
Income from operations
Interest income (expense), net
Income before income taxes
Income tax provision
Net income
Pro Forma Balance Sheet
Time 0
0
0
0
0
0
Year 1
526,010
254,589
271,421
400,571
73,115
30,000
(232,265)
(232,265)
(81,293)
150,972)
Year 2
Year 3
1,083,672 1,486,566
524,497
719,498
559,175
767,068
400,571
400,571
150,630
206,633
30,000
30,000
(22,027)
129,864
(22,027)
(7,709)
(14,317)
Year 4
1,486,566
719,498
767,068
400,571
206,633
30,000
129,864
129,864
45,452
84,412
129,864
45,452
84,412
Year 2
Year 3
Year 4
Time 0
Year 1
0
0
0
0
0
150,000
19,988
(109,938)
2,594
26,975
(60,380)
150,000
(30,000)
120,000
59,620
41,180
(82,556)
5,344
55,573
19,540
150,000
(60,000)
90,000
109,540
56,489
40,308
7,331
76,234
180,362
150,000
(90,000)
60,000
240,362
56,489
154,719
7,331
76,234
294,774
150,000
(120,000)
30,000
324,774
49,899
10,693
60,592
0
60,592
102,801
22,029
124,830
0
124,830
141,022
30,219
171,240
0
171,240
141,022
30,219
171,240
0
171,240
150,000
(150,972)
(972)
59,620
150,000
(165,290)
(15,290)
109,540
150,000
(80,878)
69,122
240,362
150,000
3,533
153,533
324,774
Assumption
Basis for Assumption
From revenue forecast
48.4% From CP common size statement (average)
26.9% From CP common size statement (at steady state)
13.9% From CP common size statement (at steady state)
5 Years, straight line—on fixed assets, gross
35% Effective rate—applies to all income
Assumption
Basis for Assumption
Assets
Current Assets
Required cash
Surplus cash
Accounts receivable
Inventory
Total current assets
Fixed assets, gross
Less: Accumulated depreciation
Net fixed assets
Total assets
Liabilities
Current Liabilities
Accounts payable
Wages and other payables
Total current liabilities
Long-term debt
Total liabilities
Equity
Common stock
Retained earnings
Total equity
Total liabilities and equity
150,000
150,000
0
0
0
0
0
150,000
150,000
150,000
3.80% Based on CP cash/revenue ratio (average)
Plugged to make statement balance
2 Days in A/R based on industry A/R turnover ratio
19.5 Based on CP sales/inventory ratio (average)
150,000 Based on fundamental analysis
19.6% Based on CP AP/cost of sales ratio (average)
4.20% Based on CP wages and other payables/cost of sales
150,000 Selected to cover start-up investments
Pro Forma Cash Flow Statement
Operating Cash Flow
Net income
Plus: Depreciation
(Increase) decrease in accounts receivable
(Increase) decrease in inventory
Increase (decrease) in accounts payable
Increase (decrease) in wages payable
Operating cash flow
Investing Cash Flow
(Increase) decrease in gross fixed assets
Investing cash flow
Financing Cash Flow
Increase (decrease) in debt
Increase (decrease) in common stock
Dividend paid
Financing cash flow
Net cash flow
Beginning cash
Ending cash
Time 0
0
0
0
0
0
0
0
Year 1
(150,972)
30,000
(2,594)
(26,975)
49,899
10,693
(89,949)
Year 2
(14,317)
30,000
(2,750)
(28,598)
52,902
11,336
48,573
Year 3
Year 4
84,412
30,000
(1,987)
(20,661)
38,220
8,190
138,174
84,412
30,000
0
0
0
0
114,412
(150,000)
0
0
0
0
(150,000)
0
0
0
0
0
150,000
0
0
0
0
0
0
0
0
150,000
0
0
0
0
(89,949)
0
(89,949)
0
48,573
(89,949)
(41,377)
0
138,174
(41,377)
96,797
0
114,412
96,797
211,209
306
Chapter Eight
and the fixed asset investment. Among the outputs, it is noteworthy that in some
trials, net income is still negative in Year 4. These are clear cases of failure since
no further growth is expected. Even some positive net income numbers would
also effectively be failures since the returns are too low to justify the investments.
Cash flow is negative over all trials in Year 1 and over more than half of the
trials in Year 2. It is positive over all trials in the last two years. Although we began the simulation with an investment of $375,000, it is apparent from the surplus
cash balances that this is sometimes not enough. This is most apparent in Year
2, where over 25% of the trials have negative surplus cash balances. The worst
case also occurs in Year 2, where the minimum surplus cash balance is negative
$173,000. Apparently, increasing the initial equity investment to $550,000 would
be sufficient to cover all of the outcomes. However, it may not be desirable to
invest so much since the negative balances in Year 2 may be associated with
outcomes that should be abandoned—possibly slow-growth, low-profit trials.
We cannot evaluate the additional investment or abandonment questions on
the basis of the summary statistics in Table 8.12. Rather, we need to investigate
the trials data. Figure 8.2 shows the results of simulating the Morebucks model.
The figure overlays results for Year 4 profitability (the year when Morebucks’s
revenue is in steady state) with results for surplus cash in Year 2 (the year when
surplus cash is usually lowest). The trials data are sorted by net income. Based
on 1,000 trials, the figure shows that Year 4 net income is negative in a few cases.
Tab le 8 .12
Morebucks: Summary of simulation results (based on 1,000 trials)
Name
Mean
Minimum
25%
50%
75%
Maximum
577,399
1,156,060
1,567,213
32.8%
153,363
696,151
1,412,934
1,822,649
37.8%
168,351
Inputs
Net revenue/Year 1
Net revenue/Year 2
Net revenue/Year 3
Operating expenses
Fixed assets, gross
533,339
1,066,675
1,500,012
31.5%
150,001
403,496
794,352
1,190,968
25.0%
134,094
486,538
970,318
1,432,526
30.1%
146,616
526,654
1,057,105
1,499,982
31.5%
149,998
Outputs
Net income/Year 4
Net cash flow /Year 1
Net cash flow/Year 2
Net cash flow/Year 3
Net cash flow/Year 4
Surplus cash/Year 1
Surplus cash/Year 2
Surplus cash/Year 3
Surplus cash/Year 4
40,964
(134,463)
(3,769)
96,522
70,965
70,270
46,234
126,289
197,254
(21,873)
(239,497)
(129,608)
27,439
9,319
(35,134)
(173,254)
(147,509)
(138,189)
27,538
(156,158)
(34,137)
80,596
57,384
48,936
(1,470)
73,609
134,482
40,562
(133,296)
(3,706)
96,282
70,745
71,331
47,863
128,409
197,640
54,278
(112,584)
26,045
112,745
84,055
91,199
93,224
179,076
260,319
102,296
(36,906)
128,127
172,728
132,020
162,835
258,024
377,067
498,951
Financial Modeling 307
While we defer a formal analysis of valuation until a later chapter, we can
use Figure 8.2 to look informally at the choice of how much to invest, presuming that negative surplus cash would lead to abandonment. Based on the
figure, if we want to cover all possible outcomes, we would need to invest an
additional $175,000 ($550,000 total) so that the Year 2 surplus cash balance
would always be positive. But, as noted above, this would fund a number of
bad outcomes. If we limit the total investment to $500,000, it appears that all
trials with Year 4 income above about $35,000 would be fully funded. But even
this investment may not be warranted. If we limit the investment to a total of
$450,000, it appears that all trials with at least $60,000 in income would be
fully funded. This rudimentary examination shows that much more can be
done with simulation to fine-tune the analysis and sharpen the investment and
abandonment decisions.
Introducing uncertainty with simulation in the Morebucks example can add
significant richness. We could continue to refine the assumptions or consider
other decision variables. We can also do more with simulation to understand
the risks of the venture. We could, for example, use the trial results to study the
$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
1
20
39
58
77
96
115
134
153
172
191
210
229
248
267
286
305
324
343
362
381
400
419
438
457
476
495
514
533
552
571
590
609
628
647
666
685
704
723
742
761
780
799
818
837
856
875
894
913
932
951
970
989
$0
–$50,000
–$100,000
–$150,000
–$200,000
Net income/year 4
Fi g u r e 8 . 2
Surplus cash/year 2
M o reb u ck s sim u lat io n of p rofit a bilit y a n d s u rp l u s ca s h
Results are shown from a 1,000-trial simulation using @Risk. Simulated variables include net income and surplus cash.
308
Chapter Eight
relation between profitability and the operating expense result or profitability
and the realized sales level.
Most new ventures would require more frequent pro formas (e.g., quarterly
or monthly) and a longer forecast horizon. The current simulation model also
allows ending cash to be negative (although, given our assumptions, this does
not occur). Negative cash would require more initial equity or additional cash
investment after start-up.
8.6 NewCo: Building an Integrated Financial Model
We now return to our earlier example, the medical technology venture called
NewCo. In Chapter 7, we developed a static revenue forecast and then extended the revenue forecasting model to incorporate uncertainty. We follow a
similar trajectory here in developing an integrated financial forecast. We begin with a static version of the NewCo financial model. The static model is
based on the assumptions that are detailed in Figure 8.3. These assumptions
reflect the expected value of each forecast. After we complete the static model,
we will modify the assumptions to introduce uncertainty.
You should assume that we developed the assumptions in Figure 8.3 on the
basis of yardsticks, industry norms, and fundamental analysis, as we did for
Morebucks. The first five assumptions were used to develop the static revenue
forecast in Chapter 7. The others are the bases for individual line items in the
pro forma income statement and balance sheet. To avoid the complexities of
dealing with depreciation, we assume that all fixed assets are leased, with lease
payments made each quarter as part of SG&A Expense. The final 3 assumptions
specify the initial investment and provide a mechanism for bringing the balance
sheet into balance, either by increasing financing or by building up surplus cash.
We establish a minimum required cash balance of the greater of $15,000 or 10%
of the quarter’s sales.
We introduce a line of credit (debt) as a device that will allow NewCo to
maintain the minimum cash balance. Consistent with the earlier discussion, if
the venture is short of cash in any period, it is assumed to draw automatically
on the credit line; if it generates free cash flow, the line is paid down. If the line
is fully paid off and the venture generates free cash flow, the excess is retained
as surplus cash. In contrast to the Morebucks model, we include an explicit inflation forecast in the assumptions so that we are forecasting in nominal terms.
Inflation will impact the selling price and most of the expenses, because they
are calculated as percentages of revenue or cost of sales. Some expenses are
fixed in nominal terms.
Financial Modeling 309
Fi g u r e 8 . 3 N ewCo
integ rated fin a n cia l
m o d el a ss u m pt io n s
1.
All assumptions shown are
expected values.
Development will require 6 quarters, during which period no sales will be
made.
2.
Initial quarterly sales of 300 units at a price of $200 beginning in at the start
of quarter 7.
3.
After quarter 7, unit sales will grow 25% per quarter for three years (through
quarter 19) and then remain constant.
4.
The sales price will increase each quarter at the inflation rate.
5.
Inflation at 4% per year (modeled as 1.0% per quarter).
6.
Operating expenses during the 6-quarter development period are
projected to be $60,000 per quarter plus inflation.
7.
Cost of sales is projected to be 45% of revenue.
8.
Beginning in quarter 7, the venture is expected to incur fixed Selling
General and Administrative (SG&A) expenses of $90,000 per quarter,
growing at the inflation rate. This includes the entrepreneur’s salary.
Variable SG&A expenses are projected to be 20% of sales.
9.
A production facility will come on line at the end of quarter 6 and is
expected to be adequate for the ensuing 5 years of operation (through
quarter 26). Quarterly lease payments for the facility and production
equipment will begin in quarter 7 and are included in fixed SG&A
expenses.
10.
The effective corporate tax rate is projected to be 35% on positive income
with no loss carry-forward; that is, any loss in a given period gets no tax
credit and cannot accumulate to offset future profits.
11.
Net working capital other than cash, including accounts receivable (A/R)
and inventory, less accounts payable (A/P), is 50% of sales in the quarter
plus 50% of cost of sales in the subsequent quarter.
12.
The company needs to maintain a minimum cash balance equal to either
10% of the prior quarter’s sales or $15,000, whichever is greater.
13.
Initial equity investment by the entrepreneur is $500,000. Additional
funding, if needed, will come from a hypothetical line of credit with no limit.
Interest on the credit line is 2% quarterly (8% annually).
14.
Free cash flow in any period will first be used to reduce the balance of the
line of credit, and then will be accumulated as surplus cash.
Table 8.13 shows the pro forma financial statements on a quarterly basis from
the start of product development through the first five years of sales. With an
expected development period of six quarters, the total forecast horizon is 26
quarters. The financial statements are simplified but appropriate for the venture.
While only selected quarters are shown in Table 8.13, the Excel file on which
the table is based contains all quarters. We selected the quarters to include in
the printed table because they correspond to major milestones: development,
start of revenue, initiation of external financing, profitability, positive operating
cash flow, and the end of five years of sales operation. We now work through the
statements chronologically, highlighting the quarters shown.
310
Chapter Eight
Modeling the Development Stage
Under the heading for Quarter 0, Table 8.13 shows the beginning balance
sheet. Based on the assumptions in Figure 8.3, NewCo has only one asset at
start-up—cash—all of which the entrepreneur invests in the venture as equity.
For now, we assume an initial investment of $500,000. In later chapters we will
use simulation to estimate the appropriate level of initial financing.
Development activity begins in Quarter 1 and is shown as the $60,000 of
development expenses on the income statement. To keep the model simple, we
do not consider tax-loss carryforwards or tax credits on negative net income.
The cash flow statement reflects this loss as the first line in operating cash flow.
Because the company has no depreciable assets and (at this point) no activity
that would create working capital, the loss is operating cash flow. The negative
operating cash flow reduces the company’s cash balance to $440,000, far in
excess of the required minimum. Except for inflationary increases in development expenses, the statements for Quarters 2 through 6 are similar to those for
Quarter 1. Over this period, losses continue to erode NewCo’s cash. At the end
of the development period, Quarter 6, there is expected to be $117,379 remaining in cash.
Modeling the Start of Sales
In Quarter 6, development is completed, and by the end of the quarter, in anticipation of the initiation of sales, NewCo has an initial investment in net
working capital (inventory less A/P) on its balance sheet. Sales commence in
Quarter 7, with $60,000 of sales revenue appearing on the income statement.
With all sales made on account, much of the revenue goes to A/R, which increases net working capital. At this point, the venture is not profitable, with
a loss in Quarter 7 of $69,000. Operating cash flow is even more negative, attributable to the increase in net working capital that is needed to support sales
growth.
Modeling External Funding
At the end of Quarter 7, NewCo’s projected cash balance is approaching zero.
The venture is not yet profitable and has a negative operating cash flow. To
maintain the minimum cash balance, NewCo must begin to draw on its line
of credit. The credit line carries 2% quarterly interest, with interest expense
computed on the basis of the prior quarter’s balance. The balance sheet shows
the outstanding balance of the credit line beginning in Quarter 7; this is the
Tab le 8 .13 NewCo pro forma financial statements
Quarter
Income Statement
0
1
Unit sales
Selling price
6
7
18
24
26
$
300
200.00
$ $ -
$ $ -
$
$
60,000
27,000
$ 779,629
$ 350,833
$ 984,394
$ 442,977
Gross profit
Development expense
SG&A expense
$ $ 60,000
$ -
$ $ 63,061
$ -
$ 33,000
$ $ 102,000
$ 428,796
$ $ 256,336
$ 541,417
$ $ 298,293
Operating profit
Interest income (expense), net
$ (60,000) $ (63,061)
$ $ -
$ (69,000) $ 172,460 $ 243,124
$ $ (12,308) $ (13,025)
$ 245,555 $ 252,996 $ 255,526 $ 260,662
$ (12,450) $ (4,044) $
(975) $ -
Profit before income tax
Tax expense
$ (60,000) $ (63,061)
$ $ -
$ (69,000) $ 160,152
$ $ 56,053
$ 230,098
$ 80,534
$ 233,105
$ 81,587
$ 248,952
$ 87,133
$ 254,551
$ 89,093
$ 260,662
$ 91,232
Net income
$ (60,000) $ (63,061)
$ (69,000) $ 104,099
$ 149,564
$ 151,518
$ 161,819
$ 165,458
$ 169,430
Revenue
Cost of sales
-
4368
4368
$
234.52 $ 236.86
$
$ 994,238
$ 447,407
$ 1,024,365
$ 460,964
$1,034,608
$ 465,574
$1,055,404
$ 474,932
$ 546,831
$ $ 301,276
$ 563,401
$ $ 310,405
$ 569,035
$ $ 313,509
$ 580,472
$ $ 319,811
$
4368
227.62
23
0
$ -
$
4368
225.37
20
0
$ -
$
3494
223.13
19
4368
241.62
Balance Sheet
Cash
Net working capital (excluding cash)
$ 500,000
$ -
$ 440,000
$ -
$ 117,379
$ 13,500
$
$
15,000
47,044
$ 61,748
$ 611,303
$ 77,963
$ 715,901
$ 98,439
$ 723,060
$ 101,422
$ 744,969
$ 210,692
$ 752,419
$ 532,751
$ 767,543
Total current assets
Fixed assets, net
$ 500,000
$ -
$ 440,000
$ -
$ 130,879
$ -
$ 62,044
$ -
$ 673,052
$ -
$ 793,864
$ -
$ 821,499
$ -
$ 846,391
$ -
$ 963,111
$ -
$1,300,293
$ -
$ 440,000
$ 130,879
$
62,044
$ 673,052
$ 793,864
$ 821,499
$ 846,391
$ 963,111
$1,300,293
$ -
$ -
Total assets
Long-term debt (credit line)
$ -
$ -
$ -
$
165
$ 651,266
$ 622,514
$ 498,631
$
48,739
Total liabilities
Equity
$ 500,000
$ $ 440,000
$ $ 130,879
$
$
165
61,879
$ 651,266
$ 21,786
$ 622,514
$ 171,350
$ 498,631
$ 322,868
$ 48,739
$ 797,653
$ $ 963,111
$ $1,300,293
Total liabilities and equity
$ 500,000
$ 440,000
$ 130,879
$
62,044
$ 673,052
$ 793,864
$ 821,499
$ 846,391
$ 963,111
$1,300,293
(continued )
Tab le 8 .13
(continued )
Quarter
Statement of Cash Flows
0
Operating Cash Flow
Net income
Less: Increase in net working
capital (excluding cash)
1
6
7
18
19
$ (60,000) $ (63,061)
$ $ 13,500
$ (69,000) $ 104,099
$ 33,544 $ 127,145
Operating Cash Flow
Investing Cash Flow
Change in gross fixed assets
Financing Cash Flow
Change in long-term debt
(credit line)
dividend
$ (60,000) $ (76,561)
$ (102,544) $ (23,046) $
$ -
$ -
$ -
$ -
$ -
$
Financing cash flow
$ -
$ -
$
Net Cash Flow
Beginning cash
Ending cash
$ 500,000
Financing Activity
New financing needed
Debt repayment
Revenue
Surplus cash
Cumulative financing needed
Net income
Operating cash flow
Test: Cash out month
$ $ 485,000
$ 149,564
$ 104,598
44,966
20
23
24
26
$ 151,518
$
7,159
$ 161,819
$
7,376
$ 165,458
$
7,450
$ 169,430
$ 7,599
$ 144,359
$ 154,443
$ 158,008
$ 161,831
$ -
$ -
$ -
$ -
$ -
$ -
165
$
35,885
$ (28,752)
$ (123,883) $ (153,439) $ (48,739) $ -
165
$
35,885
$ (28,752)
$ (123,883) $ (153,439) $ (48,739) $ -
$ (60,000) $ (76,561)
$ 500,000 $ 193,940
$ 440,000 $ 117,379
$ (102,379) $
$ 117,379 $
$ 15,000 $
12,839
48,910
61,748
$
$
$
$
$
$
$ $ -
$ $ -
$
165
$ -
$ 35,885
$ -
$ $ 28,752
$ $ 425,000
$ $ (60,000)
$ (60,000)
$ $ 102,379
$ $ (63,061)
$ (76,561)
$ 60,000
$ $
165
$ (69,000)
$ (102,544)
$ 779,629
$ $ 651,266
$ 104,099
$ (23,046)
$ 984,394
$ $ 622,514
$ 149,564
$ 44,966
0
0
0
0
16,215
61,748
77,963
0
20,477
77,963
98,439
$
1,004
$ 100,418
$ 101,422
$ 109,270
$ 101,422
$ 210,692
$ 161,831
$ 370,920
$ 532,751
$ $ 123,883
$ $ 153,439
$ $ 48,739
$ $ -
$ 994,238
$ $ 498,631
$ 151,518
$ 144,359
$1,024,365
$ $ 48,739
$ 161,819
$ 154,443
$1,034,608
$ 108,255
$ $ 165,458
$ 158,008
$1,055,404
$ 428,255
$ $ 169,430
$ 161,831
0
0
0
0
Financial Modeling 313
start of a period of three years during which NewCo is expected to increase its
borrowing every quarter. Not until Quarter 19, after the period of rapid sales
growth ends, does the credit line balance begin to decrease as cash flow is sufficient to pay down the line of credit.
By Quarter 7, the company has exhausted the entrepreneur’s initial investment, and the balance sheet shows that, from an accounting “book value” perspective, owner’s equity is almost gone. If we were to construct financial statements in terms of economic value, the statements would look very different.
Assuming that the development activities are progressing, the expenditures
during the first six quarters are actually capital investments in an intangible
asset. The value of this asset, though not reflected in book value, is a critical
element of the venture’s economic value. In fact, without it, NewCo would have
little ability to attract funding. We have assumed that perceived economic value
is sufficient to convince a lender to make a long-term loan in the form of a line
of credit.
In Quarter 8 (not shown in the table but viewable in the Excel file on the
website), the book value of equity turns negative, which persists until it becomes
positive in Quarter 18. The negative balance simply means that all of the owner’s
initial capital has been expended, plus part of what was borrowed from the line
of credit. The equity is negative because of the long period of unprofitability
coupled with the increasing net working capital needed to support growth. If
economic value is high enough, the negative balance in the equity account should
not concern us. In fact, many new ventures with negative book equity still have
positive economic value.
Achieving Profitability
Thought not shown in Table 8.13, NewCo reaches profitability in Quarter 14.
As significant as this is as a milestone, operating cash flow continues to be
significantly negative because of the continuing working capital investment.
This difference between profit and cash flow is important because even though
NewCo is profitable, it continues to need to access the line of credit to offset
the negative operating cash flow.
Operating Cash Flow and Stable Growth
Unit sales grow at 25% per quarter through Quarter 19, when the expected
growth rate of unit sales drops to zero. In Quarter 19, NewCo’s accounting profits
exceed its working capital needs for the first time, resulting in positive operating
cash flow. This is a critical milestone, because it reverses the trend of q
­ uarterly
314
Chapter Eight
borrowing. With positive operating cash flow and reduced working capital needs
due to slower growth, the company begins to repay the credit line. Repayment
continues through Quarter 23, when a final payment reduces the credit line to
zero. From this point through Quarter 26, operating cash flow accrues to each
quarter’s ending cash balance and is assumed to be retained as surplus cash.
Forecasting Financing Needs
Figure 8.4 graphically depicts NewCo’s pro forma expected financial performance over the 26-quarter forecast period. Surplus cash starts at $485,000,
which is the entrepreneur’s Time 0 equity contribution minus the $15,000 minimum cash balance. By Quarter 7, surplus cash reaches zero and remains there
until Quarter 24. In Quarter 7, NewCo begins to borrow on the credit line.
The need for external financing continues for 11 quarters, with the credit line
peaking at $651,266 in Quarter 18.
Under our expected assumptions, the venture is in sound financial shape at
Quarter 26. It is profitable, is generating operating cash flow, has no debt, and
has over $480,000 in surplus cash. However, to reach this point, the venture is
expected to need to borrow over $651,000 along the way.
Uncertainty in the NewCo Model
The NewCo financial model provokes a number of questions. For example,
how might things change if some of the assumptions prove to be wrong (as
Fi g u r e 8 . 4
NewCo expected
financial performance
$1,200,000
$1,000,000
$800,000
$600,000
$400,000
$200,000
$0
3
6
9
12
15
18
21
Quarter
–$200,000
Revenue
Net income
OCF
Credit line
Surplus cash
24
Financial Modeling 315
they undoubtedly will)? What would change if the entrepreneur were to put
more equity into the deal, or if financing were raised in the form of outside
equity, or if it were raised in stages? Finally, how should the entrepreneur select from among the various financing options that may be available? More
generally, how does uncertainty affect the financing needs of the venture and
its economic value? We begin to explore these issues here and continue the
discussion in the next chapter.
In Chapter 7, we introduced uncertainty to the NewCo sales forecast with
regard to development time, initial selling price, and duration and magnitude
of quarterly sales growth.
The simulation results in Chapter 7 illustrated how this uncertainty can
produce very different trajectories for revenue over the 26-quarter projection
period. We now incorporate this development timing and revenue uncertainty
into the integrated financial model shown in Table 8.13 and add the following
random variables to the simulation: cost of sales, quarterly development expense, and variable SG&A expense: percentage of sales. Specifically, we make
the following assumptions:
Variable
Distribution assumption
Cost of sales
Quarterly development expense
Variable SG&A expense
Uniform distribution with a minimum of 40% and maximum of 50%
Normally distributed with a mean of $60,000 and standard deviation of $600
Triangular distribution with a minimum of 18%, most likely of 20%, and maximum
of 30%
Again, you should assume that these distributions were developed using
some combination of yardstick information, industry data, and fundamental
analysis.
Linking Assumptions to the Financial Model
To facilitate use of the NewCo model for simulation, we have specified all of the
assumptions in a single worksheet that is linked directly to the financial statements. Table 8.14 shows the assumption sheet, including one random trial for the
simulated variables. In this trial, it turns out that development success is achieved
in Quarter 9 and the rapid-growth period extends for nine quarters through
Quarter 18. The initial unit sales level is 270, initial selling price is $226.74, and
cost of sales is 43.66% of price. Development expense begins at $58,934 per quarter and SG&A is 25.65%. The shaded cells can be changed to test for such things
as the effects of a larger initial investment and different expectations about cost
percentages. The unshaded cells are values derived by simulation.
Tab le 8 .14 NewCo simulation assumptions
Revenue Assumptions
Development Completion Quarter (lognormal distribution)
Preliminary quarter
Development completion quarter
Development failure (1 = yes)
Rapid Growth Period (normal distribution)
Standard deviation of rapid growth period (quarters)
Realized length of rapid growth period (quarters)
Initial Unit Sales per Quarter
Initial units/quarter
Unit Sales Growth during Rapid Growth (normal distribution)
Expected growth/quarter
Standard deviation of growth/quarter
Realized growth rate per quarter
Initial Selling Price (normal distribution)
Expected initial selling price
Standard deviation of selling price
Realized initial selling price
Inflation Rate per Quarter
Inflation/quarter
9
9
0
1.5
9
270
25.00%
6.00%
24.28%
$200.00
$20.00
$226.74
1.00%
Income Statement Assumptions
Cost of Sales (uniform distribution)
Minumum cost of sales
Maximum cost of sales
Realized cost of sales
Quarterly Development Expense (normal distribution)
Quarterly development expenses (expected)
Quarterly development expenses (standard deviation)
40.00%
50.00%
43.66%
$60,000
$600
Realized development expense
SG&A Expenses (fixed + triangular distribution)
Quarterly fixed SG&A expense
Minimum variable SG&A (expected % of sales)
Most likely SG&A expense
Maximum SG&A expense
$58,934
Realized variable SG&A percent of sales
Interest Income and Interest Expense
Interest expense per quarter
25.65%
Interest income on surplus cash per quarter
Income Tax Expense
Income tax rate (on positive income)
$90,000
18%
20%
30%
2.00%
0.00%
35%
Financial Modeling 317
Tab le 8 .14
(continued )
Balance Sheet Assumptions
Cash Balance
Minimum cash balance
Continuing cash percentage of prior quarter sales
$15,000
10.0%
Accounts Receivable Policy
Percentage of current quarter sales
50%
Inventory and Payables Policy
Percentage of next quarter cost of sales
50%
Initial Investment
Initial equity investment
$500,000
The table represents the assumptions page of the NewCo integrated financial model. Shaded cells are
inputs that can be changed; other cells are generated using the assumptions. Information in the
assumptions page links directly to the NewCo financial model.
Results of the Simulation
The NewCo simulation model is designed so the venture has an open-ended
line of credit. If the venture runs short of cash in any quarter, borrowing is
automatic and unlimited. Anytime the venture generates free cash flow, repayment of the loan is automatic. The quarterly credit line balance, which reflects
NewCo’s cumulative cash needs, is affected by how quickly development occurs, the growth and profitability of subsequent sales, and the amount of investment required for working capital.7
It may seem that cumulative financing needs would be lowest when performance is good and highest when it is not. However, the relationship is not so
simple. For example, in one simulated trial, where development fails and the
venture is assumed to keep trying through Quarter 26 and $500,000 of initial
equity funding is assumed, the credit line balance reaches $1.46 million. If this
project is abandoned after Quarter 15, the balance is $497,000. In a high-profit
trial where development success occurs early, the maximum credit line balance
is $438,000, but in another high-profit trial with a similar development success
date, the credit line balance reaches $1.89 million by Quarter 23 before turning down. The high need in this case occurs because the revenue growth rate
is very high.
The point is that large credit line balances can occur for good or bad reasons
and are not necessarily evidence of NewCo’s success or failure. In the next
chapter, we consider how operational performance impacts the venture’s need
for financing. We also consider how staging, milestones, and financing decisions
can help distinguish ventures that are likely to be successful from those that
are probable failures.
318
Chapter Eight
We use a simulation of 1,000 trials to examine some of the important outcome
variables from NewCo. Revenue in Quarter 26 ranges from $0 to more than $17.4
million, though half of the trials (the interquartile range) have revenue between
about $258,800 and $1,713,500. Quarter 26 net income is negative in more than
30% of the trials but also has the potential to be very high. While the median is
$78,800, the mean is $205,400, because there are a small number of very good
outcomes. Ending cash flow is similar to net income, but somewhat lower, with
a median of $72,700 and mean of $194,700. The maximum balance on the line
of credit ranges from a low of $339,400 to almost $3.5 million but three-quarters
of the time is less than about $1.4 million.
Ideally, we would like to invest in the good outcomes but to cut off investment
in the bad ones as quickly as we can identify them. In Chapter 9, we explore the
use of simulation to assess financing needs and opportunities to stage financing
for the purpose of creating more value from investment in the venture.
8.7
Summary
Pro forma financial statements are important to any new venture and a key
component in any entrepreneur’s or investor’s toolkit. Financial forecasting
adds discipline to the way an entrepreneur or investor thinks about the venture. It provides estimates of future cash flows, which drive financing decisions and are important determinants of value. A forecast can also be important for convincing prospective investors of the merits of the project and can
provide specific performance benchmarks around which financing and incentive contracts can be designed.
A well-constructed integrated financial model of a new venture accomplishes
two things. First, it reflects the important aspects of the venture’s business
model in the three main financial statements: income statement, balance sheet,
and cash flow statement. Second, it provides reliable estimates of the venture’s
future cash flows.
We use the venture’s cash conversion cycle to better understand the important
links between day-to-day operations and cash flow. The cash conversion cycle
visually portrays how cash moves through the firm, as well as in and out of
the firm from and to capital providers. Working capital policies are important
determinants of a venture’s operating cash flows. These include the granting
of credit to customers, levels of inventory on hand, and the ability to generate
spontaneous financing in the form of A/P.
Preparing a credible and useful financial model requires well-researched and
defensible assumptions. Yardsticks based on public or private companies and
Financial Modeling 319
industry statistics can be used to develop benchmark performance metrics and
to build the new venture’s financial model. Public SEC documents, primarily
10-K and prospectus filings, are one source of information on comparable firms.
The yardstick approach is straightforward to implement and can provide robust
estimates of revenue growth, resource requirements, and industry practices
related to working capital management.
No new venture will perform exactly like the yardstick companies, however,
and financial projections should also reflect the use of fundamental analysis to
develop the schedule of assumptions. An integrated financial model might use
assumptions developed using yardstick data and then validated with fundamental analysis or the converse.
With a schedule of the key assumptions, spreadsheet modeling can be used
to develop an integrated financial model for preparing pro forma financial forecasts. An integrated model features an income statement, a balance sheet, and
a cash flow statement, all linked to one another and across time. An integrated
financial model allows the entrepreneur or investor to conduct “what if” analysis
and facilitates the use of simulation to assess the effects of uncertainty. Both
scenario analysis and simulation analysis are valuable tools for assessing the
prospects and potential future value of any new venture.
Review Questions
1. What information does each of the three main financial statements convey about the venture’s operations? Describe three important links
across the statements.
2. Describe how to generate spontaneous financing by managing A/R and
A/P.
3. Explain the cash flow cycle in Figure 8.1.
4. What are the main components of a venture’s working capital policy?
How does each impact the firm’s operations and cash flows? How much
control does a new venture have over its working capital policies?
5. Describe several common sources of industry and firm data that can be
used as bases for developing forecast assumptions.
6. Under what conditions is the yardstick approach to preparing pro
forma financials likely to produce accurate statements? When is fundamental analysis a preferable method?
7. Describe three different approaches to making the balance sheet balance
when building an integrated financial model using spreadsheets.
320
Chapter Eight
8. Explain the concept of depreciation as a “noncash” expense. Does this
mean it has no impact at all on a venture’s cash flows?
9. What can cause the retained earnings account to change from one period to the next?
10. What is the accounting impact of negative shareholders’ equity? How
can a venture with negative book equity continue to operate and attract
capital?
Notes
1. Net working capital is usually defined as all current assets less all current liabilities. However, some current assets, such as marketable securities,
may not be central to operation of the venture, and some current liabilities,
such as notes payable, which are related to financing, are not considered part
of the operating activities of the business.
2. See Ng, Smith, and Smith (1999) for supporting evidence.
3. Smith (1987) describes credit terms with significant discounts for
prompt payment as a device the seller can use to gain timely information
about the financial health of its customers.
4. See http://​w ww​.rmahq​.org/​annual​-statement​-studies/ for more
information.
5. When developing assumptions based on comparables or industry statistics, medians can be more meaningful than means because medians are not
affected by extreme outlier values.
6. To compute days in inventory, divide 365 by the inventory turnover
ratio.
7. The simulation results discussed here are not shown, but the model is
available online for @Risk and Crystal Ball users.
References and Additional Reading
Mian, S. L., and C. W. Smith Jr. 1992. “Accounts Receivable Management
Policy: Theory and Evidence.” Journal of Finance 47: 169–​200.
Ng, C., J. K. Smith, and R. L. Smith. 1999. “Evidence on the Determinants of
Credit Terms Used in Interfirm Trade.” Journal of Finance 54: 1109–​29.
Smith, J. K. 1987. “Trade Credit and Informational Asymmetry.” Journal of
Finance 42: 863–​72.
C h a p t e r Nine
A ss e ssi n g Ca s h N e e ds
T h e o b j ec t i v e o f t h i s c h a p t e r is to enable the entrepreneur to
answer the question, “How much money do I need and when do I need it?”
For this, we build on the forecasting tools from Chapters 7 and 8 and focus on
methods of assessing financial needs.
An entrepreneur must have a good sense of how much cash will be required
to carry the venture to the point where it becomes self-sustaining or capable of
attracting additional funding, as well as a good sense of when cash infusions
are likely to be needed. An entrepreneur who does not anticipate the cash needs
over the life of the venture assumes unnecessary risk. The venture may fail, not
because the idea is bad but because the entrepreneur did not anticipate the cash
need far enough in advance to do anything about it. An entrepreneur’s failure to
anticipate the need for financing can also result in an adverse negotiating position with investors—either because the need is urgent or because the original
financing agreement impedes the ability to raise cash in the future.
“Do not run out of cash” is a common admonition to entrepreneurs. But
having too much cash can also be problematic. An entrepreneur who is overly
cautious may find that raising “enough” cash up front is not feasible. Even if
substantial early-stage financing can be arranged, it may come at a high price,
and the entrepreneur may be compelled to give up more of the venture than is
necessary or desirable. Although a venture cannot survive without cash, the
objective is not merely survival; rather, it is to finance the venture in a way that
yields the highest expected value for the parties.
As a general principle, an entrepreneur who is more confident of success than
are potential investors can benefit by raising only enough cash to carry the venture to the next milestone. At that point, the lower risk of failure will be more
321
322
Chapter Nine
apparent to investors. Reduced risk can foster competition among investors and
should enable the entrepreneur to raise capital on more favorable terms than in
the earlier round. There is, of course, a trade-off. Raising less cash in an early
round increases the probability that the venture will run out of cash before the
next milestone. Running short of cash before reaching an important milestone
can suggest to investors that the venture is not on track for success.
A cash flow breakeven analysis is one tool for assessing financial needs. It can
be used to identify the cash flow breakeven point (BEP) and to estimate cash
needs for a firm that is operating below its BEP. The BEP is the level of sales at
which a venture would be able to maintain operations without additional funding, although the venture still could need funding for growth. Hence, combining cash flow breakeven analysis with projections of sales growth can help the
entrepreneur assess the amount of investment a venture would need in order to
achieve a level of sales sufficient to maintain its operations.
The sustainable growth model is another useful tool. The model seeks to
identify the conditions under which the growth of a venture can be sustained
solely by the initial investment. We refer to this as the “sustainable growth
rate.”1 While the model is intended more for established businesses, it can also
be helpful for estimating the financing needs of early-stage ventures that are
expected to grow. If the entrepreneur anticipates or aspires to a growth rate that
is higher than the sustainable growth rate, additional financing is required and
alternatives for adding investment capital must be evaluated.
Later in the chapter, we use scenario analysis and simulation to focus on
how uncertainty affects the need for financing. These methods can be used to
assess how cash needs may be affected by uncertainty about development timing, sales levels, fixed and variable costs, and other factors. Assessing financial
need only on the basis of expected performance can expose the entrepreneur to
avoidable risk of venture failure or loss of control. When a venture’s prospects
are uncertain, staging the investment around milestones can be of great value,
but uncertainty of performance also makes the funding need in a given financing
round uncertain. In the last part of the chapter, we use simulation to fine-tune
the funding decision in the context of staged financing.
9.1
Cash Flow Breakeven Analysis
Much is written about breakeven analysis in accounting and finance textbooks. Not all of it is flattering, primarily because the accounting net income
approach to breakeven analysis ignores the time value of money and focuses
Assessing Cash Needs 323
on accounting net income rather than cash flow. In the accounting net income
approach, the BEP is the quantity of sales or amount of revenue where the
total contribution margin over all units sold equals total fixed cost. The contribution margin is the difference between price and variable cost. So, for example, if the price is $20 and variable cost is constant at $16 per unit, the contribution margin is $4. With total fixed cost of $175,000, the breakeven point
would be 43,750 units, or $875,000 in revenue.
The accounting net income approach can make sense for long-run analysis
if it is expected that depreciation expense will be offset by the new investment
necessary to maintain the capital stock. But the approach does not work well if
one desires to assess the short-run BEP or if the investment needed to maintain
the fixed assets is systematically different from depreciation expense. Moreover,
even as a long-run concept, because accounting approaches do not factor in
the opportunity cost of capital, they are not well suited for investment decision
making.
For assessing financial needs, cash flow breakeven analysis can provide insight. It addresses the question, “What level of sales generates operating cash
inflows that are sufficient to cover operating cash outflows?” The cash flow BEP
is where the venture achieves a level of sales high enough to maintain its operations at the current level. At the cash flow BEP, cash inflows from operations
are sufficient to maintain and replace current assets but not to fund growth.
This is the minimum level of revenue the venture needs to survive without additional funding.
In conjunction with a forecast of revenue, finding the cash flow BEP can
help the entrepreneur assess initial financing needs. Once a breakeven model
is constructed, the entrepreneur can use it to estimate how initial cash needs
depend on sales levels, sales growth, product prices, fixed costs, variable costs,
and noncash revenues and expenses. Breakeven analysis can also be used to
conduct a variety of “what ifs” or sensitivity analyses.
An Illustration of Net Income and Cash Flow BEPs
Consider an entrepreneur, Trinity Matrix, who has developed a low-cost virtual reality (VR) headset that she plans to market on Amazon.com for $10 per
unit. Before she can do so, she needs to invest in a production facility. The
fixed investment in the facility is expected to be $3 million. The investment
can be depreciated over five years, straight line (i.e., $600,000 per year in depreciation expense). After that point, a fixed annual expenditure of $100,000
will be sufficient to maintain the facility. She will also need to make an investment in net working capital, expected to be 10% of annual sales. Variable cost,
Panel A
Income Statement
Unit sales volume
Unit price
0
1
50,000
$10
2
250,000
$10
3
250,00 0
$1 0
4
62,50 0
$1 0
5
62,50 0
$1 0
6
62,500
$10
7
62,500
$10
$500,000
$350,000
$600,000
$950,000
$125,000
$1,075,000
($575,000)
$2,500,000
$350,000
$600,000
$950,000
$625,000
$1,575,000
$925,000
$2,500,00 0
$350,00 0
$600,00 0
$950,00 0
$625,00 0
$1,575,00 0
$925,000
$625,00 0
$350,00 0
$600,00 0
$950,00 0
$156,25 0
$1,106,25 0
($481,250)
$625,00 0
$350,00 0
$600,00 0
$950,00 0
$156,25 0
$1,106,25 0
($481,250)
$625,000
$450,000
$0
$450,000
$156,250
$606,250
$18,750
$625,000
$450,000
$0
$450,000
$156,250
$606, 250
$18,750
($575,000)
$350,000
$1,275,000
$793,750
$312,500
$331,250
$350,000
0
1
$7.50
$950,000
126,667
$1,266,667
2
$7.50
$950,000
126,667
$1,266,667
3
$7.50
$950,000
126,667
$1,266,667
4
$7.50
$950,000
126,667
$1,266,667
5
$7.50
$950,000
126,667
$1,266,667
6
$7.50
$450,000
60,000
$600,000
7
$7.50
$450,000
60,000
$600,000
0
1
$375,000
$25,000
$50,000
($25,000)
($3,025,000)
2
$1,875,000
$1,525,000
$200,000
$1,325,000
($1,700,000)
3
$1,875,000
$1,525,000
$0
$1,525,000
($175,000)
4
$468,750
$118,750
($187,500)
$306,250
$131,250
5
$468,750
$118,750
$0
$118,750
$250,000
6
$468,750
$18,750
$0
$18,750
$268,750
7
$468,750
$18,750
$0
$18,750
$287,500
1
$7.50
$350,000
46,667
$466,667
2
$7.50
$350,000
46,667
$466,667
3
$7.50
$350,000
46,667
$466,667
4
$7.50
$350,000
46,667
$466,667
5
$7.50
$350,000
46,667
$466,66 7
6
$7.50
$450,000
60,000
$600,000
7
$7.50
$450,000
60,000
$600,000
$1,325,000
$1,095,041
$1,525,000
$1,145,755
$306,250
$209,173
$118,750
$73,734
$18,750
$10,584
$18,750
$96,217
Sales
Fixed cash expenses
Depreciation expense
Total fixed expenses
Variable costs
Total expenses
Net income
Cumulative net income
Panel B
Net Income BEP
Contribution margin per unit
Total fixed costs
Unit BEP
Dollar BEP
Panel C
Cash Needs Assessment
Total contribution to fixed costs
Contribution after fixed cash expenses
Change in net working capital
Net cash flow
Cumulative cash need
Panel D
Cash Flow BEP
Contribution margin per unit
Cash fixed costs
Unit BEP
Dollar BEP
Panel E
NPV Investment Assessment
Net cash flow for period
PV of cash flow (10%)
Net present value
($3,000,000)
0
($3,000,000)
($3,000,000)
($392,223)
($25,000)
($22,727)
Fi g u r e 9.1
Net income and cash flow break-even points, cash needs assessment, and investment value
The figure shows annual net income, net income and cash flow break-even points, annual net cash flows, cash flow BEPs, cumulative cash needs, and a valuation of the
virtual reality venture.
Assessing Cash Needs 325
including production, marketing, and distribution, is estimated to be 25% of
sales. During the first five years, the cash portion of annual fixed costs (i.e., excluding depreciation) is expected to total $350,000. The entrepreneur projects
that the venture can achieve sales volume of 50,000 units in Year 1 and 250,000
units in each of Years 2 and 3. After this demand peak, annual sales volume
will stabilize at around 62,500 units. Before deciding to move forward with the
project, Ms. Matrix would like to develop a better understanding of the cash
needs of the venture.
We begin the analysis by constructing annual income statements for the
venture and using the cost information to compute the net income BEP for each
year. Panel A of Figure 9.1 shows the annual income statements. Net income is
negative in the low-sales-volume years during the time when the initial investment is being depreciated but is positive when sales volume is high and after the
investment is fully depreciated. Cumulative net income over the period of the
explicit forecast is $350,000, suggesting that the project may be worth pursuing.
Panel B shows the net income BEP for each year. During the first five years,
because of depreciation expense, the net income BEP is high, at $1.267 million
over this period. Starting in Year 6, when depreciation is zero but fixed costs to
maintain the facility increase by $100,000, the net income BEP falls to $600,000.
However, the analysis in Panels A and B is misleading because it is not focused
on cash flow. Note that changes in net working capital that are driven by sales
growth affect the cash flow BEP but do not affect the net income BEP.
Using Breakeven Analysis to Project Financial Needs
Using breakeven analysis to assess financial needs requires another step. Financial need depends on two things; one is the time until breakeven is reached
and the other is the amount of financing required to cover the shortfalls until
that time. Thus, to estimate the amount of financing required for the VR venture, we can combine breakeven analysis with a sales forecast. We can use the
cash flow BEPs in conjunction with the sales forecast to estimate cash needs.
In Panel C, we use the financial information to project annual net cash flow,
beginning with the initial $3 million investment. The first row of Panel C is
computed as unit sales volume times the $7.50 contribution margin per unit
(75% of the $10 price). In the second line, cash fixed expenses from Panel A
are deducted. The third line is computed from balance sheet information on
working capital as 10% of the annual change in sales revenue. In years when
revenue increases, an additional investment in working capital is required; in
years when revenue declines, the decline in net working capital is a source of
cash. In the fourth line, the change in working capital is subtracted from the
326
Chapter Nine
contribution after fixed expenses to get annual net cash flow. The final line in
the panel shows cumulative net working capital. The cumulative total is positive
but below cumulative net income.
In contrast to net income, net cash flow is positive in all years after the first.
Also, net cash flow is higher than net income in Years 1 through 5 because
depreciation is noncash and cash flow is equal to net income in Years 6 and 7
because all expenses are cash expenses. We can see from Panel C that beyond
the initial investment at Year 0, the venture is expected to need another $25,000
in Year 1, after which net cash flow is expected to be positive.
Panel D shows the cash flow BEP annually. This BEP of $467,000 in Years
1 through 5 is well below the net income BEP, and, at $600,000, is equal to the
net income BEP in Years 6 and 7.
One problem with the cash flow BEP analysis is that it is static in that necessary changes in investment are ignored. That is, the static BEPs in Panel D do
not take account of the necessary changes in net working capital. The net cash
flow projections in Panel C do reflect working capital changes.
The analysis in Panels A through D does not tell us whether pursuing the
venture would be a good idea (even though cumulative net income and cumulative net cash flow through Year 7 are both positive). For that, we would need
an analysis of net present value. Panel E shows a simple investment analysis,
discounting each year’s free cash flow (FCF) at an assumed rate of 10%, with
theYear 7 cash flow being capitalized as a perpetuity.2
Present Value Breakeven Analysis
Cash flow breakeven analysis enables the entrepreneur to get a better feel for
the venture by providing a way to assess how sales levels, prices, and fixed
and variable costs affect cash needs. However, the analysis does not contribute
much insight to the investment decision since it does not use the PV concept or
risk assessments that are central to investment decision making.
A modified form of breakeven analysis can determine the level of sales where
the PV of revenues is sufficient to cover the PV of cash outflows (both cash expenses and investment outlays). Present value breakeven analysis helps answer
the question, “What level of sales is needed to justify investing?” It is best suited
for projects where revenue and expense streams can be described as level annuities. The PV approach is most helpful for capital budgeting and investment
decisions. It is unlikely to be of much help for assessing cash needs.
For example, we could ask what level of perpetual free cash flow (FCF) would
justify an investment of $3 million if the cost of capital is 10%. The answer
Assessing Cash Needs 327
is $300,000 (because $300,000/0.10 = $3 million). FCF is cash flow above the
amount needed to fund expected growth and to deal with uncertainty. In this
case, if cash fixed costs are $500,000 and the contribution margin is 75%, the
annual revenue level to achieve the cash flow breakeven point is $800,000/.75 =
$1.067 million. That is, the contribution from $1.067 million of sales is $800,000,
of which $500,000 covers fixed cost, and the balance of $300,000 is the FCF
available to investors.
9.2 Sustainable Growth
The breakeven approach to estimating cash needs is based on the assumption that the investment in fixed assets does not vary over a relevant range of
sales levels. An alternative approach is to assume that the investment in fixed
assets must increase in proportion to sales. With that as a starting point, we
can use the sustainable growth model to estimate the level of resources that
can be generated through the continuing operations of the venture and, if a
higher rate of growth is projected, the additional investment that would be
required.
The model, while it is designed for established businesses that are expected to
grow in fixed proportion with constant profitability over a relevant range of asset
levels, can be modified to estimate cash needs of an asset-driven venture, where
sales are expected to grow in proportion to assets, but profitability is subject to
economies of scale. Sustainable growth, in the standard application, starts with the
assumption that as the venture grows, assets, debt, equity, sales, and net income
all grow in fixed proportion to sales.3 This means the sustainable growth rate for
an established enterprise depends on four factors:
1. Asset turnover (“turnover”)—the amount of sales revenue that can be
supported per dollar of assets, including fixed assets and net working
capital
2. Financial leverage (“leverage”)—the ratio of the venture assets to its
equity, where the difference between assets and equity represents debt
financing
3. Return on sales (“ROS”)—the profitability of sales in terms of after-tax
net income per dollar of sales
4. Dividend policy (“retention”)—the fraction of each dollar of net income
that is retained in the venture as opposed to being paid out as dividends
328
Chapter Nine
An Established Business Example
Figure 9.2 illustrates the concept of sustainable growth, denoted as g*, and
incorporates the following assumptions:
Factor
Definition and value
Asset turnover
Financial leverage
Return on sales (ROS)
Dividend retention (R)
Sales / total assets = 3.0
Total assets / equity = 1.5
Net income / sales = 10%
Fraction of net income retained = 2/3
Suppose an entrepreneur makes an initial investment of $100 in the form of
equity and wishes to calculate the rate of growth this can sustain. The leverage
ratio of 1.5 (assumed to be appropriate for the venture) implies that the venture
has $1.50 of assets for each $1 of equity. The difference of $50 is debt financing.
These amounts are reflected in the starting balance sheet shown in Figure 9.2.
Reinforcing the notion of statement integration from Chapter 8, the turnover
ratio in the figure shows that each $1 of assets (in the balance sheet) is expected
to support $3 of sales (in the income statement). This results in sustainable
sales in the first year of $450. The assumed 10% return on sales (shown in the
income statement) implies Year 1 net income of $45. In the figure, we assume
that the venture has adopted a policy of retaining two thirds of the net income
Starting Balance
Sheet
Year 1 Income
Statement
Ending Balance
Sheet
g* = 30.0%
Initial equity
investment
Sales
$100
$450
Initial equity +
retained
earnings
$130
×
×
×
Leverage
1.5
Turnover
3
=
Initial total
assets
$150
E = $100
D = $50
ROS
10%
=
Net income
$45
Retention
66.7%
$30
Leverage
1.5
Dividend
payout
33.3%
$15
Ending total
assets
$195
E = $130
D = $65
Year 2
sales
$585
=
Fi g u r e 9. 2
Sustainable growth model template
This figure is an Excel template that illustrates the key variables, relationships, and results in the sustainable growth model. Assumptions of
the model can be changed in the template to assess their impact on g* and the levels of assets, debt, and sales.
Assessing Cash Needs 329
and distributing one third as a dividend; hence, $30 goes to retained earnings
in the balance sheet, increasing equity to $130.
Because the leverage ratio remains constant at 1.5, $130 of equity supports
$195 of assets at the beginning of the second year. This level of assets, based
on the turnover ratio of 3.0, will support an increase in sales to $585. This 30%
increase in sales is the sustainable growth rate of the venture, given its leverage
and dividend policies, its profitability, and the efficiency of use of its assets.4
Financial Policy Choices
Figure 9.2 shows that the sustainable growth rate, g*, is equal to the percentage change in equity. That is:
g* = ΔE/EBeginning
(9.1)
where E is equity and ΔE is the change in equity due to retaining earnings.
Because the sustainable growth model excludes the possibility of issuing new
equity, the change in equity comes only from net additions to retained earnings; that is,
ΔE = NI − Div = NI × R
(9.2)
where NI is net income, Div is the dividend, and R is the retention ratio. For a
venture seeking the maximum sustainable growth rate, R would be 100%. Of
course, if income is negative, as it is for many new ventures, then so is the sustainable growth rate, and the venture will depend on new investment of debt
or equity, both to sustain its current size and to grow.
Substituting Eq. (9.2) into Eq. (9.1) gives:
g* = ΔE/EBeginning = (NI × R)/EBeginning = NI/EBeginning × R = ROE × R
(9.3)
where ROE is return on beginning equity, a simple measure of the venture’s
profitability.
We can separate ROE into three components as follows:
ROE = NI/E = NI/S × S/A × A/E = ROS × Turnover × Leverage
(9.4)
where S is sales and A is total assets. Substituting Eq. (9.4) into Eq. (9.3) yields
g* = ROE × R = ROS × Turnover × Leverage × R
(9.5)
Thus, the venture’s sustainable growth rate is the product of (1) its profit margin or return on sales (NI/S or ROS), (2) the asset turnover ratio (S/A or turnover), (3) financial leverage (A/EBeginning or leverage), and (4) the retention ratio
or dividend payout policy (R). The equation reflects the assumption that sales,
net income, assets, and debt all increase proportionately as the venture grows.
330
Chapter Nine
Equation (9.5) suggests that the sustainable growth rate of a venture can be
increased in several ways. The entrepreneur can try to improve the profit margin on sales, generate more sales from its asset base, or rely more heavily on
financial leverage. If the venture is already operating efficiently, then increasing
profitability and asset turnover are not feasible. This leaves leverage and the
retention ratio as the policy choices that can influence g*.
Interest Expense, Taxes, and Economies of Scale
Because of the tax deductibility of interest payments, net income is not independent of the leverage policy. To see the interdependence, as well as the tax
shelter, we restate net income as
NI = EBIT − I − T = [EBIT − r(A − E)](1 − t)
(9.6)
where EBIT is earnings before interest and taxes, I is interest expense, T is
taxes, A − E is assets minus equity (the amount of debt financing), r is the interest rate on debt, and t is the corporate tax rate. The term in square brackets
is simply income before taxes. Substituting Eq. (9.6) into Eq. (9.5) yields
g* = [EBIT − r(A − E)](1 − t)/S × Turnover × Leverage × R
(9.7)
Equation (9.7) can be used to see how the sustainable growth rate varies in response to leverage (defined as assets/equity) and payout policy choices.
Further, to adapt the model to early-stage ventures, we can express EBIT as
a function of sales. Suppose that the venture is subject to economies of scale. If
the relationship is linear, EBIT can be expressed as EBIT = a + b × S, where a
and b are parameters reflecting the scale economies of the venture, so that Eq.
(9.7) can be expanded as follows:
g* = [(a + b × S) − r(A − E)](1 − t)/S × Turnover × Leverage × R
(9.8)
The explicit incorporation of scale economies makes the sustainable growth
rate dynamic. As the venture grows, it becomes more profitable and internally
generated funds can support a higher rate of growth. Realistically, at lower
levels of sales, the venture would be more dependent on external financing to
support a targeted growth rate.
An Early-Stage Venture Example
Gill Bates is considering a venture to develop and support an online virtual world, iFree. Bates is prepared to make an initial equity investment of
$500,000. Consistent with what is normal for early-stage ventures, he plans to
use no debt financing. For strategic reasons, he believes the iFree venture must
Assessing Cash Needs 331
reach sales of $2.0 million by the start of the sixth year and seeks to grow the
venture at a constant rate of 14.87% per year to achieve this. Bates is willing to
retain all earnings in the venture to reach this goal.
In the iFree business model, we assume, reflecting the scale economies of the
venture, an operating margin (EBIT/S) of –$200,000 + 20% of sales. That is, the
venture has $200,000 in fixed costs and variable cost of 80% of sales, resulting in
a 20% contribution margin. In addition, the asset turnover ratio is expected to
be 2.0, and the corporate tax rate is 35%. Bates’s decision to avoid debt means
the leverage ratio (A/E) is initially 1.0.
We can quickly establish that the initial investment of $500,000 will not be
sufficient to achieve the Year 6 sales target. The equity investment of $500,000
will support $1 million in sales based on the turnover ratio (S/A) of 2.0. The
projected EBIT at that level of sales is $0 (i.e., 20% of sales, less the base level
loss of $200,000). At this level, the venture will just break even on profitability,
so the targeted 16.5% growth of assets would need to come from an additional
investment of equity of $82,500 (i.e., 16.5% of the initial $500,000 investment).
With no debt, assets and equity are equal. We can use Eq. (9.8) to determine the
additional equity investment needed each year for iFree to achieve the growth
rate that will enable it to achieve the sales target of $2.0 million by the sixth year.
Figure 9.3 shows iFree’s projected assessment of cash needs over the first six
years. In the first year, the targeted growth rate is achieved entirely from new
outside equity. After the first year, because of scale economies, profitability
increases and the need for additional outside equity declines. The lower panels
in the figure show key assumptions in the model, the sustainable growth rate
Year
1
2
3
4
5
6
Beginning
Equity
$500,000
$574,347
$659,748
$757,848
$870,534
$999,977
Assets
$500,000
$574,347
$659,748
$757,848
$870,534
$999,977
Assumptions
Assets/equity
Sales/assets
EBIT intercept
EBIT slope
Interest rate
Tax rate
Dividend payout pct.
Target growth rate
Sales
EBIT
$1,000,000
$0
$1,148,693 $29,739
$1,319,496 $63,899
$1,515,695 $103,139
$1,741,069 $148,214
$1,999,954 $199,991
1.0
2.0
–200,000
20.00%
10.00%
35.00%
0 .0 0 %
14.87%
Interest
$0
$0
$0
$0
$0
$0
Taxable
Income
$0
$29,739
$63,899
$103,139
$148,214
$199,991
Year
1
2
3
4
5
6
Tax
$0
$10,409
$22,365
$36,099
$51,875
$69,997
Retained
Net
Dividend
Earnings
Income
$0
$0
$0
$0
$19,330
$19, 330
$0
$41,534
$41,534
$0
$67,040
$67,040
$0
$96,339
$96,339
$0
$129,994
$129,994
Sustainable Growth Rate
0.00%
3.37%
6.30%
8.85%
11.07%
13.00%
Ending
Equity
$574,347
$659,748
$757,848
$870,534
$999,977
$1,148,666
New Equity
$74,347
$66,071
$56,565
$45,646
$33,103
$18,696
Entrepreneur Ownership
Value
Percent
$500,000
100.00%
$516,828
89.99%
$549,365
83.27%
$597,962
78.90%
$664,137
76.29%
$750,473
7 5 .0 5 %
Fi g u r e 9. 3
Dynamic sustainable growth for an early-stage venture
The figure shows the relation of sales level to EBIT, with implications for the sustainable growth rate, the amount of outside financing
required to achieve a targeted growth rate, and the fraction of entrepreneur ownership.
332
Chapter Nine
each year, and the cumulative percentage of book valued equity held by the entrepreneur. Given the assumptions, the sustainable growth rate asymptotically
approaches 20% as the venture grows and the entrepreneur’s ownership fraction
gradually declines but still remains above 75% in the sixth year.
Without knowing the market value of the venture at each year, we cannot
determine the fraction of equity Bates would have to give up in exchange for
the outside equity. That will depend on the value the investor places on the
venture at each point. Nor can we determine the value of the remaining equity
to the entrepreneur.
The sustainable growth model, even with modifications for interest expense
and taxes, is still only an approximation approach. In the example, the venture
never has negative income, so we do not confront the question of how taxes
would be affected by negative operating income. Also, the model is focused
on earnings and not on cash flow. If the assets are depreciable, net income will
understate the cash flow available to support growth. In that case, the amount
of external financing required would be less than what is estimated in the
model. To address those concerns, a fully integrated financial model would
do a better job.
9.3 Planning for Financial Needs When the Desired Growth
Rate Exceeds the Sustainable Rate
Product-market growth that is either too rapid or too slow is problematic.
Growth that is too slow threatens venture survival by encouraging competition or hastening technological obsolescence. As suggested by the prior example, growth that is too rapid threatens the entrepreneur’s control and the
value of the entrepreneur’s ownership share.
Long-run survival depends on achieving a level of sales that is sufficient for
financial viability. In some product markets, survival and profitability depend
on rapidly attaining a substantial market position. Computer software and some
Internet ventures are good examples of markets in which long-run survival can
depend on the ability to achieve a critical mass of users quickly. In the case of
software, this is because many software packages benefit from important “network externalities.” Demand for the product increases as more users affiliate
with the network. Because of these network externalities, a software manufacturer that does not quickly achieve substantial market share may be driven from
the market, even if the product is good. There are also significant switching
costs to software users, making them more likely to adopt and continue using
the market leading product.
Assessing Cash Needs 333
In some cases, network externalities are sufficiently important that a dominant product can hold competitors at bay, even when products that are technically superior are available at lower prices. In others, rapid growth is not essential for survival but still may be an element of the entrepreneur’s aspiration.
Without careful analysis, it is easy to equate rapid growth with financial success.
But as the sustainable growth model implies, this is not necessarily correct.
There are countless examples of rapid growth ultimately destroying a venture.
Webvan was one of the companies that launched in the mid-1990s to offer
home delivery of groceries ordered over the Internet. Webvan’s management
team pursued a “get big fast” strategy, launching distribution in many communities at the same time or in rapid succession. Webvan raised almost $800 million
from private and public equity sales in 1999 and had a market capitalization
of $8 billion immediately after its IPO. The company entered into a $1 billion
contract with Bechtel for construction of 26 state-of-the-art distribution centers across the country.5 The firm was betting that a significant fraction of the
population in each market would switch to ordering groceries on the Internet.
Unfortunately, consumer adoption was slower than Webvan had hoped, so that
profitable operation was never achieved. The company filed for bankruptcy in
July 2001.
Contrast Webvan with FreshDirect, another Internet-based home-delivery
grocery business. FreshDirect sells in only one market, the more densely populated and wealthier areas of New York City and surroundings, and has declined
to move into other, less dense, markets. FreshDirect introduced its service to
NYC consumers in 2002 and has chosen to remain a privately held company.
It reportedly is a highly profitable business that has succeeded on the basis of
its focused strategy.6
There are other examples where the venture survives but high growth results
in loss of control for the entrepreneur. The housing and financial system crisis
that began in 2007 provides many good examples. Developers, who hoped to
capitalize on the surging demand for housing, dramatically overbuilt in a number
of markets. When demand slowed, many of these developers failed even though
they held valuable inventories of fully or partially constructed housing. The
developers lost their equity investments, and property ownership reverted to
those who had provided credit for the development.
Commercial and investment banks encountered a similar problem. At the
time, these companies were operating with very high financial leverage. When
the market values of their assets declined, institutions such as Bear Stearns,
Lehman Brothers, Merrill Lynch, Washington Mutual, and Wachovia were
compelled to write down the values of their housing-related assets. In some
cases the drop was sufficient to wipe out the existing stockholders.
334
Chapter Nine
Ironically, during this period regulation that was intended to protect those
who traded with the institutions dramatically impeded the institutions’ ability
to raise additional capital, even though the low values of some of their housingrelated assets were likely to be transitory. Institutions such as Goldman Sachs
that barely managed to skirt bankruptcy or government takeover have now
recovered most of their losses. From a high of $236 in October 2007, Goldman
Sachs’s stock price declined by 77.5% to $53 in November 2008. By October
2009, the price was back up to $189; Goldman’s investors had recovered most
of their loss. By year-end 2016, it was trading at slightly above its 2007 high and
had paid dividends throughout the entire period. In contrast, the shareholders
of Lehman Brothers, which was slightly more aggressive than Goldman in taking risks related to the housing market, lost everything. The point is that not
providing a financing solution to cover unexpected bad outcomes can result
in permanent loss for the stockholder (or the entrepreneur) even if the lack of
financing is temporary.
9.4
Planning for Product-Market Uncertainty
Financial planning prompts the entrepreneur to assess the benefits and threats
of high-risk ventures. Financing considerations and implications for value
must be assessed before committing to a strategy. Financing considerations
can lead the entrepreneur to reject what may appear to be the best productmarket strategy in favor of one that is less ambitious but more valuable.
For iFree, the conjectural product-market strategy implied a sales goal of
$2.0 million by the sixth year. Given the amount Gill Bates was able to invest,
the strategy could not be achieved without outside financing. Whether it would
be advantageous for the entrepreneur to pursue outside financing or scale back
the Year 6 sales target depends on which approach would maximize value for
the entrepreneur, a question we take up in the next few chapters.7
For now our focus is on how uncertainty of future product-market performance affects the amount of “financial slack” the entrepreneur might want to
provide. Financial slack is liquidity that would enable the venture to deal with
surprises without the need to raise additional risk capital. Financial slack is
available in various forms, including, for example, cash flow from operations,
excess cash, or an unused line of credit. Lehman Brothers failed because declining asset values wiped out its financial slack. Any entrepreneur of a risky
venture faces a similar prospect of short-run financial adversity for an otherwise
healthy venture.
Assessing Cash Needs 335
Planning for Success
Ironically, unexpected success in the product market can be a threat to new
venture survival and control. Consider, for example, VC funds. Kaplan and
Schoar (2005) find that managers of successful funds are able to attract capital
for the next fund more easily than are their competitors. They also, however,
find that persistence of performance from one fund to the next is inversely
related to the growth of funds under management. They interpret their findings as evidence that fund managers who try to grow too rapidly after an early
success sacrifice performance, possibly because they are unable to grow their
management teams fast enough or because they have limited ability to identify attractive investment opportunities.
The same can be expected for any entrepreneur who is confronted with rapidly
growing demand. The entrepreneur may face such challenges as being unable
to (1) grow the management team rapidly enough, (2) acquire the assets necessary for production, (3) maintain consistent quality, or (4) arrange financing to
support the rate of demand growth. The last point is the focus of this chapter.
For assessing long-run financial needs, it is important to account for the risk
of unexpected success in the product market. If a venture is profitable and is
generating cash in excess of capital replacement requirements, it can finance
some growth internally. This is the lesson of the sustainable growth model.
Excess operating cash flow is an important source of investment capital. For a
venture that is debt-free and pays no dividends, the ability to finance growth
internally is approximately equal to the venture’s after-tax rate of return on
beginning assets. This was demonstrated in Eq. (9.7).
If the return on assets (ROA) is, for example, 8%, then the venture is capable of growing assets and revenue at a rate of approximately 8% by relying
exclusively on internally generated funds. A higher growth rate would require
external funding.
Conversely, if the ROA exceeds the growth rate of sales, the organization
generates FCF. FCF that is retained by the venture does not contribute to its
value but can be retained in an interest-bearing account so that retaining surplus cash does not diminish PV. To enable investors to make the best use of
their resources, a venture that generates cash flow and has no strategic reason
for holding cash reserves should distribute the surplus funds. Dividends, share
repurchase, and reducing leverage are all means of distributing FCF.8
Unexpected product-market success compels the entrepreneur to explore
alternatives for outside financing. Arranging new financing, however, takes
time, and the need to devote attention to seeking financing comes at a critical
point for the entrepreneur, a point when rapid growth is likely to generate a
336
Chapter Nine
host of organizational challenges. With effective planning, the entrepreneur can
prepare in advance for scenarios involving growth that is faster than expected.
In structuring initial financing the entrepreneur must anticipate that growth
will require additional financing and have a sense of how the necessary capital
would be raised. In negotiating the investment terms for the initial round and
other early rounds, the entrepreneur will want to preserve the option of raising
additional funds if the growth rate justifies doing so. We have already seen in
Chapter 4 that current financing decisions sometimes cause problems if the venture needs more capital at a later stage. Loan contracts and preferred stock, for
example, sometimes contain covenants or other provisions that preclude raising
additional funds without the lender’s or preferred stockholder’s approval. If that
time comes, the lender’s interests may conflict with the entrepreneur’s. Some
equity financing structures can give rise to similar difficulties. Antidilution
provisions sought by equity investors can impair a venture’s ability to raise capital, particularly where the venture has run into problems and needs cash to get
through them. Agreeing to these constraints on future fund-raising can lower the
apparent cost of financing to the entrepreneur. But if the venture grows rapidly
or runs into difficulties, those earlier decisions can be problematic. Financial
modeling of the venture, including modeling of the financing terms, is a means
of identifying, and designing around, these threats before they become problems.
Planning for Failure
Once a venture is established and is generating positive cash flows, unexpectedly slow growth may not pose a very serious problem. If the actual growth
rate is lower than expected but still high enough to assure viability, the venture may actually be able to reduce its reliance on external capital. If g* is
greater than actual growth, then the resulting FCF can be used to fund new
investments or can be distributed to investors.
A more serious problem arises if unexpectedly poor performance is encountered before the organization has reached financial viability. This may occur, for
example, if product development takes longer than expected or if sales growth is
slower than expected and the attained level of sales is below that needed to generate funds for capital replacement. Either circumstance means the organization
must depend on external financing to a greater extent than expected. This is a
particularly acute problem because a venture that has not achieved viability and
has not met growth expectations will have difficulty raising capital. A forwardlooking entrepreneur can manage the risk by maintaining financial slack and
preserving the ability to raise additional capital if growth is slower than expected.
Assessing Cash Needs 337
High-Tech, High-Growth Innovation
Some of the most challenging financing problems are associated with a product innovation that requires a long and expensive development period, which,
if successful, is followed by rapid sales growth. Negative cash flows, in such
cases, can extend over many years and can last through both the development
and rapid-growth stages. The venture will not begin to generate FCF for investors until the growth rate slows to a point where operating cash flows are
more than sufficient to fund growth. NewCo has these characteristics and we
extend the model later in this chapter.
The pattern of a long development lead time followed by rapid growth is
characteristic of many high-tech innovations (pharmaceuticals, biotechnology,
and some electronics, for example). It is not surprising, therefore, that large,
well-established companies undertake much of the development activity. Such
companies can draw on their current financing capabilities without the need
to convince investors of the merits of a specific project. They often also have
existing infrastructure that reduces the investment required during development
and can handle the operational demands of rapid growth.
9.5 Assessing Financial Needs with
Sensitivity/Scenario Analysis
Because the future is unknown, financing decisions should depend on both the
expected future of the venture and uncertainty. Methods of assessing financial
needs under uncertainty are particularly valuable.
The financial information for iFree in Figure 9.3 is based on the “expected”
scenario. But what happens if the venture is more or less successful than expected?
Sensitivity analysis is a simple way to construct scenarios that incorporate uncertainty into projections of financial need. A scenario describes one possible version of the future and specifies the key assumptions associated with the scenario.
An entrepreneur can use alternative scenarios, together with their associated assumptions, to generate alternative projections of financial needs. One might, for
example, consider “expected,” “best case,” and “worst case” scenarios, each with
its own assumptions and resultant estimate of financing need. For ventures with
long product development periods, it can be helpful to consider one scenario in
which product introduction is delayed and sales grow slowly and are not initially
profitable, and a second in which sales growth is rapid and more profitable but
product development is still slow.
338
Chapter Nine
The key to forecasting long-run financial needs is developing a model that
links product-market performance to financing requirements. We saw how this
can be done in the NewCo model in Chapter 8, as well as through the cash flow
breakeven analysis and sustainable growth model in this chapter. The first step
to forecasting financial needs is to specify a set of assumptions about productmarket performance: for example, expected sales, costs, and resulting profitability. Incorporating the assumptions into a financial model will determine
the cash flows we can anticipate the venture to generate. We can then forecast
financing needs by using estimates of asset efficiency, such as the levels of fixed
assets and working capital needed to support projected sales.
Scenario analysis can be developed in much the same way as a decision tree,
but with nature making the decisions. For example, Figure 9.4 describes the
use of sensitivity analysis to construct 27 scenarios of sales growth, cost, and
asset turnover for iFree. The key assumptions in the model are the rate of sales
growth, the profitability of sales (the variable cost percentage), and the efficiency
of asset utilization to generate sales (the sales to asset ratio). We assume that
asset turnover could be 15% higher (i.e., 2.3 times) or 10% lower (i.e., 1.8 times)
than the expected turnover of 2.0 times, the variable contribution percentage
could be 10 percentage points higher (30%) or lower (10%) than the expected
level of 20%, and the rate of sales growth could be 5 percentage points higher
(20%) or lower (10%) than the expected rate of 15%.
The most optimistic scenario would be one where asset turnover is high, profitability is high, and the rate of sales growth is high. This would be consistent
with a case where the product is well received so that it can command a high
margin and where demand grows rapidly. The most pessimistic is one where
the product is not well received so that demand is low, resulting in lower pricing and slower growth. The full set of scenarios generated from the sensitivity
analysis is shown in Figure 9.4.
Suppose Bates is faced with the issue of how much initial financing to secure
and wants to apply scenario analysis to a cash flow breakeven study. Although
there are many possible scenarios in the figure, some are more realistic than others. The scenarios differ substantially in the total amount of additional capital
required after the initial $500,000 investment. The high-turnover, high-profit,
low-growth scenario needs the least total investment, whereas the low-turnover,
low-profit, high-growth scenario needs the most—over $1 million in additional
investment. The shaded cells in the figure are the points where, for each scenario,
the cumulative cash need reaches a peak. The low-growth scenarios generally
reach their maximum cash needs sooner and begin to generate FCF.
Should Bates plan to provide funding up to the maximum amount shown
in the scenarios, or would it be better to commit to a lower level and plan to
Assessing Cash Needs 339
abandon the venture if it appears not to be doing well? To help address this,
the figure also shows Year 6 net income. Some high-cash-needs scenarios are
profitable by Year 6, but others are not, and some are only slightly profitable.
To decide on the appropriate amount to invest, it is useful to estimate the net
present value of each scenario. For this, we have assumed that the cost of capital
for the venture is 10% and that after Year 6, net income will grow at an annual
rate of 4%.9 The NPV column in Figure 9.4 is computed by valuing the growing
perpetuity plus the present value of any FCFs generated during the first 6 years,
less the present value of any investments. As can be seen in the table, all of the
low-profit scenarios have negative NPVs, whereas almost all of the others have
positive NPVs. The highest cash-need, positive NPV scenario requires a total
investment of about $1.1 million. Most of the negative NPV scenarios require
more. In this case, because the main driver of acceptability is the variable contribution to profit percentage, and that percentage is assumed to be constant,
a simple decision rule is to invest the initial $500,000 to learn what the profit
percentage will be and to abandon any scenario where the low percentage (10%)
is realized. Because all of these cases have NPVs that are more negative than
the positive $500,000 initial investment, Bates will be better off by abandoning
the venture in any of those scenarios.
Figure 9.4 provides a simple example of how staging of financing can be used
to increase the value of an opportunity. By only investing $500,000 at the beginning, the entrepreneur can learn whether to invest more in subsequent years or
abandon the venture. For example, if we assume the scenarios are equally likely
and all are pursued fully, the average NPV would be about $714,000. However,
if we assume that the low-profitability scenarios are dropped after the first year
and none of the initial investment is recoverable, the average NPV would be
about $909,000. Staging the investment increases the NPV by $195,000.
There are important limitations of using scenario analysis to assess cash
needs. Most importantly, scenarios probably are not equally likely so that simply
averaging the results over scenarios would not give an accurate estimate. If, for
example, the probabilities of low contribution margins are low (high), the gain
from using scenarios to identify outcomes that should not be pursued will be
less (more) valuable. Also, the assumptions in scenario analysis are discrete.
We cannot, for example, use Figure 9.4 to determine whether scenarios with
11% contribution percentages would be worth pursuing. Also, scenario analysis
cannot be used reliably to evaluate staging options involving continuous data.
Simulation is designed to address both of these problems. The different trials that are produced in a simulation are designed to be equally likely, and
the full dispersion of possible results is evaluated instead of just a few discrete
scenarios. If the probability distributions of key assumptions in the model are
Equity Book Value (Cumulative Cash Need)
S/A
Profit Growth
Time 0
Time 1
Time 2
Time 3
Time 4
Time 5
Time 6
Year 6 NI
NPV
High
High
High
High
High
High
High
High
High
Exp.
Exp.
Exp.
Exp.
Exp.
Exp.
Exp.
Exp.
Exp.
Low
Low
Low
Low
Low
Low
Low
Low
Low
High
High
High
Exp.
Exp.
Exp.
Low
Low
Low
High
High
High
Exp.
Exp.
Exp.
Low
Low
Low
High
High
High
Exp.
Exp.
Exp.
Low
Low
Low
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$500,000
$505,750
$480,750
$455,750
$580,500
$555,500
$530,500
$655,250
$630,250
$605,250
$535,000
$510,000
$485,000
$600,000
$575,000
$550,000
$665,000
$640,000
$615,000
$554,500
$529,500
$504,500
$613,000
$588,000
$563,000
$671,500
$646,500
$621,500
$486,650
$439,113
$394,075
$651,100
$599,825
$551,050
$815,550
$760,538
$708,025
$551,000
$502,000
$455,500
$694,000
$641,750
$592,000
$837,000
$781,500
$728,500
$593,900
$543,925
$496,450
$722,600
$669,700
$619,300
$851,300
$795,475
$742,150
$437,730
$371,729
$313,233
$709,820
$631,299
$560,655
$981,910
$890,868
$808,078
$544,200
$473,300
$410,050
$780,800
$699,013
$625,200
$1,017,400
$924,725
$840,350
$615,180
$541,014
$474,595
$828,120
$744,155
$668,230
$1,041,060
$947,296
$861,865
$353,026
$274,739
$211,306
$754,284
$647,994
$558,221
$1,155,542
$1,021,248
$905,135
$510,040
$420,795
$347,055
$858,960
$745,364
$648,720
$1,207,880
$1,069,934
$950,385
$614,716
$518,166
$437,555
$928,744
$810,278
$709,053
$1,242,772
$1,102,391
$980,552
$225,381
$143,700
$86,186
$781,641
$647,693
$542,543
$1,337,900
$1,151,686
$998,899
$443,048
$340,914
$264,761
$926,752
$779,169
$661,592
$1,410,456
$1,217,424
$1,058,424
$588,159
$472,391
$383,810
$1,023,493
$866,820
$740,958
$1,458,826
$1,261,249
$1,098,107
$46,207
($26,495)
($64,445)
$788,469
$627,846
$512,297
$1,530,730
$1,282,188
$1,089,039
$336,658
$229,551
$161,237
$982,102
$798,544
$662,751
$1,627,547
$1,367,537
$1,164,266
$530,291
$400,249
$311,691
$1,111,191
$428,006
$321,047
$231,157
$242,004
$170,698
$110,771
$56,002
$20,349
($9,614)
$355,222
$262,215
$184,049
$193,482
$131,476
$79,366
$31,741
$738
($25,317)
$306,700
$222,993
$152,645
$161,133
$105,329
$58,430
$15,567
($12,336)
($35,785)
$4,214,861
$3,168,633
$2,276,441
$1,777,566
$1,155,753
$622,881
($659,729)
($857,127)
($1,030,680)
$3,261,137
$2,380,984
$1,629,396
$1,141,750
$630,654
$191,517
($977,637)
($1,119,677)
($1,246,362)
$2,625,321
$1,855,885
$1,198,032
$717,873
$280,587
($96,059)
($1,189,575)
($1,294,710)
($1,390,150)
High
Exp.
Low
High
Exp.
Low
High
Exp.
Low
High
Exp.
Low
High
Exp.
Low
High
Exp.
Low
High
Exp.
Low
High
Exp.
Low
High
Exp.
Low
$912,343
$763,054
$1,692,092
$1,424,437
$1,214,417
Fi g u r e 9. 4
iFree scenario analysis
The figure shows 27 scenarios for iFree, varying the asset turnover (S/A = 0.8, 1.0, 1.3), the variable contribution margin to profit (Profit = 10% 20%, 30%) and the sales growth rate (Growth =
10%, 15%, 20%). Annual cumulative cash needs (Equity Book Values) are shown together with net income in Year 6. Maximum cumulative cash need in each scenario is highlighted. NPV is
estimated assuming a 10% cost of capital and 4% growth rate after Year 6.
Assessing Cash Needs 341
well supported, the decision criteria for making follow-on investments can be
designed with considerable precision.
9.6
Assessing Financial Needs with Simulation
To see how simulation can be used to assess financial needs, we reconsider
NewCo. Refer again to Table 8.13, which contains the pro forma financial
statements for the venture in selected quarters. The statements are generated
on the basis of what is expected to occur. As we discussed in Chapter 8, if we
assume that the venture begins with $500,000 of equity and raises additional
funding as needed by drawing on a line of credit, the company’s cumulative
need for debt financing peaks at about $651,000 in Quarter 18, making the cumulative (undiscounted) need from all sources about $1,151,000.
But what if NewCo does not develop as expected? We demonstrated at the
end of Chapter 8 that, given our assumptions about the uncertainty of development timing, sales growth, length of the rapid-growth period, and profitability,
the actual financial needs of the venture could be much higher or lower than
implied by the expected performance assumptions. It would not make sense for
the entrepreneur to try to fully fund all of the possible scenarios with capital
raised at the beginning. Doing so would involve raising large amounts of capital
at the point where uncertainty about ultimate success is greatest and would
include coverage of scenarios in which early abandonment would have been
a better course of action. Ultimately, we would like to approach the decision
of how much initial financing to raise on the basis of NPV; that is, we want to
select the level of financing that results in the greatest NPV for the entrepreneur
(or the investor, depending on who is making the decision). As we illustrate in
the balance of this section, a structured analysis of the results of simulating the
financial model can dramatically improve the financing decision.
Figure 9.5 provides a comprehensive view of NewCo’s future cash needs based
on 1,000 iterations of the simulation model. The results from the simulation
present information on the distribution of the initial $500,000 cash investment
plus the credit line balance at the end of each of NewCo’s first 6 years (24 quarters) and the last quarter of the simulation. The graph shows the cumulative
distribution of financing needs as of each year. The vertical axis in the graph
represents the amount of total (undiscounted) financing NewCo needs, expressed
in dollars, and the horizontal axis represents the cumulative percentage of trials
from the simulation. To generate the table and graph, we set the initial investment in NewCo at $500,000.
342
Chapter Nine
$5,000,000
$4,500,000
$4,000,000
$3,500,000
$3,000,000
Quarter 4
Quarter 8
Quarter 12
Quarter 16
Quarter 20
Quarter 24
Quarter 26
$2,500,000
$2,000,000
$1,500,000
$1,000,000
$500,000
$0
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percentage of trials
Fi g u r e 9. 5
NewCo total cash need as of quarter end
The figure is based on a simulation of the NewCo financial model with an initial investment of $500,000. Plotted lines are total cash needs
measured as the sum of the initial investment and the maximum value of the line of credit over the 26 quarters of the simulation.
For example, the trials data underlying Figure 9.5 show that an initial investment of $500,000 is sufficient to cover all of the simulated trials through the
first year (Quarter 4). By the end of the second year, almost all trials need additional funding but the amounts are generally small, resulting in an average total
funding need of $550,500. Although the distribution is skewed, the maximum
is still only $654,100. From Figure 7.5, we can determine that the probability of
development success by Quarter 4 is about 15.4% and the cumulative probability
of development by Quarter 8 is 56.5%. This means that for most of the trials,
the initial investment represents the cost associated with 4 quarters of development effort plus maintaining the $15,000 minimum cash balance, and that an
additional investment of $154,100 would cover the additional development cost
to raise the development success probability to 56.5%.
From Figure 7.5, we can determine that for an additional 30.0% of the trials this level of funding would not be enough to reach successful development.
Moreover, for the cases where development is completed by Quarter 8, we do
not know whether the outcomes are high-valued, and we do not know whether
Assessing Cash Needs 343
investing more would be valuable. From Figure 9.5, we can see that investing
$1 million would cover almost all of the trials for up to 12 quarters. We can
also see that beyond this point the trials begin to separate into some that are
producing positive FCF so that the line of credit is being paid down, and others that continue to require greater and greater investment. By Quarter 26, the
end of the simulation, Figure 9.5 shows that 36.5% of the trials have produced
enough FCF to have fully paid down the credit line and may have substantial
surplus cash balances.
Developing a Decision Rule for Staged Investment
Our goal here is to use simulation to develop a decision rule about how much
to invest initially in the venture so that we can make an NPV-maximizing decision of whether to continue to invest or abandon the venture. We begin by
rerunning the NewCo simulation model, setting the initial investment to zero
so that the line-of-credit balance shows the cumulative financing need as of
each quarter end.
To structure the analysis, we have extended the model to include some basic
present value information. Specifically, we assume an annual cost of capital of
10% (2.41% per quarter). Further, we assume that any positive surplus cash balance is distributed to investors at the end of Quarter 26, and that the Quarter 26
value of cash flows after that quarter can be estimated as 10 times the annualized
net income in Quarter 26 (i.e., a 10X multiple), which value is discounted back
to time zero at cost of capital.
Running the simulation model with 1,000 trials, we estimate that the average
NPV of investing thorough Quarter 26 in all trials would be $3.447 million and
that the average investment over all trials would be $1.828 million. Given the
positive NPV, apparently the venture would be worth pursuing, even without
any staging. However, Figure 9.6 is a plot of the NPVs from the trials sorted
from lowest to highest. While the average NPV is positive, the distribution is
highly skewed, with a much lower median of $1.553 million. Further, NPV is
negative in over 45% of the trials, and the positive average is driven by a small
number of extremely positive trial outcomes.
Given that many trials have substantially negative NPVs, we should be able
to use staging to improve the expected NPV. We begin in Figure 9.7 by sorting
the trial results by the first quarter of revenue generation and then counting
the numbers of development success trials with positive and negative NPV.
For example, from a total of 1,000 trials, 15.4% began to generate revenue in
Quarter 5. Of those, 150 trials (15% of all trials) resulted in positive NPV and 4
trials (0.4%) resulted in negative NPV.
344
Chapter Nine
Fi g u r e 9. 6
$100,000,000
NewCo NPV results
from simulation
The figure shows the distribution of estimated NPV
from 1,000 trials of the
NewCo simulation model,
with initial investment set
to zero. NPV is estimated
using a 10% annual discount rate and capitalizing
Quarter 26 income by
annualizing and applying
a 10X multiple.
$80,000,000
$60,000,000
$40,000,000
$20,000,000
95.0%
90.0%
85.0%
80.0%
75.0%
70.0%
65.0%
60.0%
55.0%
50.0%
45.0%
40.0%
35.0%
Percentage of trials, sorted by NPV
18%
Fi g u r e 9.7
NewCo simulation
results for
development
completion timing
(based on 1,000 trials)
16%
Trials with development success that
result in positive NPV
14%
Trials with development success that
result in negative NPV
12%
Percentage of trials
The figure plots the first
quarter of revenue generation from a simulation of
the NewCo financial model
with 1,000 trials. Each
bar in the figure shows the
percentage with negative
NPV and the percentage
with positive NPV.
100.0%
($20,000,000)
30.0%
25.0%
20.0%
15.0%
5.0%
10.0%
$0
10%
8%
6%
4%
2%
0%
5
6
7
8
9
10
11
12
13
14
15
16
17
Fail
First quarter of revenue generation
As reflected in the model assumptions, the value of development success
is expected to be greatest if success occurs quickly. Earlier success results in
more rapid demand growth and other benefits of being a first mover. Figure 9.7
illustrates the impact of development timing on the probability that the trial
Assessing Cash Needs 345
will result in positive NPV. The probability of positive NPV is very high if
revenue generation begins in Quarter 5. This probability diminishes the longer
development takes.
Although the model is designed to allow development efforts to continue
through Quarter 17, in the simulated trials, there are no positive-NPV trials that
begin revenue generation after Quarter 14. Based on the trials data, there is no
reason to fund development efforts beyond Quarter 14. Also, the probability of
a positive NPV outcome for an effort that begins to generate revenue in Quarter
14 is very low—only one of the 34 trials that began revenue generation in Quarter
14 is NPV-positive. Thus, it seems unlikely that funding development efforts in
Quarter 14 would be valuable.
More systematically, we can use the trials data to study how discontinuing
funding after any given quarter would affect the estimated NPV of the venture. Table 9.1 provides a summary of results of alternative decision rules of
investing in all trials through Quarter X and abandoning any trials that have
not achieved development success by that point. Because we know there are no
successful trials with positive NPV after Quarter 14, we abandon all remaining
trials after that quarter. So we can construct a correct decision rule, when a
trial is abandoned, we replace the computed NPV with the (negative) amount
invested in that trial up to that point.
As shown in the table, investing through Quarter 14 and not investing thereafter in trials that have not achieved development success results in a higher
average NPV ($4.041 million) than does continuing to invest, entirely because
the investor does not waste resources by investing in the trials that have not
Tab le 9.1 NewCo cumulative NPV by first quarter of revenue generation
Quarter
Mean
Median
Cumulative Trials with
Development Success
Cumulative Trials
with Positive NPV
5
6
7
8
9
10
11
12
13
14
All trials
$1,751,300
$3,028,335
$3,781,240
$4,144,837
$4,255,877
$4,300,509
$4,244,371
$4,187,066
$4,116,698
$4,040,740
$3,477,094
($334,606)
($403,054)
($472,912)
($543,001)
($243,299)
$357,612
$498,449
$538,526
$555,722
$562,733
$562,733
154
284
393
487
565
632
688
736
777
811
885
150
272
369
441
487
521
534
542
545
546
546
The table shows the results from the NewCo simulation trials data of implementing a decision rule to invest fully in any
trial that has Quarter X or earlier as its first quarter of revenue generation and to abandon any trial where development has
not been completed by Quarter X.
346
Chapter Nine
yet achieved development success (with negative NPV) or may never achieve
development success. At this stage in the analysis, investment continues without
limit in all trials that have been successful by the indicated quarter.
The least desirable result in Table 9.1 is to discontinue investing in any trial
that has not begun revenue generation by Quarter 5. As of that point, in only
154 trials has development been successful, and 4 of those are trials with negative NPV. Because so many potentially good outcomes are abandoned with
Quarter 5 as the decision criterion, the average NPV is only $1.751 million and
abandonment of so many trials results in the median NPV over all 1,000 trials
being negative $335,000.
Our goal with this decision rule is to select the critical quarter based on average NPV over all trials. As shown, the maximum is reached if the critical quarter
is Quarter 10, at an average NPV of $4.301 million. The median at this critical
value is also positive because most of the trials are being pursued, including
over 95% of the trials where NPV is positive (521 of 546).
Modifying the Decision Rule to Also Consider
Revenue Growth
With the decision rule of investing fully in all trials that begin revenue generation in Quarter 10 or sooner, we would end up investing fully in 111 trials
where development is successful but NPV is negative. These are the 632 successes minus the 521 with positive NPV from the Quarter 10 row in Table 9.1.
We should be able to further improve the value if we can identify factors that
can be recognized early and are key drivers of NPV. Conceptually, the value of
the development effort depends on the quality of the result and the emergence
of competing products. By inspections, we determined that, in the model,
these considerations are manifested mainly through the rate of sales growth
during the rapid growth period. That is, among the trials with development
success in any given quarter, NPV is strongly related to the realized rate of
revenue growth. Since the drivers of growth should be observable quickly after development is achieved, we decided to refine the earlier decision rule by
abandoning very-low-growth trials in each development success quarter.
Abandoning low-growth trials, while it reduces investment in some negative
NPV trials, also results in abandoning some where NPV is positive. We cannot simply focus on NPV since the realized NPV would not be observable by
investors until much later. Accordingly, using the trials data, we selected critical growth rate values for abandonment in each development success quarter.
For example, in Quarter 5, we decided to abandon any trial where the realized
growth rate was less than 14%.
Assessing Cash Needs 347
Tab le 9. 2 NewCo cumulative NPV by first quarter of revenue and revenue growth rate
Quarter
Mean NPV Based
on Critical Quarter
Adjusted Mean NPV Adding
Critical Growth Rate
Increase in NPV
Critical Growth
Rate
5
$1,751,300
$1,752,005
$705
14
6
$3,028,335
$3,032,139
$3,804
15
7
$3,781,240
$3,788,735
$7,495
15
8
$4,144,837
$4,163,002
$18,165
16
9
$4,255,877
$4,296,719
$40,842
16
10
$4,300,509
$4,360,445
$59,937
20
The table shows the gain in average NPV that results from modifying the “critical quarter” decision rule for abandonment to also incorporate a
critical growth rate for abandonment.
As shown in Table 9.2, applying this additional criterion to the trials data in
Quarter 5 modestly increases the average NPV (by $705). The increase is modest because there are only 4 negative NPV trials with development success as of
Quarter 5, 3 of which are abandoned, and there is one positive NPV trial with
a growth rate below 14%.
Moving to Quarter 6, we retain the abandonment criterion for the Quarter 5
trials and, by inspection, use 15% as the critical value of the Quarter 6 growth
rate. In general, the critical value for positive NPV outcomes is higher the later
development occurs. The result is a modest $3,804 increase in average NPV.
Continuing this stepwise process through to Quarter 10 development, the increase in average NPV that results from incorporating the growth rate criterion
is estimated to be about $60,000. We do not continue the process beyond Quarter
10 since the drop-off in baseline average NPV in Quarter 11 is too large to be
offset by the gain from modifying the decision rule. In conclusion, the highest
NPV is achieved by exercising the abandonment option if revenue generation
does not begin by Quarter 10 or if the revenue growth rate in any quarter (5
through 10) is below the critical value for that quarter, as indicated in Table 9.2.
Additional Considerations and Caveats
The preceding discussion demonstrates the process of using simulation to develop a decision rule related to abandonment. It should be possible to further
improve the decision by examining a larger number of simulated trials to improve the precision of abandonment criteria and by adding additional considerations related to the decision. Further, there may be additional choices that
should be taken into consideration. For example, abandonment is only one
option. It may also be possible to pivot or refocus the effort in response to the
learning that occurred during the development stage.
348
Chapter Nine
9.7 Summary
A number of analytical techniques are available for assessing financial needs.
They range from the simple approaches like cash flow breakeven analysis and
sustainable growth model to more complex methods that involve scenario
analysis and simulation to study how uncertainty affects financial needs.
Cash flow breakeven analysis considers financial needs in a different way.
On one level, the technique helps determine the level of sales a venture must
achieve to finance its operations from cash flow. At that point, the venture is viable on a cash flow basis, but growth beyond the breakeven point would require
additional capital. On another level, by combining cash flow breakeven analysis
with a sales forecast, the entrepreneur can estimate the investment needed to
sustain the venture until the breakeven point is reached.
The value of the sustainable growth model is that it links a venture’s ability to
finance growth from operations to a few policy decisions. For any given growth
objective, the model enables the entrepreneur to understand when growth can be
financed from operations and how much will need to be funded externally.
Scenario analysis is an approach to studying the effects of uncertainty.
When the technique is applied to assessing financial needs, the objective is to
understand how financial needs vary over a range of realistic scenarios. Scenario analysis can be combined easily with other methods of assessing financial
needs; that is, the cash flow breakeven point or the sustainable growth rate can
be ­evaluated over a range of scenarios to improve understanding of financial
needs.
Simulation is by far the most powerful analytical tool for evaluating financial
needs. Beginning with sound assumptions and an integrated financial model, the
effects of key aspects of uncertainty can be evaluated simultaneously. Skillful
application of simulation can help the entrepreneur or an investor to design a
financial structure that preserves the potential for success but does so with a
limited amount of investment capital. At the same time, the financial structure
enables the entrepreneur and investors to avoid overinvesting once it becomes
apparent that success is unlikely. Simulation can be used effectively to study
the effects of subtle but significant changes in financing provisions like staging
and to devise decision criteria for exercising real options.
Review Questions
1. Describe the main factors that determine a venture’s sustainable growth
rate. What are the key assumptions in the sustainable growth model?
Assessing Cash Needs 349
2. List four ways in which managers can increase a venture’s sustainable
growth rate. Which do you think is easiest to implement? Which is most
difficult?
3. Explain how a venture’s sustainable growth rate is related to its financing needs.
4. What is the difference between accounting breakeven and cash flow
breakeven analysis? Which is more important for a new venture and
why?
5. Explain the difference between marginal and average contribution margins. Why is it important to distinguish between them when making operational decisions?
6. Why is it important for an entrepreneur to plan for unexpected productmarket success? What are the potential consequences of failing to do so?
7. How might you determine the number of scenarios needed to analyze a
new venture’s uncertainty? Explain how you might estimate reasonable
probabilities for each scenario.
8. What benefits does simulation bring to the analysis of venture
uncertainty?
9. Explain how simulation and staging can be combined to estimate an optimal level of initial funding for a new venture.
10. Describe how you might use simulation with different assumptions
about the amount of initial financing to help determine the investment
that would help assure that good outcomes are not missed but bad outcomes do not receive funding?
Notes
1. See Higgins (2009), chap. 4, and Donaldson (1991) for additional discussion of sustainable growth.
2. In this chapter, we assume a basic knowledge of present value analysis
and an assumed cost of capital. Later in the book, we formally develop new
venture valuation.
3. This is equivalent to assuming that asset turnover, financial leverage,
and return on sales are constant.
4. Perhaps you are wondering why, in this model, we have not mentioned
cash flow. The reason is that the sustainable growth model assumes that depreciation expense each period is equal to the investment required to replenish
the assets. Thus, in order to prepare a cash flow statement, we would add back
350
Chapter Nine
the depreciation expense. But to sustain operations at the same level as in the
prior year, the entrepreneur would need to reinvest the same amount back in
the venture. Doing so would maintain the balance sheet at its initial level.
5. Information from Webvan Form 10K-405, year ended December 31,
1999; and “Technology Journal: Webvan IPO to Stir Grocery Industry,” Wall
Street Journal (Europe), November 9, 1999, p. 10.
6. See https://www.freshdirect.com/index.jsp.
7. The same question could be examined from the perspective of an investor, and similar issues would arise but the conclusions might be different.
8. Note that cash from operations that can be used to support an acquisition strategy is not FCF. Moreover, new ventures frequently raise money to
buy out a competitor to gain market share, access to a particular market, or
intellectual property. SeatGeek.com has made several acquisitions. Zipcar acquired several small competitors before, itself, being acquired by Avis.
9. In later chapters, we will focus more systematically on cost of capital and valuation. For now, we use these simple assumptions to illustrate the
approach.
References and Additional Reading
Donaldson, G. 1991. “Financial Goals and Strategic Consequences.” In Strategy: Seeking and Securing Competitive Advantage, ed. C. Montgomery
and M. Porter, 113–34. Cambridge, MA: Harvard Business School Press.
Higgins, R. C. 2009. Analysis for Financial Management, 9th ed. Boston:
­McGraw-Hill Irwin.
Kaplan, J. 1994. Startup. New York: Penguin.
Kaplan, S. N., and A. Schoar. 2005. “Private Equity Performance: Returns,
Persistence and Capital Flows.” Journal of Finance 60: 1791–823.
Ross, S. A., R. W. Westerfield, and J. Jaffe. 2008. Corporate Finance, 8th ed.
New York: McGraw-Hill Irwin.
Sahlman, W. A. 1999. “The Financial Perspective: What Should Entrepreneurs Know?” In The Entrepreneurial Venture, ed. W. A. Sahlman and
H. H. Stevenson, 2nd ed., 238–61. Cambridge, MA: Harvard Business
School Press.
C h a p t e r Ten
Fo u n datio n s o f N e w
Ve ntu r e Valuatio n
H ow d o VC s and other investors select the projects in which they invest?
And how do they settle on the ownership stakes, terms, and conditions they
require in exchange for investing? There are no simple answers to these questions. Certainly, the perceived value of the entrepreneur’s concept and capabilities are critical factors. No venture will be funded unless an investor sees the
merits of the concept and regards the entrepreneur as capable of implementing
it or of working with the investor to build a capable team. In addition, there are
issues of fit and timing: Can the investor contribute value to the project? Does
the investor have the financial and organizational capacity to take on another
project? The answers to these questions depend on the particular expertise of
the investor and on whether, at the time, the investor is looking for new projects.
In this chapter, we introduce the foundations of valuation and present the
most widely used valuation methodologies, identifying the pros and cons of
each. Chapter 11 concerns implementation and provides guidance on how to
ensure that the valuation approach is internally consistent and how to find the
information necessary to perform the valuation. In Chapter 11, we use a single
example to illustrate the use of the various valuation methods that we present
in this chapter. An important lesson is that if the assumptions are all consistent,
then, in a perfect world, every valuation method will produce the same estimate
of value. Of course, in reality they do not. The data and assumptions in each
valuation effort are estimates of the “true” underlying values. Differences in
estimated value across methods therefore reflect the effects of estimation errors.
The art of valuation is in deciding how to weight the information from each
approach and use it to reach a conclusion about value.
353
354
10.1
Chapter Ten
Perspectives on the Valuation of New Ventures
The value of any investment depends on its ability to generate future cash
flows, as well as on investor assessments of and tolerance for the riskiness of
those cash flows. Two aspects of valuation make new venture investment decisions particularly difficult. First, the future cash flows are volatile and difficult
to forecast. Second, discount rates appropriate for new venture investments
can be challenging to estimate.
In spite of the near impossibility of precision, earnings or cash flow forecasts
appear in most business plans, and forecasts are made and studied by VCs and
other investors who are shopping for deals. In addition, VCs and other investors
address the problem of determining the appropriate discount rate routinely.
We show in this chapter that financial economic theory provides considerable
guidance for estimating appropriate discount rates.
In an area as competitive and complex as investing in new ventures, the
importance of good decision making about the terms for investing cannot be
overemphasized. One indicator of the potential for good decision making to add
value is the variation in investment performance of VC funds. Several sources
compile information on the returns to investors in VC funds. In Figure 10.1,
we use information reported in the Preqin Private Equity database to produce
a histogram of annualized internal rates of return (IRRs) for 1,715 funds that
were launched between 1969 and 2013.1 The returns range from almost a total
loss (–79.2% per year) to one fund with annual returns of more than 500%. To
put this in perspective, a fund that returns 500% per year for a five-year period
would return more than $1,200 for each $1 invested. Based on its net multiple
of 19.82 (i.e., each $1 invested returned $19.82), this particular fund, however,
had a weighted average life of only about 1.5 years so the actual return is much
lower that implied by the 19.82 net multiple.
Such high returns are a rare occurrence; only 0.4% of the funds in the sample
had IRRs over 200%. The average IRR in the sample is 13.2% per year, and
the median is 8.7%. Weighting by fund size reduces the average IRR to 9.95%.
The compound average annual return on the S&P 500 over approximately the
same period (though not strictly comparable) was 8.6%, which is higher than
almost half of the VC funds. Based on this evidence, historically VC investing
has generally done better than investing in the market but the return differential
has been small. Apparently, the small return premium is sufficient to compensate
for the risk and illiquidity as VC has continued to attract funding.
Low rates of return for some VC funds can result from numerous factors:
unfortunate timing, bad luck, lack of skill or access to deal flow, and unforeseeable negative events. However, two important reasons for low rates of return
Foundations of New Venture Valuation 355
Fi g u r e 1 0.1
25%
Distribution of VC
fund average annual
IRRs (vintage years
1969–2013)
Preqin Private
Equity Database.
20%
Average fund IRRs are
computed for 1715 funds as
of returns reported though
2016.
Percentage of observations
source:
15%
10%
245%
235%
225%
215%
205%
195%
185%
175%
165%
155%
145%
135%
125%
115%
95%
105%
85%
75%
65%
55%
45%
35%
25%
5%
15%
–5%
–15%
–25%
–35%
–45%
–55%
–65%
0%
–75%
5%
Fund internal rate of return
are valuation mistakes and deal-structuring mistakes. Both of these can be
avoided, or at least minimized, by using decision-making methods that give
the investor a competitive advantage over its rivals in both project selection
and deal structuring.
The arrival of professional investment managers to VC investing has changed
the market in fundamental ways. Most important for our purpose is the changing way in which investment opportunities are valued by investors. In time, the
changes will affect the ways in which entrepreneurs evaluate projects. Because
the market is changing, some of the approaches and rule-of-thumb heuristics
that have been used historically for investing in new ventures no longer can be
relied on to identify projects that are likely to yield acceptable rates of return.
10.2
Myths About New Venture Valuation
Past practices have generated four myths about new venture investing.
Myth 1: Beauty Is in the Eye of the Beholder
Three decades ago, Gordon Baty (1990) wrote, “Pricing a new company’s stock
is much like pricing any other glamour item (such as perfume, paintings, rare
coins) where appeal is based on emotional, as well as analytical ­considerations.”2
356
Chapter Ten
While it is reasonable to expect that the entrepreneur may care about qualitative factors, it would not be a good idea for a VC fund manager to propose to
prospective investors that financial return be traded off against other factors.
Professional investors understand that the fundamental trade-off between cash
flow and risk must drive valuation. Here, we present theoretically sound valuation tools while at the same time recognizing the implementation challenges.
Myth 2: The Future Is Anybody’s Guess
This is a more reasonable-sounding version of Myth 1. The claim is that even
though cash flow is what matters, future cash flows of new ventures are so
uncertain that forecasting them is of little value. Although new venture financial forecasts are subject to great uncertainty, such uncertainty—rather than
making the forecast worthless—makes forecasting critical. In particular, it is
important to try to understand the extent, nature, and implications of the uncertainty. It is true that a single-scenario forecast for a new venture is unlikely
to be of much value. Scenario analysis and simulation are, however, of considerable practical value for understanding and dealing with the risks, establishing strategy, assessing cash needs, and valuing the venture.
Myth 3: Investors Demand Very High Rates of Return to
Compensate for Risk
New ventures are high-risk investments that tie up the investor’s capital for
several years with no easy means of exit. This has led to a broadly held perception that required rates of return for VC investing must be very high. On this
subject Michael Roberts and Howard Stevenson write, “In order to compensate for the high risk of their investments, give their own investors a handsome
return, and make a profit for themselves, venture firms seek a high rate of
return. Target returns of 50% or 60% are not uncommon.”3 As summarized in
the following table, Jeffrey Timmons and Stephen Spinelli (2007, p. 449) provide a more comprehensive summary that echoes the same point:
Stage
Annual ROR (%)
Total expected holding period (years)
Seed and start-up
First stage
Second stage
Expansion
Bridge and mezzanine
LBO
Turnaround
50–100% or more
40–60%
30–40%
20–30%
20–30%
30–50%
50%+
More than 10
5–10
4–7
3–5
1–3
3–5
3–5
Foundations of New Venture Valuation 357
Scholars and others who remark on the high rates of return base their statements on historical practice or statements of “sought for” returns cited in PPMs,
which generally are indicating the rates of return VC investors apply when
discounting the entrepreneur’s projected cash flows. Approaching the question in this way, they find that the rates are typically quite high. For example,
Gompers, Gornall, Kaplan, and Strebulaev (2016) report, based on a survey of
a large sample of VCs, that the median “required” IRR for investment selection
was 30% and the “required” net multiple (cash-on-cash return) was 5.5 times.
They also report that in marketing to potential investors VCs indicate seeking
investments that can offer IRRs of 24% and net multiples of 3.5 times.
These high returns, however, are not supported by historical evidence; an
examination of the actual average realized returns for investing in new ventures
tells a very different story. In addition to the information in Figure 10.1, numerous studies by academics and industry participants over more than four decades
report average annual rates of return to investors in VC funds in the mid- to
high teens.4 For the recent 20-year period, ending with the 2013 vintage year, the
average annual return from a sample of U.S. VC funds was 17.3%. Figure 10.2
shows estimates of VC IRRs since 1981 by vintage year: the series exhibits very
high volatility, including vintage years with very high returns and others with
negative or near-zero returns.5
100
Fi g u r e 1 0. 2
VC returns to limited
partners by vintage
year
Thomson Reuters, Venture Economics,
VentureXpert database,
extracted August 2009;
PitchBook data are from
the PitchBook 2017 PE VC
Fund Performance Report
and Preqin
90
80
sources:
60
50
40
30
20
10
0
–10
–20
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Annualized IRR (percentage)
Data through 2006 are
pooled average VC returns
by vintage year compiled
by Thomson Reuters and
measured through 2009.
Data from 2007 on are VC
fund medians reported
by PitchBook/NVCA and
measured through 2017.
70
Vintage year
358
Chapter Ten
How can the common practice of using very high discount rates to value
projects be reconciled with the evidence that over many years the average realized return has been much lower? Do investors suffer from chronic unfounded
optimism, so that actual returns have been disappointingly low? Or is there
another interpretation of the evidence, one that does not rely on biased decision
making? Surely, if rates of return in the neighborhood of 30% could reasonably
be expected, capital would flood the new venture market, driving the returns to
levels that are more consistent with those for other forms of investment. True required rates of return are much closer to the range documented in the empirical
studies than to the very high rates that are sometimes sought by investors when
they evaluate individual projects. Later in this chapter, we offer a reconciliation
of the preceding statements and evidence that places the statements in a more
useful context and demonstrates that actual required rates are much lower. The
point for now is that the contradiction is more apparent than real.
Myth 4: The Investor Determines the Value of the Venture
A common contention is that it is pointless for the entrepreneur to undertake
a valuation. The argument is that investors do not accept the entrepreneur’s
valuation anyway, so the entrepreneur’s efforts are better spent in other ways.
The problem with this view is that it fails to recognize the pivotal role that valuation plays in reaching agreement between the entrepreneur and the investor,
as well as the role it can play in helping the entrepreneur decide whether to
undertake the venture. VC investors report in the Gompers et al. (2016) survey that less than 2% of the companies they consider for investment result in
completed funding rounds and that many of their negotiations fail because of
concerns about the management team, which can arise partly from unrealistic
perceptions of value.
In an interview, Sonja Hoel, a managing director at Menlo Ventures, cited
valuation issues as an important factor.
We almost always get it right if we turned down a deal because there wasn’t
a market. Where we don’t always get it right is valuation. If we turned it
down because of valuation, we had a 10% error rate. Of all the decisions
we made because of valuation, 90% were good but 10% were bad.6
Good working knowledge of valuation can help the entrepreneur avoid a
breakdown of negotiations. It is true that investors commonly prepare valuations
based on their own research and assumptions. However, there is more to new
venture financing negotiations than a simple exchange of cash for a percentage
of the equity. In the context of a financing negotiation, valuation is important
Foundations of New Venture Valuation 359
to the entrepreneur for at least three reasons. First, the entrepreneur can better understand how the venture is likely to be valued by prospective investors.
Second, the entrepreneur can better understand what the venture should be
worth to him and how that differs from its value to the investor. Third, the
entrepreneur needs to understand how alternative deal structures affect overall
value and the values of the financial claims of investors and the entrepreneur.
Even for very early-stage ventures, where “unpriced” rounds are common, it
would be difficult to reach agreement on deal terms without a sense of the value
of the opportunity.
The entrepreneur might expect that competition among prospective investors
can eliminate the need to value complex financial claims. This expectation is
incorrect. Even if several investors are vying to participate in a venture, they
may have varying views of the venture’s strategy and will probably seek different
structures of ownership claims and financing commitments. Without studying
the valuation consequences of different proposals, choosing the best alternative
can be problematic for the entrepreneur. A solid understanding of basic valuation techniques can ensure that entrepreneurs better understand how investors
perceive the opportunity and help to reach a mutually beneficial agreement.
10.3 An Overview of Valuation Methods
For an investor who cares only about financial return, the value of any investment is the PV of its future cash flows. Although a variety of methods exist
for estimating value, ranging from explicit discounting of future cash flows to
valuation based on simple multiples to valuation based on comparable firms,
they all are attempting to measure, directly or indirectly, the PV of the right to
receive future cash flows.
As discussed in Chapter 1, valuation is guided by two fundamental principles:
that a dollar today is worth more than a dollar received in the future, and that
a safe dollar is worth more than a risky one, that is, a safe dollar is more valuable than a gamble with an expected payoff of one dollar. Thus, the PV of any
investment depends on the timing and riskiness of expected future cash flows.
In theory, if a person could correctly identify expected cash flows, risk, and
cost of capital, the result of a discounted cash flow analysis would be the “true”
PV. In practice, however, there is considerable judgment involved in valuation.
Because we must rely on estimates, we can be sure our calculations yield only
approximations of true PV. Rather than despair, we should recognize that imperfect PV estimates provide opportunity. There are potentially large rewards
360
Chapter Ten
for doing a better job of estimating PV than do your rivals. Other things being
equal, the entrepreneur or investor who consistently does a good job of estimating value will outperform one who is right on average but makes large over- and
underestimation errors. There are many theoretically sound and empirically
validated valuation tools for analyzing new ventures. In this chapter, we introduce the following methods and highlight their strengths and weaknesses:
1. Discounted cash flow (DCF) methods
a. The risk-adjusted discount rate (RADR) approach
b. The certainty equivalent (CEQ) approach
2. Relative value (RV) method
3. The Venture Capital (VC) Method
4. The First Chicago Method
These methods vary in their complexity and the directness of their connection
to underlying economic theory, but all are used in practice and therefore are
important to entrepreneurs and investors.
The Discounted Cash Flow (DCF) Methods
We consider two approaches for estimating PV by DCF. The first is the RADR
approach and the second the CEQ approach. The difference between them
lies in how the adjustment for risk is incorporated into the calculation. If the
approaches are applied in a consistent manner and the underlying assumptions are consistent, they will yield identical estimates of value. However, the
information needed to implement the two approaches differs. Consequently,
availability and quality of information are important determinants of which
to use.
The RADR approach is the more widely known and used DCF approach. In
RADR, an expected future cash flow is converted to PV by applying a discount
rate that reflects both the time value of money and the riskiness of the future
cash flow. This rate is known as the “risk-adjusted discount rate” because the
effect of risk on value is built into the discount rate. The RADR approach is
used most commonly in corporate finance because it is convenient and because
the information requirements are easily satisfied by using data on comparable
public firms.
With the CEQ approach, instead of adjusting the discount rate, the risk adjustment is made directly to cash flow. This adjustment yields a risk-adjusted
(or certainty equivalent) cash flow that is converted to PV by discounting at the
risk-free rate. It turns out that for new ventures, it can be easier to estimate the
Foundations of New Venture Valuation 361
CEQ cash flow than the RADR. In subsequent discussion, we examine both
approaches.
The Relative Value (RV) Method
Relative valuation uses market data on other companies or other transactions
as bases for inferring the value of the subject venture. This method is sometimes referred to as “comparables” or “multiples valuation.” It is widely used
in practice, especially for established private companies, and can provide a
quick and easy ballpark estimate of value. The underlying logic is arbitrage.
If two different companies are expected to produce identical future cash flows
and are subject to identical risks, they should have the same value. If they
did not, then investors should want to sell the higher-valued one and buy the
lower-valued one. By inference, if firms have similar characteristics, the arbitrage reasoning should provide a good approximation.
Relative valuation is thus an effort to finesse questions of discount rates and
explicit cash flow projections. If RV and DCF could be correctly and perfectly
applied to a given venture, then they should yield identical values. Suppose, for
example, that we find a perfect comparable for a venture we want to value and
that the comparable has been the subject of a recent transaction that implied
a specific value. Under RV, we could simply say that the values are the same;
under DCF, in contrast, we could use the expected future cash flows and implied
value of the comparable to infer the discount rate that we should use to value the
cash flows of the subject venture. We would get the same answer either way. In
practice, when RV and DCF yield different estimates of value, there is something
inconsistent about the assumptions. Either the assumptions need to be revised
or judgment must be used to assess the significance to place on each estimate.
In RV, market prices or prices from public or private transactions are collected along with information on observable characteristics of the comparable
firms. This information may include accounting ratios and operating data that
can be used to estimate the value of a private firm or new venture. While the
logic behind RV is straightforward, there are many challenges to valid implementation. Appropriate comparables for a new venture (with transactions at the
same stage of development) can be difficult or impossible to find or to verify,
and common metrics such as P/E ratios are not useful for a start-up that has not
reached profitability. Because some multiples are based on accounting data, the
usual caveats about quality of earnings and adjustments to ensure comparability apply. More generally, while the idea of valuation based on comparisons to
other firms sounds simple, it is not. There are many dimensions of comparability,
including industry, business model, stage, size, and accounting ratios. Ideally,
362
Chapter Ten
a comparable firm has expected cash flows and risk that are similar to those of
the new venture.
Certainly, no public company can be a direct comparable for an early-stage
venture. Moreover, there are no readily accessible databases of transactions
related to early-stage private companies that have sufficient data to be useful
in a valuation. Use of an RV approach is more common and more defensible
for a company that is being valued at the time of an assumed harvesting event,
such as an IPO or acquisition. At that stage, information on comparable firms
and transactions is likely to be more plentiful and the necessary analogies are
easier to make and defend. In fact, RV is often the approach that is used in DCF
methods to estimate the harvest-date cash flow. That said, even for a mature
private company there are likely to be no truly comparable public companies.
Companies that are similar in some key respects but not identical to the subject company are likely to yield a wide range of value estimates. Deciding how
to select the companies and how to weight the evidence are part of the art of
valuation.
The Venture Capital (VC) Method
The VC Method combines elements of DCF and RV and has been popular
in the private equity arena. It is computationally straightforward and parallels the way some VCs have traditionally approached their investments.7
The method starts with an estimate of future value conditional on success—­
possibly the projections in the entrepreneur’s business plan, which typically
seek to demonstrate the potential success of the venture. Success scenario
projections incorporate an assumed timetable for exit, typically three to five
years, and a conjecture as to the form of exit and resulting future value. The
estimate of exit value conditional on success is sometimes developed using RV
multiples to a projected performance measure such as net income or EBIT.
The success-scenario exit value is then discounted to PV, typically using a
very high annual discount rate such as the “sought for” rates we presented
earlier. This hurdle rate is intended to take account of (1) time value, (2) risk,
(3) the bias associated with discounting only the success-scenario cash flows,
and (4) the dilutive effect of subsequent financing rounds.8 The result is an estimate of the venture’s PV.
In spite of its widespread use and intuitive appeal, the VC Method has theoretical and practical shortcomings. Most important, the hurdle rate selection
implicitly combines so many conditions that are difficult to specify so that there
is no reliable way to determine the rate that should be used. Because only a success scenario is used, the assumed timing of the harvest cash flow can ­impart
Foundations of New Venture Valuation 363
systematic bias to value estimates. We discuss these shortcomings in greater
depth below. Because of these, we regard the VC Method as more of a device
for negotiating with the entrepreneur than a reliable valuation method.
The First Chicago Method
The First Chicago Method is also used widely by VC and PE practitioners and
represents an improvement on the VC Method.9 The goals of the First Chicago
Method are to provide a simple way of performing DCF valuation and to mitigate the valuation biases of the VC Method. Rather than limiting the analysis to a success scenario, the First Chicago Method uses probability-weighted
scenarios to come up with a more reliable estimate of expected (in the statistical sense) cash flows, rather than just the optimistic cash flows used in the VC
Method. These expected cash flows are then discounted using a more realistic cost of capital, rather than the high hurdle rates used in the VC Method.
If the scenario probabilities are correctly weighted, the appropriate discount
rate is identical to the one that would be used in DCF valuation by the RADR
approach. A benefit of the First Chicago Method is that it requires the analyst to think about the range of possible outcomes for the venture and their
probabilities.
The question of which valuation tool is best for a particular situation does
not have a clear answer. Like any quantitative methodology, the results of each
approach depend on the availability and quality of the information it requires.
There is a benefit to using as many of the techniques as the data will allow.
Doing so will inevitably produce a range of value estimates. The final step is to
compare the results, seeking to understand the reasons for material differences.
On reflection, you may conclude that in a given instance, one approach is more
reliable than are the others. More likely, each provides some useful information,
and your task is to decide how best to weight the different estimates.
10.4 Discounted Cash Flow Valuation
As noted earlier, we consider two approaches to DCF valuation, the RADR
approach and the CEQ approach. The main difference between them is in the
way we adjust for risk. Most introductory finance texts cover only RADR or
only briefly describe CEQ. Because of its different information requirements,
however, the CEQ method can be better suited than RADR for new venture
valuation.
364
Chapter Ten
The Risk-Adjusted Discount Rate Approach
Under RADR, expected future cash flows are discounted to PV using a discount factor that reflects the time value of money and the riskiness of the future cash flows. The present value, PVj, of an investment that offers a series of
expected future cash flows, Cjt, is given as:
C jt
PV j = Σ t
(10.1)
(1 + rt )t
In Eq. (10.1), rt is the risk-adjusted discount rate that is appropriate for computing the PV of an expected time t cash flow. The expression is general in that
it allows for cash flows to be received at any time and for the cost of capital to
be specific to the period in which the flow is expected.
Note that PVj is the value today of all of the cash flows that are generated by
the venture’s operations, whether they are positive or negative. It is not the same
as net present value (NPV), which is net of the investment required to receive
the venture’s future cash flows. To illustrate, consider a delivery business such
as Postmates. The PV would include positive cash flows (e.g., revenue) and negative cash flows (e.g., compensation to independent contractors, lease payments
on vehicles, salaries, and fuel). Let’s assume that discounting all of the positive
and negative cash flows using Eq. (10.1) produces a PV of $150,000. If the owner
agrees to sell you the venture for $130,000, the resultant NPV is $20,000. That is,
NPV is equal to PV minus the investment required to acquire the venture. Applying Eq. (10.1) requires that we (1) identify the cash flows that are appropriate
to include in the valuation model and (2) determine the appropriate discount
rate for valuing each cash flow.
Identifying relevant cash flows. Conceptually, determining the cash
flows to include in an RADR valuation is straightforward. They are the cash
flows the investor can expect (statistically) to receive in exchange for investing.
To identify the relevant cash flows, we need to understand exactly what asset is
being valued. The asset may be the entire venture or a particular financial claim
on the venture, such as a portion of the common stock, preferred stock, or debt.
A share of stock, for example, yields a stream of cash flows in the form
of dividends. An investor who owns the share for a finite period, of course,
does not receive the entire dividend stream but receives dividends paid by the
firm during the holding period and a lump-sum payment when the share is
sold. The payment when the share is sold depends on the stream of dividends
that are expected to be received from that point forward. Implicitly, then,
the stock price at any point reflects the value of all subsequent dividend cash
Foundations of New Venture Valuation 365
flows.10 The expected proceeds from selling the stock at a future date can
be thought of as the “continuing value” of the stock at that point, based on
cash flows (dividend payments) expected after the sale. Because we normally
project dividends in simple ways, such as by assuming that they are expected
to grow at a constant rate, the continuing value approach (of an actual or
hypothetical sale in the future) is a shortcut approach to discounting a long
stream of future cash flows.
Debt is expected to yield a stream of interest payments and the eventual
repayment of principal. If the debt is risky, the interest payments and principal
repayment to be valued are not those specified in the debt contract. Rather,
they are the cash flows that are expected to be received, recognizing that the
borrower may prepay or default on the obligation.
If the asset to be valued is a venture, the relevant cash flows are the (positive
and negative) periodic free cash flows generated by the venture and available to
all capital providers. Depending on your purpose, it may be advantageous to
measure these cash flows net of any retention for the purpose of capital replacement or growth and (for consistency) to measure investment as only the cash
infusions that investors make in the venture.
Like a share of stock, a venture can, in principle, last forever. Conceptually,
we would like to know the value of all future cash flows. However, at some point
after the first few years it is more convenient (and possibly no less accurate) to
assume a hypothetical sale where the selling price is based on the value (at the
time of sale) of all free cash flows from that point forward. We refer to this value
as the continuing value of the venture. The “explicit value period” is the span
over which periodic cash flows are estimated explicitly and each is discounted to
its PV. Total PV thus consists of two parts: (1) the PV of the expected cash flows
from the explicit value period and (2) the PV of the expected continuing value.
Determining the discount rate (cost of capital). The next step in
valuation by RADR is determining the discount rate for valuing each cash flow.
Because in the RADR approach the discount rate takes account of both time
value and cash flow risk, we can think of the discount rate used in Eq. (10.1) as
having two components. The first is the return for investing in a risk-free asset
that would pay off at the same time as the project cash flow. The second is a
risk premium that depends on the riskiness of the expected future cash flow.
For a particular project j that yields a single uncertain cash flow at time t, the
appropriate discount rate can be stated as follows:
rjt=rFt+RPjt
(10.2)
366
Chapter Ten
where rFt is the required rate of return for investing in a risk-free asset that
would pay off at time t, and RPjt is a risk premium or risk adjustment. The risk
adjustment depends, in some fashion, on the riskiness of the time t cash flow.
Because the cash flows being valued are expected cash flows, the appropriate
discount rate is the opportunity cost (or rate of return) the investor could expect
to realize by investing in an alternative financial claim with the same holding
period and risk. In contrast to the ad hoc determination of the hurdle rate in
the VC Method, the financial economic theory underlying determination of the
discount rate for RADR valuation is well established.
In summary, the steps for using the RADR approach are as follows: (1) forecast expected future cash flows, (2) estimate the risky discount rate, rjt, for each
cash flow, (3) discount each future cash flow to its PV, and sum the PVs. Equation (10.2) is useful because it is sometimes easier to estimate rFt and RPjt than
to estimate rjt directly. A simple way of estimating rFt is to examine currently
available returns on zero-coupon government bonds of similar duration to the
cash flow that is being valued. Estimating the risk premium is more difficult:
first, we need a measure of risk; second, we need a metric to determine the associated risk premium.
The measure of risk. The measure of risk that has become the norm for
investment valuation is the standard deviation of holding-period returns.11 A
holding-period return is a rate of return that is measured from the point of
investment to the point when the return is realized. That is, the holding-period
return is expressed as a percentage based on the amount invested. Take, for
example, a common stock. Its holding-period return consists of two parts, a
dividend yield and a capital gain (or loss). Uncertainty about the size of the
dividend and the price at which the shares can be sold in the future are the risks
faced by investors. Assume that a share of stock that has a market value of $20
today has the following equally likely payoffs and returns in one year and pays
an annual dividend (in one year from the time of investment):
State of the economy
Dividend (D1)
Price (P1)
Good
Moderate
Bad
$2.00
$1.00
$0.00
$24.00
$22.00
$18.00
Expected
$1.00
$21.33
Dividend yield
Percent appreciation
Total return
10%
5%
0%
20%
10%
(10%)
30%
15%
(10%)
5%
6.67%
11.67%
With equally likely outcomes, the expected return over the holding period
is the average of the three possible returns, which is 11.67%, and the standard
deviation of the holding-period return is 16.5%.12 Note that we calculated the
Foundations of New Venture Valuation 367
holding-period returns and standard deviation based on today’s market price
of $20. We can use this calculation to infer cost of capital because the stock is
traded in a market such that buying the stock today and holding it for one year
is expected to be a zero-NPV transaction.
Here, we find an important distinction between new ventures and publicly
traded stock. Because new ventures are not like shares of stock that trade in a
market, we cannot assume that investing is a zero-NPV choice. We will see later
that this feature makes RADR difficult to apply unless we can find some way
to infer the appropriate discount rate.
The price for bearing risk. How do we go from measuring risk to inferring a price for bearing risk? To answer, we must first distinguish between the
entrepreneur and prospective investors in a new venture. For investors, we follow
the standard corporate finance approach to estimating a risk premium, which
relies on the following assumptions:
• There is active competition to invest in new ventures.
• Investors view new venture investing as an alternative to other investment opportunities.
• Investors assess project risk based on its contribution to the risk of a diversified portfolio.
• Illiquidity does not affect the investors’ valuation of new venture
investment.
These assumptions allow us to distinguish between market and nonmarket
risk. The total risk, measured as the standard deviation of holding-period returns, is the sum of two categories of risk. The first—called market, systematic,
or nondiversifiable risk—consists of marketwide risk factors that affect all ventures. Economic recession and boom, changes in inflation, and changes in the
flow of funds into the capital markets are among the factors that tend to affect
asset values marketwide. Because market risk affects all assets, it cannot be
eliminated by diversifying the investor’s portfolio. The second category—called
firm-specific, idiosyncratic, nonmarket, or diversifiable risk—includes all risk
other than market risk. Examples of diversifiable risk include success or failure
in achieving a technology milestone and idiosyncratic product demand that is
higher or lower than expected.
Because of diversification, the unexpected positive and negative nonmarket
components of returns to different investments are random so that, in diversified portfolios, they tend to cancel each other out. In a portfolio with even a­
Chapter Ten
Fi g u r e 1 0. 3
How portfolio risk
depends on the
number of assets in
the portfolio
The figure shows how
diversification reduces
total risk as the number of
assets in the portfolio increases. Risk is measured
by the standard deviation
of holding-period returns.
The risk that remains is
shown as σM and is the
market, or nondiversifiable, risk.
Risk
σPortfolio
Standard deviation
368
Diversifiable
risk
σM
Nondiversifiable
risk
Number of assets in portfolio
modest number of assets, the idiosyncratic component of risk can be substantially reduced. Diversification cannot be used, however, to reduce exposure to
the component of risk that is due to the overall market.
Figure 10.3 demonstrates the diversification principle that the total risk of
a portfolio, σPortfolio, varies as a function of the number of randomly selected
securities. As shown, the total risk of the portfolio is composed of diversifiable
(nonsystematic) and nondiversifiable (systematic) risk. By holding more securities, diversifiable risk approaches zero and total risk approaches the risk of the
market, σM.
Any investor can diversify by investing in an equity mutual fund that is designed to match the performance of a standard market index, such as the S&P
500. Because diversification is easy, investors cannot expect to be compensated if
they choose not to take advantage of the simple opportunity to diversify. Thus,
the expected return on a risky asset depends only on risk that is not diversifiable.
The nondiversifiable component of risk is known as beta (β) risk, or market
risk. By convention, the beta risk of the market portfolio is defined as one unit
of risk. The risk-free asset, by definition, has no risk and therefore has a beta of
zero. An asset with nondiversifiable risk that is half as great as the market has
a beta of 0.5, and so on.
The notion that investors care only about time value and nondiversifiable
risk is reflected in the Capital Asset Pricing Model (CAPM).13 Figure 10.4 shows
the relationship between beta risk and expected return that is implied by the
CAPM. The CAPM tells us that different investments with different amounts
of total risk but equal amounts of beta risk will have the same expected return.
Accordingly, all assets and portfolios with the same beta risk plot at the same
Foundations of New Venture Valuation 369
Expected
return
Security
market
line (SML)
rm
Market
portfolio
rf
1.0
Beta (b )
Fi g u r e 1 0. 4
The capital asset pricing model (CAPM)
The CAPM describes the required return on an asset as a function of its nondiversifiable (market) risk as measured by beta. The market
portfolio by definition has a beta of 1.0 and is expected to earn rM. The riskless asset has a beta of zero and earns rF, the risk-free rate. The
difference between rM and rF is called the market risk premium. If the CAPM correctly describes investor behavior, then all market assets
offer risk and expected return combinations that plot on the security market line (SML).
point in the figure. The sloping line in Figure 10.4 is known as the security
market line (SML) because all risky assets must plot on the line. The result is a
single price, or expected return, for bearing a unit of nondiversifiable, or beta,
risk. The difference between the expected return on the market portfolio and
the return on a risk-free asset is called the market risk premium.
In algebraic form, the CAPM is:
rj = rF + βj(rM − rF)
(10.3)
Comparing Eq. (10.2) and Eq. (10.3), we can see that the CAPM defines the
risk premium for an asset as βj (rM − rF), which has two components. The term
in parentheses, (rM − rF), is the market risk premium and is computed as the
difference between the expected return on the market portfolio and the return
on a risk-free asset. This market risk premium is then scaled by βj, the beta or
systematic risk of asset j relative to the risk of the market portfolio. The product
of the two is the risk premium for asset j. The beta of asset j is the measure of
its nondiversifiable risk. Specifically, βj is computed as follows:
Covr ,r
ρ r ,r σ j
j M
j M
βj =
=
(10.4)
2
σ
σM
M
In Eq. (10.4), Covr ,r is the covariance of holding-period returns of asset j with
the holding-period returns of the market, ρrj , rM is the correlation coefficient of
holding-period returns between the asset and the market, σ M2 is the variance
of market returns, and σM and σj are the standard deviations of returns of the
market and of asset j.
j
M
370
Chapter Ten
Equation (10.3) identifies the information necessary to use the CAPM to estimate the required rate of return on an investment. Estimating the risk premium
for use in the RADR model requires estimating both the beta of the asset and
the market risk premium. Equation (10.4) shows two different ways to estimate
beta. As an alternative to estimating beta directly, we can calculate it from estimates of the standard deviations of holding-period returns for both the asset
and the market and an estimate of the correlation between asset returns and
market returns. In Chapter 11, we examine methods of estimating the information needed to use the CAPM in RADR valuation.
Given its focus on nondiversifiable risk, the CAPM is appropriate when investors are able to diversify at low cost. The typical investors in new ventures and
in VC funds (pension plans, endowments, and insurance companies) are able to
do so. The CAPM suggests that these investors should not require an increase
in expected return for bearing venture-specific risk that is diversifiable. Other
kinds of investors in new ventures may find diversification more difficult to
achieve. In particular, private corporations and high-net-worth individuals, if
they want to invest in venture capital, may be compelled to hold portfolios that
are not well diversified. If such investors hope to be compensated for sacrificing
diversification, they must either find opportunities that well-diversified investors
do not recognize or be able to contribute unique value to the venture in other
ways that well-diversified investors cannot.
The Certainty Equivalent Approach
It sometimes is difficult to use the RADR form of the CAPM to value real
investment opportunities with risky cash flows. The model requires that risk
be measured as the standard deviation of holding-period returns for an asset
that is correctly valued—an equilibrium holding period return. However, even
if we can estimate the expected cash flows of a project and their riskiness (i.e.,
standard deviation), we cannot correctly determine the expected holding-period return or the standard deviation of holding-period returns without knowing the PV of the project. When it is possible to rely on market assets to infer
cost of capital, this is not a problem because market assets are zero NPV by
definition (cost and present value are the same). However, this does not work
so simply for new ventures, where we cannot easily refer to market assets.
We can demonstrate the difficulty of using RADR by making a couple of
substitutions to Eq. (10.3). Equation (10.5) makes the appropriate substitutions
for rj and βj in Eq. (10.3), where Cj is the expected cash flow of project j, σC j is the
standard deviation (risk) of this cash flow, and ρC j , rM is the correlation between
the project j cash flow and the market return. For convenience, we will define
Foundations of New Venture Valuation 371
rates of return in the preceding expressions over the holding period for Cj (one
year, two years, etc.).
 σC 
ρC j , rM  j 
Cj
 PV j 
− 1 = rF +
(10.5)
( rM − rF )
PV j
σM
In this modified CAPM, the left-hand side of the equation is the expected
return and the right-hand side is the expression for the required rate of return. In
other words, we must find the required returns at the point where the expected
return equals the required return. Note, however, that PVj appears on both sides
of the equation. The two sides will be equal only at the point where PVj is the
PV of Cj. That is, Eq. (10.5) can be used to search for the PV of a future cash
flow by using trial and error to find the value of PVj that equates the two sides
of Eq. (10.5). Although this obviates the need to assume a standard deviation of
holding-period returns (using, instead, the standard deviation of cash flows), it
does not resolve the fundamental problem that the correct risky discount rate
depends on the (unknown) value of the cash flows.
An illustration of the difficulties of using the RADR approach. To
see the nature of the problem, consider a wager that will pay either $1.00 or
$2.00, with equal probability. We can easily determine that the risk of the bet (in
terms of the standard deviation of payoff cash flows) is $0.50.14 But what about
the standard deviation of holding-period returns? Suppose the wager can be
acquired for $1.25. If so, the $2.00 payoff is a return of 60% and the $1.00 payoff is a return of −20%. This makes the expected return 20% and the standard
deviation of holding-period returns 40%.15
But what if the wager costs $1.50? In this case, the $2.00 payoff yields a return
of 33.3% and the $1.00 payoff yields −33.3%. At this price, the expected return is
zero, and the standard deviation of holding-period returns is 33.3%. Thus, the
standard deviation of holding-period returns depends on the cost of the wager.
To find the PV of the wager, we need to know the standard deviation of
holding-period returns that corresponds to investing exactly the PV of the expected payout. If the required investment is different from the PV of the expected
payout, we can then correctly measure NPV as the difference between the actual
investment and the investment that would yield a zero NPV. You can see that
applying the conventional RADR form of the CAPM is challenging when it is
being used to value real assets. The problem is the inherent simultaneity and
the lack of a zero-NPV market asset to use as a basis for inferring the correct
discount rate. Valuing the expected cash flows requires knowing the discount
rate, but the discount rate calculation requires a beta, which depends on the
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Chapter Ten
standard deviation of holding-period returns, which, in turn, depends on the
PV of the project.
In corporate settings, these problems are finessed by analogizing the investment decision to an existing market asset that is publicly traded (and, by inference, fairly priced). If that is possible, a two-step approach can be used: first,
estimate the beta of the market asset; second, discount project cash flows using
that beta. Unfortunately, convincing analogies are hard to find if the project
is a new venture or a financial claim on a new venture. In Chapter 11, we seek
to overcome this problem by providing some empirical estimates of betas for
publicly traded corporations that are similar to entrepreneurial ventures. If those
estimates are reliable, then using the RADR approach is straightforward, just
as it is for public corporations. If not, the CEQ approach can be easier to use.
Advantage of the CEQ method. The certainty equivalent of a risky cash
flow is the certain cash flow that, if received at the same time as the risky cash
flow, would be equally valuable to the investor. For risk-averse investors, the
certainty equivalent is less than the expected risky cash flow. Thus an investor
might regard a risky cash flow with an expected value of $200 and a standard
deviation of $50 as being equal in value to a certain $125 cash flow.
In the CEQ approach of DCF valuation, instead of adjusting the discount
rate, the risk adjustment is made to the cash flow:
CE(Cjt) = Cjt − RDjt
(10.6)
where CE(Cjt) is the certainty equivalent of the period t expected cash flow, Cjt,
and RDjt represents the dollar-valued discount to Cjt that is required to convert the risky expected cash flow to its certainty equivalent. Once we have this
stream of certainty equivalent cash flows, we can convert them to PV using a
risk-free discount rate as follows:
PV j = Σ t
CE (C jt )
(1 + rFt )t
(10.7)
where rFt is the risk-free rate for period t cash flows.
To apply Eq. (10.7), we need a way to compute the CEQ cash flow. Because
the CEQ form of the CAPM uses the standard deviation of cash flows instead
of the standard deviation of holding-period returns, it avoids the simultaneity
problem of the RADR form.
The CEQ approach is general in that it does not impose any particular tradeoff between risk and return. If we assume that the CAPM is the correct asset
pricing model, then Eq. (10.5), the expanded version of the RADR form of the
CAPM, can be solved for PVj to yield the CEQ form of the CAPM.
Foundations of New Venture Valuation 373
PV j =
C jt
ρC
j , rM
σ
Cj
(rM − rF )
σM
1 + rF
(10.8)
The numerator in Eq. (10.8) is the CAPM-based certainty equivalent of the
risky cash flow, Cj, which corresponds to Eq. (10.5). The denominator is a discount factor that is used to determine the PV of a riskless cash flow.16 When
the CEQ form of the CAPM is used, the risky cash flow is adjusted by a factor that makes the PV of the CEQ cash flow, when discounted with a risk-free
rate, exactly equal to the value derived by discounting the uncertain expected
cash flow at the appropriate risky rate.
To see how the CEQ approach simplifies finding PV, let’s revisit the wager
that will pay either $1.00 or $2.00 with equal probability. We assume that the
risk-free rate is 4%; the market risk premium is 6%; the standard deviation
of holding-period returns of the market portfolio is 20%; and the correlation
between the payoff of the bet and the return you could earn by investing in the
market portfolio is 0.6. Given this information, it is easy to use Eq. (10.8) to
determine the PV of the wager.
PV j =
cj
pc j , rM σ C j
σM
1 + rF
( rM − rF )
=
$1.50 −
0.60$0.50
(0.06) $1.41
0.200
=
= $1.356
1 + 0.04
1.04
The CEQ cash flow of the expected (risky) $1.50 is $1.41, which when discounted at the riskless rate yields a PV of $1.356 for the wager. You can verify
that if this value is used in Eq. (10.5), then the two sides are equal (i.e., required return equals expected return at a PV of $1.356).
Given the PV of the wager, the NPV of the opportunity to acquire it for
$1.25 is $0.106 (i.e., $1.356 − $1.25 = $0.106). We also can determine that the
correct risk-adjusted discount rate (the cost of capital), given the PV of $1.356, is
(Cj /PVj ) − 1, or 10.62%.
Because Eq. (10.8) adjusts for risk by using the correlation between project
cash flows and market returns, it circumvents the need to determine the riskiness of the holding-period returns. However, it does raise another question:
How can we estimate the correlation between project cash flows and market
returns? We address this problem later in the chapter. A similar information
requirement exists if the RADR form is used, but in that case, the need is
to estimate the correlation between project returns and market returns. The
problem for the RADR form is sometimes finessed by analogizing the project
to a publicly held corporation and using estimates of beta from published
information.
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10.5 The Relative Value Method
Relative valuation (RV) uses data from public companies and public and private
market transactions to estimate value. It is widely used in the real estate industry, especially for residential real estate, for which it is particularly well suited.
Owner-occupied housing does not produce observable cash flow streams that
can be valued by DCF. Moreover, there are many trades of highly similar properties, where differences that affect value tend to be associated with observable
factors such as square footage and number of bedrooms. For ventures, the RV
method is commonly used to value companies at the time of IPO or acquisition.
Relative Valuation and New Ventures
We can apply RV to new ventures by gathering information on the value drivers of comparable firms. A firm’s value is driven by its profitability, expected
growth, and risk. In RV analysis, we can classify multiples into two categories:
(1) multiples based on the capitalized value of equity and (2) multiples based
on enterprise value, where enterprise value is defined as the market value of
equity, plus the market value of interest-bearing debt, minus excess cash.
For consistency of comparison, before computing the multiples, excess cash
(cash that is not needed for operation of the enterprise) should be subtracted
from both the subject asset and the comparables. After the multiples are applied
to estimate the enterprise value of the subject, any excess cash that is expected to
be accumulated by the subject can be separately discounted to PV at the risk-free
rate, analogous to distributing the cash as a dividend. For a firm such as Apple,
which in 2017 had over $74 billion of cash and short-term investments (24% of
total assets), this is not a trivial adjustment. We can also classify multiples as
either (1) accounting based or (2) non-accounting-based.
Accounting-based approaches. Accounting-based multiples relate the
value of venture equity or total capital to reported accounting information. The
most familiar of these, the P/E ratio, is a way to estimate the value of equity on
the basis of reported net income. Other accounting-based approaches to valuing
equity include the following:
• Price to cash income, which is measured as net income plus noncash
expenses
• Price to levered free cash flow, which differs from the previous measure
by subtracting increases in net working capital and fixed assets
Foundations of New Venture Valuation 375
• Price to book value of equity (more commonly referred to as market
value to book value of equity)
Accounting-based approaches can also be used to estimate the combined
value of equity and debt, or enterprise value. The commonly used measures
are similar to those for valuing equity, except that they are usually based on
cash flow measures including interest expense. Such approaches include the
following:
• Enterprise value to EBITDA (earnings before interest, taxes, and depreciation and amortization)
• Enterprise value to unlevered free cash flow, which is EBIT minus theoretical taxes as if the company were entirely equity financed
• Enterprise value to book value of debt and equity
• Enterprise value to sales
While accounting data are readily available for public firms, accountingbased valuation multiples have two major problems. First, they are subject to
accounting choices that can make comparison across firms inaccurate. Second,
the data in public financial statements are based on historical results, whereas
value today depends on expected future performance. While accounting-based
comparisons can work well for valuing established private businesses with simple
business formats, such as dry cleaners or donut shops, they are not easily applied to early-stage ventures.
Because new ventures are often unprofitable at the valuation date, may not
have initiated sales, and may also have negative net worth, many of the commonly used accounting measures may not be meaningful. The VC Method,
discussed next, is designed to get around this problem while retaining the simplicity of RV.
Problems related to differences in accounting practices can be overcome
with careful analysis. We can also overcome the backward-looking nature of
accounting data by performing the RV analysis using forward projections for
the comparable firms. For example, instead of calculating a P/E ratio based on
the prior year’s earnings, we can base the value estimate on multiples tied to
earnings forecasts.
Of course, simple ratio-based comparisons are only helpful if the subject
venture and the comparables have similar expected growth rates. There are
other, more formal, approaches, as well, for incorporating growth expectations
into multiples analysis. The following are two of the most common:
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Chapter Ten
• The PEG ratio (or P/E/growth), where growth is the expected growth
rate of earnings
• Enterprise value to EBITDA/growth, where growth is the expected future growth rate of EBITDA
The growth-adjusted approaches are ways of dealing with differences in
growth expectations between the subject venture and the comparables.
Non-accounting-based approaches. The other category of multiple we
consider is industry-specific nonfinancial metrics. An online subscription-based
venture could be valued on the basis of the projected number of subscribers or
estimated web pages of advertising. A proxy for the value of a biotechnology
venture might be the number of patents, while a pharmaceutical venture’s value
might be estimated using the number of products in various stages of FDA
approval. Internet ventures have been valued based on the number of website
visits and time spent on the site.17
Depending on the venture’s stage of development, the focus of non-accounting-based valuation might be on the drivers of revenue. Revenue-related metrics
include, for example, the number of visitors per month, the fraction of visitors
who become customers, the percentage of visitors who linger on the website
instead of leaving quickly, average order size, monthly active users, revenue per
active user, recurring (subscription) revenues, and annualized monthly revenue.
For a more seasoned venture, useful metrics might include contribution margin
per order or customer, the average cost of acquiring a customer, the customer
churn rate (lost customers/total customers), the cash burn rate per month, and
the average lifetime value of a customer. A number of websites provide benchmarking data that can be used to estimate value relative to other companies
with established values. See, for example, SimilarWeb​.com, JumpShot​.com,
and SemRush​.com.
There is some evidence that industry-specific non-accounting-based measures
can be better value predictors than accounting-based multiples.18 For example,
for early-stage Internet companies, commonly used valuation metrics include
monthly unique visitors, conversion rates (visitors into customers), bounce rates,
and average order value. Industry-specific multiples may be used to estimate
either equity or enterprise value. The important consideration is consistency.
If the comparables used in the valuation are materially debt financed but the
venture is entirely financed with equity, then the comparables ratios need to be
based on enterprise value.
Foundations of New Venture Valuation 377
An illustration: initial public offering. Here is a simple example to illustrate the concepts. Suppose your social media company is considering an
IPO. The company’s most recent annual EBIT is $2.2 million on revenue of $35
million, coming primarily from the sale of advertising. The company currently
has 15 million users per day and the growth rate of users per day in the most
recent year was 10%. You have also collected the following accounting-based
and non-accounting-based data on recent IPO transactions:
Comparable
transaction
Value at
IPO
(million)
Revenue
(million)
EBIT
(million)
Company A
Company B
Company C
Company D
Average
80
120
135
55
100
$36.360
$38.710
$79.410
$13.750
$42.058
$2.96
$1.88
$3.75
$0.71
$2.325
Users per
Value/
day
Growth rate Revenue at
(million) of daily users
IPO
33.10
11.00
16.80
9.20
17.53
6.0%
12.0%
9.0%
21.0%
12.0%
2.20
3.10
1.70
4.00
2.75
Value/
EBIT
at IPO
Value/ Daily
user at IPO
27.03
63.83
36.00
77.46
51.08
2.42
10.91
8.04
5.98
6.83
There are different ways to use the information from the comparables to
estimate the enterprise value of the subject company. One simple approach, the
one we illustrate here, is to consider each factor one at a time and compare the
averages to the subject. If the comparables include large outliers, it can also be
better to focus on medians instead of averages. A better approach, if sufficient
data are available, is to fit a statistical model to the data, for example, by using
multiple regression. The model can be used to assess the marginal effect on value
of each factor in conjunction with the effects of the others.
The last row of the preceding table shows the averages of the key variables
and enterprise value (EV) multiples. Averaging the results across the four comparable companies yields the following estimates of enterprise value:
Mkt Value
) × RevenueSubject = 2.75 × $35.0 = $96.25 million
Revenue
Mkt Value
= Avg (
) × EarningsSubject = 51.08 × $2.2 = $112.38 million
Earnings
Mkt Value
= Avg (
) × UsersSubject = 6.83 × 15.0 = $102.52 million
Users
EV Est .subject = Avg (
EV Est .subject
EV Est .subject
All three approaches yield similar estimates. Averaging the three produces an
estimated enterprise value of $103.72 million.
How reliable is this value? One positive indication is that the 10% growth rate
in average users per day is similar to the 10.5% average over the comparable
companies. Another indication is that the valuation based on the averages of
comparable companies is almost the same as the median (not shown) for the
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comparables. The implied value based on the medians of each factor would be
$105.10.
We can also look separately at each comparable. Company A yields the lowest implied value, $57.57 million based on the average of the three measures.
But Company A has the lowest user growth rate of any of the comparables.
Company B yields the highest implied value, $137.52 million, but has a higher
growth rate of users and a high value per existing user. Overall, it appears that
our valuation is reasonable.
Private value versus market value. It is important to be cognizant of
the assumptions implicit in the choice of comparables. If, for example, a private
venture is valued on the basis of public company multiples, then the result is an
estimate of the value as if the venture were public. If the comparables are from
private transactions, such as acquisitions, then the estimate is as if the venture
were to be acquired.
In principle, if everything is properly controlled, there may be no difference
between the values implied by the two different kinds of comparables data.
However, the choice to go public or exit via acquisition is not arbitrary. Some
companies are better suited for IPO and others are better suited for acquisition.
The valuation is likely to be more credible if the comparables represent the exit
choice that is most appropriate for the subject venture. The main drawback of
relative value multiples is that current financial measures are linked primarily
to assets in place, which may bear little relation to likely future performance.19
Furthermore, comparable firm and comparable transaction data can be difficult to collect.20 That said, practitioners frequently use multiples, so we include them here in our discussion and offer suggestions to help overcome their
shortcomings.
10.6
Valuation by the Venture Capital Method
The VC Method is the traditional approach for VC investment valuation. It
is also the simplest approach for valuing early-stage ventures.21 In the VC
Method, value is estimated on the basis of projected harvest-date cash flows
under the assumption that the venture meets its performance objectives. The
scenario in which the venture meets its objectives is referred to as a “success
scenario.”
The procedure is as follows:
Foundations of New Venture Valuation 379
1. Select a terminal year for the valuation by determining a point where, if
the venture is successful, harvesting by acquisition or IPO would be feasible. Estimate net income or other cash flows in that year based on the
success scenario.
2. Use the appropriate P/E or other multiple and the harvest-date earnings
or cash flow projection to compute continuing value. The multiple should
reflect the expected capitalization of earnings or cash flow for a company
that has achieved the level of success reflected in the scenario.
3. Convert the continuing value estimate to PV by discounting at a hurdle
rate that is high enough to compensate for time value, risk, the probability that the success scenario will not be achieved, and dilution from anticipated future funding rounds.
4. Based on estimated PV, compute the minimum fraction of ownership an
investor would require in exchange for contributing a given amount of
capital.
The VC Method is problematic for several reasons. Most fundamentally, it is
based on an optimistic forecast of the future.22 While the hurdle rate is intended
to compensate for the optimistic cash flow estimate, there is no indication of
the likelihood that the success scenario will be achieved. To compensate for the
optimistic forecast of continuing value, the hurdle rate must be well above cost
of capital. The approach can easily be based on hurdle rates in excess of 50%.
In addition, the VC Method does not consider cash flows in the explicit value
period. This could make sense if the expected cash flows during the explicit
value period are very small compared to continuing value. If they are not, the
problem can be addressed by adding the PV of the cash flows from the explicit
value period to the PV of continuing value. Because these cash flows are from
a success scenario, the model implies that they again would be discounted at
a high rate.
To illustrate, recall the stock price example from Section 10.4. If we were to
value the stock using the VC Method, we would consider only the good state
(i.e., the success scenario) as shown here.
State of the economy
Good
Dividend (D1)
Price (P1)
Dividend yield
Percent appreciation
Total return
$2.00
$24.00
10%
20%
30%
We know that the actual PV is equal to today’s market price of $20, and in
Section 10.4, we established that the cost of capital (i.e., the expected return)
taking account of all three possible outcomes is 11.67%. To get the same
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Chapter Ten
$20 market value by the VC Method, we would need to discount the Time 1
success-scenario price of $24, plus the $2 dividend by a 30% hurdle rate, well
above the actual 11.67% cost of capital.
The Achilles heel of the VC Method is that intuition and experience form
the basis of the hurdle rate to use. So while the method might work reasonably
well for an investor who has years of experience of experimenting with different hurdle rates in different circumstances, there is no real guidance for an
entrepreneur, a first-time investor, or a student who aspires to become a VC or
entrepreneur. In spite of these problems and challenges, the VC Method continues to have some appeal because of its simplicity, intuitiveness, and potential
usefulness in negotiating with an entrepreneur.
10.7
Valuation by the First Chicago Method
Under the First Chicago Method, a user identifies a small number of discrete
scenarios and values them using a discount rate that is reflective of cost of
capital. The First Chicago Method is designed to be simple to use but also
to address the biases of the VC Method. Usually the First Chicago Method
is based on three scenarios: “success,” “sideways,” and “failure.” The success
scenario might be the same as in the VC Method, possibly the entrepreneur’s
projection. The failure scenario is one in which investors realize essentially no
return on their investment and lose the principal. The sideways scenario is one
of moderate performance in which the venture languishes with no prospect of
a high-valued outcome. In this scenario, investors might earn a preferred dividend return and recoup the initial investment but nothing more.
If the First Chicago Method is intended to correct the biases of the VC
Method, the scenarios and their probability weights will be chosen so that opportunity cost of capital is the correct discount rate to use to value the scenario
cash flows. Because the stock price example in Section 10.4 simplifies the stock
price outcomes to three equally likely scenarios, we can think of it as an example
of the First Chicago Method.
Implementation of the method involves the following steps:
1. Select a terminal year for the valuation based on the likely harvest date in
the event of success.
2. Estimate the cash flows during the explicit value period based on a small
number of discrete scenarios.
Foundations of New Venture Valuation 381
3. Compute the continuing value by applying a multiplier to the financial
projection. The multiplier for the success scenario should reflect the expected capitalization for a company that has achieved the level of success
reflected in the scenario. The multiplier for the sideways scenario may be
different, depending on differences in expected growth rates used in the
capitalization. In the failure scenario, the venture probably is not sold,
but the liquidation value, if any, of the assets should be incorporated in
the cash flows.
4. Compute the expected cash flow in each period by appropriately weighting the scenarios.
5. Compute PV by discounting the expected cash flows, including expected
continuing value at opportunity cost of capital.
6. Based on PV, determine the minimum fraction of ownership the investor
should require in exchange for contributing a given amount of capital.
The First Chicago Method makes fundamental sense because the goal is to
value expected cash flows at opportunity cost of capital. Traditional application
of the First Chicago Method, however, does not offer much guidance on how
to determine the cost of capital. It might seem that the dispersion information
reflected in the cash flow scenarios should be useful. Later, we will see that it
is, but in the context of the CEQ model. In fact, if the CEQ model is applied to
similar, discrete scenarios, it is the same as the First Chicago Method with the
CAPM being used to value the cash flows.
Ultimately, it is not possible to value future cash flows by either the VC
Method or the First Chicago Method without relying on some asset pricing
model to determine the appropriate discount rates. The asset pricing model
might be based on experience and rules of thumb. It might be based on simple
comparisons to other assets in the market. Or it might be derived from a formal
asset pricing model, such as the CAPM.
10.8 Reconciliation with the Pricing of Options
Option pricing theory implies that the values of some kinds of claims increase
with risk.23 Because new ventures are, in effect, portfolios of options, you
might ask whether the Option Pricing Model (OPM) would be a better valuation model for our purposes. To answer, we need to examine the principles
underlying the OPM.
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Chapter Ten
Risk contributes positively to the value of options because options partition
risk into the risk of an increase in the price of an underlying asset and the risk
of a price decrease. An investor who is optimistic about the future performance
of a share of stock can buy a call option on the stock instead of the underlying
share. If the stock value increases, the call option will increase as well. However,
unlike an investor in the stock, the call option investor is protected against price
declines. If, on the date the option expires, the stock value has fallen to below
the exercise price of the option, then the option is worthless. Conversely, if, on
the day the call option expires, the stock price is above the exercise price, the
value of the option will equal the difference between the stock price and the
option exercise price.
Because the exposure to risk is not symmetric (only the upside matters),
the value of a call increases with anything that increases the riskiness of the
underlying shares. There is no inconsistency between using the OPM to value
puts and calls and using the CAPM to value the underlying asset. The key to
reconciliation is that an option is a derivative asset: its value is “derived” from
the value of the underlying asset and from the risk characteristics of the underlying asset. Because the underlying asset does not partition risks into good and
bad, an investor in the asset must bear the risk of loss to acquire the potential
for gain. In that setting, risk aversion leads to the conclusion that investors will
require compensation for bearing risk that is not diversifiable.
Although the CAPM and OPM are consistent, certain aspects of the OPM
make its application to valuation of new ventures difficult. One problem is that
the OPM is derived under an assumption of market completeness and continuous trading of assets. In general terms, a complete market is one with negligible
transaction costs and where there is a price for every asset in every possible
state of the world. A complete market, in our narrower context, means that the
underlying asset, matched pairs of puts and calls (with the same exercise price
and expiration date), and riskless debt must all be continuously available, and
it must be possible, at low cost, to take long or short positions in each. In standard pricing of options on freely tradable assets, by either the Black-Scholes
Option Pricing Model or the Binomial Option Pricing Model, the current price
of a freely traded underlying asset is established in a market so that, implicitly,
there is agreement on value. Riskless debt with the same maturity as an option
on the underlying asset is also freely traded.
The logic of option pricing is arbitrage. Suppose, for example, that there are
only two future states of the world and that a share of stock that sells for $10
will, in one year, pay $15 if the good state occurs or $9 if the bad state occurs.
Riskless debt with the same maturity will return 10%. You would like to price
a one-year call option with an exercise price of $10. The option will pay either
Foundations of New Venture Valuation 383
$4 or $0. In this case, we can replicate the payoffs of the call option by investing
in two thirds of a share of the underlying stock on margin. The stock investment
will cost $6.67. To construct the call option payoff profile, you will pay $1.22 of
this price yourself and will borrow the remaining $5.45. At the time of option
expiration, the stock investment will pay either $10 or $6, but you will need to pay
off the margin loan and accrued interest, at a total cost of $6, leaving you with
either $4 or $0, the same as if you had invested in the call option directly. Hence,
by this replication argument, the current price of the call option must be $1.22.
In this example, we know the current stock price, the conditional payoffs of
the stock in one year, and the interest rate on riskless debt, and we can use this
information to find the conditional payoffs of the call option. We do not know
the probabilities of the good and bad states in one year. Nor do we know how
the market determined that those conditional payoffs had a current value of $10
per share. We can, however, narrow the feasible range of state probabilities. If
investors are risk-averse to any extent, since $11 is the one-year payoff on a $10
investment in riskless debt, the true probabilities from investing in the one share
of stock must result in an expected payoff greater than $11. Mathematically, we
can determine that a 16.7% probability of the good outcome (a payoff of $15)
would make the expected payoff $11, exactly the same as the risk-free payoff. So
the implied probability of the bad state must be 83.3%. These state probabilities
that would make the expected payoff the same as the risk-free payoff are what
are referred to in standard option valuation as “risk-neutral probabilities.” If
investors are risk-averse, the true probability of the good state must then be
more than 16.7%. Since standard option pricing relies on riskless arbitrage and
a known set of current and contingent future prices, we can price the option
even though we do not know the true probabilities.
These conditions or conditions like them that hold reasonably well for publicly traded options on publicly traded stock are the core of the Black-Scholes
and binomial option pricing models. They also may provide reasonable approximations for options on assets such as gold mines, the values of which are
closely linked to the value of gold, a freely traded asset. Clearly, however, they
do not hold for many high-risk, high-potential new ventures. Investors and
entrepreneurs are likely to disagree about the current value and about future
state-contingent payoffs. The underlying asset, one that can service the full
potential product-market demand and does not provide for abandonment, is
virtually never observed or priced. As a consequence, riskless arbitrage between
options and underlying assets is not possible. The upshot is that a replicating
portfolio is unlikely to exist and the standard OPM logic is likely to misvalue the
real options that comprise most new ventures. Moreover, as a new venture is an
amalgam of many interrelated options, some with complex exercise ­provisions
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Chapter Ten
that are controlled by different parties, use of the OPM can quickly become
impractical. The alternative we use is pricing by simulating the uncertainties
in a decision tree that incorporates the important real options.
In summary, the choice of real option valuation methods depends on what
you are believe you know, and what you don’t. If you believe you know the statecontingent payoffs and that you can replicate the payoffs with a freely traded
asset that has the same risk profile, but you do not know the state probabilities,
you can use market data and the standard OPM replication approach to value
the venture and price the real options. Alternatively, if the option structure is
complex and there is no market asset that can replicate the venture, but you think
you know the probabilities of option exercise and associated future values, you
can use the decision tree simulation approach similarly to what was illustrated
in Chapter 6 to value the venture. In a later chapter, we present a simulation
approach to valuing new ventures and financial claims based on the CEQ form
of the CAPM. The approach provides a convenient way to estimate the effects
of embedded real options on the value of a new venture.24
10.9 Required Rates of Return for Investing in New Ventures
Early in the chapter we promised a resolution of the paradoxical difference
between the rates of return sought by VC investors and those realized by the
investors. The CAPM helps with the explanation and offers some supporting
evidence.
Assuming that the CAPM is a reasonable way to estimate the rates of return
investors in new ventures require, how high would those rates be? Clearly, a new
venture is a risky proposition, which might suggest that the expected returns
should be very high. On the other hand, the point of the CAPM is that only
nondiversifiable risk matters.
It seems likely that much of the risk associated with new ventures is diversifiable or firm specific. Consider, for example, a biotechnology venture. How much
of the risk is likely to be nondiversifiable? In many respects, a biotechnology
venture is like a lottery: by investing, you place a bet today, and at some future
date, you learn whether you have a winning ticket. The future payoff from
such an investment is highly unpredictable and depends on factors such as how
significant the innovation proves to be. The payoff is not likely to depend very
much on economic fluctuations or similar factors that would have much greater
effects on such sectors as discretionary consumer durables.
Foundations of New Venture Valuation 385
Because beta risk depends on how the venture’s payoff varies with marketwide
fluctuations, a biotech venture is likely to have a low beta despite its high total
risk. At the same time, the cash flows from investing in a portfolio of 100 different biotech ventures might not be very risky at all. Most of the risk is specific to
the individual venture and would be substantially reduced by diversifying, even
within the same industry. In fact, for a large sample of public biotechnology
ventures, we estimated an average beta of 0.75 but an average total risk almost
five times as high.25
We will see in Chapter 11 that estimating the beta of an individual new venture requires judgment. However, we can gain information about the betas of
new ventures as a group by studying the price performance of publicly traded
firms that were funded by VCs. Such firms typically have betas in the range of
1.0 to 2.0.
What do betas in this range imply for the required rates of return on new
venture investments? We can answer by looking back at Eq. (10.3) and filling in
the other pieces of the equation with reasonable values. Suppose we use 4.0%
as an estimate of the riskless rate of return, rF, and 8.0% as the historical average market risk premium, rM − rF.26 Beta values in the 1.0 to 2.0 range would
imply required rates of return in the 12–20% range. Such low required rates of
return seem inconsistent with the earlier-mentioned claims that, for investing
in new projects, VCs sometimes seek rates of return in excess of 50%. On the
other hand, they are fully consistent with the historical evidence on VC returns
cited earlier in this chapter.
To look more specifically at the rates of return required by VC firms, the
discount rate for VC investing can be disaggregated into four components as
follows:
VC
rproj
= rF + β proj (rM − rF ) + Effort + Liquidity
(10.9)
VC
where rproj
is the discount rate used to value the VC’s claim on the value of a
project, is the beta risk of the project, Effort is a measure of the cost of effort
committed to the project by the VC and expressed in returns form, and the last
term is a measure of the required return differential due to illiquidity of the investment. Note that rF + β proj (rM − rF ) is just the CAPM applied to the project.
With the model from Eq. (10.9) in mind, an average gross realized return as
high as 25% or 30% on VC investing could be understandable. The gross return
is different from the net return because, as discussed in Chapter 3, it includes
the fees charged by the GP and the portion of capital appreciation (the carried
interest) that is retained by the GP.
386
Chapter Ten
We previously calculated, based on historical data, that a beta of 2.0 yields a
cost of capital for an entrepreneurial venture of about 20%. This corresponds to
the returns realized by LPs. VC limited partnerships segregate returns to the GP
(the active investor) from returns to LPs (passive investors). The GP’s return is
typically 20% of fund returns after initial investment capital has been returned
to the LPs, and after the annual management fee, which is equal to about 2.5%
of committed and invested capital.27 A reasonable estimate, then, is that one
fifth of a 25% gross return is actually compensation to the GP.
This return leaves nothing to compensate for the illiquidity of the investment. However, an adjustment for illiquidity of near zero comports with recent
empirical evidence on the cost of illiquidity.28 Low compensation for bearing
illiquidity risk also makes sense given the fact that most investors in VC limited
partnerships are institutions with long-term investment horizons, at least for
some fraction of their portfolios. It is reasonable to expect that competition
would drive them to demand a negligible return premium to compensate for
illiquidity.
Why does the CAPM accurately predict average returns of VC investors? In
large part, the answer appears to be that assumptions underlying the CAPM
are reasonably well satisfied. Most importantly, any venture is likely to represent only a small fraction of the asset portfolio of a large pension plan or life
insurance company. For such investors, a substantial component of the total
risk is diversifiable.
How, then, do we account for the high sought-for rates of return? Our answer
is that those returns represent hurdle rates that investors sometimes use to value
cash flow projections that are developed on the presumption that the venture will
be successful. VCs know that a large fraction of their deals will fail completely
and others will limp along offering breakeven returns at best. These will be offset by a few home runs that will offer spectacular returns, sometimes returning
5 or 10 times the initial investment. All of the prospective ventures have rosy
forecasts at the time of investment, and there is no reliable way for the VC to
know ex ante which will be home runs and which will fail. To compensate for
the optimism built into the cash flow projections, the investor applies a hurdle
rate that is substantially above the required return for investing in the project.29
10.10 The Entrepreneur’s Opportunity Cost of Capital
A unique challenge to new venture contracting is that the entrepreneur necessarily invests a large fraction of his financial wealth and human capital in the
venture. The resulting underdiversification implies that the entrepreneur faces
Foundations of New Venture Valuation 387
a different risk-return trade­off than does a well-diversified investor such as an
LP in a VC fund.
The entrepreneur’s decision to pursue a new venture is among the most difficult to make correctly. Yet research and books on entrepreneurial finance
take the entrepreneur’s investment decision as a forgone conclusion. A common
perception is that the entrepreneur’s investment problem is less a quantitative
decision than a qualitative one, having to do with wanting to be one’s own boss,
one’s lifestyle preferences, and one’s tolerance for risk. This focus is too narrow.
The entrepreneur’s investment decision problem is fundamentally different
from the investor’s. Even though the underlying financial economic theory is the
same, because of necessary underdiversification, the opportunity cost of capital
that is appropriate for the entrepreneur is different from that of the investor.
This is true even if the parties hold identical financial claims.
The entrepreneur should take a separate look at value for three compelling
reasons:
1. Underdiversification causes differences in required rates of return. Because
an entrepreneur is necessarily sacrificing some ability to diversify, the entrepreneur’s opportunity cost of capital is higher than that of investors.
2. Ownership claims of investors and entrepreneurs usually are not identical.
As we have seen, investors often receive “sweeteners” such as options and
preferences that make the deal more attractive. Sweeteners raise the value
of an investor’s ownership claims and therefore reduce the value of the
entrepreneur’s claim.
3. The parties may have different beliefs about expected performance and
risk. The entrepreneur and the investor are unlikely to agree about the
risk and expected return of the venture. Differences in expectations about
such things as development timing, achievable sales, and profitability
make contracting more difficult.
Building on financial economic theory from this chapter, we provide a valuation framework that addresses the entrepreneur’s underdiversified risk. The
framework can be applied in the context of differences in expectations and
financial claims that differ in priority.
Opportunity Cost and Choosing Entrepreneurship
What drives the decision to become an entrepreneur? Opportunity cost is
key to answering this question, along with related questions such as: Why,
compared to in the U.S., do greater percentages of the populations in Brazil,
Lebanon, and South Africa, for example, engage in entrepreneurial activities,
388
Chapter Ten
and why do lower percentages in countries like France and Sweden? Why do
students drop out of college to start businesses? And why is the probability
of starting a new business in the U.S. positively related to wealth whereas in
some other countries there is a negative relationship between per capita wealth
and engagement in entrepreneurship?
Research indicates that, in general, the decision to become an entrepreneur
rests on rational assessments that individuals make based on two components of
opportunity cost—the opportunity cost of their committed effort (their human
capital) and the opportunity cost of their invested wealth (financial capital). At
least intuitively, individuals try to compare the expected value of starting their
own businesses with the value of the next best alternative, which for many is
staying on the employment track.
The patterns of entrepreneurial activity described here are explained by two
factors, both reflective of opportunity cost. First, in countries with fewer good
employment opportunities, individuals are more likely to turn to self-employment
out of necessity. In countries such as the U.S., where attractive career opportunities are usually available, there are fewer entrepreneurs as a percentage of the
population than in countries with chronically limited employment opportunities.
However, the opportunity cost of pursuing an entrepreneurial opportunity is not
very high for a student, who can easily return to student status if the venture fails,
or for an employee, who can quickly return to similar employment. Second, the
ability to diversify financial assets can significantly reduce the entrepreneur’s
opportunity cost of capital and make entrepreneurship more attractive. Wealthy
individuals are able to commit smaller fractions of total wealth to an entrepreneurial venture and thus can be better diversified. Other things being equal,
greater wealth lowers the opportunity cost of becoming an entrepreneur.
Of course, factors other than opportunity cost also affect the decision to
become an entrepreneur, such as a desire to be one’s own boss or to pursue a
particular lifestyle, as well as differences in risk tolerance. In this section, we
set these other considerations aside to focus on opportunity cost. We analyze,
at a conceptual level, how risk and the opportunity cost affect the decision.
To ensure clear understanding, we provide formal analysis of the underlying
foundations and derivations. To make application more practical, Chapter 11
includes discussion of shortcut approaches that make the entrepreneur’s valuation problem more tractable.
The Entrepreneur as an Underdiversified Investor
Often, an entrepreneur must commit most of his time, at least for a few years,
as well as a substantial fraction of his financial capital to the venture. As a
Foundations of New Venture Valuation 389
result, the entrepreneur necessarily bears not only the nondiversifiable risk of
the venture but also the total risk. The focus on total risk as a determinant of
the entrepreneur’s required rate of return is appropriate in part because entrepreneurial ventures are not market assets.
To illustrate the differences between a well-diversified investor and the entrepreneur, consider an individual who has total wealth of $300,000, which includes
both financial and human capital. The entrepreneur is considering whether to
invest one third of his total wealth ($100,000) in a venture that will pay off in one
year. Excluding the entrepreneur’s salary, there are three equally likely payoffs
on his investment, as shown:
Scenario
Probability
Year 1 payoff
Return
Success
Likely
Failure
1/3
1/3
1/3
$200,000
$125,000
$53,000
100%
25%
(47%)
Using the required investment of $100,000, the expected return and standard
deviation of returns on the investment are 26% and 60%, respectively. The 60%
standard deviation represents the total risk of the new venture. Assuming a 4%
risk-free rate, an expected return on the market of 12%, a standard deviation
of market returns of 15%, and a correlation coefficient of the venture’s returns
with the market of 0.2, our estimate the venture’s beta is
ρrj , rM σ j
0.2 × 0.60
= 0.80
0.15
σM
With this beta, the CAPM return that a well-diversified investor would require
in exchange for investing in the new venture is 10.4%:
βj =
=
rj = rF + βj(rM − rF) = 4% + 0.8(12% − 4%) = 10.4%
The anticipated 26% return indicates a positive NPV for a well-diversified
investor. However, based on the opportunity cost of bearing risk, considering
his inability to fully diversify, the entrepreneur possibly should not settle for a
26% return. Suppose the entrepreneur were to use leverage to achieve the same
level of risk by investing only in the market portfolio. His expected return would
be higher than 10.4%. But how much higher?
We can use this opportunity cost reasoning to compute the entrepreneur’s
required rate of return for investing in the new venture. We start by considering the standard deviation of the entrepreneur’s portfolio, which (we assume)
is divided between the new venture and the market. The standard deviation of
a portfolio of two risky assets is calculated as follows:
390
Chapter Ten
σ port = xM2 σ 2M + xP2 σ 2P + 2 xM xP ρM , P σ M σ P (10.10)
The variables xM and xP are the value weights of total risky investments in
the market and the project, respectively, where the weights sum to 100%. The
variable ρM,P is the correlation coefficient between the market and the project.
In our example, the weights in the market and the venture are 2/3 and 1/3, respectively, which yields a portfolio standard deviation of 24.1%:
σ port = (2 3) 2 (0.15) 2 + (1 3) 2 (0.60) 2 + 2(2 3)(1 3)(0.2)(0.15)(0.60)
σ port = 0.241 or 24.1%
The following formula allows us to calculate a portfolio’s required return,
using the total risk of the portfolio as compared to the market:
rport = rF + (σ port σ M ) RPM
(10.11)
Using the CAPM-based approach, the entrepreneur’s required return on his
risky portfolio is
rport = rF + (σ port σ M )(rM − rF ) = 4% + (24.1% 15%)(12% − 4%) = 16.85%
Now that we have the required return on the portfolio, we can use the fact
that the required return on the portfolio is equal to the weighted average of the
required return on the project and the required return on the market. That is,
rport = x p rp + xM rM
(10.12)
Because we know everything except the required return on the project, we
can rearrange to solve for rp, the project required return:
rport =
rport − xM rM
xP
=
16.8% − (2 3)12%
= 26.55%
13
(10.13)
Therefore, the opportunity cost of investing one third of total wealth in the
new venture is a 26.55% return—slightly higher than the 26% expected return
on the project. From a purely financial perspective, this is the return the entrepreneur would need in order to forgo an investment in the market portfolio
that was leveraged to achieve the same total risk as the entrepreneur’s portfolio.
We can conclude that if the entrepreneur would have to invest one third of total wealth in the venture, the NPV of the investment would be negative. Note,
however, that if the entrepreneur could find a way to invest a smaller fraction
of wealth in the venture, his required return would be lower, and possibly the
project NPV would be positive.
Foundations of New Venture Valuation 391
This example makes clear that the entrepreneur’s required return, even with
less than a full commitment of wealth to the new venture, is substantially higher
than the return required by a diversified investor.
Factors That Offset the Entrepreneur’s Cost-of-Capital
Disadvantage
Based on the preceding discussion, the entrepreneur’s cost-of-capital disadvantage may seem daunting. In a competitive market for launching new ventures, we should find that, because of their lower cost of capital, public corporations can spend more resources searching for viable ventures than can
entrepreneurs and that corporations can legitimately undertake ventures before they reach the threshold of economic profitability for underdiversified
entrepreneurs.
Offsetting their cost-of-capital advantage, however, corporations face challenges when they seek to engage in entrepreneurship. For example, because of
internal equity concerns, the maximum reward a corporation can offer to an
employee for perceiving and pursuing entrepreneurial opportunities can be
less than what an individual can realize by acting alone. Consequently, corporate employees with good new venture opportunities frequently jump ship to
pursue them on their own. As a result, the venture cash flows that are available
to entrepreneurs may not be equally available to large corporations. Hence, a
trade-off exists between some of the organizational efficiencies and expediencies of stand-alone entrepreneurial ventures and the cost-of-capital advantage
of a public corporation.
This trade-off gives insight into the kinds of ventures that are likely to be
pursued by corporations rather than individuals.
• The larger the scale of the venture and the more complex the organization that is required to undertake it, the more likely the venture is to be
pursued by a corporation.
• The higher the level of total risk, as compared to beta risk, the greater
the cost-of-capital advantage of the diversified investor, and hence the
more likely the venture is to be pursued by a public corporation.
• With a longer expected holding period between investment and harvesting, the diversified investor’s cost-of-capital advantage compounds,
making corporate entrepreneurship more likely.
There are additional reasons why, in the market for new venture investing,
large public corporations and diversified investors do not entirely displace entrepreneurs. As one example, for any given venture, investors may not perceive
392
Chapter Ten
the opportunity or may be less optimistic than is the entrepreneur. In such cases,
the entrepreneur may need to invest more resources that are personal because
money from diversified investors is not available, or the entrepreneur may need
to make a larger initial investment to help convince prospective investors of the
merits of the venture.
10.11
Matching Cash Flows and Discount Rates
Cash Flow Definitions
Many valuation errors, even by employees of well-known investment banks,
arise from incorrect matching of cash flows and discount rates. The cash flow
definitions used in a valuation need to be tied to the specific financial claim
being valued and must be matched correctly with the appropriate discount
rates. As an aid to identification, Table 10.1 includes specific definitions of the
most important measures of expected cash flow that are appropriate for valuing debt and equity claims and for valuing the enterprise:
• Cash flow to all investors represents the amount of cash available to all
capital providers, after funding net working capital and capital expenditures. Because this measure is after actual taxes in light of the venture’s
leveraging decision, the tax benefit of debt financing (i.e., tax deductibility of interest payments) is reflected in the cash flow measure.
• Cash flow to creditors is the net of what the firm’s creditors expect to receive in the form of interest and principal payments less any new borrowing. If there is a risk of default or prepayment, then the expected
cash flows to creditors are not the same as the contractual cash flows.
• Cash flow to stockholders represents the residual cash flow available to
the equity investors in the venture. This is a measure of residual free
cash flow after expected debt service (principal and interest payments to
creditors less new borrowing), taxes, and investments in working capital
and fixed assets.
• Unlevered free cash flow is the cash flow the venture would generate if
it were unlevered, that is, financed entirely with equity (as is typical for
high-risk, early-stage ventures). By calculating tax on EBIT, we ignore
the beneficial tax effect of the actual financing choice. To offset, we need
to incorporate the tax benefits of debt financing into the discount rate.
Tab le 1 0.1
Measures of expected cash flow
Cash flow to all investors (both stockholders and creditors)
Cash Flow to All Investors = EBIT − Actual Taxes + D&A − ΔNWC − ΔFixed Assets
Cash flow to creditors
Cash Flow to Creditors = Expected INT - Expected ΔDebt
Cash flow to stockholders (residual, in light of expected cash flows to creditors)
Cash Flow to Stockholders = EBIT − Actual Taxes + D&A − ΔNWC – Δ Fixed Assets − Expected INT + Expected ΔDebt
Unlevered free cash flow (as if financed with no debt)
Unlevered Free Cash Flow = EBIT − Theoretical Taxes without Debt + D&A − ΔNWC − ΔFixed Assets
EBIT = earnings before interest and taxes, or operating profit
D&A = depreciation and amortization
ΔFixed Assets = change in fixed assets = net capital expenditures
ΔNWC = change in net working capital = NWC investment
INT = net interest payments
ΔDebt = net change in debt financing = proceeds from new debt – principal payments on outstanding debt
394
Chapter Ten
Consistent Discount Rates
In Table 10.2, we match these cash flow measures with the appropriate discount rates. Under the RADR method, expected cash flows are discounted to
PV using a discount rate that is based on the market risk of the cash flow. The
tax deductibility of interest payments is a complicating factor. In a correct
valuation, the tax benefit can be recognized in either the cash flow measure or
the discount rate, but not in both. Therefore, it is important to make sure the
cash flow definition and discount rate assumption are consistent.
• Unlevered cost of equity. One way to achieve consistency is to estimate
expected after-tax cash flows given the target capital structure and to
discount those flows at a rate that is not adjusted for the tax deductibility
of interest expense. The appropriate rate is called the “unlevered cost of
equity.” Under this approach, the tax benefit (if any) is incorporated as
an adjustment to cash flows and not to the discount factor.
• Weighted average cost of capital (WACC). The other way to achieve consistency is to estimate theoretical cash flows as if the venture were financed entirely with equity and then discount those cash flows using a
discount rate that is adjusted for the benefit of the debt tax shelter at the
target capital structure. Under this approach, the tax benefit (if any) of
debt financing is incorporated in the discount factor and not in the cash
flow.
In principle, either approach can yield a correct estimate, but the net tax
advantage is difficult to determine directly. The tax advantage of debt financing
depends on the aggregate supply and demand for debt and equity, the statutory corporate tax rate, the structure of personal tax rates, and the company’s
profitability and nondebt tax shelters. Empirical evidence suggests that the tax
advantage of debt financing is usually small as a result of low effective corporate tax rates and partially offsetting personal tax rates. Thus, in the calculation of WACC, the appropriate tax rate is probably well below the statutory
rate. The preferred approach depends on ease of estimation and availability
of information.
There are other ways to calculate cash flows beyond these definitions. Some
informal valuation approaches are based on a narrower concept of operating
cash flow: EBIT or EBITDA. While EBITDA is convenient to compute, it does
not provide for capital replacement or expected growth. Consequently, EBITDA
may not be a good measure of the cash flows investors can expect to receive.
The important point is to match the cash flow of the claim being valued with a
consistent discount rate.
Foundations of New Venture Valuation 395
Tab le 1 0. 2
Matching cash flows to discount rates for various financial claims
Financial claim
Discount rate
Discount rate formula (CAPM)
Comment
Cash flows to all
investors
Unlevered cost
of equity
rA = rF + βA(rM − rF)
Cash flow to
creditors
Cost of debt
rD = rF + βD(rM − rF)
Cash flow to
stockholders
Cost of equity
rE = rF + βE(rM − rF)
Unlevered free
cash flow
Weighted average cost of
capital
WACC = (D/V)(1 − tc)rD + (E/V)rE
The required rate of return on assets, or
the unlevered cost of equity, is used to
value cash flows that are expected to be
received by all claimants given the target capital structure. The effect of tax
deductibility of interest payments is reflected in the cash flows.
The cost of capital for debt depends on
the extent to which debt service payments are subject to market risk.
The cost of capital for equity depends
not on the total risk of equity but on the
market component of the risk.
The weighted average cost of capital (WACC) is used to value hypothetical cash flows as if the venture were financed entirely with equity. D and E are
market values of debt and equity, V =
D + E. The tax benefit of debt financing is an adjustment to the cost of debt
capital.*
rA = return on assets rD = return on debt rE = return on equity
WACC = weighted average cost of capital
βD = debt beta
βE = equity beta
βA = asset beta
rF = risk-free rate rM = expected return on the market (rM − rF) = market risk premium
tc = corporate tax rate
D/V = market value debt/total firm value (debt + equity)
E/V = market value equity/total firm value (debt + equity)
*The correct tax adjustment should be the net advantage of debt financing, giving consideration to the offsetting effects of personal taxes.
However, this number is unobservable and difficult to estimate, so in practice, tc , the marginal corporate tax rate, is typically used. See
Miller (1977) and DeAngelo and Masulis (1980).
Consistency in the Use of Continuing Value
The same issues of consistency can arise when we use continuing value in a PV
calculation. Continuing value is a shortcut way of projecting future cash flows.
Instead of explicitly projecting each period’s cash flow, discounting it back to
PV, and then summing, with continuing value a single cash flow number is
capitalized using a capitalization rate that takes account of both the riskiness
of the cash flow and its expected growth rate.
Consider a simple example in which, after several years of rapid growth,
the cash flow available to all investors is expected to grow at a constant rate
of 4%. Based on Table 10.2, if we are valuing cash flow to all investors, then
the appropriate discount rate is the unlevered cost of equity. Suppose we have
396
Chapter Ten
determined that the unlevered cost of equity is 10%. We can determine the appropriate continuing value multiple in this example as
Multiple =
(1 + g )
1.04
=
= 17.33
(rA − g) (0.10 − 0.04)
Thus, we would estimate continuing value by multiplying cash flow to all investors in the last year of the explicit value period by 17.33. To determine the
PV of the continuing value, we would discount the continuing value back to
Time 0 at rA. If we assume that the D/E ratio stays constant, we can use the
same approach to find the PV of debt but would use rD to determine the multiple and to discount the continuing value.
This example demonstrates the importance of consistency. If we were to use
a different cash flow measure and not adjust the cost of capital to be consistent,
the result would be a different (and biased) estimate of continuing value.
Many of the cash flow measures in the earlier discussion of relative value do
not have obvious discount rates and multiples that are theoretically consistent.
Instead, the normal approach is to estimate the multiple based on data for comparable companies. The consistency point, however, is the same. If we are using
comparables to value cash flow to all investors, then the same cash flow should
be used for the comparable companies, and either the comparables should be
selected to have similar leverage ratios or the cash flows should be adjusted to
be based on similar leverage ratios. Our overarching point is that consistency
is important to the valuation. This is true whether you are using explicit DCF
or RV and whether you are using an approach based on cost of capital or a
simplified approach such as the VC Method.
10.12 Summary
Valuation is central for entrepreneurs and investors when they are making decisions about business planning, capital raising, and deal structuring. In the
last three decades, private equity investing has become more competitive, increasing the role and importance of theoretically sound and empirically supported valuation tools. The valuation is a key contributor to a successful negotiation between the entrepreneur and investors.
We describe several valuation methodologies that fall into two basic categories: discounted cash flow and relative value. Discounted cash flow approaches
start with the premise that the value is simply the PV of all future cash flows.
This is a theoretically sound approach, but DCF can also be challenging to
Foundations of New Venture Valuation 397
implement. To use DCF, we need estimates of future cash flows and the corresponding discount rates.
We stress the importance of matching the financial claim with the appropriate cash flow measure and discount rate and also introduce the concept of
continuing value. To estimate discount rates, the notion of nondiversifiable or
market risk is highlighted. Because the investors who participate in VC investing
will be those best able to diversify and hold illiquid investments, we are able to
rely on CAPM-based measures for calculating the appropriate discount rates.
Continuing value represents a shortcut to estimating the value of the venture’s long-term cash flows while avoiding the need for extensive, explicit cash
flow forecasts. We review the RADR approach of DCF valuation and introduce
the CEQ approach as a way to overcome the inherent challenges of using RADR
to value new ventures.
The relative valuation method uses the values of comparable firms, relative
to operating or financial multiples, to provide a basis for estimating value. The
challenge in using RV is in finding comparables that are truly representative of
the new venture at the point when the venture is being valued.
The Venture Capital Method is simple and intuitively appealing but has
theoretical shortcomings that raise challenges to validity and call for caution
in its implementation critical. Because the VC Method assumes a successful
outcome, the corresponding annual discount rate is typically very high (30–80%)
to compensate for the cash flow bias.
The First Chicago Method was developed to address some of the shortcomings of the VC Method. It is a scenario-based valuation methodology, which
means it incorporates a range of possible outcomes and therefore uses a more
reasonable, lower discount rate. The First Chicago and RADR approaches
yield similar value estimates when the scenarios in the First Chicago approach
are weighted to yield expected cash flows.
A key point in this chapter is to reconcile the high hurdle rates associated
with use of the Venture Capital Method with reasonable estimates of cost of
capital. We demonstrate that high hurdle rates are simply a heuristic device that
investors can use to compensate for optimistic cash flow projections.
Another key point is that because entrepreneurs cannot be fully diversified,
they bear the unsystematic risk of their investment of financial and human
capital in the venture. As a result, the entrepreneur’s cost of capital is higher
than that of well-diversified investors. The extent of the disadvantage depends
on the fraction of wealth the entrepreneur must commit to the venture.
The chapter concludes with a structured discussion of the importance of
matching cash flow measures to discount rates and emphasizes that the tax
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Chapter Ten
a­ dvantage of debt financing can only be incorporated into the cash flow estimate
or in the discount rate, but not in both.
Review Questions
1. How might you respond to an entrepreneur who says, “There is so much
risk and uncertainty associated with new ventures that forecasting future revenue and cash flows is a waste of time”?
2. Describe the recent performance of VC funds. Do the empirical results fit with your prior expectations? If not, how can you explain the
discrepancy?
3. Explain the theoretical basis for discounted cash flow valuation. What
are the key inputs required for DCF analysis?
4. Explain the intuition behind the Capital Asset Pricing Model. Is the
CAPM a reasonable tool for estimating the required return for an investor? For an entrepreneur? Why or why not?
5. Why is it important to match the cash flows and discount rate when using DCF valuation?
6. What are the main differences between the RADR and CEQ approaches
to DCF valuation? Why is the CEQ approach often preferable when
valuing new ventures?
7. Explain the intuition behind each of the following valuation techniques:
(a) relative valuation (b) the VC Method (c) the First Chicago Method.
Describe the strengths and weaknesses of each approach and the caveats
regarding its implementation.
8. How would you reconcile the following? (a) VCs often use very high
hurdle rates when valuing potential new investments. (b) The historical
average return for a large sample of VC funds is around 14%.
9. Why should an entrepreneur’s cost of capital for valuing an opportunity depend on the fraction of the entrepreneur’s wealth that must be invested in the venture?
10. How is the benefit of diversifying related to the correlation of returns or
cash flows between different assets in the portfolio?
Notes
1. Funds with vintage years later than 2013 do not have enough history to
produce reliable IRRs.
Foundations of New Venture Valuation 399
2. Baty (1990, p. 63).
3. See Roberts and Stevenson (1992).
4. For more detail on historical VC returns in the early years of the industry, see Poindexter (1976), Ibbotson and Brinson (1987, pp. 99–100), Martin
and Petty (1983), and Cochrane (2005).
5. All of these estimates are likely to be positively biased. Information
on fund IRRs is not reported formally to any official source; rather, both
Thomson Reuters and PitchBook obtain the information from voluntary disclosures, from third parties such as limited partners of the fund, and through
Freedom of Information Act (FOIA) disclosures. Funds that have not performed well are less likely to disclose performance voluntarily.
6. See Roberts and Barley (2004, p. 8).
7. The term “Venture Capital Method” was first coined by HBS Professor William Sahlman in a 1987 publication.
8. We use the term “hurdle rate” to distinguish it from cost of capital,
which is the rate used to discount expected cash flows as opposed to an optimistic projection.
9. The First Chicago Method was developed by the VC group of First
Chicago Corporation. Sahlman and Scherlis (2009) describe it as a method
developed to address valuation biases inherent in the VC Method.
10. See Brealey, Myers, and Allen (2017, chap. 4) for elaboration of the
relation between share value and expected dividends.
11. A pioneer of modern portfolio theory, Harry Markowitz, first proposed use of the standard deviation of holding-period returns as the measure
of risk; see Markowitz (1952). Markowitz was awarded the Nobel Prize in economics in 1990 for his contributions to the theory of decision making.
12. The expected return is computed as [(30% × 1/3) + (15% × 1/3) + (–10%
× 1/3)] = 11.67%. The standard deviation is computed as [(30% – 11.67%)2 × 1/3
+ (15% – 11.67%)2 × 1/3 + (–10% – 11.67%)2 × 1/3]0.5 = 16.5%.
13. Development of the CAPM from its roots in modern portfolio theory was the contribution of several individuals, working independently. See
Sharpe (1964), Lintner (1965), and Mossin (1966). The model is based on a
number of assumptions, including homogeneity of beliefs about risk and return, a one-period time horizon, availability of a risk-free asset, and quadratic
utility functions for investors or normally distributed risk. Empirical evidence
from testing of the CAPM is roughly consistent with the CAPM’s theoretical
predictions. This suggests that, in most uses, violations of the assumptions are
not very important and do not impede its application as a venture valuation
tool.
400
Chapter Ten
14. The expected return is computed as [($1 × 0.5) + ($2 × 0.5)] = $1.50.
The standard deviation is computed as [($1 − $1.50)2 × 0.5 + ($2 − $1.50)2 ×
0.5]0.5 = $0.50.
15. The expected holding-period return is computed as [(60% × 0.5) +
(−20% × 0.5)] = 20%. The standard deviation is computed as [(60% − 20%)2 ×
0.5 + (−20% − 20%)2 × 0.5]0.5 = 40%.
16. For additional discussion of the CEQ valuation approach, see
Brealey, Myers, and Allen (2017).
17. See Demers and Lev (2001), Kozberg (2001), and Rajgopal, Venkatachalam, and Kotha (2003) for evidence of nonfinancial value drivers for
Internet companies.
18. See Amir and Lev (1996), who explore the valuation impact of nonfinancial metrics such as population growth and market penetration in the
wireless industry. See also the survey results summarized by Prinz (2013).
19. Black (2003) finds no value relevance of earnings in a sample of startup and growth-phase firms. The author reports that cash flow measures are
more value relevant than earnings in early stages of a firm’s existence.
20. Wright and Robbie (1996) survey VC firms in the U.K. regarding their
valuation approaches. They find that capitalization of historical or prospective earnings is the most widely relied-upon approach.
21. For discussion and examples using the VC Method, see Lerner and
Willinge (2002) and Sahlman and Scherlis (2009). Sahlman and Scherlis describe the VC Method and illustrate its application. See also Timmons and
Spinelli (2007).
22. The relation between net income and cash flow is not explicit. In our
discussion, we assume that net income is used as an approximation of steadystate cash flow.
23. Fischer Black, Myron Scholes, and Robert Merton are the original
developers of the OPM. See Black and Scholes (1973) and Merton (1973). Scholes and Merton were awarded the Nobel Prize in economics in 1998 for their
contributions. Black died in 1996, and the prize is not awarded posthumously.
24. Most proposals for valuing options that are not traded or where the
market is incomplete involve some means of backing into completeness. For
example, Mason and Merton (1985) and Kasanen and Trigeorgis (1994) propose that real options that are not traded can be valued by appealing to the
existence of a “twin security” that is traded and has risk characteristics that
are perfectly correlated with the real option. Short of that possibility, several
researchers suggest that untraded options can be valued in the presence of
nondiversifiable risk by replacing expected cash flows with their certainty
equivalents and valuing the certainty equivalent cash flows at the risk-free
Foundations of New Venture Valuation 401
rate of interest. They generally do not address how to determine the certainty
equivalent. See Constantinides (1978); Cox, Ingersoll, and Ross (1985); and
Harrison and Kreps (1979). For an overview and summary of the literature,
see Trigeorgis (1996) and Borison (2005).
25. See Kerins, Smith, and Smith (2004).
26. These are roughly the long-run historical averages over the 1928–
2016 period, as estimated by Damodaran. See http://​
pages​
.stern​
.nyu​
.edu/​
~adamodar/​New​_Home​_ Page/​datafile/​h istretSP​.html.
27. See Sahlman (1990) and Gompers and Lerner (1999).
28. For evidence that illiquidity premia tend to be small, see Blackwell
and Kidwell (1988), Hertzel and Smith (1993), and Smith and Armstrong (1993).
29. See Bhagat (2014), who makes a similar argument.
References and Additional Reading
Achleitner, A., and E. Nathusius. 2005. “First Chicago Method: Alternative
Approach to Valuing Innovative Start-Ups in the Context of Venture
Capital Financing Rounds.” Betriebswirtschaftliche Forschung und Praxis
(BFuP) 57: 333–47.
Amir, E., and B. Lev. 1996. “Value-Relevance of Nonfinancial Information:
The Wireless Communications Industry.” Journal of Accounting and Economics 22: 3–30.
Baty, G. 1990. Entrepreneurship in the Nineties. Englewood Cliffs, NJ:
Prentice-Hall.
Bhagat, S. 2014. “Why Do Venture Capitalists Use Such High Discount
Rates?” Journal of Risk Finance 15: 94–8.
Black, E. 2003. “Usefulness of Financial Statement Components in Valuation:
An Examination of Start-Up and Growth Firms.” Venture Capital: An
International Journal of Entrepreneurial Finance 5 (1): 47–69.
Black, F., and M. Scholes. 1973. “The Pricing of Options and Corporate Liabilities.” Journal of Political Economy 81 (3): 637–54.
Blackwell, D. W., and D. S. Kidwell. 1988. “An Investigation of Cost Differences between Public Sales and Private Placements of Debt.” Journal of
Financial Economics 22: 253–78.
Borison, A. 2005. “Real Options Analysis: Where Are the Emperor’s Clothes?”
Journal of Applied Corporate Finance 17: 17–31.
Brealey, R. A., S. C. Myers, and F. Allen. 2017. Principles of Corporate Finance,
12th ed. New York: McGraw-Hill.
Cochrane, J. H. 2005. “The Risk and Return of Venture Capital.” Journal of
Financial Economics 75: 3–52.
402
Chapter Ten
Constantinides, G. 1978. “Market Risk Adjustment in Project Valuation.”
Journal of Finance 33: 603–16.
Cox, J., J. Ingersoll, and S. Ross. 1985. “An Intertemporal General Equilibrium Model of Asset Prices.” Econometrica 53: 363–84.
Demers, E., and B. Lev. 2001. “A Rude Awakening: Internet Shakeout in
2000.” Review of Accounting Studies 6: 331–59.
Fama, E., and K. French. 1995. “Size and Book-to-Market Factors in Earnings and Returns.” Journal of Finance 50: 131–55.
Gompers, P., W. Gornall, S. N. Kaplan, and I. A. Strebulaev. 2016. “How Do
Venture Capitalists Make Decisions?” NBER Working Paper 22587.
Gompers, P., and J. Lerner. 1999. “An Analysis of Compensation in the U.S.
Venture Capital Partnership.” Journal of Financial Economics 51: 3–44.
Harrison, J., and D. Kreps. 1979. “Martingales and Arbitrage in Multiperiod
Securities Markets.” Journal of Economic Theory 20: 381–408.
Hertzel, M. G., and R. L. Smith. 1993. “Market Discounts and Shareholder
Gains for Placing Equity Privately.” Journal of Finance 48: 459–85.
Ibbotson, R. G., and G. P. Brinson. 1987. Investment Markets. New York:
McGraw-Hill.
Jones, C. M., and M. Rhodes-Kropf. 2013. “The Price of Diversifiable Risk
in Venture Capital and Private Equity.” Review of Financial Studies 26:
1854–89.
Kasanen, E., and L. Trigeorgis. 1994. “A Market Utility Approach to Investment Valuation.” European Journal of Operational Research 74: 294–309.
Kerins, F., J. K. Smith, and R. L. Smith. 2004. “Opportunity Cost of Capital
for Venture Capital Investors and Entrepreneurs.” Journal of Financial
and Quantitative Analysis 39: 385–405.
Kozberg, A. 2001. “The Value Drivers of Internet Stocks: A Business Models Approach.” SSRN Working Paper Series. http://​papers​.ssrn​.com/​sol3/​
papers​.cfm​?abstract​_ id​=​256468.
Lerner, J., and J. Willinge. 2002. “A Note on Valuation in Private Equity Settings.” Harvard Business School Note 9-297-050. Cambridge, MA: Harvard Business Publishing.
Lintner, J. 1965. “The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgeting.” Review of Economics and Statistics 47: 13–37.
Markowitz, H. 1952. “Portfolio Selection.” Journal of Finance 7: 77–91.
Martin, J. D., and W. Petty. 1983. “An Analysis of the Performance of Publicly
Traded Venture Capital Companies.” Journal of Financial and Quantitative Analysis 18: 401–10.
Foundations of New Venture Valuation 403
Mason, S. P., and R. C. Merton. 1985. “The Role of Contingent Claims Analysis in Corporate Finance.” In Recent Advances in Corporate Finance, eds.
E. I. Altman and M. G. Subrahmanyam, 9–54. Homewood, IL: Irwin.
Merton, R. C. 1973. “Theory of Rational Option Pricing.” Bell Journal of Economics and Management Science 4 (Spring): 141–83.
Miller, M. H. 1977. “Debt and Taxes.” Journal of Finance 32: 261–75.
Mossin, J. 1966. “Equilibrium in a Capital Asset Market.” Econometrica 34:
768–83.
Poindexter, J. B. 1976. “The Efficiency of Financial Markets: The Venture
Capital Case.” PhD dissertation, New York University.
Post, T., M. J. van den Assem, G. Baltussen, and R. H. Thaler. 2008. “Deal or
No Deal? Decision Making Under Risk in a Large-Payoff Game Show.”
American Economic Review 98: 38–71.
Prinz, K. F. 2013. “Determinants of Valuation of Early-Stage High-Growth
Start-ups.” https://​ssrn​.com/​abstract​=​2398567 .
Rajgopal, S., M. Venkatachalam, and S. Kotha. 2003. “The Value Relevance
of Network Advantages: The Case of E-Commerce Firms.” Journal of Accounting Research 41: 135–62.
Roberts, M. J., and L. Barley. 2004. “How Venture Capitalists Evaluate Potential Venture Opportunities.” Harvard Business School Case 9-805-019.
Cambridge, MA: Harvard Business Publishing.
Roberts, M. J., and H. H. Stevenson. 1992. “Alternative Sources of Financing.” In The Entrepreneurial Venture, ed. W. A. Sahlman and H. H. Stevenson, 171–8. Boston: Harvard Business School Press.
Robichek, A. A., and S. C. Myers. 1966. “Conceptual Problems in the Use of
Risk-Adjusted Discount Rates.” Journal of Finance 21: 727–30.
Sahlman, W. A. 1990. “The Structure and Governance of Venture Capital Organizations.” Journal of Financial Economics 27: 473–521.
Sahlman, W. A., and D. Scherlis. 2009. “A Method for Valuing High-Risk
Long-Term Investments: The ‘Venture Capital Method.’” Harvard
Business School Note 9-288-006. Cambridge, MA: Harvard Business
Publishing.
Sharpe, W. F. 1964. “Capital Asset Prices: A Theory of Market Equilibrium
Under Conditions of Risk.” Journal of Finance 19: 425–42.
Smith, R. L., and V. Armstrong. 1993. “Misperceptions About Private Placement Discounts: Why Market Reaction to Rule 144A Has Been Lukewarm.” In Modernizing US Securities Regulation: Economic and Legal
Perspectives, ed. K. Lehn and R. Kamphuis, 175–91. Homewood, IL:
Irwin.
404
Chapter Ten
Timmons, J. A., and S. Spinelli Jr. 2007. New Venture Creation: Entrepreneurship for the 21st Century, 7th ed. Chicago: McGraw-Hill Irwin.
Trigeorgis, L. 1996. Real Options. Cambridge, MA: MIT Press.
Venture Economics. 1997. Investment Benchmarks Report: Venture Capital.
New York: Venture Economics.
Wright, M., and K. Robbie. 1996. “Venture Capitalists, Unquoted Equity Investment Appraisal and the Role of Accounting Information.” Accounting and Business Research 26: 153–68.
C h a p t e r Ele ven
N e w Ve ntu r e Valuatio n
i n P r ac tice
W e saw i n the previous chapter that many tools are available for valuing new
ventures. Most are conceptually straightforward and intuitive; the challenges
come with implementation. In this chapter, after presenting a framework for
selecting a valuation method, we address use of the continuing value approach
as a way to make the valuing of going concerns tractable. We then address the
challenges of making the assumptions that are needed for applying any CAPMbased approach to DCF valuation. In the last part of the chapter, we use a
single example to illustrate all of the valuation approaches and to highlight the
advantages and disadvantages of each.
The chapter can serve as a handbook for generating the information that
is required to implement each valuation approach and illustrates how the approaches are related. It can aid in selecting the approaches that will be most
useful for addressing a particular valuation problem.
11.1 Criteria for Selecting a New Venture Valuation Method
As we have seen, there is a discrepancy between what we frequently observe
in practice among VCs in their valuation approaches and what theory would
suggest. No doubt, this is attributed in part to the obvious difficulty in forecasting cash flows for risky ventures that will evolve/pivot over time and determining opportunity cost for discounting. However, we have seen that the
heuristics that many VCs use (net multiples and hurdle rates of IRRs) are not
incompatible with theory in that when we observe successful decisions (ex post
405
406
Chapter Eleven
correct decisions of which ventures to invest in and at what valuations) those
decisions will mimic theoretically correct valuation approaches. Improving on
heuristics requires the decision maker to evaluate the criteria for deciding on
a valuation approach and to get as close as possible to the true intrinsic value
of the venture.
The following questions are relevant for assessing the relative merits of different approaches.
Is cost of capital used as the discount rate? Attempting to compensate for positively biased estimates of cash flow by discounting with positively
biased hurdle rates tends to cause projects with more distant payoffs to be
rejected incorrectly. Similarly, discount rates based on total risk rather than
nondiversifiable risk can lead to rejecting projects that should be accepted by
an investor who is well diversified.
How does the approach deal with cash flows that vary in risk? Different cash flow streams that occur in the same period can differ in risk. The
appropriate discount rates will vary accordingly. Models that do not distinguish
among cash flows that differ in risk can produce distorted estimates of value.
Cash flows that occur at different times can also differ in risk. If the discount
rates do not account for such differences, valuation errors can result.
Can the model be used to value embedded options and complex
financial claims? Complexity affects both expected returns and risk. A
financial structure that includes real or financial options can alter the overall
value of a venture relative to a simple accept/reject approach. The values of the
options depend on both expected cash flows and the risk of the option cash flows.
How difficult is it to estimate the information required for the valuation? There is virtue in simplicity. Valuation approaches that are complex
or difficult to use are sometimes too costly to justify. This is true particularly
if the project is clearly worth pursuing and agreements for sharing gains and
losses can be reached informally.
Are there sufficient data available to have confidence in relative
valuation estimates? Relative value works best if the expected future cash
flows (including expected growth), total risk, and market risk of the comparables
are believed to be proportional to those of the subject venture.
New Venture Valuation in Practice 407
11.2 Implementing the Continuing Value Concept
It is not practical to value a going concern by forecasting cash flows explicitly
into the indefinite future and then discounting each periodic cash flow back to
PV. Instead, the normal approach is to project explicit cash flows over a period
until it can be assumed that subsequent growth is likely to stabilize and then
to summarize the value of cash flows beyond that point as a single “continuing
value.” The continuing value concept is used to convert cash flows after the
explicit value period to a single estimate of value that is equivalent to valuing
each subsequent cash flow. In other words, the cash flows after the first few
years are valued implicitly by applying a multiplier to the last explicit cash
flow. The multiplier is intended to take account of expected growth of cash
flows from that point forward and the riskiness of those cash flows. Normally,
continuing value is estimated by using a theoretically determined discount
rate or is based on observed multipliers of market assets that are similar to the
one being valued. For many new ventures, continuing value is an important
component of total PV.1
The overall valuation is thus divided into two periods. For the first period, an
explicit cash flow projection is made for each year, quarter, or other appropriate
$1,600
$1,400
$1,200
Cash flow
$1,000
$800
$600
$400
$200
$0
–$200
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Year
Explicit Value Period:
Compute the present value of
each periodic cash flow.
Continuing Value Period: Estimate the continuing
value of the stream as of year 5, and then convert
the continuing value to present value.
Fi g u r e 11 .1
Using continuing value to estimate the value of a new venture
A common approach used in DCF valuation is to divide the forecast into two periods. During the explicit value period, cash flows are
forecasted individually and valued directly. During the continuing value period, cash flows are converted to a capitalized value at the end of
the explicit value period. This capitalized value is then discounted back to Time 0. Normally, the continuing value period begins when the
venture reaches a point of stable growth.
408
Chapter Eleven
interval. We refer to this as the “explicit value period.” We refer to the period
after the explicit value period as the “continuing value period.”2
Figure 11.1 illustrates a forecast of the future cash flows of a venture and their
segregation into explicit value and continuing value periods. In the example,
Year 5 is the last explicit forecast. Projections beginning in Year 6 are implicit,
based on assumptions about the growth rate from that point forward. The venture’s continuing value is an estimate as of the end of the explicit value period
(Year 5 in the figure). To estimate PV, continuing value must be discounted to
PV, as if it were a single cash flow that was expected to be received for selling
the venture or the financial claim.
Equation (11.1) describes the value of a venture in terms of explicit and continuing value components, based on annual data:
PV = Σ Tt =1
Ct
CVT
+
(1 + rt )t (1 + rT )T
(11.1)
where PV is the present value of the venture; Ct is the annual (or other periodic) cash flow for each explicit year, t; CV T is the continuing value at the end
of the explicit value period, year T; and rt is the discount rate for year t cash
flows.
As Figure 11.1 and Eq. (11.1) suggest, continuing value is a DCF valuation
method. However, it is not one that relies entirely on explicit forecasts of cash
flows. Instead, the cash flow forecast during the continuing value period is implicit
in the assumptions used to estimate continuing value. Continuing value is estimated based on information from the last explicit forecast along with assumptions
about the expected growth rate and cost of capital or on the values of comparables.
Determining the explicit value period. The first step in using the continuing value concept is to decide where to draw the line between the explicit and
continuing value periods. Continuing value estimates are most reliable if they are
made for a period when a firm has established a track record and has reached a
point of stable growth. Continuing value does not work well for valuing the early
stages of a new venture because of the expectation of temporary high growth and
the volatility of revenue and cash flows. Thus, explicit cash flows are normally
estimated during development, for periods when the venture has not yet achieved
a profitable level of sales, and during periods of rapid growth. In Figure 11.1,
the explicit value period, Years 1 through 5, includes years when expected cash
flows are negative and years when they are growing rapidly. The continuing value
period begins at the point where the venture is expected to be in steady state.
Sometimes continuing value estimates are applied at earlier stages of development. Rapidly growing ventures with large capital needs sometimes seek
New Venture Valuation in Practice 409
public equity financing before establishing a stable track record. If comparable
companies have gone public at similar stages, then underwriters are likely to
use a combination of continuing value methods and explicit DCF methods to
estimate the price at which shares will be offered to the public. In such a case,
it could be appropriate to use data from IPOs of other early-stage ventures to
estimate continuing value.
Determining which multiplier to use. Multipliers can be tied to any
accounting item or nonaccounting item that can be measured at the end of the
explicit value period, and they can be derived from theory or based on comparables. A free cash flow measure seems like the obvious choice for estimating the
multiple, since that is the source of the investor’s return. Sometimes, however,
other measures, such as EBIT or sales, or even website visits, for example, can
yield more reliable multipliers. We showed in Chapter 10 that multipliers implicitly include an assumed growth rate and discount rate for cash flows during
the continuing value period. In that example, the multiple was based on explicit
assumptions about the discount rate and growth rate of cash flows. In other scenarios, we might use a multiple based on comparable transactions. For example,
a firm planning to go public at the end of the explicit value period might collect
multiples from the IPO valuations of comparable ventures.
Choosing the right multiplier or set of multipliers requires judgment and
benefits from practical experience. Multiples of operating cash flow, net income,
sales, or assets are commonly used. For example, continuing value might be
capitalized at 10 times expected cash flow in the last year of the explicit value
period, or 1.5 times expected sales. The relative merits of different multipliers
(e.g., sales versus cash flow) are discussed in a variety of sources.3
Continuing value is sometimes estimated on the basis of sales or asset multiples, not because investors care specifically about sales or assets but because
sales or asset levels at the end of the explicit value period bear a stronger relationship to expected future cash flows over the continuing value period than
does cash flow at the end of the explicit value period.
The continuing value approach works best when the growth rate of the accounting stream (sales, earnings, etc.) or nonaccounting stream (web clicks,
site visits, etc.) on which the valuation is based has stabilized and when the
relation between the stream and value is strong. The strength of the relationship between the stream and value can be assessed by comparing measures of
dispersion of alternative multipliers across a sample of comparable firms. In
general, a multiplier with low dispersion, standardized by the mean or median
value, yields a better estimate.
410
Chapter Eleven
If you decide to use a multiple based on comparables, it is still helpful to use
the theory-based approach to test whether the multiple derived from the comparables really makes sense for your venture. Whatever the multiple, it implies
something about expected growth and cost of capital, and those implications
should be assessed. Equation (11.2) summarizes the implicit assumptions. You
can easily assess whether a multiple derived from comparables implies a sensible
value for the expected growth rate and cost of capital for your venture.
Determining the multiplier. Equation (11.2) describes the relation between
value in one period (the end of the explicit value period) and all future cash flows:
Vt =
Ct +1 Ct (1 + g )
=
r−g
r−g
(11.2)
where Vt is value at time t, Ct+1 is cash flow at time t + 1, r is the discount rate,
and g is the expected growth rate of cash flows. Generally, Ct is the cash flow
generated over a year, and Vt is value at the end of that same year (i.e., an earnings/price ratio). This is sometimes referred to as a trailing value. It does not
imply that value is determined by prior earnings. Rather, it implies that prior
earnings can be used in a consistent way, as shown in the equation, to predict
future earnings. Vt, the value at the end of period t, is a function of the cash
flows from period t + 1 onward. In the equation, Ct is increased by g, which
effectively means that the numerator represents the next period’s cash flow.
Equation (11.2) is the standard expression for the PV of a growing perpetuity of cash flows. The equation can be rearranged to calculate the cash flow
multiplier, as shown in Eq. (11.3):
Vt (1 + g )
=
Ct
r−g
(11.3)
where Vt /Ct is the cash flow multiplier.
Equation (11.3) shows how the expected growth rate and discount rate affect
the multiplier. The assumptions of a 10% discount rate and 4% annual growth
rate in cash flows yield the following calculation and cash flow multiple:
Cash Flow Multiple =
Vt (1 + g )
1.04
=
= 17.33
=
Ct
r−g
(.10 − .04)
It is easy to see that a higher rate of expected growth would increase the multiplier, as it simultaneously increases the numerator and makes the denominator
smaller. A higher discount rate would reduce the multiplier by increasing the
denominator. This makes intuitive sense, as cash flows that are growing more
quickly are worth more, and riskier cash flows are worth less.
Although the connections between value and other accounting and nonaccounting streams are indirect, the same principle applies. Higher expected
New Venture Valuation in Practice 411
growth rates imply higher multiples, and larger discount rates imply lower multiples. This suggests a way to use market data to estimate a multiplier. If, relative
to comparable firms, the venture being valued has a high expected growth rate,
a larger multiplier is implied. If the comparable firms are selected correctly and
if the cash flow used in Eq. (11.3) is the expected cash flow, there is no reason for
the discount rate to be different from that for the comparables.
The other determinant of Vt in Eq. (11.2) is Ct. There are two important issues
to keep in mind about Ct. First, the cash flows of comparable public firms are
based on publicly reported information. They have been prepared under U.S.
Generally Accepted Accounting Principles (GAAP) or International Financial
Reporting Standards (IFRS) and have been subject to independent audit, whereas
the venture’s forecast has not been. Second, the comparable firms have survived
long enough to have gone public, whereas the venture is at an earlier stage.
How best to deal with these issues depends on the purpose of the valuation
and on your comfort level with the financial projections. Suppose you believe
that the projections of the venture were prepared consistently with GAAP and,
though not audited, are unbiased. In this case, it is reasonable to assume that the
projections are comparable to the reported numbers of the public companies.
Suppose, instead, that the entrepreneur prepared the projections. Such projections are likely to reflect the entrepreneur’s inherent optimism that the venture
will be successful. In that case, direct application of multipliers from comparable
public companies could result in an overestimate of continuing value.
One way to avoid this survival bias is to base the continuing value estimate on
multipliers from private transactions. Such information can be useful, especially
if the selected transactions are for companies at similar stages of development
to the subject venture. But information about private transactions is difficult
to acquire and to verify.
A second solution is to adjust the public company multiplier for an estimate
of the bias in the accounting projections for the venture. If, for example, you
believe that the venture’s probability of failure is 30% and is not reflected in
its projected cash flows, it would be appropriate to adjust the public company
multiplier down by 30%. The latter solution is implicit in the actual multipliers
that are frequently used in private transaction valuations. Such adjustments are
often characterized (incorrectly, we believe) as “illiquidity discounts.” The true
nature of the discount is not illiquidity but rather biased cash flow estimates. By
recognizing this, you can do a better job of applying the data from comparable
public firms to your own valuation questions.
This leads to the third solution. If the lack of comparability is due to positively biased projections for the venture, then one way to solve the problem is to
develop a set of projections that reflect the true expectations, including the risk
412
Chapter Eleven
of failure. You would then value the asset using the comparable market data
without additional adjustment.
Forecasting the multiple. A correct valuation must be based on a multiple that is expected to be accurate at the time when the continuing stream of
cash flows is being capitalized. This is most obvious if it is assumed that there
will be a liquidity event, such as an IPO, at the end of the explicit value period.
It follows that the multiples you can observe today are not the ones you would
want to use in the valuation.
To illustrate, consider the data shown in Figure 11.2. In 2002, the price/earnings (P/E) ratio of the S&P 500 Index was around 37.3, a historical high relative
to prior years, and the aggregate dividend yield was near a historical low. If the
basis for equity valuation is the PV of expected future dividends, then either the
expected rate of dividend growth must have been very high in 2002 or the cost of
equity capital must have been very low. The true explanation probably involves
a combination of both factors: an expectation of rising dividends combined with
a low cost of equity capital.
Imagine that you are back in 2002 and are planning to value a company based
on its expected future earnings. You believe that the P/E ratio of the S&P 500
Index is appropriate to use as a benchmark for estimating continuing value five
years later. Knowing that the 2002 P/E ratio is at a historical high, you should
not base the continuing value estimate on the 2002 S&P 500 P/E ratio. Because
Fi g u r e 11 . 2
90
Historical price/
earnings ratios of the
S&P 500 Index
80
60
50
40
30
20
10
0
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
The P/E ratio each year is
calculated as the monthly
average of the value of the
S&P 500 divided by aggregate earnings over the
preceding (trailing)
12 months.
70
Prices as a multiple of earnings
source: http://​w ww
​.quandl​.com.
Year
New Venture Valuation in Practice 413
there is no plan to harvest the investment in 2002, the multiple that existed at
that time is not the best one to use. A better approach is to forecast the multiple
that is likely to exist at the time of a hypothetical sale of the investment.
P/E multiples are influenced by factors that can affect either the numerator
or the denominator. In 2002, they soared in part because earnings levels of
technology-related firms declined sharply after the Internet bubble. In contrast,
the 2009 multiple of 83.6—a clear outlier—was caused by a large, widespread
drop in corporate earnings brought on by the subprime and financial crises. In
fact, the S&P 500 Index fell almost 40% in 2008, its worst annual return since
1931, but in 2008 and 2009 earnings fell by an even greater percentage, so the
2009 S&P 500 P/E ratio increased.
Suppose that in 2009 you want to select a multiple to use in estimating continuing value where harvesting is expected to occur in five years. Figure 11.2
shows that between 1980 and 2008 there was a great deal of variation in the
annual S&P 500 P/E ratio, from a low of 7.9 in 1980 to a high of 37.8 in 2002.
The simple average over the 53-year period is 19.6, but it appears that the most
recent historical period has seen higher multiples, on average. Finally, from
Figure 11.2 it appears that the change in P/E from one year to the next is not
random; instead, a high value in one year tends to be followed by a high value
in the next. So how can we best estimate an appropriate multiple for 2013?
One valid approach is to use statistical techniques—regression analysis, exponential smoothing, or the like—to estimate future P/E multiples. This level of
rigor may or may not be warranted in practice. For some valuation problems it
may be sufficient to recognize that historical P/E multiples appear to be mean
reverting and that the multiple in 2008 is historically high. In recognition of the
uncertainty as to what the future exit multiple will be, we suggest using several
different values of exit multiples and assessing their impact on the resultant valuation. If the accept/reject decision is not sensitive to multiples over a reasonable
range, that alone may be sufficient to proceed with the venture.
Even if the appropriate multiple for your venture is not the S&P 500 Index
P/E ratio, whatever multiple you do use, if it is based on comparables, is likely
to be highly correlated with the S&P Index multiple. You can update Figure 11.2
and use the data to make inferences about whether other multiples are currently
likely to be too high or too low.
11.3 Implementing DCF Valuation Methods
CAPM-based approaches to DCF valuation require specific assumptions
about the risk-free rate, the market risk premium, and beta. In lieu of beta,
414
Chapter Eleven
they may require specific assumptions about market risk, project risk, and
correlation. In the RADR form, project risk is the standard deviation of holding-period returns, and the correlation is between project returns and market
returns. In the CEQ form, project risk is the standard deviation of cash flows,
and the correlation is between project cash flows and market returns.
There are many subtleties and challenges involved in making good and internally consistent assumptions. In practice, it is common to use some simplifying
assumptions that make implementation easier. In this section, we first outline
approaches to making valid and internally consistent assumptions; we then
discuss shortcuts that are commonly used.
Estimating the Risk-Free Rate of Interest
The appropriate risk-free rate for valuing a future cash flow is one that is available in the market as of the valuation date and is for a holding period of the same
duration as the cash flow being valued. Thus, for a cash flow that is expected in
five years, ideally we would use the currently available risk-free rate for an instrument that would mature in five years. We normally assume that U.S. government
debt has no risk of default, so we can infer the appropriate risk-free rate directly
from current interest rates of U.S. Treasury securities of appropriate maturities.4
We also must be cognizant of whether the cash flow projection is in real or
nominal terms. If it is in nominal terms, then the appropriate risk-free rate is also
the nominal rate that can be inferred directly from market data. If the forecast
is in real terms, then the risk-free rate must be converted to an estimate of the
real rate by subtracting the rate of inflation that is expected in the market for
the holding period. For this, we can search for publicly available forecasts of
inflation or use historical data to infer the normal relationship between risk-free
interest rates and inflation. In most cases, the expected cash flows of the venture
will have been projected in nominal terms.
U.S. Treasury yields at the time of this writing were as follows:5
U. S . Trea s u r y yield s , J a n u a r y 2 6 , 201 8
Maturity
Yield %
Maturity
Yield %
3-month
6-month
1-year
2-year
3-year
1.50
1.87
1.83
2.04
2.21
5-year
10-year
20-year
30-year
2.47
2.61
2.74
2.94
source : Based on http://​w ww​.wsj​.com/​m dc/​p ublic/​p age/​2 ​_ 3020​- tstrips​.html. Maturities of one year or less are
from zero-coupon securities. Maturities greater than one year are based on STRIP (Separate Trading of
Registered Interest and Principal) yields.
New Venture Valuation in Practice 415
These data suggest the use of a different risk-free rate in calculating each
period’s discount rate. The cash flow from Year 1 would be discounted using a
rate based on the 1-year U.S. Treasury bill. The cash flow in Year 5 would use
a rate computed with the 5-year Treasury bond.
Estimating the Market Risk Premium
The market risk premium that is used in the CAPM is the expected difference
between the return on the market and the risk-free rate over the period from
investment until a cash flow is received. In contrast to the current risk-free
rate, which is observable, the current market risk premium is not. Because it
is not, three main approaches are used to estimate the market risk premium:
(1) a long-term historical average, (2) a risk premium that is implied by discounting a forecast of future dividends (i.e., the IRR that makes the PV of expected
future dividends equal to today’s market price), and (3) a consensus estimate.
The easiest but not necessarily most accurate way to estimate the expected
market risk premium is to extrapolate from historical data. These data are
readily available from numerous sources. Most introductory finance textbooks
contain some version of Table 11.1, which presents historical data on stock and
bond returns.
The table shows arithmetic average returns since 1928 and since 1960 and the
respective standard deviations of returns, and also shows the geometric average
returns over the same periods. Geometric averages are calculated based on the
beginning and ending values from 1928 through 2017 and 1960 through 2017.
Tab le 11 .1 Historical stock and bond returns
1928–2017
Series
S&P 5001, 2
U.S. Treasury Bonds (LT)2
U.S. Treasury Bills (ST)2
Inflation3
Series
S&P 5001, 2
U.S. Treasury Bonds (LT)2
U.S. Treasury Bills (ST)2
Inflation3
1960–2017
Arithmetic Mean
Standard Deviation
Arithmetic Mean
Standard Deviation
11.53%
5.15%
3.44%
3.05%
19.62%
7.72%
3.05%
3.84%
11.27%
6.64%
4.64%
3.78%
16.31%
9.05%
3.11%
2.81%
Geometric Mean
Geometric Mean
10.15%
6.18%
4.62%
3.78%
9.32%
4.93%
3.40%
3.04%
Composite Total Return Index (includes dividend reinvestment).
Sources: Stock, T-bond, and T-bill annual returns data downloaded from http://​pages​.stern​.nyu​.edu/​~ adamodar/​New​_ Home​_ Page/​d atafile/​
histretSP​.html.
3
Annual CPI (inflation) data from U.S. Department of Labor, Bureau of Labor Statistics.
1
2
416
Chapter Eleven
To estimate the expected market risk premium with these data, we would
calculate the market risk premium as the difference between the historical average return on the S&P 500 (the market) and the historical average risk-free rate.
If we are valuing a near-term cash flow (such as a year or two), we would use
the historical short-term risk-free rate in the calculation. If we were valuing a
longer-term cash flow, such as a return in five years, we would use the historical
long-term rate, represented by the yield on U.S. Treasury bonds.
Using historical data to estimate the expected market risk premium, while
simple, still requires some choices for implementation. For example, over what
period should the average returns be measured, and is it better to use arithmetic
or geometric averages?
In using historical data to develop a forward-looking estimate of the market
risk premium, we need to balance the relevance of older data with the statistical unreliability that comes with using fewer observations. Table 11.1 includes
historical average data for two time periods, but other windows could be used.
In principle, the historical returns averages will be more reliable when measured
over a long period, as long as the fundamentals that drive the risk premia are
consistent over time. In this case, the period since 1960 represents a time when
modern portfolio theory was generally accepted so that investors would mainly
have been concerned about compensation for bearing systematic risk. Arguably,
the earlier years are ones where the premium could be affected by total risk.
As to the choice between arithmetic and geometric average returns, it is common practice among academics and practitioners to use the arithmetic averages.6
On balance, evidence favors using arithmetic averages, though geometric averages may be more appropriate for long-term projects. In Figure 11.3, we explain
why arithmetic averages work well.
Table 11.1 also reports historical inflation rates. Consistent with our discussion of the need to adjust the risk-free rate for inflation to derive an estimate of
the real risk-free rate, we could use this information to infer the historical real
rates for short- and long-term riskless debt. Based on the period since 1960,
Treasury bill rates have averaged 0.86% higher than the average rate of inflation,
so the apparent short-term real risk-free rate is 0.86% and the long-term Treasury
rate averaged 2.86% above the inflation rate. Similarly, the risk premium of the
S&P 500 averaged 6.63% above the short-term risk-free rate and 4.63% above
the long-term Treasury rate.
For a variety of reasons, the historical average may not be the best measure
of the market risk premium. Recent forward-looking estimates of the risk premium (derived by discounting forecasts of future dividends) suggest that the
long-term historical average overstates the market risk premium somewhat. For
example, Fama and French (2002) use dividend and earnings growth rates to
New Venture Valuation in Practice 417
Fi g u r e 11 . 3
Using arithmetic or
geometric average
returns
Suppose an asset that is correctly priced is equally likely to return either 50% or 0% each year. If the
cost of capital for an investment is inferred by calculating the average annual return, the resulting
estimate is 25% (i.e., (50% + 0%)/2). The geometric return, in contrast, is 22.5% [i.e., ((1 + 50