The Pennsylvania State University

The Graduate School

The Mary Jean and Frank P. Smeal College of Business

VENTURE CAPITAL, BOARDS OF DIRECTORS, AND THE MARKET

FOR CORPORATE CONTROL

A Dissertation in

Business Administration

by

Colin R. Jones

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

May 2013

ii

The dissertation of Colin R. Jones was reviewed and approved* by the following:

Chris Muscarella

Professor of Finance

Co-Chair of Committee

Dissertation Co-Advisor

Laura Field

Associate Professor of Finance

Co-Chair of Committee

Dissertation Co-Advisor

David Haushalter

Associate Professor of Finance

N. Edward Coulson

Professor of Economics

William Kracaw

Professor of Finance

Head of the Department of Finance

*Signatures are on file in the Graduate School

iii

ABSTRACT

This dissertation studies the effect of venture capitalists on the decisions of newly-public corporations

after the IPO. I investigate venture capitalists’ role as information conduits in mergers and acquisitions,

specifically, their role while serving on the board of directors of acquiring firms, target firms, or both.

Chapter 1 finds that the information sharing between a VC and a firm’s management results in value-

enhancing acquisitions of VC-backed private targets, especially when the acquirer and target share a

venture capital board member. I also document that this holds true with public targets, which is evidence

in support of the information sharing hypothesis rather than the cheap targets hypothesis. Chapter 2

shows that VC-backed firms are significantly more likely to receive bids and get acquired following their

initial public offering. I also find that they receive higher premiums while the venture capitalist sits on

the board of directors. This seems to come from extracting the negotiation surplus, as the acquirer of VC-

backed targets experiences more pronounced negative announcement returns while the combined

announcement return is on average unaffected. I interpret this as evidence of a wealth transfer from the

acquirer to the target firm. I attribute this to the certification and negotiating abilities of the venture

capitalist serving on the target firm’s board of directors, documenting a further method venture capitalists

can add value to corporations.

iv

TABLE OF CONTENTS

LIST OF TABLES v

LIST OF FIGURES vii

ACKNOWLEDGEMENTS viii

INTRODUCTION 1

CHAPTER 1 - THE INFORMATION ROLE OF VENTURE CAPITALISTS:

HOW VENTURE CAPITALISTS AFFECT THE M&A DECISIONS OF

THEIR PREVIOUS IPOs

3

1.1 INTRODUCTION 3

1.2 BACKGROUND 6

1.3 HYPOTHESES 14

1.4 DATA AND METHODOLOGY 17

1.5 RESULTS 21

1.6 CONCLUSION 28

CHAPTER 2 - THE INFORMATION ROLE OF VENTURE CAPITALISTS

ON BOARDS OF TARGET FIRMS

36

2.1 INTRODUCTION 36

2.2 BACKGROUND 39

2.3 HYPOTHESES 45

2.4 DATA AND METHODOLOGY 47

2.5 RESULTS 50

2.6 CONCLUSION 59

REFERENCES 92

v

LIST OF TABLES

TABLE 1.1: The Effect of Venture Capital Backing on M&A Activity for Recent IPOs

30

TABLE 1.2: Logistic Regression for Likelihood That Target is VC-backed

31

TABLE 1.3: Acquirer CARs in Acquisitions of Private Targets

32

TABLE 1.4: Multivariate Analysis of Acquirer Cumulative Abnormal Announcement

Returns

33

TABLE 1.5: Announcement CARs in Acquisitions of Young VC-backed Public Targets

by Young VC-backed Public Acquirers

34

TABLE 1.6: Multivariate Analysis of Target, Acquirer, and Combined Announcement

Returns in Acquisitions of Young VC-backed Public Targets by Young VC-backed

Public Acquirers

35

TABLE 2.1: IPOs, Venture Capital Backing, and Deal Activity

60

TABLE 2.2: Venture Capital Backing and Deal Activity Through Time

62

TABLE 2.3: Venture Capital Backing and Percentage of Firm Sold in IPO

73

TABLE 2.4a: Venture Capital Backing and Likelihood of an Acquisition Attempt

74

TABLE 2.4b: Venture Capital Backing and Likelihood of Being Acquired

75

TABLE 2.4c: Venture Capital Backing and Likelihood of An Acquisition Attempt

Within 3 years of the IPO

76

TABLE 2.5: IPOs, Venture Capital, and Announcement Returns to Acquisition Targets

in IPO Sample

77

TABLE 2.6a: Acquisition Announcement Returns to Bids on Recent IPOs (VC vs. Non-

VC)

78

TABLE 2.6b: Acquisition Announcement Returns to Completed Acquisitions of Recent

IPOs (VC vs. Non-VC)

79

TABLE 2.6c: Acquisition Announcement Returns to Bids on Recent IPOs (Tech vs.

Non-Tech)

80

TABLE 2.6d: Acquisition Announcement Returns to Completed Acquisitions of Recent

IPOs (Tech vs. Non-Tech)

81

vi

TABLE 2.6e: Acquisition Announcement Returns to Completed Acquisitions of Recent

IPOs (High Tech Only)

82

TABLE 2.6f: Acquisition Announcement Returns to Completed Acquisitions of Recent

IPOs (Non-Tech Only)

83

TABLE 2.7a: VCs, Board Presence, and Announcement Returns to IPOs Receiving Bids

within 3 years of IPO

84

TABLE 2.7b: VCs, Board Presence, and Announcement Returns to IPOs Receiving Bids

within 3 years of IPO (High-Tech Only)

85

TABLE 2.7c: VCs, Board Presence, and Announcement Returns to IPOs Receiving Bids

within 3 years of IPO (Non-Tech Only)

86

TABLE 2.8: Target, Acquirer, and Combined Announcement Returns to Bids on Recent

IPOs

87

TABLE 2.9: Summary Statistics of Regression Variables, VC vs. Non-VC

88

TABLE 2.10a: Regression Analysis of Target Acquisition Announcement CARs

89

TABLE 2.10b: Regression Analysis of Bidder Acquisition Announcement CARs

90

TABLE 2.10c: Regression Analysis of Combined Acquisition Announcement CARs 91

vii

LIST OF FIGURES

FIGURE 2.1: IPOs, Venture Capital Backing, and Acquisition Announcements in Years

Following IPO

64

FIGURE 2.2: Cumulative Total Acquisition Announcements in Years Following IPO

65

FIGURE 2.3: IPOs, Venture Capital Backing, and Acquisitions in Years Following IPO

66

FIGURE 2.4: Cumulative Total Acquisitions in Years Following IPO

67

FIGURE 2.5: Acquisition Announcements on IPO Sample Chronologically

68

FIGURE 2.6: Cumulative Total Bids in Years Following IPO, Technology Firms

69

FIGURE 2.7: Cumulative Total Bids in Years Following IPO, Non-Technology Firms

70

FIGURE 2.8: Cumulative Total Completed Acquisitions in Years Following IPO,

Technology Firms

71

FIGURE 2.9: Cumulative Total Completed Acquisitions in Years Following IPO, Non-

Technology Firms

72

viii

ACKNOWLEDGEMENTS

I’ll start by thanking my incredibly patient advisors, Chris Muscarella and Laura Field,

and committee members, David Haushalter and Ed Coulson. I’d like to acknowledge the Penn

State Smeal College of Business for financial support. I would also like to thank Vladimir

Atanasov, Jean Helwege, Michelle Lowry, Tom Miller, Brad Goldie, and Marco Rossi, as well

as seminar participants at the University of South Carolina, University of Denver, East Carolina

University, the College of William & Mary, and the 2008 FMA meetings for insightful

comments. Special thanks to Spencer Kushner, whose interest in my research at times exceeded

my own, for his research assistance down the stretch.

Whatever it is I’ve done here, I couldn’t have done it without the great group of friends

and colleagues I forged in Happy Valley. From The Threadbare Orchestra to G. Rickey & Co.,

and everything in between, there was nary a dull moment outside of the office. Thanks for

keeping it interesting.

Last but not but least, thanks to my family, for helping me become who I am today.

1

INTRODUCTION

This dissertation studies the effect of venture capitalists on the decisions of newly-public

corporations after the IPO. I investigate venture capitalists’ role as information conduits in

mergers and acquisitions, specifically their role while serving on the board of directors of

acquiring firms, target firms, or both. Chapter 1 studies tightly networked transactions in which

a venture capitalist serves on the board of directors of a recently public firm that acquires a

private target out of the same VC’s portfolio. Chapter 2 examines VC’s effect on the likelihood

of a takeover attempt, the likelihood of takeover completion, and the target, acquirer, and

combined announcement returns to bids (acquisition announcements) on recently public firms.

Given the level of involvement between a venture capitalist and a portfolio firm’s

management, and a VC’s propensity to facilitate collaborations within its network, it is likely

that a VC continues to affect a firm beyond the initial public offering. This could manifest itself

in many ways, including the M&A decisions of management, with a VC facilitating and

encouraging mergers and acquisitions within its network. Chapter 1 seeks determine what role

venture capital firms play in the acquisition decisions of their previous IPOs, and what effect this

has on acquisition announcement returns.

Chapter 1 explores the announcement returns to publicly traded acquirers of venture

capital backed private targets. I find that tightly networked transactions in which a venture

capitalist concurrently sits on the board of both the acquirer and target result in abnormally high

announcement returns. I interpret my findings as evidence that venture capitalists function as

information conduits to alleviate asymmetric information in acquisitions, rather than acting in

their own self-interest at the expense of acquiring firm shareholders. Chapter 1 documents a

further mechanism through which venture capitalists can add value to corporations.

2

It has been well established that venture capitalists are heavily involved with the private firms

in which they invest (Gorman and Sahlman (1989), Gompers (1995), Lerner (1995), Baker and

Gompers (2003)). Less well studied is the role of a venture capitalist after they’ve taken a firm

public. We know that venture capitalists tend to stay on the boards of their companies well after an

IPO (Barry et al. (1990), Gompers (1996)), but we know little about how this affects firm decisions

and firm performance.

Chapter 2 shows that VC-backed firms are significantly more likely to receive bids and

get acquired following their initial public offering. I also find that they receive higher premiums

while the venture capitalist is still on the board of directors. This seems to come from extracting

the negotiation surplus, as the acquirer of VC-backed targets experiences more pronounced

negative announcement returns while the combined announcement return is on average

unaffected. I interpret this as evidence of a wealth transfer from the acquirer to the target firm. I

attribute this to the certification and negotiating abilities of the venture capitalist serving on the

target firm’s board of directors, documenting a further method venture capitalists can add value

to corporations.

I find that venture capitalists continue to affect firm M&A decisions well after the initial

public offering, and that this results in higher returns to firm shareholders. Chapter 1 shows that

the information sharing between a VC and a firm’s management results in value-enhancing

acquisitions of VC-backed private targets. Chapter 2 finds strong evidence that VC involvement

is associated with an increased likelihood of being acquired, and that VC board presence on the

target firm increases the premium paid in acquisition.

3

CHAPTER 1

The Information Role of Venture Capitalists: How Venture Capitalists Affect the M&A

Decisions of Their Previous IPOs

1.1 INTRODUCTION

ON OCTOBER 6, 2006, GOOGLE INC. announced that it would acquire YouTube.com in a

$1.65 billion stock-for-stock transaction. Less than a year earlier, YouTube LLC received its

first and only venture capital round when Sequoia Capital invested $11.5 million in November

2005.1 When the “GooTube” acquisition was completed in November 2006, Sequoia Capital

received 1.07 million shares of Google stock, worth approximately $504 million (a return of

nearly 4300% in a single year). Interestingly, and perhaps not coincidentally, Sequoia Capital

was also a major investor in Google during the search engine’s first and only venture round in

1999. In fact, Sequoia partner Michael Moritz served on the Google board of directors from

May 1999 until his resignation in May 2007.

I suggest that tightly networked transactions like this one are not coincidental, but that the

VC continues to impact the decisions of the newly public firm. Given the level of involvement

between a venture capitalist and a portfolio firm’s management, and a VC’s propensity to

facilitate collaborations within its network, it is likely that a VC continues to affect a firm

beyond a successful public offering2. This could manifest itself in many ways, including the

M&A decisions of management, with a VC facilitating and encouraging mergers and

acquisitions within its network. Specifically, the VC could promote the sale of private targets

from its own portfolio to public acquirers that the VC previously took public. This paper

1 According to Securities Data Company’s VentureXpert database.

2 See Section II of this paper (Background) for an in depth review of the applicable literature.

4

examines the role that venture capital firms play in the acquisition decisions of their previous

IPOs.

I show that public companies that were VC-backed at the IPO [VC-acquirers] are more

likely to acquire VC-backed private targets [VC-targets] in the five years following their IPO. In

a logistic regression for 2,969 acquisitions by firms in my IPO sample, a dummy variable for a

VC-acquirer is positive and significant in determining the likelihood that the target is VC-

backed. This supports the hypothesis that VC-backing at the IPO affects a firm’s M&A activity

following the IPO.

Furthermore, I examine the effect of a venture capitalist’s presence on the announcement

returns of these tightly networked acquisitions. It is possible that the venture capitalist’s role as

an information conduit leads to the identification of profitable acquisitions within its network, ala

Aoki (2000) and Lindsey (2008). In this case, VCs could share valuable information with the

acquiring firm’s management, improving the acquirer’s bidding ability, leading to value-

enhancing acquisitions. On the other hand, it could be that the VC’s propensity to facilitate deals

within its network leads to managerial hubris in the acquiring firm, ala Roll (1986). If acquiring

firm management trusts the VC, management may place too much weight in the information that

is being shared, leading to hubris and overbidding. This would result in value-destructive

acquisitions.

Following a sample of firms that went public between 1996 and 2005, I examine their

acquisitions of private targets within 5 years of the acquirer’s IPO. I identify 188 acquisitions of

VC-backed private targets, 114 involved VCs on both sides, and 34 involving the same VC. I

find mild support for the information sharing hypothesis and strong support against the

managerial hubris hypothesis. Acquisitions in which the same VC backed the acquirer at the

5

IPO and was invested in the private target at the acquisition (SAME_VC transactions)

experience higher cumulative abnormal returns around the acquisition announcement date than

do typical acquisitions of VC-backed private targets by public acquirers. This supports the

information sharing hypothesis.

Masulis and Nahata (2011) also documents the abnormal announcement returns to similar

transactions in the self-dealing portion of their paper, arguing that the positive acquirer

announcement returns are caused by a wealth transfer from the target to the acquirer. They argue

that venture capitalists are selling the target firm “cheap” to the acquirer, resulting in positive

abnormal returns to the acquirer around the acquisition. That is, the acquirer is getting a good

deal on the target due to the self-dealing of the venture capitalist. As the target is a private firm,

we do not observe a market price for the company, so cannot distinguish between my

information sharing hypothesis and their cheap target hypothesis.

However, I distinguish between the information sharing hypothesis and the cheap target

hypothesis by finding acquisitions of VC-backed public targets by VC-backed public acquirers

that had concurrent, overlapping venture capital representation on their respective boards of

directors. While they are few in number, they are quite telling. The highly positive

announcement returns to these transactions disprove the cheap target, or wealth transfer,

hypothesis, and lend strong evidence to the information sharing hypothesis.

I conclude that venture capitalists continue to affect firm M&A decisions well after the

initial public offering, but that this is generally of benefit to acquiring firm shareholders. The

information sharing that occurs between a VC and a firm’s management appears to result in

value-enhancing acquisitions of VC-backed private targets, despite the potential for managerial

6

hubris or self-dealing. This paper documents a further mechanism through which venture

capitalists can add value to corporations.

1.2 BACKGROUND

It is well documented that venture capitalists play a valuable information gathering and

monitoring role for start-up firms, reducing informational asymmetry and agency problems

(Gompers (1995), Lerner (1995), Baker and Gompers (2003)). But in addition to their role as

monitors, existing literature has shown that venture capitalists provide additional value-added

services to the entrepreneurial firm.

Gorman and Sahlman (1989) show that the venture capitalist serves an important role in

fund raising, strategic analysis, management recruitment, and operational planning.

Additionally, they find that the average venture capitalist visits a portfolio company nineteen

times per year, spends 80 hours per year onsite and 30 hours per year on the phone with the

CEO. Sahlman (1990) finds that venture capitalists, while generally not involved in day-to-day

operations, regularly serve on the board of directors. Barry, Muscarella, Peavy, and Vetsuypens

(1990) show that venture capitalists typically control about one-third of the board seats.

Furthermore, the presence of a venture capitalist at an IPO leads to the involvement of

higher quality underwriters and auditors (Barry et al. (1990), Megginson and Weiss (1991)).

Hellman and Puri (2002) examine the differences between venture-backed and non venture-

backed companies and show that the presence of a venture capitalist in a start-up firm leads to a

more professional internal organization (advanced human resource policies, stock option plans,

the hiring of a VP of marketing, etc.). They also find that venture capitalists can play both

7

supportive and controlling roles in the start-up firm, depending on the needs of the particular

firm.

Serving as much more than a monitoring financial intermediary, the venture capitalist

takes on an integral role in the firm during start-up, working to maximize the odds that their

equity investment will lead to a successful exit and superior return. Barry et al. (1990) and

Gompers (1996) show that venture involvement continues long after a firm goes public, where

venture capitalists continue to hold large equity stakes and serve on the board.

In fact, many venture capital firms pride themselves on value-added keiretsu-style

behavior, facilitating strategic alliances among portfolio firms and fostering interaction within

their networks. Lindsey (2008) finds that strategic alliances are more frequent among firms

backed by the same venture capitalist. She also finds that this effect is concentrated in alliances

where high informational asymmetries exist and venture capitalists can utilize their informational

advantage. Venture-backed firms often operate in highly competitive and information-sensitive

industries, where the costs of sharing proprietary information are high. Lindsey (2008) suggests

that venture capitalists can use their information to identify profitable collaborations between

firms, overcoming this hurdle. Furthermore, she shows that these alliances create value for

portfolio firms by increasing the probability of a successful exit (IPO or acquisition).

It is imperative that VCs be able to exit their investments in order to provide liquidity and

returns to their limited partners. This is done either through an initial public offering, an

acquisition, or liquidation. Most prior finance literature focuses on the IPO exit route for venture

capitalists, as it is the most observable. According to Gompers and Lerner (2001), the proportion

of VC-backed IPOs increased from 10% of all IPOs in the 1980s, to 31% in the 1990s, to 56% in

1999, demonstrating the importance of VC-backed firms in the IPO market. Empirical work has

8

shown that VC-backed IPOs experience less underpricing (Barry et al. (1990), Megginson and

Weiss (1991)) and have better long-run performance than non-backed IPOs (Brav and Gompers

(1997)). The authors argue that this is due to the certification role that the venture capitalist

plays in the offering process.

In a Gompers (1995) sample of 794 VC-backed companies, 127 (22.5%) went public,

134 (23.8%) were acquired, 88 (15.6%) were liquidated, and 215 (38.1%) were still privately

held at the end of the sample. In the same sample, Gompers (1995) finds that IPO exits offered

an average return of 59.5% per year (7.1x invested capital in 4.2 years), M&A exits averaged

15.4% per year (1.7x invested capital in 3.7 years), and liquidations lost 80% of their value in 4.1

years. Gompers’ findings demonstrate that while IPOs are a primary source of large returns for

venture capitalists, acquisitions are also an important form of exit, especially in a dry IPO

market. Therefore, venture capitalists may be incentivized to leverage every available tool to

generate exits, including their relationship with the management of a potential acquirer that the

VC previously took public.

“No conflict, no interest” is an old venture capital saying, and this is not the first paper to

address potential conflicts of interest in the venture capital realm. Gompers and Lerner (1999)

address a conflict of interest inherent when an investment bank underwrites an IPO in which its

holds equity through a venture capital subsidiary. The authors find that investors require greater

underpricing for offerings in which the lead underwriter is also a venture investor in the firm.

The authors interpret this as proof of the rational discounting hypothesis, or that investors are

smart enough to anticipate conflicts of interest and compound them into the price at the IPO.

Given the level of involvement between a venture capitalist and a firm’s management,

and a VC’s propensity to facilitate alliances within its network, it is likely that a VC continues to

9

affect a firm beyond the initial public offering. This could manifest itself in many ways,

including the M&A decisions of management, with a VC facilitating and encouraging mergers

and acquisitions within its network. Specifically, the VC could promote the sale of private

targets from its own portfolio to public acquirers that the VC previously took public. The nature

of the VC’s effect on M&A decisions of public firms is an empirical question. This paper seeks

to determine how the VC’s role affects acquiring firm value as measured by acquirer

announcement returns.

The determinants of acquisition announcement returns to publicly-held bidders have been

heavily studied in the financial literature. Earlier event studies show that bidders have no or

slightly negative announcement returns (Jensen and Ruback (1983), Andrade, Mitchell, and

Stafford (2001)). This is generally attributed to managerial hubris, in which acquiring managers

overestimate their ability to run the target (Roll (1986)), or managerial empire building behavior,

in which managers’ personal objectives are inconsistent with maximizing shareholder value

(Jensen (1986), Morck, Schleifer, and Vishny (1990), Harford (1999)). Furthermore, earlier

studies documented that cash deals resulted in better acquirer returns than stock deals (Jensen

and Ruback (1983), Andrade, Mitchell, and Stafford (2001)). This was attributed to the concept

of stock acquisitions as a signal of overvalued acquirer equity (Myer and Majluf (1984), Travlos

(1987)). But more recently, the financial literature has demonstrated the determinants of acquirer

CARs to be much more nuanced.

Chang (1998) and Fuller, Netter and Stegemoller (2002) show that target ownership is an

important factor in predicting acquirer returns, where acquirers of public targets lose 1% on

average but acquirers of private targets gain about 1.5%. Additionally, the method of payment

effect was also shown to depend on target ownership structure. Acquisitions of private targets

10

fair better when financed with equity, which runs counter to public target acquisitions. This is

attributed to the contingency pricing effect, in which target shareholders partially bear the cost of

overpayment when the acquirer pays with stock (Hansen (1987)). Interestingly, Moeller,

Schlingemann, and Stulz (2007) find that the method of payment is irrelevant after controlling

for acquiring firm idiosyncratic volatility. Using the acquirer’s excess stock return volatility as a

proxy for information asymmetry, the authors show that acquirer CARs are negatively related to

idiosyncratic volatility.

Asquith et al. (1983) show that relative deal size is also a determinant of acquirer

announcement returns, where returns are increasing in the ratio of target market capitalization to

bidder market capitalization. Furthermore, Moeller, Schlingemann, and Stulz (2004) find that

acquirer size (as separate from relative size) is an additional determinant of acquirer

announcement returns for both public and private targets, with small acquirers fairing better than

large acquirers on average. The effect of Tobin’s q and the highly correlated market-to-book

ratio of the acquirer is unclear. Lang et al. (1991) and Servaes (1991) find evidence that high-q

bidders have higher announcement returns, but Dong et al. (2006) find the opposite and Moeller

et al. (2004) find no relation at all. This is likely due to sample period differences. It is

generally acknowledged that pre-1990 announcement returns were increasing in acquirer q, but

reversed in the 1990s.

Interestingly, Moeller, Schlingemann, and Stulz (2005) point out that average

announcement returns may be a misleading measure of returns to acquiring firm shareholders.

They find that from 1991-2001, while 3-day announcement cumulative abnormal returns [CARs]

averaged 1.2%, on the whole acquiring firm shareholders lost $216 billion. The authors compare

this to the 1980s, in which acquiring firms’ shareholders averaged 0.6% in CARs but lost only $4

11

billion in acquiring firm value. Most of the losses in their sample came between 1998 and 2001,

where CARs averaged 0.7% but lost $240 billion in acquiring firm value. Specifically, most of

the dollar losses came in 87 large loss deals (defined as an acquiring firm loss of at least $1

billion). The authors find that the acquirer’s in the large loss deals had high Tobin’s q and low

book-to-market ratios, which is consistent with the Jensen (2005) hypothesis that high firm

valuations give managers leeway to make poor acquisitions.

However, only recently have researchers begun to explore the effect of venture capital

involvement on acquisition announcement returns. A related working paper by Gompers and

Xuan (2009) examines the characteristics of public acquirers of private venture-backed

companies, finding that these acquirers are larger, have higher Tobin’s Q, and operate in a

related industry. Their study shows that the market reacts less positively to the announcement of

the acquisition of a venture-backed firm, but that longer-run returns and operating performance

were higher following these acquisitions. The authors attribute the smaller announcement

returns to the view that venture capitalists are strong negotiators or that an adverse selection

problem exists. But nowhere in the paper do they examine whether the public acquirer had been

venture-backed before going public.

A related paper by Masulis and Nahata (2011) studies the acquisition announcement

returns for public acquirers of private targets. The authors find that acquirers of VC-backed

targets experience larger announcement CARs than acquirers of non-backed targets. They

suggest that this is the result of VCs selling their firms cheap due to conflicts of interest between

VCs and other target shareholders (i.e. entrepreneurs, angel investors). However, the paper only

finds support for the self-dealing hypothesis, in which a VC has a concurrent financial stake in

both the acquirer and the target.

12

While at first this paper may appear similar to Masulis and Nahata (2011), my paper has

significant differences and additions. I look at acquisitions by recent IPOs, which allows me to

comment on the effect of VC-backing on post-IPO acquisition behavior. Masulis and Nahata

(2011) look at acquisitions of VC-backed targets by all acquirers, and make no mention of

acquirer VC-backing (with the exception of the self-dealing observations). Masulis and Nahata

(2011) impose a strong filter on their sample of acquisitions, requiring the target size to be at

least 10% of the acquirer size. They do this so that they can get financial data on the target.

Acquirers are not required to disclose target financials unless the target is 10% of the acquirer.

To get an idea of the sample selection, Andrade, Mitchell, and Stafford (2001) find that the

median target size is 11.7% of the acquirer from 1973-1998 and 11.2% from 1990-1998 (a

similar time frame to Masulis and Nahata (2011)). For comparison, the Masulis and Nahata

(2011) sample mean target size is 45% and median 24%. This censors the sample to deals with

high relative target size, which Asquith et al. (1983) shows have higher announcement returns. It

may also censor the sample to high profile deals where witnessing self-dealing is the least likely.

In the Masulis and Nahata (2011) self-dealing section, the authors identify 25

acquisitions in which a VC has a concurrent financial stake in both the acquirer and the target.

This is a dummy variable, so the authors cannot comment on how much of the acquirer or target

the VC owns (in fact, this information is difficult to ascertain). The authors find that these deals

have higher CARs than acquisitions where the VC doesn’t have a stake in the acquirer, and

conclude that it is because the VC (as target representative) isn’t incentivized to negotiate as hard

with the acquirer when holding a concurrent financial stake. However, Masulis and Nahata

(2011) is unable to show if the VC is the lead investor in the target, which could limit the VC’s

effect on negotiations. And again, in such large deals, it is unlikely that the VC would have as

13

much influence over the target’s decisions, making self-dealing less likely. My paper examines

SAME_VC transactions, in which the VC was invested (concurrent or not) in the acquirer and

the target, over a different time period, and offers an alternative explanation (information

sharing) for abnormal announcement returns.

Furthermore, I distinguish between the information sharing hypothesis and the cheap

target hypothesis by finding acquisitions of VC-backed public targets by VC-backed public

acquirers that had concurrent, overlapping venture capital representation on their respective

boards of directors. While they are few in number, they are quite telling. The highly positive

target, acquirer, and combined announcement returns to these transactions disprove the cheap

target, or wealth transfer, hypothesis, and lend strong evidence to the information sharing

hypothesis.

Both Gompers and Xuan (2009) and Masulis and Nahata (2011) explore target venture-

backing as a determinant of acquirer announcement returns. This paper documents that the

effect of this determinant depends on whether the acquirer was also VC-backed at the IPO. I

follow Aoki (2000) and Lindsey (2008) in examining whether the venture capitalist, acting as an

information conduit, can facilitate value-added activities within its network. Specifically, the

venture capitalist may help an acquirer overcome informational asymmetries and alleviate

managerial hubris in bidding on private venture-backed targets.

The lasting effect that venture capitalists have on their previous IPOs requires further

investigation. This paper explores whether public acquirers that were VC-backed at the IPO are

more likely to acquire VC-backed private targets, and how this bias affects acquiring firm

announcement returns. I extend both the M&A literature on acquisitions of private targets by

14

public acquirers and the venture capital literature on the effect of venture capital backing on

post-IPO behavior.

1.3 HYPOTHESES

Do VC-backed IPOs Differ In Their M&A Behavior?

Given the level of involvement between a venture capitalist and a portfolio firm’s

management, and a VC’s propensity to facilitate alliances within its network (Lindsey (2008)), it

is likely that a VC continues to affect a firm beyond the initial public offering. This could

manifest itself in many ways, including the M&A decisions of management, with a VC

facilitating and encouraging mergers and acquisitions within its network. Specifically, the VC

could promote the sale of private targets from its own portfolio to public acquirers that the VC

previously took public. In either case, I would expect to see that acquirers that were VC-backed

at the IPO [VC-acquirers] are more likely to acquire VC-backed private targets [VC-targets].

H1: Acquirers that were VC-backed at the IPO are more likely to acquire VC-

backed private targets, even after accounting for industry and time.

Information Sharing or Managerial Hubris?

Information Sharing: It is possible that the venture capitalist’s informational advantage

leads to the identification of profitable acquisitions within its network, ala Aoki (2000) and

Lindsey (2008). In this case, VCs could share asymmetric information with the acquiring firm’s

management, improving the acquirer’s bidding ability, leading to value-enhancing acquisitions.

Of course, we cannot observe what a target is truly worth. Examining acquirer acquisition

15

announcement returns will reveal whether the market interprets these tightly networked

transactions as information sharing or managerial hubris.

H2: The market believes the venture capitalist’s involvement reduces asymmetric

information and improves the acquirer’s bidding ability. The acquirer would

experience above average abnormal returns over the announcement window.

Managerial Hubris: It could be that the VC’s propensity to facilitate deals within its

network leads to managerial hubris in the acquiring firm, ala Roll (1986). If acquiring firm

management trusts the VC, management may place too much weight in the information that is

being shared, leading to hubris and overbidding. (It is also possible that the VC pressures

acquiring firms to make acquisitions from the VCs network under threat of retribution, but this is

not necessary to explain overbidding by the acquirer.) In either case, we’d expect to see

overbidding by the acquirer which would lead to value-destructive acquisitions.

H2A: The market believes the venture capitalist’s involvement leads to

overbidding for the target. The acquirer would experience below average

abnormal returns over the announcement window.

Differentiating Between Information Sharing and Cheap Target Hypotheses

Of course, as Masulis and Nahata (2011) suggest, above average abnormal announcement

returns could also be interpreted as a result of the venture capitalist selling its targets cheap due

to conflicts of interest with target shareholders. In their self-dealing hypothesis, the venture

capitalist is not incentivized to achieve a high price for its target if it also holds a concurrent

financial stake in the acquiring firm since it would also gain from any positive announcement

16

return to the acquirer. Unfortunately, my paper faces the same data limitations as Masulis and

Nahata (2011), making it difficult to differentiate between these two hypotheses with the private

target sample.

If accurate information on venture capitalist percentage holdings in the acquirer and

target were available, then it would be straight forward to look at the relationship between VC

holdings in the acquirer and acquisition announcement returns. If the cheap targets hypothesis is

correct, then abnormal CARs should be increasing in the VC’s stake in the acquirer. If

information sharing is correct, the VC’s stake shouldn’t matter in determining announcement

returns. It would also be possible to compare concurrent financial stakes (which Masulis and

Nahata (2011) examine) against historical financial acquirer stakes (the VC held a stake at the

IPO, but has since exited the position). If the information sharing hypothesis is correct, these

shouldn’t differ. If the cheap target hypothesis is correct, concurrent-stake acquisitions should

have higher CARs. But for a variety of reasons, including database limitations, reporting

requirements, and the opaque nature of venture capital share distributions, accurate venture

capital holdings could not be ascertained for private targets.

However, I am able distinguish between the information sharing hypothesis and the

cheap target hypothesis by finding acquisitions of VC-backed public targets by VC-backed

public acquirers that had concurrent, overlapping venture capital representation on their

respective boards of directors. While they are few in number, they are quite telling. The highly

positive target, acquirier, and combined announcement returns to these transactions disprove the

cheap target, or wealth transfer, hypothesis, and lend strong evidence to the information sharing

hypothesis.

17

Predictions

Whatever the effect of the venture capitalist on the M&A behavior of their previous

IPOs, I expect that the effect will:

1) Decrease if management changes between IPO and acquisition. During the startup,

the venture capitalist develops a working relationship with the firm’s management. If

management changes, I expect the VC’s influence on management to diminish.

2) Increase if the VC sits on the board at the IPO and the acquisition. I expect that the

VC’s relationship with management is more pronounced by a presence on the board.

3) Decrease with time from IPO. The further the VC is from the close involvement with

management during the startup and IPO, the VC’s influence on management should

decrease.

1.4 DATA AND METHODOLOGY

The initial sample is compiled from SDC’s Global New Issues, Mergers & Acquisitions,

and VentureXpert databases. The IPO sample consists of firms that went public between

January 1, 1996 and December 31, 2005 on the American, Nasdaq, and New York stock

exchanges. It must be the firm’s original public offering and the offering price must be at least

$5. I exclude utilities, financials, unit offerings, rights offerings, REITs, and ADRs. This results

in sample of 2,491 IPOs, of which 1,153 (46.3%) are venture-backed.

The M&A sample consists of completed acquisitions announced between January 1, 1996

and Decemeber 31, 2006 of private targets by public acquirers whose stock is listed on the

American, Nasdaq, and New York stock exchanges. Acquirers and targets must be U.S. based,

the acquisition must be for 100% of the target, and no toe-hold positions exist before the

18

announcement. I exclude all financial acquirers and targets. I then cross-reference the M&A

sample with the IPO sample to determine which acquirers were in the IPO list and, of those,

which were venture-backed. Acquirer stock returns must be available in the CRSP database.

This results in a sample of 2,781 acquisitions of non-VC-backed targets and 188 acquisitions of

VC-backed targets that occurred within 5 years of a firm’s IPO.

I compile a list of venture-backed companies from VentureXpert, and cross-reference that

list with the M&A group to find a sample of venture-backed targets. The venture-backed targets

sample was created by cross-referencing the M&A sample on CUSIP and company name for all

firms in the VentureXpert database of 19,317 portfolio companies. This results in a sample of

188 venture-backed targets. It is clear that these 188 acquisitions are not a comprehensive

sample, but the current depth of the database and my methodology limit my ability to collect a

larger sample. However, there is little reason to think that this sample is biased or

unrepresentative of the population.

From VentureXpert, I gather the names of venture capital firms invested in both the VC-

backed acquirers and VC-backed targets, and hand-check for the presence of overlapping VCs,

which I call SAME_VC transactions. I construct a dummy variable for SAME_VC that equals 1

if there is at least one overlapping VC and 0 if it is a DIFFERENT_VC transaction. The final

sample consists of 34 SAME_VC transactions, 114 DIFFERENT_VC, and 40 acquisitions of

VC-backed targets by acquirers that were not VC-backed at the IPO.

For the 34 SAME_VC acquisitions, I hand-collect from SEC filings whether the CEO

was the same at both the IPO and acquisition. SAME_CEO equals 1 if so and 0 if the CEO

changed. I also check if the VC remained on the board of directors from the IPO through the

19

acquisition. VC_BOARD equals 1 if the VC was on the board at the IPO and the acquisition,

and 0 if not.

From CRSP, I gather the announcement returns for all 2,969 acquisitions. Using a 3-day

window [-1, +1] relative to the announcement date, I use market-adjusted returns using the CRSP

equal-weighted index to define cumulative abnormal returns. Using a market-model or the

CRSP value-weighted index to calculate abnormal returns did not appreciably alter my results.

Later, I Winsorize the CAR data at the 1% and 99% tails to account for the possibility of outliers

biasing the small sample.

High technology companies are defined as those operating in SIC codes 283 (biological

products, genetics, and pharmaceuticals), 357 (computers), 365-369 (electronic equipment), 481

(high-tech communications), 482-489 (communications services), 737 (software services).

As control variables, I gather acquirer size, market-to-book ratio, and idiosyncratic excess

stock return volatility. I define size as shares outstanding multiplied by acquirer stock price the

two days before the acquisition announcement. I define market-to-book ratio as the market value

of equity divided by the book value of equity, where book equity is equal to Total Assets – Total

Liabilities – Preferred Stock + Deferred Taxes + Convertible Debt (Kayhan and Titman (2007)).

I measure idiosyncratic volatility as the standard deviation of the acquirer’s daily stock return

minus the equal weighted market return from 205 days to 6 days prior to the acquisition

announcement (Moeller et al. (2007)).

To analyze acquisitions of public targets, I construct a new sample of recently public

firms that get acquired. This IPO sample consists of initial public offerings on the NYSE,

AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based

in the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I

20

remove IPO firms that had an offer price of less than $5, raised less than $5 million, or had an

unidentifiable PERMNO. The M&A sample consists of transactions announced between

January 1, 1985 and May 17, 2010, where both the acquirer and target must be based in the

United States and publicly traded. The deals must have been completed or withdrawn. I

eliminate financials and utilities. A maximum of 5% toehold could have existed prior to

announcement, and the acquirer must seek to own 100% of the target after the acquisition. I

eliminate observations that had material alternative news around the announcement period.

Merging the samples I find 1,404 acquisition announcements of firms in the IPO sample, and

1,216 completed acquisitions of those firms. I filter for VC-backed acquirers within 5 years of

their IPO making acquisitions of VC-backed targets within 3 years of their IPO, where

overlapping venture capitalists would be most likely, which results in 90 acquisition attempts.

For both bidders and targets, I use a market-adjusted 5-day window [-2,+2] to measure

cumulative abnormal returns [CARs], where the announcement date is day 0. I subtract off the

return to the CRSP value-weighted index over the same period to define cumulative abnormal

returns. All returns data is obtained from CRSP.

∑ ( )

where

21

I also look at market-adjusted 13-day window [-10,+2] to capture any short-term run-up

to the acquisition announcement. For robustness, I run the data with both the CRSP equal- and

value-weighted market indices.

As in Bradley et al (1988) and Mulherin and Boone (2000), combined return (or synergy

return) is defined as the value-weighted CAR of the transaction, where bidder and target values

are their respective market caps 3 trading days prior to the announcement date.

( ) ( ) ( ) ( )

( )

1.5 RESULTS

The Effect of Venture Capital Backing on M&A Activity

Table 1 demonstrates the strong inclination of VC-backed IPOs to acquire VC-backed

private targets in the five years following the IPO. Of the 188 VC-targets in my sample, 148

were acquired by VC-acquirers (78.7%). Of the 2,969 acquisitions in my full sample, VC-

acquirers made 1256 (42.3%) of all acquisitions, of which VC-targets represent 148 (11.8%) of

the acquisitions. This is interesting when compared to the activity of non-VC-backed acquirers,

where VC-targets make up only 2.3% of their acquisitions. A binomial test for the difference in

proportions was strongly significant.

Furthermore, of the 188 observed VC-backed private targets, 34 (18.1%) came from

acquirers that were backed by the same venture capital firm at the IPO. 114 (60.6%) came from

acquirers that were backed by different VC firms, and 40 (21.3%) came from acquirers that were

22

never VC-backed. This indicates that a VC-backed private target is nearly as likely to be

acquired by a firm that was backed by the same VC than any non-backed firm in the IPO sample.

This could of course be due to the VC’s tendency to invest in firms operating in high-tech

SIC codes. High-tech companies should be more likely to acquire high-tech targets, which could

present an omitted variable bias if I don’t control for industry effects. To account for this, I run a

logistic regression which includes control variables to determine the likelihood of acquiring a

VC-target. Table 2 shows a logistic regression where the dependent variable is a dummy that

equals 1 if the target is VC-backed and 0 if not. In addition to the dummy for VC-acquirer, I

include a dummy if the acquirer operates in a high-tech industry and a time variable to capture

any time trends or bubble effects. The VC-acquirer dummy is positive and significant in

determining the likelihood of acquiring a VC-backed private target.

It is clear from this data that VC-backed IPOs disproportionately acquire VC-backed

targets, even after accounting for industry. This is strong support for Hypothesis 1, that VC-

backed acquirers are more likely to acquire VC-backed private targets in the years following

their IPO.

Acquisition Announcement Returns

Table 3 Panel A shows acquirers’ 3-day cumulative abnormal returns for acquisition

announcements. My full sample of 2,969 acquisitions by public acquirers of private targets

appears similar to those in previous studies (Chang (1998), Fuller, Netter, and Stegemoller

(2002)), with a mean acquirer announcement return of 1.3%. But contrary to both Masulis and

Nahata (2011) and Gompers and Xuan (2009), I find that acquirer announcement CARs don’t

23

significantly differ for VC-backed targets than they do for non-VC-backed private targets (2.3%

vs. 1.3% respectively).

However, within the sample of VC-backed targets, SAME_VC transactions experience

larger returns than other acquisitions of VC-backed targets. SAME_VC acquisitions experience

mean returns of 4.7%, compared to 1.6% for DIFFERENT_VC transactions and approximately

0% for acquisitions of VC-targets by non-VC-acquirers. However, mean difference tests fail to

attain acceptable significance, which could be due to the presence of outliers in the small sample.

These results still show mild support for the information sharing hypothesis, and strong support

against the managerial hubris hypothesis.

With a sample of only 34 SAME_VC acquisitions, the results could be strongly affected

by statistical outliers. Panel B reports the results after Winsorizing the CARs data to account for

outliers. Winsorizing at the 1% and 99% tails increases significance in means and mean

differences without drastically altering the absolute values. Here, SAME_VC transactions

significantly outperform acquisitions of VC-targets by non-VC-acquirers (5.6% difference). The

difference between SAME_VC and DIFFERENT_VC acquisitions was 3.2%, but only

significant at the 20% level. This is further support for the information sharing hypothesis, and

strong support against the managerial hubris hypothesis. The information sharing that occurs

between a VC and a firm’s management appears to result in value-enhancing acquisitions of VC-

backed private targets, despite potential managerial hubris.

Segmenting the 34 SAME_VC acquisitions on whether or not management has changed

yields interesting results. For 26 of the 34 acquisitions, the CEO remained the same from the

IPO to the acquisition. Acquisitions where the CEO was different experience average

announcement CARs 4.1% larger than those acquisitions in which the CEO remained the same.

24

This runs counter to my prediction that the effect of the VC would decrease with management

turnover. However, a mean difference test was insignificant. The fact that 26 of the 34

acquisitions involved the same CEO is support for the lasting effect of venture backing on post-

IPO acquisition activity.

Segmenting the 34 SAME_VC acquisitions on whether or not the venture capitalist

served on the board at both the IPO and the acquisition also proves interesting. For 18 of the 34

acquisitions, the venture capitalist remained on the board from the IPO through the acquisition.

These acquisitions had a mean announcement CAR of nearly 7%, significantly different from

zero at the 5% level, but not significantly different from acquisitions in which the VC left the

board before the acquisition. Though lacking significance, this supports my prediction that the

effect of the VC will increase with board presence.

Multivariate Analysis of Acquirer Announcement CARs

Table 4 presents the results of ordinary least squares regressions. The dependent variable

is 3-day [-1, +1] acquirer announcement CARs, measured using market-adjusted returns on the

CRSP equal weighted index (Winsorized at the 1% and 99% tails). The results, though generally

insignificant, still tell a story. If the managerial hubris hypothesis were correct, the SAME_VC

variable would be a negative and significant determinant of acquirer announcement CARs. The

fact that it is positive and fails to significantly load in the regression indicates that I can reject the

managerial hubris hypothesis. This is further support that the market believes venture capitalists

don’t influence their previous successes to make value-destructive acquisitions.

Regression 1 shows that when acquiring a VC-backed private target, public acquirers fair

better if they were VC-backed at the IPO. Regression 2 shows that SAME_VC transactions are

25

likely to fair better, though the coefficient wasn’t strongly significant. In regression 3, I include

variables for management turnover and VC board presence at the acquisition. Interestingly,

these coefficients run against each other. VC board presence at the IPO and the acquisition has a

significantly positive effect on acquisition announcement CARs, while a static CEO has an

insignificantly negative effect. On one hand, this supports my prediction that VC board presence

would facilitate information sharing. On the other, it runs counter to my prediction that a change

in management would hinder information sharing.

Since VCs generally remain on the board for a few years after an IPO before stepping

down, it is possible that the VC board presence is picking up some characteristic of more recent

IPOs. Therefore, all regressions include a control variable that measures the number of years

from the IPO to the acquisition announcement date. This control variable is insignificant, so

from the regressions it is clear that information sharing is strongly facilitated by the VC’s

presence on the board, but not significantly affected by a change in management or the passage

of time. In addition, I include five other control variables in all regressions to account for other

possible determinants of announcement returns: 1) log of acquirer’s market cap at the

acquisition, which should be negatively related to acquirer CARs (Moeller, Schlingemann, and

Stulz (2004)), 2) a dummy variable for acquisitions financed with pure-equity, which should be

positively related to acquirer CARs since my sample is all private targets (Fuller, Netter, and

Stegemoller (2002)), 3) a dummy variable if the acquisition is in a different two-digit SIC code

to account for the possibility of empire building behavior (Morck et al. (1990)), 4) acquirer

idiosyncratic excess stock return volatility to account for information asymmetry, which should

be negatively related (Moeller, Schlingemann, and Stulz (2007)), 5) acquirer market-to-book

ratio as a proxy for Tobin’s Q (Lang et al (1991), Servaes (1991), Dong et al. (2006)). Size is the

26

only control variable to achieve significance at the 10% level. Though most controls lack

significance, in all regressions these controls have the proper sign as predicted by the literature.

Information Sharing Vs. Cheap Targets

Table 5 documents the remarkable returns to SAME_VC acquisitions of public targets by

public acquirers. I identify 90 acquisitions of VC-backed targets within 3 years of their IPO by

VC-backed acquirers within 5 years of their IPO. I searched the IPO and merger documents on

EDGAR to identify overlapping board presence and shareholdings by the same venture capital

firm. While I only have 3 observations of SAME_VC public/public acquisitions, the returns to

these acquisitions lend evidence in favor of the information sharing hypothesis and strongly

against the cheap target hypothesis.

The average 5-day value-weighted announcement return to the target was 50.4%. This is

28.9% higher than the average of similar acquisitions involving different venture capitalists. The

average 5-day value-weighted announcement return to the acquirer was 5.6%, compared to an

average of -4.3% for similar acquisitions with different VCs. The 5-day value-weighted

combined announcement return, a metric more aligned with the overall quality of the business

combination, was 11.7%. This can be compared to the average of -0.6% for similar acquisitions

without an overlapping venture capitalist. Looking at these announcement returns, it is obvious

that the market reacts very well to acquisitions involving firms that share a venture capitalist.

The cheap target hypothesis argues that the positive announcement return to acquirers in

SAME_VC acquisitions is due to the venture capitalist selling the target firm cheap due to the

self-dealing interests of the venture capitalist. This is describing a wealth transfer from target

firm shareholders to acquiring firm shareholders. If this were the case, the acquirer

27

announcement return would be more positive than expected, the target announcement return (a

proxy for the premium paid in acquisition) would be lower than expected, and the combined

announcement return would be no different than expected. However, my data shows that all

three announcement returns are drastically higher than expected, strongly supporting the

information sharing hypothesis rather than the cheap target hypothesis.

Table 6 further documents this effect, laying out multivariate regression results for target,

acquirer, and combined announcement returns. A dummy variable for the presence of

overlapping board representation of the same venture capitalist is positive and significant in

explaining all three announcement returns. This is further evidence for the information sharing

hypothesis.

Discussion of Results

It is clear from my results that the market believes venture capitalists are not leveraging

their relationship with acquiring firm management to encourage overbidding which would result

in value-destructive acquisitions. An obvious question is, why not?

It could be that the venture capitalist’s role in the acquiring firm presents legal

disincentives for acting in their own self-interest. In 18 of the 34 SAME_VC acquisitions, the

VC sat on the board of directors at the IPO and the acquisition. In these instances, the VC is

expected to act in the interest of shareholders. If this were the case, I’d expect to see higher

acquirer announcement CARs for firms in which the VC remains on the board at the acquisition

and is therefore bound not to act self-interested. While I don’t find a significant difference in

means for acquisitions in which the VC remains on the board and those where the VC did not

(7% and 2.3% respectively), I do find that it is significant in my regression at the 10% level. Of

28

course, I predicted that information sharing would also be facilitated by VC presence on the

board, which would also explain this result.

More likely, reputational concerns deter venture capitalists from over-exerting their

influence on management for the VC’s own benefit. Gompers (1996) and Krishnan, Ivanov,

Masulis, and Singh (2010) point out that reputational concerns are important to venture

capitalists, as they repeatedly bring companies public. Reputation affects the VC firm’s ability

to certify companies, raise new funds, and gain access to quality deal flow. Other recent

literature has shown the importance of networks in the venture capitalist industry, providing

further evidence on the importance of reputation (Hochberg, Ljungqvist, and Lu 2007). The

results of my event study indicate that the market believes that venture capitalists avoid selfish

one-time wealth transfers in favor of long-run value maximization through improved reputation

and strong networks.

1.6 CONCLUSION

I find that venture capitalists continue to affect firm M&A decisions well after the initial

public offering, but that this results in higher returns to acquiring firm shareholders. The

information sharing that occurs between a VC and a firm’s management appears to result in

value-enhancing acquisitions of VC-backed private targets, despite the potential for elevated

managerial hubris or self-dealing. I reject the managerial hubris hypothesis that venture

capitalist are using their role as information conduit to encourage overbidding at the expense of

acquiring firm shareholders. I also demonstrate that the information sharing present in the sale

of private targets to public acquirers holds in acquisitions of public targets as well, allowing me

to reject the cheap targets hypothesis. Using this sample of public targets, I reject the cheap

29

targets hypothesis that argues venture capitalists are self-dealing in these transactions selling

target firms cheap as they have an interest in the acquirer as well. This paper demonstrates a

further mechanism through which venture capitalists can add value to corporations.

30

TABLE 1.1

The Effect of Venture Capital Backing on M&A Activity for Recent IPOs Sample is comprised of 2,969 acquisitions from 1996-2006 of private targets by public firms within 5 years of their

IPO. Acquirers must be companies that went public between 1996-2005, listed on the NYSE, Nasdaq, or AMEX,

with an offering price of at least $5, excluding financials and utilities. Within the sample, there are 188 VC-backed

private targets and 2781 non-backed private targets. ***, **, and * denote significantly different from zero at the 1,

5, and 10 percent levels respectively (binomial test for difference in proportions).

PANEL A: FULL SAMPLE (n=2,969)

Private Targets

Public Acquirers VC Backed Non VC Backed

VC Backed At IPO

Acquisitions 148 1108

Row % 11.8% 88.2%

Non VC Backed At IPO

Acquisitions 40 1673

Row % 2.3% 97.7%

Difference 9.5%***

PANEL B: VC-BACKED PRIVATE TARGETS (n=188)

VC-backed Private Targets

Public Acquirers SAME VC DIFF VC

VC Backed At IPO

Acquisitions 34 114

% of n 18.1% 60.6%

Non VC Backed At IPO

Acquisitions 40

% of n 21.3%

PANEL C: AVERAGE YEARS BETWEEN IPO AND ACQUISITION

Public Acquirer

SAME VC DIFF VC NON VC

Mean (years) 1.81 1.68 1.97

Median (years) 1.47 1.22 1.77

n 34 114 40

31

TABLE 1.2

Logistic Regression for Likelihood That Target is VC-backed Sample is comprised of 2,969 acquisitions from 1996-2006 of private targets by public firms within 5 years of their

IPO. Within the sample, there are 188 VC-backed private targets and 2781 non-backed private targets. VC_TGT is

the dependent variable, which equals 1 if the target was VC-backed. VC_AQ equals 1 if the acquirer was VC-

backed at its IPO. TECH_SIC is a dummy if the firm operates in one of the following high-tech SIC codes:

283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. Year is the year in which the acquisition was

announced to capture possible time trends or bubble effects. Acquirers must be companies that went public between

1996-2005, listed on the NYSE, Nasdaq, or AMEX, with an offering price of at least $5, excluding financials and

utilities. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Logistic Model: VC_TGT = VC_AQ + TECH_SIC + YEAR

Estimate Std Errors Wald Chi-Sq Pr > Chi Sq

Intercept -138.0* 71.953 3.6776 0.0551

VC_AQ 1.1831*** 0.1905 38.5717 <.0001

TECH_SIC 1.7016*** 0.2063 68.041 <.0001

YEAR 0.0668* 0.036 3.4421 0.0636

32

TABLE 1.3

Acquirer CARs in Acquisitions of Private Targets Sample is comprised of 2,969 acquisitions from 1996-2006 of private targets by public firms within 5 years of their

IPO. Acquirers must be companies that went public between 1996-2005, listed on the NYSE, Nasdaq, or AMEX,

with an offering price >$5, excluding financials and utilities. Cumulative abnormal return is calculated using a

market-adjusted returns method subtracting the return on the CRSP equal-weighted index over a 3-day window

[-1,+1] surrounding the announcement date. SAME CEO equals 1 if the CEO was the same at the IPO and

acquisition, 0 if not. VC ON BRD = 1 if the same venture capitalist was on the firm’s board at both the IPO and the

acquisition, 0 if not. In Panel B, sub-sample data is Winsorized as the 1% and 99% tails with a minimum of 1

observation winsorized at each end. ***, **, and * denote significantly different from zero at the 1, 5, and 10

percent levels respectively (two-tailed t test for means, signed rank test for medians).

PANEL A: ACQUIRER ANNOUNCEMENT RETURNS (CAR [-1,+1])

N Mean p-value Median p-value % > 0

FULL SAMPLE 2969 0.013*** <.0001 0.006*** <.0001 55.6%

NON-VC TARGETS 2781 0.013*** <.0001 0.006*** <.0001 55.8%

VC-BACKED TARGETS 188 0.017* 0.08 0.005 0.25 52.7%

NON-VC ACQUIRER 40 -0.008 0.54 -0.008 0.36 45.0%

VC-BACKED ACQUIRER 148 0.023** 0.04 0.009 0.10 54.7%

DIFFERENT VC 114 0.016 0.17 0.005 0.36 52.6%

SAME VC 34 0.047 0.13 0.033* 0.09 61.8%

SAME CEO 26 0.037 0.33 0.022 0.35 57.7%

DIFFERENT CEO 8 0.079 0.11 0.071* 0.08 75.0%

VC ON BOARD 18 0.052 0.21 0.043* 0.03 83.3%

VC NOT ON BOARD 16 0.041 0.40 -0.018 1.00 37.5%

PANEL B: WINSORIZED ACQUIRER ANNOUNCEMENT RETURNS (CAR [-1,+1])

VC-BACKED TARGETS 188 0.016* 0.06 0.005 0.23 52.7%

NON-VC ACQUIRER 40 -0.009 0.44 -0.008 0.35 45.0%

VC-BACKED ACQUIRER 148 0.023** 0.03 0.009* 0.09 54.7%

DIFFERENT VC 114 0.015 0.19 0.005 0.36 52.6%

SAME VC 34 0.048** 0.04 0.033* 0.08 61.8%

SAME CEO 26 0.038 0.15 0.022 0.31 57.7%

DIFFERENT CEO 8 0.079 0.11 0.071* 0.08 75.0%

VC ON BOARD 18 0.070** 0.03 0.043** 0.02 83.3%

VC NOT ON BOARD 16 0.023 0.50 -0.018 1.00 37.5%

PANEL C: MEAN DIFFERENCES Difference p-value

(VC AQ) - (NON VC AQ) 0.031** 0.04

(SAME VC) - (DIFF VC) 0.032 0.18

(SAME VC) - (NON VC AQ) 0.056** 0.03

(DIFF VC) - (NON VC AQ) 0.024 0.14

(DIFF CEO) - (SAME CEO) 0.041 0.44

(VC BOARD) - (VC NOT BOARD) 0.047 0.29

33

TABLE 1.4

Multivariate Analysis of Acquirer Cumulative Abnormal Announcement Returns This table reports ordinary least squares estimates. Sample is comprised of 2,969 acquisitions from 1996-2006 of

private targets by public firms within 5 years of their IPO. Acquirers must be companies that went public between

1996-2005, listed on the NYSE, Nasdaq, or AMEX, with an offering price >$5, excluding financials and utilities.

Cumulative abnormal return is calculated using a market-adjusted returns method subtracting the return on the

CRSP equal-weighted index over a 3-day window [-1,+1] surrounding the announcement date. The dependent

variable is Winsorized at the 1% and 99% tails. ***, **, and * denote significantly different from zero at the 1, 5,

and 10 percent levels respectively (p-values in brackets).

OLS REGRESSIONS

Dependent Variable = CAR [-1, +1]

(1) (2) (3) (4)

VC ACQUIRER 0.041*

[0.09] 0.035

[0.14]

0.036

[0.12]

0.035

[0.14]

SAME VC 0.025

[ 0.27]

0.032

[ 0.47]

VC ON BOARD 0.074*

[0.07]

0.066

[0.11]

SAME CEO -0.030

[0.39]

-0.056

[0.27]

LOG SIZE -0.009**

[0.04]

-0.009*

[ 0.06]

-0.010**

[0.04]

-0.009*

[ 0.06]

M/B RATIO -0.009

[0.79]

-0.008

[0.82]

-0.011

[0.73]

-0.007

[0.84]

ALL STOCK 0.018

[0.35]

0.015

[ 0.45]

0.019

[0.34]

0.018

[0.36]

DIFFERENT SIC 0.010

[ 0.62]

0.009

[ 0.62]

0.010

[0.59]

0.008

[0.66]

VOLATILITY -0.560

[ 0.22]

-0.539

[0.24]

-0.522

[0.26]

-0.543

[0.24]

TIME SINCE IPO 0.007

[ 0.34]

0.006

[ 0.38]

0.006

[ 0.36]

0.006

[0.39]

Adjusted R2 0.010 0.011 0.019 0.016

n 188 188 188 188

INDEPENDENT VARIABLE DEFINITIONS

VC ACQUIRER Equals 1 if the acquirer was VC-backed at its IPO

SAME VC Equals 1 if the acquirer and the target were backed by the same VC firm

SAME CEO Equals 1 if the CEO was the same at the IPO and at the acquisition

VC ON_BOARD Equals 1 if the VC in question was on the board at the IPO and acquisition

TIME SINCE IPO Years from the acquirer's IPO to the announcement date of the acquisition

LOG SIZE Log of acquirer's market capitalization at announcement date

ALL STOCK Equals 1 if the transaction was financed with 100% stock

DIFFERENT SIC Equals 1 if the acquirer and the target operate in different two-digit SIC code

VOLATILITY Std deviation of excess acquirer stock returns from 205 to 6 days prior

M/B RATIO Acquirer’s market value of equity divided by book value of equity

*TECH SIC defined as being in 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737

34

TABLE 1.5

Announcement CARs in Acquisitions of Young VC-backed Public Targets

by Young VC-backed Public Acquirers This table reports target, acquirer, and combined cumulative abnormal announcement returns. Same VC indicates that target and acquirer had concurrent board

representation of the same venture capital firm at their respective IPOs and at the acquisition announcement. DIFF VC indicates that the acquirer and target were

both VC-backed at the IPO, the acquirer was within 5 years of its IPO, and the target was within 3 years of its IPO. Equal (Value) indicates that the CRSP equal

(value) weighted index was used in calculating market adjusted returns. IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq

between January 1, 1985 and December 31, 2009. The firm had to be based in the United States. I exclude utilities, unit offerings, rights offerings, REITs, and

ADRs. I remove IPO firms that had an offer price of less than $5, raised less than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of

transactions announced between January 1, 1985 and May 17, 2010, where both the acquirer and target must be based in the United States and publicly traded.

The deals must have been completed or withdrawn. I eliminate financials and utilities. A maximum of 5% toehold could have existed prior to announcement,

and the acquirer must seek to own 100% of the target after the acquisition. Merging the samples results in 1,404 acquisition announcements of firms in the IPO

sample, and 1,216 completed acquisitions of those firms. Screening for acquirers within 5 years of their IPO making acquisitions of targets within 3 years of their

IPO results in 90 acquisition attempts. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Mean CARs Same VC (1) Diff VC (2) All Others (3)

(1)-(2) (1)-(3)

Target [-2,+2] CAR (Equal) 50.07% 21.19% 24.41%

28.88%

25.66% ***

Target [-2,+2] CAR (Value) 50.44% 21.52% 24.61%

28.92% *

25.83% ***

Target [-10,+2] CAR (Equal) 71.76% 23.86% 28.83%

47.90% **

42.93% ***

Target [-10,+2] CAR (Value) 74.20% 24.47% 29.43%

49.73% **

44.77% ***

Acquirer [-2,+2] CAR (Equal) 5.21% -4.66% -2.02%

9.87%

7.23% ***

Acquirer [-2,+2] CAR (Value) 5.58% -4.33% -0.33%

9.91%

5.91%

Acquirer [-10,+2] CAR (Equal) 12.18% -4.13% -0.92%

16.31%

13.10% **

Acquirer [-10,+2] CAR (Value) 14.62% -3.51% -0.33%

18.13%

14.95%

Combined CAR (Equal Weighted) 11.32% -0.88% 1.07%

12.20%

10.25% ***

Combined CAR (Value Weighted) 11.69% -0.55% 1.28%

12.24%

10.41% ***

n 3 87 1314

35

TABLE 1.6

Multivariate Analysis of Target, Acquirer, and Combined Announcement Returns in Acquisitions

of Young VC-backed Public Targets by Young VC-backed Public Acquirers This table reports ordinary least squares estimates for acquisitions of targets within three years of their IPO by acquirers within 5 years of their IPO. Same VC

indicates that target and acquirer had concurrent board representation of the same venture capital firm at their respective IPOs and at the acquisition

announcement. DIFF VC indicates that the acquirer and target did not have the same VC concurrently sitting on their board, or the information was not

identifiable on EDGAR. Equal (Value) indicates that the CRSP equal (value) weighted index was used in calculating market adjusted returns. The sample

formation is described in the methodology section. The sample consists of 1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed

acquisitions of those firms. Filtering for VC-backed acquirers within 5 years of their IPO making acquisitions of VC-backed targets within 3 years of their IPO

results in 90 acquisition attempts. Diversifying acquisition equals 1 if the firms operated in different 2-digit SIC codes. Tender offer equals 1 if the bid was a

tender offer. All Stock equals 1 if the offer was a 100% stock offer. ln(Acquirer (Target) Market Cap) is the natural log of the acquirer’s (target’s) share price 3-

days prior to the announcement multiplied by the shares outstanding. Relative size is the target’s market cap divided by the acquirer’s market cap. Tech Bubble

equals 1 is the acquisition was in the years 1998,1999, or 2000. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Dependent Variable Target [-2,+2] CAR Acquirer [-2,+2] CAR Combined [-2,+2] CAR

Coeff. p-value Coeff. p-value Coeff. p-value

Intercept 0.623 ** 0.020

0.175

0.291

0.360 ** 0.017

Same VC 0.375 ** 0.016

0.176 * 0.069

0.206 ** 0.019

Diversifying Acquisition -0.077 0.289

0.058 0.197

0.045 0.267

Tender Offer 0.175 0.124

-0.143 ** 0.045

-0.112 * 0.081

All Stock 0.062 0.419

-0.045 0.354

-0.029 0.499

ln(Acquirer Market Cap) 0.202 *** <.0001

0.039 0.189

0.000

1.000

ln(Target Market Cap) -0.272 *** <.0001

-0.064 ** 0.037

-0.029

0.295

Relative Size 0.182

0.288

0.137

0.202

-0.014

0.882

Tech Bubble (1998-2000) 0.048

0.411

0.012

0.733

0.002

0.944

N 90

90

90

Adj R-squared 0.464 0.083 0.096

36

CHAPTER 2

The Information Role of Venture Capitalists on Boards of Target Firms

2.1 INTRODUCTION

On February 14, 2002, PayPal Inc. held their initial public offering. Just 144 days later,

on July 8, 2002, eBay Inc. announced that it had entered an agreement to buy PayPal for nearly

$1.5 billion, an 18% premium over the previous day’s closing price, and an 82% premium over

PayPal’s IPO offering price. Prior to PayPal’s IPO, eBay had repeatedly been in negotiations to

buy PayPal. These attempts ultimately failed. At both the time of the IPO and the time of the

acquisition, several venture capitalists sat on PayPal’s board of directors. This leads one to

wonder why PayPal would go through the expensive IPO process just to sell the firm to eBay 4.5

months after the IPO. Zingales (1995) would argue that this is precisely why PayPal went

public, to intentionally create a free-riding shareholder problem with which to extract the surplus

in negotiation. In this context, it would appear that PayPal went public in order to extract a

higher premium in negotiating the later sale of the firm to eBay.

This paper explores whether the venture capitalists’ presence affects the likelihood of

takeover, and how their presence affects the target, acquirer, and combined announcement

returns around the acquisition announcement of recently public firms. I find strong evidence that

venture-backed firms are more likely to get acquired and light evidence that they receive higher

premiums in acquisition.

It has been well established that venture capitalists are heavily involved with the private

firms in which they invest (Gorman and Sahlman (1989), Gompers (1995), Lerner (1995), Baker

and Gompers (2003)). Less well studied is the role of a venture capitalist after they’ve taken a

firm public. We know that venture capitalists tend to stay on the boards of their companies well

37

after an IPO (Barry et al. (1990), Gompers (1996)), but we know little about how this affects

firm decisions and firm performance. One way VCs may affect firm decision making is through

merger and acquisition choices. A few recent working papers (Gompers and Xuan (2009),

Masulis and Nahata (2011)) have explored how venture capitalists who maintain their seat on the

board influence decisions of public companies that choose to make an acquisition (acquiring

firms), but there has been little research on how VCs may affect the decisions of publicly-held

firms being acquired (target firms). This paper documents how venture capital backing at the

IPO increases the likelihood of takeover after an IPO, with little effect if any on the target,

acquirer, and combined announcement returns.

When Valero Energy announced on April 25, 2005 that it would acquire Premcor Inc. for

$6.9 billion in a combination of cash and stock, Valero’s stock price fell 1.8% in the three day

announcement window, while Premcor’s rose 16.5%. This is typical of many acquisitions of

public targets by public acquirers. Andrade, Mitchell, and Stafford (2001) find that from 1973-

1998, acquisitions result in a 16% return to targets and -0.7% return to bidders on average. With

this knowledge, a vast financial literature has struggled to explain why a public company such as

Valero Energy would undertake such an acquisition, knowing that the expected announcement

return is most likely negative. Popular explanations include managerial hubris (Roll (1986)),

agency costs of free cash flow (Jensen (1986), Harford (1999)), empire building (Morck,

Schleifer, and Vishny (1990), Masulis, Wang, and Xie (2007)), and the signaling of over-valued

equity in the case of stock-based acquisitions (Rhodes-Kropf and Viswanathan (2004), Moeller,

Schlingemann, and Stulz (2005)).

The gist of these theories is that, for whatever reason, management’s interests are

divergent from those of its shareholders. However, for a value-weighted diversified shareholder,

the Valero/Premcor acquisition would have generated a combined announcement return of 2.2%,

38

and Andrade, Mitchell, and Stafford (2001) find that these combined announcement returns

averaged 1.8% from 1973-1998. Using combined announcement returns as a key metric of

interest, a proxy of the overall quality of a business combination, I investigate the role that

venture capitalists play in identifying and facilitating synergistic mergers and acquisitions.

Hansen and Lott (1996) argue that if shareholders are well diversified, the objective of

firm management is not to maximize firm value, but rather to maximize the value of their

shareholder’s entire portfolio. Therefore, diversified investors may be indifferent to wealth

transfers between acquirer and target, and only concerned with the wealth gain/loss associated

with the entire merger. If it is indeed true that managers are acting in the interest of diversified

shareholders, then bidder announcement returns alone are an incomplete metric to evaluate and

study acquisitions. A more relevant question is: was wealth created through the acquisition for a

diversified shareholder?

Bradley, Desai, and Kim (1988), Berkovitch and Narayanan (1993), Mulherin and Boone

(2000), and Andrade, Mitchell, and Stafford (2001) show that while acquisitions tend to transfer

wealth from acquirers to targets, acquisitions generally create wealth on the whole. While many

finance papers examine the determinants of acquirer and/or target announcement returns, there

has been little research on the determinants of total wealth creation, or synergistic gains, in

acquisitions. I use the involvement of a venture capitalist prior to and/or after the IPO, and also

explore the effect on total wealth creation, or lack thereof, in acquisitions.

This paper studies the effect of venture capitalists on the decisions of newly-public

corporations after the IPO. I extend both the merger and acquisition literature and the venture

capital literature by investigating the venture capitalist’s role as an information conduit in

mergers and acquisitions, specifically, their role while serving on the board of directors of a

target firm. I find that VC-backed firms are significantly more likely to receive bids and get

39

acquired following their initial public offering. I find light evidence that they may also receive

higher premiums, and strong evidence that they at least don’t receive lower premiums.

2.2 BACKGROUND

It has been well established that venture capitalists are heavily involved with the private

firms in which they invest (Gorman and Sahlman (1989), Gompers (1995), Lerner (1995), Baker

and Gompers (2003)). Less well studied is the role of a venture capitalist after they’ve taken a

firm public. We know that venture capitalists tend to stay on the boards of their companies well

after an IPO (Barry et al. (1990), Gompers (1996)), but we know little about how this affects

firm decisions and firm performance. One way VCs may affect firm decision making is through

merger and acquisition choices. A few recent working papers (Gompers and Xuan (2009),

Masulis and Nahata (2011)) have explored how venture capitalists who maintain their seat on the

board influence decisions of public companies that choose to make an acquisition (acquiring

firms), but there has been little research on how VCs may affect the decisions of publicly-held

firms being acquired (target firms).

Venture capitalists are generally thought to be value-added investors, who, through a

strong network of contacts, are able to raise capital, gain access to deal flow, invest in quality

companies, and add value to firm management. After the IPO, it is less clear how VCs are

involved with a firm, but oversight of a large public enterprise is probably low on their list of

core competencies. It would make sense that selling the company they brought public in an

acquisition would not only alleviate these responsibilities, but also lead to a nice return.

Mulherin and Boone (2000) show that targets receive an average premium of 20.2% in

40

acquisitions. The private benefits captured by the VC (reduced responsibility, grandstanding,

increased network, lock ups, etc) may lead firms with VC presence to sell for lower premiums.

In addition, VCs who want to sell off their shares in a company they’ve taken public run

the risk of signaling negative information to the market (Field and Hanka (2001)), resulting in a

decrease in share price. Selling the entire public company through a merger or acquisition after

an IPO avoids this problem. VCs may be willing to sell at a lower premium in order to avoid

this dilemma.

However, VCs are also considered to be strong monitors, certifiers, and shrewd

negotiators, well informed about the value of the companies and industries in which they invest.

This could lead them to command a higher premium for their target companies. Whether VC

presence on the board (and/or as a large shareholder) commands a higher premium as a target of

an acquisition is an empirical question. This paper studies the effect of venture capitalists on the

decisions of newly-public corporations after the IPO. I investigate the venture capitalist’s role as

an information conduit in mergers and acquisitions, specifically, their role while serving on the

board of directors of a target firm. I document the increased likelihood of a takeover attempt, the

likelihood of takeover completion, and the target, acquirer, and combined returns around the

acquisition announcement of newly public VC-backed firms.

The finance literature has clearly documented that announcement returns to public

acquirers of public targets are negative/null on average (Andrade, Mitchell, and Stafford (2001)).

With this knowledge, the literature has struggled to explain why managers/firms continue to

undertake acquisitions that have a negative expected impact on firm value. Popular explanations

include managerial hubris (Roll (1986)), agency costs of free cash flow (Jensen (1986), Harford

(1999)), empire building (Morck, Schleifer, and Vishny (1990), Masulis, Wang, and Xie (2007)),

and the signaling of over-valued equity in the case of stock-based acquisitions (Rhodes-Kropf

41

and Viswanathan (2004), Moeller, Schlingemann, and Stulz (2005)). The gist of these theories is

that, for whatever reason, managers are not acting in the interest of their shareholders.

Hansen and Lott (1996) argue that if shareholders are well diversified, the objective of

firm management is not to maximize firm value, but rather to maximize the value of their

shareholder’s entire portfolio. Therefore, diversified investors may be indifferent to wealth

transfers between acquirer and target, and only concerned with the wealth gain/loss associated

with the entire merger. While this may initially seem like a stretch, recent research indicates this

line of logic may actually be quite accurate.

A recent paper by Matvos and Ostrovsky (2008) shows that institutional shareholders

gain on average around acquisition announcements because they have substantial ownership in

both the target (who gains on average) and the acquirer (who loses on average). They also

provide evidence that institutional shareholders with cross-holdings in both the acquirer and the

target are more likely to approve an acquisition than shareholders that only hold the acquirer.

This could explain why mergers that apparently destroy acquirer value continue to get

shareholder approval. It is not farfetched to think that investors are more concerned with the

value of their entire portfolio than just the value of the bidder. A related paper, Harford, Jenter,

and Li (2011), shows that there exist significant institutional cross-holdings in acquirers and

targets and argue that this influences bidding firm management’s choice of target.

If it is indeed true that managers are acting in the interest of diversified shareholders, then

bidder and target announcement returns alone are an incomplete metric to evaluate acquisitions.

A more relevant question is: what are the determinants of wealth creative acquisitions?

A large financial literature examines the determinants of acquirer announcement returns,

but there has been little research on the determinants of total wealth creation, or synergistic

gains, in acquisitions.

42

Bradley, Desai, and Kim (1988) show that successful tender offers result in a synergistic

wealth gain of 7.48% to a value-weighted investor. They also show that this gain is mostly

captured by the target shareholders, especially in multiple-bidder contests. However, they don’t

address firm or deal characteristics in explaining this wealth gain.

Berkovitch and Narayanan (1993) find that total gains are positive in 76.4% of

acquisitions. They use the correlation between target gains and total gains to test for takeover

motive. They argue that a positive correlation indicates a synergy motive, a negative correlation

indicates an agency motive, and zero correlation indicates hubris. They find that synergy is the

primary motive for takeover, but that agency and hubris are also prevalent. However, they stop

short of examining the characteristics that may determine the returns to these deals.

Mulherin and Boone (2000) document that though acquirer’s tend to transfer wealth to

targets, mergers and acquisitions generally create wealth on the whole. Returns to targets

average 20.2%, which more than makes up for the small average losses to bidders, which

benefits a diversified shareholder. In their sample, acquisitions average a 3.56% gain to the

value-weighted shareholder who holds both acquirer and target. Andrade, Mitchell, and Stafford

(2001) find qualitatively similar results.

The vast majority of finance studies have used bidder, target, and deal characteristics to

explain acquirer announcement returns. Several finance studies show that cash deals result in

better acquirer announcement returns than stock deals (Jensen and Ruback (1983), Andrade,

Mitchell, and Stafford (2001)), which is attributed to theory that stock acquisitions are a signal of

overvalued equity (Myers and Majluf (1984), Travlos (1987)).

Morck, Shleifer, and Vishny (1990) find that diversifying acquisitions are associated with

negative bidder announcement returns. In a sample of 326 acquisitions, the authors show that

bidder returns are lower when the acquirer and target don’t share the same 4-digit SIC code.

43

Jensen (1986) argues that high amounts of firm leverage disciplines managers and

reduces free cash flow that would otherwise be left to management’s discretion, suggesting it

should lead to better acquisitions. Maloney et al. (1993) finds empirical support for this

hypothesis.

Jensen (1986) argues that managers with firm free cash flows will waste it on negative

NPV projects rather than distribute it to shareholders. Lang, Stulz, and Walking (1991) find that

free cash flow is negatively related to bidder announcement returns for low-Q bidders, but not

for high-Q bidders. They argue that the agency costs of free cash flow are higher for low-Q

bidders because they are less likely to have positive NPV projects. Harford (1999) finds that

cash-rich firms make worse acquisitions regardless of Tobin’s-Q.

The evidence on the effect of Tobin’s Q on bidder returns is mixed. Lang et al. (1989,

1991) and Servaes (1991) find evidence that high-Q bidders have higher announcement returns,

but Dong et al. (2006) and Moeller et al. (2004) find the opposite. This is likely due to sample

period differences. It is generally acknowledged that pre-1990 announcement returns were

increasing in acquirer Q, but reversed in the 1990s. These papers have also shown that returns

are higher with the acquisition of a low-Q target by a high Q bidder, and lower with the

acquisition of a high-Q target by a low-Q bidder.

Asquith et al. (1983) show that relative deal size is a determinant of acquirer

announcement returns, where returns are increasing in the ratio of target market capitalization to

bidder market capitalization.

Moeller, Schlingemann, and Stulz (2004) find that acquirer size (as separate from relative

size) is an additional determinant of acquirer announcement returns for both public and private

targets, with small acquirers faring better than large acquirers on average. They argue that this is

due to the prevalence of agency costs and managerial hubris in large acquirers.

44

Masulis et al. (2007) find that bidders with high G-indexes make acquisitions with worse

announcement returns than low G-index firms. They argue that weakly governed firms result in

entrenched managers and that high G-index firms aren’t subject to the market for corporate

control.

Stulz, Walking, and Song (1990) show that target announcement returns are negatively

related to target institutional ownership, but that value-weighted announcement returns (total

return to bidder and target) are not related to target institutional ownership. They propose this is

due to the idea that institutions are willing to sell at less of a premium because they are in lower

tax brackets. However, they do not control for the endogeneity of size and institutional

ownership.

Duggal and Millar (1999) find a positive relationship between bidder gains and bidder

institutional ownership in a simple OLS regression. However, they also find that institutional

ownership is endogenous with firm size, inside ownership, and presence in the S&P 500.

Employing a two-stage regression of bidder gains on predicted institutional ownership, they do

not find a relationship between institutional ownership and bidder announcement returns.

Bargeron, Schlingemann, Stulz, and Zutter (2008) find that shareholders of public targets

receive higher premiums from publicly held bidders than privately held bidders or private equity

firms. They also show that the premiums paid by public bidders is positively related to target

institutional ownership. While the authors argue for an agency cost story, their results are also

consistent with the concept that managers in public firms are acting to maximize the value of

their investors’ portfolios, and not just bidder value.

Huang and Walking (1987) find that announcement returns to targets are higher with cash

offers than with stock offers. Song and Walking (1993) find that targets tend to have lower

managerial ownership than their industry competitors, but that managerial ownership can lead to

45

higher announcement returns in contested offers. They interpret this as evidence that an optimal

level target managerial ownership exists, in which managers can better negotiate but not stop

acquisitions, per Stulz (1988).

This paper extends both the merger and acquisition literature and the venture capital

literature. I find that VC-backed firms are significantly more likely to receive bids and get

acquired following their initial public offering. I find light evidence that they may also receive

higher premiums, and strong evidence that they don’t receive lower premiums.

2.3 HYPOTHESES

Likelihood of being acquired after the IPO

H1: Venture capital backed firms are more likely to be acquired within 3 years of their IPO.

Due to the venture capitalist’s networks, industry knowledge, and informational

advantage, VCs are able to identify profitable business combinations and promote their firms as

takeover targets. This increases the likelihood of takeover, and is financially beneficial to target

firm shareholders. In modeling the likelihood of takeover for newly public firms, a dummy

variable for VC board presence at both the IPO and takeover announcement would be positive

and significant.

Acquisition attempts on venture capital-backed firms

H2: Acquisition likelihood is inversely related to the percentage of the firm sold in the IPO.

Venture capitalists who are interested in going public and then selling the firm in an

acquisition may offer a small portion of the firm for sale in the IPO. This allows the shares to

trade in the public markets, establishes a price, and creates a free-riding shareholder problem, but

46

the venture capitalist, as well as other insiders, still maintains a large block with which to

negotiate.

Premiums paid in acquisitions of VC-backed IPOs

H3: Newly public VC-backed firms that become targets command a higher premium.

Venture capitalists are thought to be well-informed, shrewd negotiators. Their presence

on the board of directors of a newly public firm at both the IPO and takeover announcement will

be positive and significant in predicting the premium paid in acquisition.

Acquirer announcement returns

H4: Acquirer announcement returns are lower in acquisitions of VC-backed targets.

Hypothesis 3 posits that venture capitalists are negotiating for higher premiums for the

target firm, which should result in a wealth transfer from the acquirer to the target. Their

presence on the board of directors of a newly public firm at both the IPO and takeover

announcement will be negative and significant in predicting the announcement return to the

acquirer.

Combined announcement returns

H5: Combined announcement returns are higher in acquisitions of VC-backed targets.

The wealth transfer between acquirer and target should have no effect on the combined

wealth change in the acquisition. But in the VC’s unique role as an information conduit, their

presence on the board of directors of a newly public firm at both the IPO and takeover

announcement will be positive and significant in predicting the combined announcement return

in the acquisition.

47

2.4 DATA AND METHODOLOGY

The sample is compiled from SDC’s Global New Issues and Mergers & Acquisitions

databases. The initial IPO sample consists of initial public offerings on the NYSE, AMEX, and

Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in the

United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove

IPO firms that had an offer price of less than $5 or raised less than $5 million. I also require that

firms have identifiable PERMNOs. This results in a sample of 4,819 IPOs. Of the 4,819 IPOs,

2,204 (45.7%) were VC-backed at the IPO.

The Mergers & Acquisitions sample consists of transactions announced between January

1, 1985 and May 17, 2010. Both the acquirer and target must be based in the United States and

publicly traded. The deals must have been completed or withdrawn. I eliminate financials and

utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer

must seek to own 100% of the target after the acquisition. This results in 4,205 announced deals,

and 3,292 completed deals. Requiring targets to have returns listed on CRSP results in a sample

of 3,119 acquisitions.

Merging the IPO sample with the M&A sample, I create a set of IPOs that had

acquisition announcements. After requiring the acquired firm to have a PERMNO and returns

on CRSP on the acquisition date, my I find 1,440 acquisition announcements on firms that are

also in my IPO sample. 1,237 of these acquisition announcements resulted in a completed

acquisition. After gathering returns data, I find several transactions that involved material

alternative news in and around the acquisition announcement. I eliminate these from my later

samples, as the market reaction to alternative news obscures the effect of the acquisition

announcement. After eliminating transactions with errors or alternative news, I have 1,404

48

acquisition announcements on firms that are also in my IPO sample. 1,216 of these bids were

completed.

High technology companies are defined as those operating in SIC codes 283 (biological

products, genetics, and pharmaceuticals), 357 (computers), 365-369 (electronic equipment), 481

(high-tech communications), 482-489 (communications services), 737 (software services).

Venture capitalist board data was hand collected from SEC filings on EDGAR. I would

first go to the IPO documents to find the lead venture capitalist. In the common event that there

was more than one VC invested, I would choose the VC firm with the largest percentage

ownership in the company that also had a board seat. Where available, I would gather their

percentage ownership before the offering and after the offering. I would then go to the

documents in and around the acquisition announcement to discover if the venture capitalist was

still on the board of directors, and where available and applicable, the percentage ownership in

the target company. If the same lead venture capitalist was on the board at the IPO and at the

acquisition announcement, VC On Board is a 1. If the VC had departed the board between the

IPO and acquisition, VC On Board is a 0.

Of the 313 VC-backed IPOs that received bids within 3 years of their IPO, I was able to

identify VC board presence at the acquisition for 203 bids (missing data was mostly due to the

fact that the firm went public and was acquired before EDGAR begins). Several of my

observations went public before EDGAR begins, but received bids within 3 years of their IPO.

As such, I identified VC percentage ownership before and after the IPO in 179 and 180 cases

respectively. There were 6 observations where I could identify VC board presence at the bid, but

not find the VC percentage ownership in the firm, resulting in 197 observations of VC ownership

at the bid.

49

Returns Data

For both bidders and targets, I use a market-adjusted 5-day window [-2,+2] to measure

cumulative abnormal returns [CARs], where the announcement date is day 0. I subtract off the

return to the CRSP value-weighted index over the same period to define cumulative abnormal

returns. All returns data is obtained from CRSP.

∑ ( )

where

I also look at market-adjusted 13-day window [-10,+2] to capture any short-term run-up

to the acquisition announcement, as well as a preliminary 30 day window before the

announcement windows [-40,-10] to capture early run-up before my announcement windows.

For robustness, I run the data with both the CRSP equal- and value-weighted market indices.

As in Bradley et al (1988) and Mulherin and Boone (2000), combined return (or synergy

return) is defined as the value-weighted CAR of the transaction, where bidder and target values

are their respective market caps 2 days prior to the announcement date.

( ) ( ) ( ) ( )

( )

50

Variable Definitions

Market Cap is defined as the share price multiplied by shares outstanding. Market-to-

Book Ratio is the market value of equity divided by the book value of equity. Property is the

ratio of a firm’s property, plant, and equipment to total assets at the time if the IPO. Liquidity is

current assets minus current liabilities, divided by total assets at the time if the IPO. TGT VC

equals 1 if the firm had venture capital backing at the IPO. TGT VC Young Firm (Acquirer VC

Young Firm) is defined as a VC-backed firm within 3 (5) years of its IPO. Acquirer Tech and

TGT TECH equal 1 if the firm operates in SIC codes 283 (biological products, genetics, and

pharmaceuticals), 357 (computers), 365-369 (electronic equipment), 481 (high-tech

communications), 482-489 (communications services), or 737 (software services). Diversifying

Acquisition equals 1 if firms operate in different 2-digit SIC codes. Tender offer equals 1 if the

bid was a tender offer. All Cash equals 1 if the offer was a 100% cash offer. All Stock equals 1

if the offer was a 100% stock offer. ln(Acquirer (Target) Market Cap) is the natural log of the

acquirer’s (target’s) share price 3-days prior to the announcement multiplied by the shares

outstanding. Relative size is the target’s market cap divided by the acquirer’s market cap. Tech

Bubble equals 1 is the acquisition was in the years 1998,1999, or 2000.

2.5 RESULTS

Table 1 presents a breakdown of my sample, showing that at first glance, VC-backed

IPOs are acquired at a much higher rate than non-VC-backed IPOs. Of the 4,819 IPOs in my

sample, 2,204 (45.7%) were VC-backed. Of the 1,440 bids (announced takeovers) made on the

firms in my IPO sample, 814 (56.5%) were VC-backed IPOs. Of the bids on the IPOs sample

(1,440), 1,237 are completed, with VC-backed IPOs representing 719 (58.1%) of the completed

takeovers of firms in my IPO sample. That is to say that while VC-backed IPOs make up only

51

46% of my IPO sample, they represent 58% of the IPOs that end up being acquired. Of the

2,204 VC-backed IPOs in my sample, 814 (36.9%) receive acquisition bids at some point, of

which 719 (32.6%) are completed. Compare this with the 2,615 non-VC-backed IPOs, where

just 626 (23.9%) receive acquisition bids at some point, with 518 (19.8%) being completed.

That is to say that in my sample, approximately 1 out of 3 VC-backed IPOs end up being

acquired (32.6%), while only 1 out of 5 non-VC-backed IPOs get acquired (19.8%).

Table 1 Panel B shows that of the 3,381 announced deals (bids) in my full M&A sample,

2,686 (79.4%) end up being completed. Bids on firms that are also in my IPO sample are more

likely to be completed. Of the 1,440 targets that were also in my IPO sample, where 1,237

(85.9%) were completed. Within this sample of IPOs firms that end up receiving bids, VC-

backed IPO firms are more likely to complete/accept the acquisition than non-VC-backed IPO

firms (88.3% vs. 82.7%). This demonstrates that not only are VC-backed IPOs more likely to

receive bids (Table 1 Panel A), but the bid is more likely to be completed (Table 1 Panel B).

Table 2 breaks down my sample by both calendar year and year following the respective

IPO, while figures 1 through 5 present Table 2 graphically. Figure 1 present bids on IPO firms

by year following the respective IPO. Figure 1 shows that VC-backed IPO firms receive bids at

a higher rate the years following their IPO than do non-VC-backed IPO firms. Figure 2 shows

the cumulative percentage of the firms in my IPO sample that are acquired by the year following

respective IPOs. Within the first 3 years of a the respective IPO, 15% of VC-backed IPOs are

the target of an acquisition attempt, compared to less than 10% of non-VC-backed IPOs. Figure

2 shows that this gap only widens as time passes from the IPO.

Figure 3 shows the percentage of firms in my IPO sample actually acquired (successful

acquisitions) by the year following their respective IPO. Again, VC-backed IPOs are acquired at

a much higher rate than non-VC-backed IPOs. Figure 4 presents the cumulative percentage of

52

firms acquired by the year following their respective IPO. Again, we see that VC-backed IPOs

are acquired at a higher rate than non-VC-backed IPOs, and that this cumulative gap widens over

time.

Figure 5 lays out the bids received by firms in my IPO sample chronologically. The

years 1998, 1999, and 2000 (the bubble) were very active periods for acquisitions of firms in my

IPO sample. 393 (31.8%) of the 1,237 completed acquisitions in my sample occurred during the

years 1998, 1999, and 2000. However, this was an active acquisition period for both VC- and

non-VC-backed IPO firms. 224 (31.2%) of the 719 VC-backed IPO firms were acquired during

these years, compared to 169 (32.6%) of the 518 non-VC-backed IPO firms.

Venture capitalists generally concentrate their investments in high technology industries.

As a result, this increased likelihood of an acquisition could be due to the fact that the majority

of venture capital investments are in high technology companies, and not due to any effect of the

venture capitalist. Figure 6, 7, 8, and 9 separate target firms on their SIC codes into high

technology-based and non-technology based companies. High technology companies are

defined as those operating in SIC codes 283 (biological products, genetics, and pharmaceuticals),

357 (computers), 365-369 (electronic equipment), 481 (high-tech communications), 482-489

(communications services), 737 (software services).

It is clear from Figures 6, 7, 8, and 9 that the increased likelihood of being acquired due

to venture capitalist involvement holds true for both high-technology and non-technology

companies. In my IPO sample, there were 2,053 high-technology IPOs. Of these, 1,373 were

VC-backed, while 680 were not. In non-technology SIC codes, there were 2,840 IPOs in my

sample, of which 1,969 were non-VC backed, and 871 that had venture capital backing. My

acquisition sample, after removing errors and firms with alternative news around the acquisition,

contains 1,404 bids made on my IPO sample. Of those, coincidentally, 702 were high

53

technology targets and 702 were non-technology targets. Within the high technology sample,

506 (72.1%) of the targets were VC-backed while 196 (27.9%) were not. Within the non-

technology sample, 283 (40.4%) were VC-backed while 419 (59.6%) were not.

Looking at the high-technology sample, 33% of VC-backed IPOs receive an acquisition

bid within 10 years of their IPO, while only 25% of non-VC backed IPOs receive a bid over the

same respective time period. Within the non-technology sample, 29% of VC-backed IPOs

receive an acquisition bid within 10 years of their IPO, while only 17% of non-VC backed IPOs

do. This shows that venture capital involvement seems to increase the likelihood of an

acquisition attempt for both high technology and non-technology companies.

Of the 1,404 firms in my acquisition attempts sample, 1,216 were completed. Of the

1,216 completed acquisitions, 626 were high technology companies, while 590 were in non-

technology sectors. Within the high technology sample, 30% of the VC-backed IPOs are

acquired within 10 years, while only 21% of non-VC backed IPOs are. Within the non-

technology sample, 25% of VC-backed IPOs are acquired within 10 years, while only 14% of

non-VC backed IPOs are. This demonstrates that venture capital involvement seems to increase

the likelihood of being acquired, regardless of whether a firm is considered high-tech or not.

Table 3 shows that VC-backed firms sell a smaller portion of the firm in the initial public

offering than non-VC-backed firms, a variable I’ll utilize in my logistic regressions. VC-backed

firms sell an average of 27.8% of the firm in the IPO, while non-VC-backed firms sell an

average of 37.99%. Table 3 also shows that VC-backed firms are more likely to sell very small

portions in the IPO, with 4.3% of the firms selling less than 10%, 13.8% selling less than 15%,

and 45.6% selling less than 25%. I also break the sample into tech and non-tech subgroups,

where the same pattern holds true.

54

Table 4a lays out logistic regression results modeling the likelihood of receiving a bid. A

dummy variable for venture capital backing is strongly significant in predicting the likelihood of

being the target of an acquisition attempt.

Table 4b lays out logistic regression results modeling the likelihood of being acquired. A

dummy variable for venture capital backing is strongly significant in predicting the likelihood of

being acquired. Table 4c lays out regression results modeling the likelihood of being acquired

within 3 years of the initial public offering. A dummy variable for venture capital backing at the

IPO is strongly significant in predicting the likelihood of being quickly acquired, where no other

control variable.

Table 5 lays out an initial breakdown of acquisition announcement returns to my sample,

for all bids and for the subset of completed acquisitions. Of the 1,404 IPO firms that eventually

receive an acquisition bid, firms that had VC-backing at the IPO receive higher abnormal

announcement returns over both the 5-day (25.8% vs. 22.3%) and 13-day windows (30.2% vs.

26.6%). Both differences are statistically significant at the 5% level, and hold for both the equal-

weighted and value-weighted index. When examining differences in abnormal announcement

returns for the 1,216 acquisitions of IPO firms that were eventually completed, VC-backed firms

again have a higher mean than Non-VC backed firms (26.4% vs. 24% over the 5-day window,

30.1% vs. 28.1% over the 13-day window), but the difference in means lacks statistical

significance at the 10% level.

Table 6 examines my subjects of interest: IPO firms that are acquired relatively quickly

after the IPO. Table 4a looks at acquisition announcement returns to recent IPOs receiving bids

within 1, 3, and 5 years of their respective IPO. There were 123 IPO firms that were acquired

within 1 year of their respective IPOs (73 VC-backed vs. 50 Non-VC), 545 acquired within 3

years of their IPO (313 VC-backed vs. 232 Non-VC backed), and 828 acquired within 5 years of

55

their IPO (478 VC-backed vs. 350 Non-VC backed). VC-backed firms average higher abnormal

announcement returns over the 5-day and 13-day windows with both the equal-weighted and

value-weighted index. However, difference in means tests lacked statistical significance at the

10% level. Looking at firms that receive bids within 5 years of their respective IPO, VC-backed

firms average higher mean abnormal returns over the 5-day window (26.1% vs. 22.5%) and over

the 13-day window (31% vs. 27.4%), both statistically different at the 10% level.

Table 6b looks at acquisition announcement returns to targets were eventually acquired

(as opposed to just receiving bids) within 1, 3, and 5 years of their respective IPO. There were

116 IPO firms that were acquired within 1 year of their respective IPOs (73 VC-backed vs. 43

Non-VC), 483 acquired within 3 years of their IPO (277 VC-backed vs. 206 Non-VC backed),

and 721 acquired within 5 years of their IPO (420 VC-backed vs. 301 Non-VC backed). Again,

VC-backed firms average higher abnormal announcement returns over the 5-day and 13-day

windows with both the equal-weighted and value-weighted index. However difference in means

tests lacked statistical significance at the 10% level.

Table 6c segments the sample of IPO firms receiving bids on high-tech SIC codes and

non-technology-based SIC codes. There were 123 IPO firms that received bids within 1 year of

their respective IPO (67 High-tech vs. 56 Non-tech), 545 that received bids within 3 years (273

vs. 272), and 828 that received bids within 5 years (417 vs. 411). High-technology IPO firms

average higher abnormal announcement returns than non-technology-based IPO firms. High-

tech IPO firms that receive bids within 1 year of their IPO average abnormal announcement

returns 10.25% higher than non-tech firms (30.9% vs. 20.6%) over the 13-day announcement

window, significant at the 10% level. Looking at firms that receive bids within 5 years of their

respective IPO, high-tech firms average higher mean abnormal returns over the 5-day window

56

(27% vs. 22.2%) and over the 13-day window (32.4% vs. 26.5%), both statistically different at

the 5% and 1% level respectively.

Table 6d looks at announcement returns of completed acquisitions to recent IPO firms,

segmented on high-tech classification. The same relationship observed in Table 4c holds here as

well. In my sample of recent IPOs that get acquired, high-tech firms receive significantly higher

announcement returns than non-tech firms. Recent high-tech IPOs that are acquired with 1 year,

3 years, and 5 years of their respective IPO average 10.7%, 6.6%, and 5.9% higher abnormal

announcement returns than non-tech firms. Qualitative results are the same using the equal-

weighted or value-weighted CRSP index in calculating abnormal returns.

Table 6e looks at announcement returns to only the high-tech IPO firms that are acquired

relatively quickly after their IPO. While few of the difference in means test were significant,

segmenting on VC-backing produces some intriguing results. Of the 64 high-tech firms that are

acquired within1 year of their IPO, 49 were VC-backed and 15 were not. VC-backed high-tech

firms acquired within 1 year averaged a 32.6% announcement return over the 13-day

announcement window, versus a 26.9% average for non-VC-backed firms (a 5.6% difference).

However, looking at high-tech firms acquired within 3 years (5 years) of their IPO, VC-backed

firms experience lower announcement returns by 2.7% (3.8%).

The opposite is true for non-technology firms. VC-backed non-tech firms experience

slightly lower announcement returns if acquired within 1 year of their IPO over the 13-day

announcement window (-0.91% difference), but higher announcement returns if acquired within

3 years or 5 years of their IPO (5.01% and 5.29 % respectively).

Table 7a, 7b, and 7c examine announcement returns associated with the hand collected

board data. Table 5a looks at returns to IPO firms that are acquired within three years,

segmented on VC-backing and VC board presence. When the venture capitalist maintains their

57

seat on the board of directors from the IPO to the acquisition announcement, target firms

experience a 35% announcement return over the 13-day window, as opposed to a 28.2%

announcement return when the venture capitalist has departed the board before the acquisition

announcement is made. When the VC is still on the board at the acquisition announcement, VC-

backed firms receive a 7.75% higher announcement return than non-VC-backed firms. This

difference goes away if the VC no longer maintains a seat on the board of directors.

Tables 7b and 7c break Table 7a into high-tech and non-tech subsets. Table 7b looks at

announcement returns to only high-tech IPO firms that are acquired within three years of their

IPO, segmenting on VC-backing and VC board presence. VC and non-VC firms have about the

same announcement returns when the venture capitalist is still on the board at the acquisition

announcement. However, in instances where the VC no longer maintains a board seat,

announcement returns are significantly lower than both VC-backed firms in which the VC is still

on the board and non-VC backed firms.

Conversely, as shown in Table 7c, VC board presence is associated with lower

announcement returns when looking at non-technology companies. Though not statistically

significant, firms in which the VC maintained their board seat had lower average announcement

returns than firms in which the VC had departed the board. Table 7c shows that VC-backed

firms, regardless of board presence, are associated with higher announcement returns than non-

VC backed non-technology firms.

Table 8 documents the target, acquirer, and combined announcement returns by whether

the target and acquirer were both VC-backed IPOs at some point.

Table 9 contains summary statistics for key variables to be used in regressions. Of

particular interest, Table 9 includes summary statistics of the hand collected board data from the

SEC website. Of the 313 VC-backed IPOs that received bids within 3 years of their IPO, I was

58

able to identify VC board presence at the acquisition for 203 bids. The lead venture capitalist

maintained their board seat through the acquisition announcement in 87.2% of the cases. The

lead venture capitalist held 22.46% of the firm on average before the IPO, 16.73% after the IPO,

and 11.34% at the acquisition announcement. The average venture capitalist held onto 65.9% of

their stake between the post-IPO period and acquisition announcement.

Table 9 also shows that VC-backed targets tend to be smaller, and acquirers of VC-

backed targets tend to be larger. As such, the relative size of the target to the acquirer is lower.

Target size, acquirer size, and relative size will be controlled for in the regression tables to

follow.

Table 10a, 10b, and 10c lay out multivariate regressions results of target, bidder, and

combined announcement returns. A dummy variable for venture capital backing has little

explanatory power in explaining target announcement returns. For bidders however, if the target

is VC-backed the bidder has lower announcement returns. I interpret this as light evidence that

VC-backed targets extract more of the surplus in acquisitions than non-VC backed firms.

Looking at the regressions on combined announcement returns reinforces this. The dummy

variable for VC backing is insignificant in explaining combined returns, indicating that there is a

wealth transfer from acquirer to target, rather than value-destruction. I attribute this to the

certification and negotiating ability of the venture capitalist.

Discussion of Results

VC-backed IPOs are significantly more likely to be acquired within 3 years of their IPO.

Firms offering a small percentage (less than 10%) of the firm for sale in the IPO are significantly

less likely to be acquired, which is the opposite of my prediction. There is light evidence to

59

indicate that VC-backed firms receive higher premiums, acquirers of VC-backed recent IPOs

overpay (wealth transfer), and that this has little effect on combined wealth effects.

2.6 CONCLUSION

This paper studies the effect of venture capitalists on the decisions of newly-public

corporations after the initial public offering. I extend both the merger and acquisition literature

and the venture capital literature by investigating the venture capitalist’s role as an information

conduit in mergers and acquisitions, specifically, their role while serving on the board of

directors of a target firm. I find that VC-backed firms are significantly more likely to receive

bids and get acquired following their initial public offering. I also find that they receive higher

premiums while the venture capitalist is still on the board of directors. This seems to come from

extracting the negotiation surplus, as the acquirer of VC-backed targets experiences more

pronounced negative announcement returns while the combined announcement return is on

average unaffected. I interpret this as evidence of a wealth transfer from the acquirer to the

target firm. I attribute this to the certification and negotiating abilities of the venture capitalist

serving on the target firm’s board of directors, documenting a further method venture capitalists

can add value to corporations.

60

Table 2.1: IPOs, Venture Capital Backing, and Deal Activity IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition.

***, **, and * denote significantly different from zero at the 1, 5, and 10 percent levels respectively (binomial test for difference in proportions).

PANEL A All IPOs VC-Backed IPOs Non-VC-Backed IPOs Difference

# of IPOs 4,819 2,204 2,615

% of IPOs 100% 45.7% 54.3%

# Announced “IPO” Takeovers 1440 814 626

% of Announced “IPO” Takeovers 100% 56.5% 43.5%

# Completed “IPO” Takeovers 1237 719 518

% of Completed “IPO” Takeovers 100% 58.1% 41.9%

Announced Deals / # IPOs 29.9% 36.9% 23.9% 13%***

Completed Deals / Announced Deals 85.9% 88.3% 82.7% 5.6%***

Completed Deals / # IPOs 25.7% 32.6% 19.8% 12.8%***

61

Table 2.1 (cont’d): IPOs, Venture Capital Backing, and Deal Activity IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition.

***, **, and * denote significantly different from zero at the 1, 5, and 10 percent levels respectively (binomial test for difference in proportions).

PANEL B Announced M&A Deals Completed M&A Deals % Completed

Full M&A Sample 3,381 2,686 79.4%

All Announced "IPO" Takeovers 1,440 1,237 85.9%

% of M&A Sample 42.6% 46.1%

VC-backed IPOs 814 719 88.3%

% of M&A Sample 24.1% 26.8%

Non-VC IPOs 626 518 82.7%

% of M&A Sample 18.5% 19.3%

62

Table 2.2: Venture Capital Backing and Deal Activity Through Time This table show deal activity on the IPO sample both chronologically and in relation to the year following IPO. The IPO sample consists of initial public

offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in the United States. I exclude utilities,

unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less than $5 million, or had an unidentifiable

PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where both the acquirer and target must be based

in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials and utilities. A maximum of 5% toehold

could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. ***, **, and * denote significantly

different from zero at the 1, 5, and 10 percent levels respectively (binomial test for difference in proportions). (Begins on following page.)

63

Table 2.2 (Cont’d)

Bids on IPO Sample Completed Acquisitions Bids on IPO Sample Completed Acquisitions

Year Following IPO

Non-VC IPOs VC IPOs Total

Non-VC IPOs VC IPOs All IPOs Year

Non-VC IPOs VC IPOs Total

Non-VC IPOs VC IPOs Total

1 48 75 123 41 74 115

1987 2 3 5 2 3 5

2

85 116 201

77 99 176

1988

13 5 18

9 4 13

3

100 136 236

88 114 202

1989

12 5 17

8 4 12

4

68 93 161

53 79 132

1990

7 5 12

5 5 10

5

53 76 129

44 66 110

1991

2 4 6

1 4 5

6

54 66 120

38 58 96

1992

7 6 13

4 3 7

7

30 58 88

23 54 77

1993

7 14 21

2 11 13

8

29 57 86

22 50 72

1994

23 29 52

17 24 41

9

24 33 57

22 31 53

1995

31 43 74

27 39 66

10

25 17 42

22 15 37

1996

44 41 85

35 33 68

11

29 25 54

21 22 43

1997

58 57 115

46 51 97

12

29 20 49

26 18 44

1998

72 75 147

60 69 129

13

16 10 26

11 10 21

1999

65 95 160

57 84 141

14

6 7 13

5 6 11

2000

60 81 141

52 71 123

15

11 6 17

8 6 14

2001

38 71 109

33 59 92

16

3 9 12

3 8 11

2002

27 43 70

25 38 63

17

9 2 11

7 2 9

2003

19 44 63

18 41 59

18

2 3 5

2 2 4

2004

31 28 59

27 25 52

19

2 1 3

2 1 3

2005

19 40 59

19 36 55

20

1 2 3

1 2 3

2006

32 34 66

27 33 60

21

1 0 1

1 0 1

2007

24 37 61

22 35 57

22

1 0 1

1 0 1

2008

21 28 49

11 23 34

23

0 0 0

0 0 0

2009

10 23 33

9 21 30

24 0 2 2 0 2 2 2010 2 3 5 2 3 5

Total 626 814 1440 518 719 1237 Total 626 814 1440 518 719 1237

64

Figure 2.1: IPOs, Venture Capital Backing, and Acquisition Announcements in Years Following IPO This graph shows the percentage of firms in the IPO sample that that are the target of an acquisition announcement by year following their IPO. The IPO sample

consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in the United

States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less than $5

million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where both the

acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials and

utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition.

Merging the samples results in 1,440 acquisition announcements of firms in the IPO sample, and 1,237 completed acquisitions of those firms.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 24

% o

f IP

O S

amp

le R

ece

ivin

g B

ids

Year Following IPO

Bids on Firms in IPO Sample by Year Following IPO

Non-VC IPOs VC IPOs

65

Figure 2.2: Cumulative Total Acquisition Announcements in Years Following IPO IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition.

Merging the samples results in 1,440 acquisition announcements of firms in the IPO sample, and 1,237 completed acquisitions of those firms.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 24

Cu

mu

lati

ve %

of

IPO

Sam

ple

Re

ceiv

ing

Bid

s

Year Following IPO

Cumulative % of Bids on IPO Sample by Year Following IPO

Non-VC IPOs VC IPOs

66

Figure 2.3: IPOs, Venture Capital Backing, and Acquisitions in Years Following IPO This graph shows the percentage of firms in the IPO sample that were acquired by year following their IPO, segmented on whether the IPO was VC-backed at

the IPO. The IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had

to be based in the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5,

raised less than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17,

2010, where both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate

financials and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the

acquisition. Merging the samples results in 1,440 acquisition announcements of firms in the IPO sample, and 1,237 completed acquisitions of those firms.

0

0.01

0.02

0.03

0.04

0.05

0.06

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 24

% o

f IP

O S

amp

le A

cqu

ire

d

Years Following IPO

% of IPO Sample Acquired by year following IPO

Non-VC IPOs VC IPOs

67

Figure 2.4: Cumulative Total Acquisitions in Years Following IPO This graph shows the cumulative percentage of the IPO sample that is acquired in the years following the IPO. IPO sample consists of initial public offerings on

the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in the United States. I exclude utilities, unit

offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less than $5 million, or had an unidentifiable

PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where both the acquirer and target must be based

in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials and utilities. A maximum of 5% toehold

could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. Merging the samples results in 1,440

acquisition announcements of firms in the IPO sample, and 1,237 completed acquisitions of those firms.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 24

Cu

mu

lati

ve %

of

IPO

Sam

ple

Acq

uir

ed

Year Following IPO

Cumulative % of IPO Sample Acquired

Non-VC IPOs VC IPOs

68

Figure 2.5: Acquisition Announcements on IPO Sample Chronologically This graph shows the number of acquisition announcements made on IPO sample chronologically. IPO sample consists of initial public offerings on the NYSE,

AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in the United States. I exclude utilities, unit offerings, rights

offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less than $5 million, or had an unidentifiable PERMNO. The

M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where both the acquirer and target must be based in the United

States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials and utilities. A maximum of 5% toehold could have

existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. Merging the samples results in 1,440 acquisition

announcements of firms in the IPO sample, and 1,237 completed acquisitions of those firms.

0

10

20

30

40

50

60

70

80

90

100

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

# o

f A

cqu

isit

ion

An

no

un

cem

en

ts o

n IP

O S

amp

le

Year

Bids on IPO Sample Chronologically

Non-VC IPOs VC IPOs

69

Figure 2.6: Cumulative Total Bids in Years Following IPO, Technology Firms This graph shows the percentage of firms in the IPO sample that were acquired by year following their IPO, segmented on whether the IPO was VC-backed at

the IPO. IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be

based in the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5,

raised less than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17,

2010, where both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate

financials and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the

acquisition. Merging the samples results in 1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. High-

tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1 2 3 4 5 6 7 8 9 10

Cu

mu

lati

ve %

of

IPO

s

Year Following IPO

Cummulative % of Tech IPOs Receiving Bids by Year Following IPO

Non-VC IPOs

VC-backed IPOs

70

Figure 2.7: Cumulative Total Bids in Years Following IPO, Non-Technology Firms This graph shows the percentage of firms in the IPO sample that were acquired by year following their IPO, segmented on whether the IPO was VC-backed at

the IPO. IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be

based in the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5,

raised less than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17,

2010, where both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate

financials and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the

acquisition. Merging the samples results in 1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. High-

tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1 2 3 4 5 6 7 8 9 10

Cu

mu

lati

ve %

of

IPO

s

Year Following IPO

Cummulative % of Non-tech IPOs Receiving Bids by Year Following IPO

Non-VC

VC

71

Figure 2.8: Cumulative Total Completed Acquisitions in Years Following IPO, Technology Firms This graph shows the percentage of firms in the IPO sample that were acquired by year following their IPO, segmented on whether the IPO was VC-backed at

the IPO. IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be

based in the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5,

raised less than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17,

2010, where both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate

financials and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the

acquisition. Merging the samples results in 1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. High-

tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

1 2 3 4 5 6 7 8 9 10

Cu

mu

lati

ve %

of

IPO

s

Year Following IPO

Cummulative % of Tech IPOs Acquired by Year Following IPO

Non-VC

VC

72

Figure 2.9: Cumulative Total Completed Acquisitions in Years Following IPO, Non-Technology Firms This graph shows the percentage of firms in the IPO sample that were acquired by year following their IPO, segmented on whether the IPO was VC-backed at

the IPO. IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be

based in the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5,

raised less than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17,

2010, where both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate

financials and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the

acquisition. Merging the samples results in 1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. High-

tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737.

0

0.05

0.1

0.15

0.2

0.25

0.3

1 2 3 4 5 6 7 8 9 10

Cu

mu

lati

ve %

of

IPO

s

Year Following IPO

Cummulative % of Non-Tech IPOs Acquired by Year Following IPO

Non-VC

VC

73

Table 2.3: Venture Capital Backing and Percentage of Firm Sold in IPO VC equals 1 if the firm had venture capital backing at the IPO. High-tech equals 1 if the firm operates in the following SIC codes: 283, 481, 365, 366, 367, 368,

369, 482, 483, 484, 485, 486, 487, 488, 489, 357, or 737. Less than 10% sold in the IPO equals 1 if the firm sold less than 10% of their equity in the IPO. ***,

**, and * denote significance at the 1, 5, and 10 percent levels respectively.

VC-backed Non-VC Backed Difference

Average Percent Sold in IPO 27.82% 37.99% 10.17% ***

Sell Less than 10% 4.30% 2% 2.30% ***

Sell Less than 15% 13.81% 7.21% 6.60% ***

Sell Less than 25% 45.59% 28.10% 17.49% ***

n 2244 2648

High Tech Non Tech

VC-backed Non-VC Backed Difference VC-backed Non-VC Backed Difference

Average Percent Sold in IPO 25.29% 35.20% -9.91% ***

31.81% 38.95% -7.14% ***

Sell Less than 10% 5.68% 3.53% 2.15% **

2.07% 1.52% 0.54%

Sell Less than 15% 17.92% 11.19% 6.72% ***

7.35% 5.84% 1.51%

Sell Less than 25% 54.62% 35.49% 19.13% ***

31.34% 25.55% 5.80% ***

n 1373 679 871 1968

74

Table 2.4a: Venture Capital Backing and Likelihood of an Acquisition Attempt Logistic regression in which the depenent variable equals 1 if the IPO firm is at some point the target of an acquisition attempt. Each regression includes fixed

year effect dummy variables. VC equals 1 if the firm had venture capital backing at the IPO. High-tech equals 1 if the firm operates in the following SIC codes:

283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. Less than 10% sold in the IPO equals 1 if the firm sold less than 10% of their equity

in the IPO. Market Cap is the natural log of the firm’s market cap in 2007 dollars. Leverage is the ratio of the total book value of debt to total assets at the time

of the IPO. Market to book is the ratio of the market cap at the first day close to the book value of equity at the IPO. Property is the ratio of net property, plant,

and equipment to total assets at the time of the IPO. Liquidity is the ratio of current assets minus current liabilities to total assets at the time of the IPO. ***, **,

and * denote significance at the 1, 5, and 10 percent levels respectively.

Model 1 Model 2 Model 3

Coef. Estimate p-value Coef. Estimate p-value Coef. Estimate p-value

VC

0.509 *** <.0001

0.512 *** <.0001

High Tech

0.324 *** <.0001

0.327 *** <.0001

Less than 10% Sold in IPO

-0.464 ** 0.027

Market Cap 0.119 *** 0.000 0.089 *** 0.008

0.113 *** 0.001

Leverage -0.312 * 0.090 -0.295 0.102

-0.309 * 0.088

Market to Book 0.000 0.620 0.000 0.676

0.000 0.663

Property -0.140 0.527 0.069 0.758

0.065 0.773

Liquidity 0.411 *** 0.008 0.029 0.857

0.025 0.875

Intercept -2.248 *** <.0001 -2.084 *** <.0001

-2.360 *** <.0001

Likelihood Ratio Test Statistic 220.71 ***

303.81 ***

308.96 ***

Max Rescaled R2 0.07

0.09

0.09

n 4662

4662

4662

Acquisition Attempts ("1") 1380

1380

1380

No Bids Made ("0") 3282 3282 3282

75

Table 2.4b: Venture Capital Backing and Likelihood of Being Acquired Logistic regression in which the depenent variable equals 1 if the IPO firm is at some point acquired. Each regression includes fixed year effect dummy

variables. VC equals 1 if the firm had venture capital backing at the IPO. High-tech equals 1 if the firm operates in the following SIC codes:

283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. Less than 10% sold in the IPO equals 1 if the firm sold less than 10% of their equity

in the IPO. Market Cap is the natural log of the firm’s market cap in 2007 dollars. Leverage is the ratio of the total book value of debt to total assets at the time

of the IPO. Market to book is the ratio of the market cap at the first day close to the book value of equity at the IPO. Property is the ratio of net property, plant,

and equipment to total assets at the time of the IPO. Liquidity is the ratio of current assets minus current liabilities to total assets at the time of the IPO. ***, **,

and * denote significance at the 1, 5, and 10 percent levels respectively.

Model 1 Model 2 Model 3

Coef. Estimate p-value Coef. Estimate p-value Coef. Estimate p-value

VC

0.513 *** <.0001

0.517 *** <.0001

High Tech

0.337 *** <.0001

0.340 *** <.0001

Less than 10% Sold in IPO

-0.586 *** 0.009

Market Cap 0.114 *** 0.001 0.083 ** 0.019

0.114 *** 0.002

Leverage -0.218 0.244 -0.200

0.274

-0.217 0.237

Market to Book 0.000 0.608 0.000

0.655

0.000 0.643

Property -0.170 0.468 0.050

0.834

0.044 0.854

Liquidity 0.539 *** 0.001 0.150

0.370

0.145 0.385

Intercept -2.555 *** <.0001 -2.385 *** <.0001

-2.732 *** <.0001

Likelihood Ratio Test Statistic 191.146 ***

270.491 ***

277.898 ***

Max Rescaled R2 0.06

0.08

0.09

n 4662

4662

4662

Acquired ("1") 1198

1198

1198

Not Acquired ("0") 3464 3464 3464

76

Table 2.4c: Venture Capital Backing and Likelihood of An Acquisition Attempt Within 3 years of the IPO Logistic regression in which the depenent variable equals 1 if the IPO firm is acquired within 3 years of their respective IPO. Each regression includes fixed year

effect dummy variables. VC equals 1 if the firm had venture capital backing at the IPO. High-tech equals 1 if the firm operates in the following SIC codes:

283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. Less than 10% sold in the IPO equals 1 if the firm sold less than 10% of their equity

in the IPO. Market Cap is the natural log of the firm’s market cap in 2007 dollars. Leverage is the ratio of the total book value of debt to total assets at the time

of the IPO. Market to book is the ratio of the market cap at the first day close to the book value of equity at the IPO. Property is the ratio of net property, plant,

and equipment to total assets at the time of the IPO. Liquidity is the ratio of current assets minus current liabilities to total assets at the time of the IPO. ***, **,

and * denote significance at the 1, 5, and 10 percent levels respectively.

Model 1 Model 2 Model 3

Coef. Estimate p-value Coef. Estimate p-value Coef. Estimate p-value

VC

0.4463 *** <.0001

0.4507 *** <.0001

High Tech

0.0907

0.3912

0.0939 0.375

Less than 10% Sold in IPO

-0.4183 0.1408

Market Cap 0.0689 0.1307 0.0501

0.2852

0.0738 0.1359

Leverage -0.0406 0.8488 -0.0338

0.8701

-0.0446 0.8302

Market to Book -0.00035 0.5927 -0.0003

0.6353

-0.00029 0.6345

Property -0.3783 0.2332 -0.2641

0.4107

-0.2731 0.3953

Liquidity 0.1398 0.5041 -0.1185

0.5825

-0.1232 0.568

Intercept -3.6455 *** <.0001 -3.5587 *** <.0001

-3.8268 *** <.0001

Likelihood Ratio Test Statistic 116.8936 ***

138.3374 *** <.0001

140.6652 ***

Max Rescaled R2 0.05

0.06

0.06

n 4662

4662

4662

Acquired with 3 years ("1") 539

539

539

Not Acquired within 3 years ("0") 4123 4123 4123

77

Table 2.5: IPOs, Venture Capital, and Announcement Returns to Acquisition Targets in IPO Sample IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition.

Merging the samples results in 1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. ***, **, and *

denote significance at the 1, 5, and 10 percent levels respectively.

Market Adjusted Returns CARs on Full Sample (All Bids), n=1404

Equal Weighted Index VC-Backed Non-VC Backed Difference p-value

(-2,+2) 25.77 22.33 3.44** 0.024

(-10,+2) 30.17 26.62 3.55** 0.025

(-40,-10) 3.21 4.90 -1.69 0.188

Value Weighted Index VC-Backed Non-VC Backed Difference p-value

(-2,+2) 25.99 22.53 3.46** 0.024

(-10,+2) 30.8 27.19 3.61** 0.024

(-40,-10) 5.12 6.36 -1.24 0.339

Market Adjusted Returns CARs on Completed Acquisitions, n=1216

Equal Weighted Index VC-Backed Non-VC Backed Difference p-value

(-2,+2) 26.36 23.97 2.39 0.15

(-10,+2) 30.91 28.12 2.79 0.10

(-40,-10) 3.82 5.40 -1.58 0.25

Value Weighted Index VC-Backed Non-VC Backed Difference p-value

(-2,+2) 26.58 24.18 2.40 0.15

(-10,+2) 31.50 28.69 2.82 0.10

(-40,-10) 5.68 6.97 -1.29 0.35

78

Table 2.6a: Acquisition Announcement Returns to Bids on Recent IPOs (VC vs. Non-VC) IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition.

Merging the samples results in 1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. ***, **, and *

denote significance at the 1, 5, and 10 percent levels respectively.

Mean Target CARs in Bids on Recent IPOs

(Equal Weighted Market Adjusted Returns)

Received Bid Within 1 Year of IPO

(n=123)

Received Bid Within 3 Years of IPO

(n=545)

Received Bid Within 5 Years of IPO

(n=828)

VC-Backed Non-VC Difference VC-Backed Non-VC Difference VC-Backed Non-VC Difference

N 73 50 313 232 478 350

(-2,+2) Window 22.54% 20.01% 2.53% 25.08% 22.37% 2.71% 26.13% 22.52% 3.61%*

(-10,+2) Window 28.44% 22.96% 5.48% 30.54% 26.75% 3.80% 31.02% 27.36% 3.66%*

(-40,-10) Window 0.02% -0.50% 0.51% 2.10% 4.37% -2.27% 2.64% 4.78% -2.14%

Mean Target CARs in Bids on Recent IPOs

(Value Weighted Market Adjusted Returns)

Received Bid Within 1 Year of IPO

(n=123)

Received Bid Within 3 Years of IPO

(n=545)

Received Bid Within 5 Years of IPO

(n=828)

VC-Backed Non-VC Difference VC-Backed Non-VC Difference VC-Backed Non-VC Difference

N 73 50 313 232 478 350

(-2,+2) Window 22.53% 20.35% 2.18% 25.23% 22.65% 2.58% 26.29% 22.69% 3.60%*

(-10,+2) Window 29.12% 23.80% 5.32% 31.12% 27.33% 3.80% 31.60% 27.88% 3.72%*

(-40,-10) Window 2.07% 0.25% 1.82% 3.95% 5.67% -1.72% 4.57% 6.17% -1.60%

79

Table 2.6b: Acquisition Announcement Returns to Completed Acquisitions of Recent IPOs (VC vs. Non-VC) IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition.

Merging the samples results in 1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. ***, **, and *

denote significance at the 1, 5, and 10 percent levels respectively.

Mean Target CARs in Completed Acquisitions on Recent IPOs

(Equal Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=116)

Acquired Within 3 Years of IPO

(n=483)

Acquired Within 5 Years of IPO

(n=721)

VC-Backed Non-VC Difference VC-Backed Non-VC Difference VC-Backed Non-VC Difference

N 73 43 277 206 420 301

(-2,+2) Window 22.54% 20.61% 1.94% 25.76% 23.64% 2.12% 27.08% 24.59% 2.49%

(-10,+2) Window 28.44% 23.05% 5.40% 31.25% 27.83% 3.43% 31.93% 29.27% 2.66%

(-40,-10) Window 0.02% 0.79% -0.78% 2.52% 5.38% 2.86% 3.49% 5.44% -1.95%

Mean Target CARs in Completed Acquisitions on Recent IPOs

(Value Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=116)

Acquired Within 3 Years of IPO

(n=483)

Acquired Within 5 Years of IPO

(n=721)

VC-Backed Non-VC Difference VC-Backed Non-VC Difference VC-Backed Non-VC Difference

N 73 43 277 206

420 301

(-2,+2) Window 22.53% 20.99% 1.54% 25.91% 23.94% 1.97% 27.24% 24.79% 2.45%

(-10,+2) Window 29.12% 23.83% 5.29% 31.82% 28.42% 3.40% 32.47% 29.81% 2.66%

(-40,-10) Window 2.07% 1.46% 0.61% 4.22% 6.82% -2.59% 5.29% 6.99% -1.70%

80

Table 2.6c: Acquisition Announcement Returns to Bids on Recent IPOs (Tech vs. Non-Tech) IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials and

utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of 5%

toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. Merging the samples results in

1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. ***, **, and * denote significance at the 1, 5, and

10 percent levels respectively.

Mean Target CARs in Bids on Recent IPOs

(Equal Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=123)

Acquired Within 3 Years of IPO

(n=545)

Acquired Within 5 Years of IPO

(n=828)

Tech Non-Tech Difference Tech Non-Tech Difference Tech Non-Tech Difference

N 67 56 273 272 417 411

(-2,+2) Window 24.46% 17.99% 6.47% 0.2578 0.2206 0.0371 27.01% 22.16% 4.86%**

(-10,+2) Window 30.88% 20.63% 10.25%** 0.3149 0.2636 5.13%* 32.43% 26.47% 5.96%***

(-40,-10) Window 3.37% -4.46% 7.83% 0.0516 0.00966 4.20%* 5.49% 1.57% 3.93%**

Mean Target CARs in Bids on Recent IPOs

(Value Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=123)

Acquired Within 3 Years of IPO

(n=545)

Acquired Within 5 Years of IPO

(n=828)

Tech Non-Tech Difference Tech Non-Tech Difference Tech Non-Tech Difference

N 67 56 273 272 417 411

(-2,+2) Window 24.55% 18.16% 6.39% 26.05% 22.21% 3.84% 27.28% 22.22% 5.06%**

(-10,+2) Window 31.83% 21.13% 10.70%** 32.36% 26.65% 5.71%** 33.29% 26.73% 6.56%***

(-40,-10) Window 5.60% -3.79% 9.39%* 7.22% 2.12% 5.10%** 7.59% 2.86% 4.73%***

81

Table 2.6d: Acquisition Announcement Returns to Completed Acquisitions of Recent IPOs (Tech vs. Non-Tech) IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. Merging the samples results in

1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. ***, **, and * denote significance at the 1, 5, and

10 percent levels respectively.

Mean Target CARs in Completed Acquisitions on Recent IPOs

(Equal Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=116)

Acquired Within 3 Years of IPO

(n=483)

Acquired Within 5 Years of IPO

(n=721)

Tech Non-Tech Difference Tech Non-Tech Difference Tech Non-Tech Difference

N 64 52 245 238 370 351

(-2,+2) Window 24.83% 18.13% 6.69% 27.11% 22.54% 4.57%* 28.27% 23.69% 4.58%**

(-10,+2) Window 31.23% 20.55% 10.68%** 33.04% 26.45% 6.59%** 33.68% 27.81% 5.87%**

(-40,-10) Window 2.75% -2.71% 5.46% 5.62% 1.79% 3.83%* 6.23% 2.27% 3.96%**

Mean Target CARs in Completed Acquisitions on Recent IPOs

(Value Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=116)

Acquired Within 3 Years of IPO

(n=483)

Acquired Within 5 Years of IPO

(n=721)

Tech Non-Tech Difference Tech Non-Tech Difference Tech Non-Tech Difference

N 64 52 245 238 370 351

(-2,+2) Window 24.94% 18.29% 6.65% 27.38% 22.68% 4.70%* 28.54% 23.77% 4.77%**

(-10,+2) Window 32.12% 21.05% 11.07%** 33.90% 26.74% 7.16%** 34.50% 28.04% 6.46%***

(-40,-10) Window 4.97% -2.00% 6.97% 7.65% 2.94% 4.72%** 8.32% 3.55% 4.77%**

82

Table 2.6e: Acquisition Announcement Returns to Completed Acquisitions of Recent IPOs (High Tech Only) IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. Merging the samples results in

1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. ***, **, and * denote significance at the 1, 5, and

10 percent levels respectively.

Mean Target CARs in Completed Acquisitions on High Tech IPOs

(Equal Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=64)

Acquired Within 3 Years of IPO

(n=245)

Acquired Within 5 Years of IPO

(n=370)

VC-Backed Non-VC Difference VC-Backed Non-VC Difference VC-

Backed Non-VC Difference

N 49 15 178 67 265 105

(-2,+2) Window 25.86% 21.43% 4.43% 25.92% 30.26% -4.34% 27.35% 30.60% -3.24%

(-10,+2) Window 32.55% 26.92% 5.63% 32.30% 35.00% -2.71% 32.61% 36.37% -3.75%

(-40,-10) Window 3.76% -0.54% 4.30% 4.50% 8.61% -4.11% 5.84% 7.21% -1.37%

Mean Target CARs in Completed Acquisitions on High Tech IPOs

(Value Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=64)

Acquired Within 3 Years of IPO

(n=245)

Acquired Within 5 Years of IPO

(n=370)

VC-Backed Non-VC Difference VC-Backed Non-VC Difference VC-

Backed Non-VC Difference

N 49 15 178 67 265 105

(-2,+2) Window 25.98% 21.54% 4.44% 26.17% 30.60% -4.43% 27.59% 30.94% -3.35%

(-10,+2) Window 33.45% 27.78% 5.67% 33.02% 36.22% -3.20% 33.31% 37.51% -4.20%

(-40,-10) Window 6.49% 0.00% 6.49% 6.65% 10.33% -3.69% 8.06% 8.96% -0.90%

83

Table 2.6f: Acquisition Announcement Returns to Completed Acquisitions of Recent IPOs (Non-Tech Only) IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. Merging the samples results in

1,404 acquisition announcements of firms in the IPO sample, and 1,216 completed acquisitions of those firms. ***, **, and * denote significance at the 1, 5, and

10 percent levels respectively.

Mean Target CARs in Completed Acquisitions on Non-Tech IPOs

(Equal Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=52)

Acquired Within 3 Years of IPO

(n=238)

Acquired Within 5 Years of IPO

(n=351)

VC-Backed Non-VC Difference VC-Backed Non-VC Difference VC-

Backed Non-VC Difference

N 24 28 99 139 155 196

(-2,+2) Window 15.76% 20.16% -4.40% 25.47% 20.44% 5.03% 26.63% 21.37% 5.25%

(-10,+2) Window 20.06% 20.97% -0.91% 29.38% 24.37% 5.01% 30.76% 25.47% 5.29%

(-40,-10) Window -7.63% 1.51% -9.14% -1.05% 3.82% -4.88% -0.54% 4.49% -5.03%**

Mean Target CARs in Completed Acquisitions on Non-Tech IPOs

(Value Weighted Market Adjusted Returns)

Acquired Within 1 Year of IPO

(n=52)

Acquired Within 3 Years of IPO

(n=238)

Acquired Within 5 Years of IPO

(n=351)

VC-Backed Non-VC Difference VC-Backed Non-VC Difference VC-

Backed Non-VC Difference

N 24 28 99 139 155 196

(-2,+2) Window 15.48% 20.69% -5.21% 25.44% 20.72% 4.71% 26.64% 21.50% 5.14%

(-10,+2) Window 20.28% 21.71% -1.43% 29.66% 24.66% 5.00% 31.03% 25.68% 5.35%

(-40,-10) Window -6.96% 2.25% -9.21% -0.13% 5.12% -5.25% 0.54% 5.93% -5.39%**

84

Table 2.7a: VCs, Board Presence, and Announcement Returns to IPOs Receiving Bids within 3 years of IPO IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. A maximum of 5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. I

hand collect board and ownership data from EDGAR for firms acquired with 3 years of their respective IPO to determine if the lead VC continues to maintain a

board seat from the IPO through the acquisition. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Mean Announcement CARs [-10,+2] Value Weighted Index VC-backed (n=313) Non VC-backed (n=232) Difference

VC On Board (n=177) 35.08% 27.33%

7.75%**

VC Not On Board (n=25) 28.16% 0.83%

Difference 6.92%

VC-backed, But Board Information Unidentifiable (n=111) 25.48%

Mean Announcement CARs [-2,+2] Value Weighted Index VC-backed (n=313) Non VC-backed (n=232) Difference

VC On Board (n=177) 27.89% 22.65%

5.24%*

VC Not On Board (n=25) 28.89% 6.24%

Difference -1.01%

VC-backed, But Board Information Unidentifiable (n=111) 20.17%

Mean Announcement CARs [-10,+2] Equal Weighted Index VC-backed (n=313) Non VC-backed (n=232) Difference

VC On Board (n=177) 34.30% 26.75%

7.55%**

VC Not On Board (n=25) 27.81% 1.06%

Difference 6.49%

VC-backed, But Board Information Unidentifiable (n=111) 25.17%

Mean Announcement CARs [-2,+2] Equal Weighted Index VC-backed (n=313) Non VC-backed (n=232) Difference

VC On Board (n=177) 27.62% 22.37%

5.25%*

VC Not On Board (n=25) 28.82% 6.45%

Difference -1.20%

VC-backed, But Board Information Unidentifiable (n=111) 20.19%

85

Table 2.7b: VCs, Board Presence, and Announcement Returns to IPOs Receiving Bids within 3 years of IPO (High-Tech Only) IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. I hand collect board and

ownership data from EDGAR for firms acquired with 3 years of their respective IPO to determine if the lead VC continues to maintain a board seat from the IPO

through the acquisition. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Mean Announcement CARs [-10,+2] Value Weighted Index VC-backed (n=199) Non VC-backed (n=74) Difference

VC On Board (n=119) 36.41% 34.40%

2.00%

VC Not On Board (n=14) 22.89% -11.51%*

Difference 13.51%**

VC-backed, But Board Information Unidentifiable (n=66) 24.77%

Mean Announcement CARs [-2,+2] Value Weighted Index VC-backed (n=199) Non VC-backed (n=74) Difference

VC On Board (n=119) 28.27% 29.00%

-0.73%

VC Not On Board (n=14) 22.31% -6.69%

Difference 5.96%

VC-backed, But Board Information Unidentifiable (n=66) 19.53%

Mean Announcement CARs [-10,+2] Equal Weighted Index VC-backed (n=199) Non VC-backed (n=74) Difference

VC On Board (n=119) 35.43% 33.16%

2.26%

VC Not On Board (n=14) 22.66% -10.51%

Difference 12.77%**

VC-backed, But Board Information Unidentifiable (n=66) 24.37%

Mean Announcement CARs [-2,+2] Equal Weighted Index VC-backed (n=199) Non VC-backed (n=74) Difference

VC On Board (n=119) 27.81% 28.69%

-0.88%

VC Not On Board (n=14) 22.61% -6.08%

Difference 5.20%

VC-backed, But Board Information Unidentifiable (n=66) 19.52%

86

Table 2.7c: VCs, Board Presence, and Announcement Returns to IPOs Receiving Bids within 3 years of IPO (Non-Tech Only) IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. I hand collect board and

ownership data from EDGAR for firms acquired with 3 years of their respective IPO to determine if the lead VC continues to maintain a board seat from the IPO

through the acquisition. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Mean Announcement CARs [-10,+2] Value Weighted Index VC-backed (n=114) Non VC-backed (n=158) Difference

VC On Board (n=58) 32.37% 24.01%

8.36%

VC Not On Board (n=11) 34.86% 10.85%

Difference -2.49%

VC-backed, But Board Information Unidentifiable (n=45) 26.51%

Mean Announcement CARs [-2,+2] Value Weighted Index VC-backed (n=114) Non VC-backed (n=158) Difference

VC On Board (n=58) 27.10% 19.68%

7.42%

VC Not On Board (n=11) 37.27% 17.59%

Difference -10.17%

VC-backed, But Board Information Unidentifiable (n=45) 21.11%

Mean Announcement CARs [-10,+2] Equal Weighted Index VC-backed (n=114) Non VC-backed (n=158) Difference

VC On Board (n=58) 31.99% 23.74%

8.25%

VC Not On Board (n=11) 34.37% 10.63%

Difference -2.38%

VC-backed, But Board Information Unidentifiable (n=45) 26.33%

Mean Announcement CARs [-2,+2] Equal Weighted Index VC-backed (n=114) Non VC-backed (n=158) Difference

VC On Board (n=58) 27.22% 19.41%

7.81%

VC Not On Board (n=11) 36.73% 17.32%

Difference -9.51%

VC-backed, But Board Information Unidentifiable (n=45) 21.16%

87

Table 2.8a: Target, Acquirer, and Combined Announcement Returns to Bids on Recent IPOs IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. A firm is considered a VC-

backed IPO if they were VC-backed and in my IPO sample. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Mean Announcement CARs [-2,+2] Value Weighted Index

VC-backed IPO Target VC-backed IPO Acquirer N Variable Mean Median

0

0 506

Target

22.37% 17.46%

Acquirer

-1.07% -0.65%

Combined

2.35% 1.70%

1 109

Target

23.30% 18.86%

Acquirer

-1.35% -1.03%

Combined

1.37% 1.56%

1

0 466

Target

28.11% 21.97%

Acquirer

-2.10% -1.23%

Combined

0.97% 0.48%

1 323

Target

22.94% 19.67%

Acquirer

-3.32% -2.67%

Combined -0.38% 0.27%

88

Table 2.9: Summary Statistics of Regression Variables, VC vs. Non-VC IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. I hand collect board and

ownership data from EDGAR for firms acquired with 3 years of their respective IPO to determine if the lead VC continues to maintain a board seat from the IPO

through the acquisition. ***, **, and * denote test for mean differences were significant at the 1, 5, and 10 percent levels respectively.

VC-backed Target Non-VC Backed Target

Variable N Mean Median N Mean Median

Target Market Cap (000s) (3 days prior to bid) 789 666,641 161,079 615 805,231 171,734

Acquirer Market Cap (000s) (3 days prior to bid)*** 789 19,404,092 1,791,790 615 8,945,513 1,302,016

RelativeSize** 789 28.5% 11.3% 615 35.2% 17.3%

Stock (at least some stock used)*** 789 63.5% 1 615 55.1% 1

All Stock*** 789 49.9% 0 615 35.6% 0

All Cash 789 27.9% 0 615 25.5% 0

Tender Offer 789 18.0% 0 615 21.0% 0

Diversifying Acquisition 789 33.3% 0 615 35.9% 0

Target Age (Years from IPO to Bid)** 789 4.9 3.9 615 5.5 4.2

Target Young Firm (<3 years) 789 39.7% 0 615 37.7% 0

VC Still On Board at Bid 203 87.2% 1

% VC Owns Before IPO 179 22.46% 18.90%

% VC Owns After IPO 180 16.73% 15.05%

% VC Owns at Bid 197 11.34% 8.60%

% of VC Stake Held from IPO to Bid 173 65.9% 81.3%

[-2,+2] CAR to Acquirer (Value Weighted)** 789 -2.6% -1.6% 615 -1.1% -0.8%

[-2,+2] CAR to Target (Value Weighted)** 789 26.0% 21.6% 615 22.5% 17.6%

[-2,+2] CAR to Acquirer (Equal Weighted)*** 789 -2.8% -2.0% 615 -1.3% -1.1%

[-2,+2] CAR to Target (Equal Weighted)** 789 25.8% 21.0% 615 22.3% 17.3%

Combined Announcement CAR (Value Weighted)*** 789 0.4% 0.4% 615 2.2% 1.7%

Combined Announcement CAR (Equal Weighted)*** 789 0.2% 0.3% 615 2.0% 1.7%

89

Table 2.10a: Regression Analysis of Target Acquisition Announcement CARs IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. I hand collect board and

ownership data from EDGAR for firms acquired with 3 years of their respective IPO to determine if the lead VC continues to maintain a board seat from the IPO

through the acquisition. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Dependent Variable: [-2,+2] Target CAR (Value Weighted Index)

Coeff. p-value

Coeff. p-value

Coeff. p-value

Coeff. p-value

Intercept 0.436*** <.0001

0.436*** <.0001

0.443*** <.0001

0.899*** <.0001

TGT VC

0.012 0.412

TGT VC YOUNG

0.013 0.475

TGT HIGH TECH

0.016 0.283

0.006 0.744

0.042 0.343

% of Firm VC Owns at Bid

-0.004** 0.046

VC On Board at Bid

-0.060 0.410

Acquirer Tech

0.021 0.298

Acquirer VC Young Firm

-0.017 0.447

Control Variables:

Diversifying Acquisition -0.017 0.2662

-0.012 0.412

-0.011 0.450

-0.045 0.329

Tender Offer 0.103*** <.0001

0.110*** <.0001

0.111*** <.0001

0.074 0.234

All Cash 0.031 0.1237

All Stock -0.024 0.1581

-0.040** 0.012

-0.040** 0.014

-0.046 0.376

ln(Acquirer Market Cap) 0.045*** <.0001

0.045*** <.0001

0.044*** <.0001

0.053*** <.0001

ln(Target Market Cap) -0.069*** <.0001

-0.070*** <.0001

-0.068*** <.0001

-0.103*** <.0001

Relative Size -0.025 0.1458

-0.026 0.134

-0.027 0.113

-0.140** 0.036

Tech Bubble (1998-2000) -0.026* 0.0913

-0.027* 0.078

-0.027* 0.085

-0.027 0.534

N 1404

1404

1404

196

Adj R2 0.1923

0.1919

0.1915

0.3083

90

Table 2.10b: Regression Analysis of Bidder Acquisition Announcement CARs IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. I hand collect board and

ownership data from EDGAR for firms acquired with 3 years of their respective IPO to determine if the lead VC continues to maintain a board seat from the IPO

through the acquisition. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Dependent Variable: [-2,+2] Bidder CAR (Value Weighted Index)

Coeff. p-value

Coeff. p-value

Coeff. p-value

Coeff. p-value

Intercept 0.043* 0.0721

0.051** 0.032

0.050** 0.037

0.167* 0.058

TGT VC

-0.012** 0.046

TGT VC YOUNG

0.003 0.721

TGT TECH

-0.007 0.251

-0.002 0.809

0.000 0.998

% of Firm VC Owns at Bid

0.000 0.751

VC On Board at Bid

-0.048 0.199

Acquirer Tech

-0.012 0.126

Acquirer VC Young Firm

-0.007 0.444

Control Variables:

Diversifying Acquisition 0.012** 0.0494

0.010 0.100

0.009 0.125

0.004 0.866

Tender Offer 0.007 0.42

0.010 0.201

0.009 0.241

-0.022 0.500

All Cash 0.011 0.1985

All Stock -0.019*** 0.0073

-0.019*** 0.003

-0.019*** 0.003

-0.028 0.293

ln(Acquirer Market Cap) 0.005** 0.0276

0.006*** 0.003

0.006*** 0.005

0.006 0.368

ln(Target Market Cap) -0.011*** <.0001

-0.012*** <.0001

-0.012*** <.0001

-0.019** 0.036

Relative Size 0.012* 0.0918

0.012* 0.077

0.012* 0.086

0.018 0.606

Tech Bubble (1998-2000) -0.007 0.2668

-0.008 0.179

-0.007 0.239

0.029 0.197

N 1404

1404

1404

196

Adj R2 0.0376

0.0407

0.0388

0.0074

91

Table 2.10c: Regression Analysis of Combined Acquisition Announcement CARs IPO sample consists of initial public offerings on the NYSE, AMEX, and Nasdaq between January 1, 1985 and December 31, 2009. The firm had to be based in

the United States. I exclude utilities, unit offerings, rights offerings, REITs, and ADRs. I remove IPO firms that had an offer price of less than $5, raised less

than $5 million, or had an unidentifiable PERMNO. The M&A sample consists of transactions announced between January 1, 1985 and May 17, 2010, where

both the acquirer and target must be based in the United States and publicly traded. The deals must have been completed or withdrawn. I eliminate financials

and utilities. High-tech is defined as operating in the following SIC codes: 283,481,365,366,367,368,369,482,483,484,485,486,487,488,489,357,or 737. A maximum of

5% toehold could have existed prior to announcement, and the acquirer must seek to own 100% of the target after the acquisition. I hand collect board and

ownership data from EDGAR for firms acquired with 3 years of their respective IPO to determine if the lead VC continues to maintain a board seat from the IPO

through the acquisition. ***, **, and * denote significance at the 1, 5, and 10 percent levels respectively.

Dependent Variable: [-2,+2] Combined CAR (Value Weighted Index)

Coeff. p-value

Coeff. p-value

Coeff. p-value

Coeff. p-value

Intercept 0.107*** <.0001

0.113*** <.0001

0.115*** <.0001

0.256*** 0.002

TGT VC

-0.009 0.126

TGT VC YOUNG FIRM

0.005 0.488

TGT TECH

-0.008 0.173

-0.004 0.592

-0.009 0.677

% of Firm VC Owns at Bid

-0.001 0.185

VC On Board at Bid

-0.050 0.146

Acquirer Tech

-0.009 0.263

Acquirer VC Young Firm

-0.012 0.143

Control Variables:

Diversifying Acquisition 0.007 0.2075

0.005 0.354

0.005 0.404

-0.005 0.806

Tender Offer 0.016** 0.032

0.019** 0.010

0.018** 0.014

-0.030 0.311

All Cash 0.007 0.3459

All Stock -0.026*** <.0001

-0.025*** <.0001

-0.025*** <.0001

-0.040 0.104

ln(Acquirer Market Cap) -0.007*** 0.0004

-0.006*** 0.003

-0.006*** 0.002

-0.007 0.314

ln(Target Market Cap) 0.001 0.6466

0.000 0.992

0.000 0.916

-0.005 0.554

Relative Size -0.001 0.898

0.000 0.948

-0.001 0.873

-0.052* 0.098

Tech Bubble (1998-2000) -0.004 0.4882

-0.005 0.380

-0.004 0.502

0.024 0.236

N 1404

1404

1404

196

Adj R2 0.0420

0.0447

0.0443

0.0423

92

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VITA

Colin R. Jones

AREAS OF INTEREST

Private Equity, Venture Capital, M&A, IPOs

ACADEMIC EXPERIENCE

University of South Carolina, Moore School of Business 2013-Present

Clinical Assistant Professor of Finance

College of William & Mary, Mason School of Business 2011-2013

Visiting Assistant Professor of Finance

EDUCATION

The Pennsylvania State University, Smeal College of Business

PhD in Finance

2013

College of William & Mary, Mason School of Business

Master of Business Administration

2006

New Mexico State University, College of Business

Bachelor of Business Administration

2002

INDUSTRY EXPERIENCE

Flywheel Ventures

Summer Analyst

2006

Los Alamos National Laboratories

Graduate Research Assistant

2005

Green Garlic Records

Founder

2002 - 2004

New Mexico State University Center for Entrepreneurship

Research Assistant

2002

INVITED PRESENTATIONS

The Information Role of Venture Capitalists: How Venture Capitalists Affect the M&A Decisions of Their

Previous IPOs, FMA Annual Conference (2008), University of South Carolina (2011), College of

William & Mary (2011), University of Denver (2010), East Carolina University (2010)

Discussant for The Corporate Acquisition Policy of Financially Distressed Firms by Dror Parnes, FMA

Annual Conference (October 2008)

HONORS AND AWARDS

Nominated for Ossian R. MacKenzie Teaching Award, Penn State Smeal College of Business, 2010

Doctoral Research Award, Penn State Smeal College of Business, 2008- 2010

Beta Gamma Sigma, William & Mary Mason School of Business, 2006

Portfolio Manager, Batten Student Managed Investment Fund, W&M Mason School of Business, 2006

MBA Community Fellow, William & Mary Mason School of Business, 2005-2006

Crimson Scholar, New Mexico State University, 2000-2002