GHENT UNIVERSITY

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

ACADEMIC YEAR 2014 – 2015

Who benefits most from venture capital investments? Early versus late investors.

Dissertation submitted in fulfilment of the requirements for the degree of Master in Business Economics

Jean Flammang

Under guidance of

Prof. dr. ir. Sophie Manigart

GHENT UNIVERSITY

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

ACADEMIC YEAR 2014 – 2015

Who benefits most from venture capital investments? Early versus late investors.

Dissertation submitted in fulfilment of the requirements for the degree of Master in Business Economics

Jean Flammang

Under guidance of

Prof. dr. ir. Sophie Manigart

I

PERMISSION

The undersigned declares that the content of this master dissertation may be consulted

and/or be reproduced, provided the source is acknowledged.

Jean Flammang

II

DUTCH SUMMARY

In dit onderzoek wordt nagegaan welke venture capital investeerder het meest

profiteert van beursintroducties (IPOs) van biotechnologische bedrijven. Is dit de vroege of

de late venture capitalist (VC)? Om een antwoord te kunnen bieden op deze vraag werden

alle Amerikaanse biotech IPOs van de laatste 10 jaar onderzocht. Een IPO is slechts één van

de uitstapmogelijkheden van de VC en zeker niet de meeste courante, maar het is wel het

enige exit-scenario waarover genoeg gegevens beschikbaar zijn om rendementen te kunnen

bepalen.

In deze studie wordt de invloed van het tijdstip waarop geïnvesteerd wordt

gerelateerd aan de behaalde rendementen van de VC. Volgens de traditionele financiële

theorie zullen vroege investeerders een hoger rendement eisen voor de hoger genomen

risico’s. Volgens de principaal-agenttheorie zullen vroegere investeerders typisch ook een

hoger rendement vragen. De reden hiervoor is dat er in een vroeg stadium meer

informatieasymmetrie is tussen de VC (principaal) en de leiding van het bedrijf (agent)

waarin geïnvesteerd wordt. Naast het tijdstip van de investering worden enkele belangrijke

VC kenmerken onderzocht zoals ervaring en reputatie, die een invloed op het rendement

zouden kunnen hebben. Uit vorig onderzoek blijkt dat ervaren en gereputeerde VCs meer

macht hebben om lagere waarderingen te onderhandelen (Hsu, 2004). Daarnaast zouden

deze VCs meer waarde kunnen creëren (Rosenstein, Bruno & Bygrave, 1993; Sapienza,

Manigart & Vermeir, 1996).

De dataset bestaat uit 402 rendementen die manueel berekend werden uit investeringen in

106 verschillende biotech IPOs. In totaal konden 167 verschillende VCs opgenomen worden

in de dataset. Dit betekent dat een VC uit de dataset in gemiddeld 3.79 biotech bedrijven

heeft geïnvesteerd die tot een beursintroductie zijn overgegaan.

Opvallend tonen de resultaten aan dat late investeerders hogere rendementen

behalen dan vroege investeerders. Deze bevindingen druisen in tegen wat beweerd wordt in

de traditionele financiële theorie en de principaal-agenttheorie. Tevens heeft de ervaring en

reputatie van de VC een positieve invloed op de prestaties, maar ongeacht het tijdstip

waarop de VC investeert. Verder is er een negatief kwadratisch verband tussen het venture

capital syndicatie-niveau en de rendementen. Bovendien zijn de behaalde rendementen

hoger wanneer het biotech bedrijf jonger is.

III

ACKNOWLEDGEMENTS

This master dissertation has been written as a completion of my studies in Business

Economics at Ghent University. I would like to thank a few people who helped, advised, and

guided me through this stimulating journey. First of all, I would like to express my deepest

gratitude to my promotor Prof. dr. ir. Sophie Manigart, who gave me the opportunity to

investigate the subject I desired the most. She was always available to provide clear

guidance and answer my questions. On a second note, I would like thank Thomas

Verschueren, who was very supportive during this academic year and was always available

whenever I needed feedback. On a personal note, I would like to thank my family who has

been a tremendous support throughout my years at Ghent University.

IV

TABLE OF CONTENTS

1. INTRODUCTION .......................................................................................................... 1

2. RESEARCH CONTEXT ................................................................................................... 4

2.1. Venture capital ............................................................................................................ 4

2.2. Exit strategies .............................................................................................................. 4

2.3. Venture capital performance ...................................................................................... 5

2.4. Early versus late stage investments ............................................................................ 6

2.5. Biotechnology sector ................................................................................................... 7

3. THEORETICAL FRAMEWORK ....................................................................................... 8

3.1. Hypotheses .................................................................................................................. 9

3.1.1. Early versus late ............................................................................................................... 9

3.1.2. Venture capitalist experience and reputation .............................................................. 11

4. RESEARCH METHODOLOGY ...................................................................................... 12

4.1. Data collection ........................................................................................................... 12

4.2. Sample description .................................................................................................... 13

4.3. Measures ................................................................................................................... 14

4.3.1. Dependent variables ..................................................................................................... 14

4.3.2. Independent variables ................................................................................................... 14

4.3.3. Control variables............................................................................................................ 17

5. ANALYSIS ................................................................................................................. 21

5.1. Summary statistics ..................................................................................................... 21

5.2. Regression models ..................................................................................................... 23

5.3. Results ........................................................................................................................ 24

5.3.1. Early versus late VCs ...................................................................................................... 27

5.3.2. VC experience/reputation ............................................................................................. 28

5.3.3. Early versus late VCs & VC experience/reputation ....................................................... 29

5.3.4. Control variables............................................................................................................ 31

6. DISCUSSION ............................................................................................................. 34

6.1. Conclusion ................................................................................................................. 34

6.2. Limitations and directions for further research ........................................................ 35

7. REFERENCES ............................................................................................................. VII

8. APPENDICES .............................................................................................................. XI

V

LIST OF ABBREVIATIONS USED

Biotech = Biotechnology

EDGAR = Electronic Data Gathering, Analysis and Retrieval

IPO = Initial Public Offering

IPO company = venture capital-backed biotechnology company that went through an IPO

between 2004 and 2014

IRR = Internal Rate of Return

LBO = Leveraged Buyout

MBI = Management Buy-in

MBO = Management Buyout

MM = Money Multiple

OLS = Ordinary Least Squares

SEC = U.S. Securities and Exchange Commission

US = United States of America

VC = Venture capital; venture capitalist; venture capital firm

VIF = Variance Inflation Factor

VI

LIST OF TABLES AND FIGURES

TABLES

Table 1: Summary all measures ............................................................................................... 20

Table 2: Summary statistics: means, medians, standard deviations, minima and maxima .... 22

Table 3: Regression models C1,1,2,3,4 with IRR as dependent variable ................................. 25

Table 4: Regression models C2,5,6,7,8 with MM as dependent variable ................................ 26

Table 5: Model I1 and I2; interaction early VC late and VC experience/reputation ................ 30

Table 6: Model S1 and S2; VC syndication squared ................................................................. 33

Table 7: Description deleted IPOs ............................................................................................. XI

Table 8: Mean and median returns per vc type ........................................................................ XI

Table 9: Method calculation economic significance (illustration for model 1) ...................... XIII

Table 10: Correlation matrix ................................................................................................... XIV

FIGURES

Figure 1: Economic significance of investment timing estimates in models 1 to 4 (IRR) ........ 27

Figure 2: Economic significance of investment timing estimates in models 5 to 8 (MM) ....... 28

Figure 3: VC experience/reputation distribution per VC ......................................................... XII

Figure 4: VC syndication level distribution per IPO company .................................................. XII

Figure 5: Distribution of the biotech companies operating stage at IPO ............................... XIII

1

1. INTRODUCTION

“If I had invested in that company 10 years ago, I would have been very rich today!”

There is nothing wrong with this sentence. It could be used when someone refers to

companies like Facebook, GoPro, or even Omega Pharma. But does investing early in a

company guarantee high returns? I am sure we all know that the answer is no. Investing is a

risky business, especially in young and technological companies. Venture capitalists

specialize in making these risky investments. Their goal is to spot promising young

companies, invest (large) amounts of capital, provide them with guidance and assistance,

and eventually obtain high returns upon exiting them.

Venture capital is an important source of financing for young companies that cannot

obtain funds through more traditional ways. The risky and illiquid nature of these companies

hinders them to attract bank financing. Young technological companies often lack the

tangible assets required as collateral to obtain debt financing. Moreover, early stage

companies are often not profitable and cannot project stable cash flows. As a result, the

pool of well-resourced capital providers available to young entrepreneurs is limited. For

companies with high growth perspectives, venture capital is often the way to go. When

venture capital firms invest in these firms with high potential, they require a substantial

equity stake and take on a role as active investors. They typically sit on the board of directors

and participate actively in the decision-making process (Sahlman, 1990).

In this master dissertation, a closer look is taken at venture capitalists (VCs) and their

returns. The US is one of the most mature markets for venture capital in the world and will

be the main area of focus. Given the heterogeneity of venture capital markets across

different countries and its implications towards generalization of the results, only the US

venture market will be examined (Manigart et al., 2002).

Software, Biotechnology, Media and Entertainment are the 3 main industries that

received the largest amounts of venture capital financing according to the 2014 PWC

MoneyTree ReportTM,1. “The Software industry maintained its status as the single largest

investment sector for the year, with dollars rising 77% over 2013 to $19.8 billion, which was

invested into 1,799 deals, a 10% rise in volume over the prior year. Biotechnology investment

1 The MoneyTreeTM Report is a quarterly study of venture capital investment activity in the United States made by PriceWaterhouseCoopers in collaboration with the National Venture Capital Association and is based upon data from Thomson Reuters. It is the only industry-endorsed research of its kind.

2

dollars rose 29% while volume decreased 4% in 2014 to $6.0 billion going into 470 deals,

placing it as the second largest investment sector for the year in terms of dollars invested.

The Media and Entertainment sector accounted for the second largest number of deals in

2014 at 481, however it was third largest in terms of dollars invested with an annual total of

$5.7 billion.” (PriceWaterhouseCoopers, 2014)

This paper will focus on the biotechnology industry for 3 main reasons. First, as

indicated above, the biotechnology industry occupies an important place in the venture

capital market. Second, the biotech industry is seen as one of the riskiest industries due to a

long path to deliver market ready products, regulatory difficulties and a technology which is

difficult to understand (Baeyens, Vanacker & Manigart, 2005). Analyzing potential venture

capital performance in these circumstances is even more interesting and difficult. Third,

venture capital performance across all industries has extensively been covered in the past,

but more progress can be made in analyzing the correlation between the timing at which

investments are being made and the returns upon exit.

In this study, the venture capital returns in US biotech IPOs over the last 10 years

(2004-2014) will be examined. This time frame is chosen to incorporate data from before as

well as after the 2008 financial crisis. By doing so, several macroeconomic factors are taken

into account that could have an influence on the results. Previous studies have shown that

exit conditions are highly cyclical and strongly depend on the level of stock markets. (Lerner,

1994; Gompers & Lerner, 1998). This study focuses on IPOs for two main reasons. First, the

so-called Initial Public Offering is seen as the preferred exit strategy for a venture capitalist

(Gompers, 1995), and second, companies have standard disclosure requirements regarding

IPO prospectuses, which implicates that information with respect to share composition is

available via the EDGAR database of the SEC.

Although it is known that VCs do not exit entirely at the IPO date (Bienz & Leite,

2004), the assumption is made that the potential returns at IPO are a good indication of the

returns that could eventually be obtained. The performance of the VCs is assessed by

calculating the internal rate of returns and money multiples at IPO, which will be related to

the timing of the initial investment made by the VC. This examination will enable to answer

the research question of this dissertation: “Who benefits most from VC investments in US

biotechnology companies? Early versus late investors.”

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This study will also delve deeper into some important performance determinants, such as VC

experience and reputation, and will try to relate them to the investment timing.

The remainder of the paper is structured as follows: the next section explores the

research context of this subject. Section 3 focuses on the theoretical framework and the

buildup of the hypotheses. Thereafter, the research methodology will be explained in

section 4 and the analysis will be performed in section 5. Finally, in the last section the

results will be discussed, followed by limitations of this study and some directions for further

research.

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2. RESEARCH CONTEXT

2.1. Venture capital

Next to leveraged buyouts, mezzanine financing and distressed debt investing,

venture capital can be seen as one of the main strategies within “private equity”. The key

component in either case is the private nature of the securities purchased (Anson, 2003). In

venture capital, equity financing is supplied to young companies with high potential growth.

As mentioned before, these companies are in most cases unable to attract capital from

traditional sources, such as banks, because of the high rate of uncertainty and the illiquid

nature of the businesses. By investing in those risky young companies, venture capitalists

hope to obtain a high rate of return (Baeyens et al., 2005). Average annual required rates of

return in the US range from 26% up to 55% depending on the stage in which the VC invests

(Sapienza, Manigart & Vermeir, 1996). By 1988 the typical venture capital fund was

organized as a limited partnership, with the venture capitalists serving as general partners

and the investors, often institutional investors or wealthy individuals, serving as limited

partners. Typically, the general partners provide only a small proportion of the capital raised

by a given fund. In each new fund, the capital is invested in new ventures during the first

three to five years of the fund (Sahlman, 1990). Eventually, VCs will try to find the best way

to exit the portfolio companies. By doing so, they will try to obtain a high rate of return.

2.2. Exit strategies

As VCs raise money via closed-end funds that are normally dissolved after ten years,

the exit decision is highly important (Bienz & Leite, 2008). There are quite some possible exit

strategies, such as IPOs, trade sales or acquisitions, secondary sales or even liquidations.

Although a trade sale is the more universal exit strategy, which is available to many

companies and not only to the most successful ones (Cumming & Macintosh, 2001; Lerner,

1994; Schwienbacher, 2004), this study will only focus on IPOs, for the reasons mentioned in

the introduction. Based on previous literature, these so-called initial public offerings are

considered as the preferred exit strategies for venture capitalists (Gompers, 1995).

Moreover, active stock markets allow venture capitalists to exit more easily while leaving the

entrepreneur in control of the firm (Black & Gilson, 1998). Bienz and Leite (2004) even

suggest that there exists a pecking order of exit channels: IPOs normally yield higher returns

5

than trade sales. They base their premise on empirical evidence that shows IPOs generate

higher returns than trade sales. According to Beinz and Leite’s study, IPOs yield median

returns of 58.39%, whereas trade sales yield medians returns of 18.32%. Gompers (1995)

also stated that IPOs yield the highest returns with an average of 59.20% per year while

acquisitions yield average returns of only 15.4% per year. But higher average returns are not

the only reason why an IPO is seen as the more favored exit scenario. Several authors

explain that VCs wish to generate a reputation for presenting high quality firms to the public

markets (Barry, Muscarella, Peavy, & Vetsuypens, 1990; Fleming, 2004; Lin & Smith 1998).

Lerner (1994) even examined the ability of venture capitalists to time IPOs in the

biotechnology industry by going public when equity values are high. It is clear that IPOs have

been covered extensively in previous literature but adding the important dimension of

investment timing, early versus late, will be a worthy addition to existing literature.

2.3. Venture capital performance

The internal rate of return (IRR) is the most frequently used measure to evaluate the

performance of a venture capital fund. The IRR calculates the rate of return at which cash

flows are discounted so that the net present value amounts to zero. As a consequence, the

calculation requires data on the amounts and dates of the cash-flows that occurred (Burgel,

2000). It should be noted that the existing literature focuses primarily on the IRRs at fund-

level, whereas in this study the IRRs of the individual VC investments will be calculated and

assessed. The money multiple or cash-on-cash multiple is a useful alternative measure that

is not biased by the time held of the investment (e.g. an IRR may be very high if the venture

capitalist makes a return multiple of 1.1 times cost in a few months) (Fleming, 2004). The

money multiple is calculated by dividing the value of shares at IPO by the sum of the

investments made.

The bigger part of the returns is given back to the investors (limited partners). The VC

earns a percentage of the returns generated upon exit, which is called the performance fee

or carried interest. This performance fee being earned on the capital gains induces strong

alignment between the VC and investors (Fleming, 2004). The typical performance fee is 20%

but more experienced or reputed VCs can charge higher fees (Anson, 2003). Apart from

these performance related fees, the VC also receives management fees, which are agreed

upon commitment of the capital by the investors. These management fees are based on a

6

percentage of the funds committed by the limited partners, and typically range between 2%

and 5% annually (Manigart & Meuleman, 2004). They are used to compensate the VC for the

daily managerial costs.

2.4. Early versus late stage investments

In this study, the different stages at which VCs commit initial capital to several

companies will be related to the returns. In general, four main development stages of a

company can be differentiated (Smith & Smith, 2000). The first two stages are the seed stage

and the start-up or early growth stage. In these first stages, the technology often has to be

further developed, the cash flows are typically negative, and the funds are often allocated

towards R&D. The third stage is the expansion phase of the company. At this point some

companies start to generate positive cash flows and/or profits. Large amounts of funding are

required to facilitate the expansion and distribution of the business. In the latest stage, the

company will be able to attract more traditional sources of financing such as debt financing,

or will be able to go to the capital markets to obtain public equity financing.

To a certain extent however, the stages of development in biotech companies are

different compared to companies in other sectors. The technology in biotech is more

complex and the product development typically takes more than a decade (Baeyens et al.,

2005; Lerner, 1994). Moreover, biotech companies require very large amounts of financing

early on (Baeyens et al., 2005). As a consequence, going public is often necessary to satisfy

these high and recurring capital demands. Biotech companies do not necessarily wait until

they have a market-ready product to go public. Lerner (1994) confirms this by saying that

biotech companies remain in the R&D phase until well after going public. Furthermore, he

states that each financing round involves an explicit decision to go public or remain private.

Nonetheless, this study will analyze the time at which VC investments are being made

and try to find a relationship with the potential returns at IPO. VCs decide whether to invest

at an earlier stage or later stage. Some VCs deliberately choose not to invest in early stage

companies, while other VCs do not focus on a particular stage but only specialize in specific

industries. In the US, the distinction is made between financiers of LBOs and MBO/MBIs on

the one hand, which are referred to as private equity firms, and financiers of early stage and

development capital for young entrepreneurial companies on the other hand (Burgel, 2000).

In Europe however, venture capital is synonymous for private equity. For the sake of the

7

comparison of early and late investors in this research, all biotech investments categorized

as “venture capital investments” in the Thomson One VentureXpert database will be

analyzed.

2.5. Biotechnology sector

Biotechnology, as defined by the OECD is “the application of science and technology

to living organisms as well as parts, products and models thereof, to alter living or non-living

materials for the production of knowledge, goods and services" (Pomykalski, Bakalarczyk &

Weiss, 2010). As mentioned in the introduction, the biotechnology industry in the US is one

of the most active sectors for venture capital measured in number of deals and in dollar

amounts in 2014 (PricewaterhouseCoopers, 2014). Gompers (1995) also showed that the

biotechnology industry is one of the four sectors that receive the highest total funding per

firm. Biotech ventures operate in an extremely risky environment because of the long path

to deliver market ready products and the difficulty to understand the technology (Baeyens et

al., 2005). Multiple aspects of venture capital in biotechnology have been covered in

previous literature, but the comparison between early and late VC investments is an area in

which progress can be made.

8

3. THEORETICAL FRAMEWORK

The dynamics between the VC and the company in which the VC invested can be

explained by two main theories; the agency theory and the traditional finance theory. These

two theories will be part of the underlying assumptions in the hypotheses and clearly

indicate the line of reasoning.

According to Sahlman (1990), the best way to analyze the relationship between the

VC and the entrepreneur is through the agency theory. This relationship between the VC

(principal) and the entrepreneur (agent) is characterized by differing interests and high

information asymmetry. The assumption is that the agent is driven by self-interest

(Strömsten & Waluszewski, 2012). The actions an agent may take in his/her personal interest

could destroy value for the venture capital investor (Manigart et al., 2002). As a

consequence, the principal will try to establish control elements to govern the invested

company. Venture capitalists design contracts with entrepreneurs that reduce potential

agency costs (Gompers, 1995; Kaplan & Strömberg, 2004). Moreover, contractual

arrangements guarantee the venture capitalists’ explicit intervention rights and cover

potential exit issues (Cumming & Macintosh, 2003; Gompers, 1996; Kaplan & Strömberg,

2004). But even if clear goals for the company are set, contracts have been negotiated and

interests of both parties are aligned in theory, information asymmetry remains a big concern

for venture capitalists (Gompers, 1995). When business and agency risks are high, VCs will

employ more elaborate governance structures to control and monitor the company’s

management closely (Sapienza, 1996). Close monitoring helps to align interests between the

VC and the company to create shareholder value (Manigart et al., 2002; Fama & Jensen,

1985). Staging of capital infusions is a frequently used method to monitor a company closely

and gives the investor the option to periodically abandon the projects (Gompers, 1995).

Sahlman (1990) claims that staged capital investments are the most potent control

mechanism a venture capitalist can employ. When VCs provide funds in stages, each

financing round is accompanied by a formal review of the firm’s status (lerner, 1994). As a

result, the company’s entrepreneurs and/or managers will be incentivized to reach

predetermined goals.

The agency risks are not the venture capitalists’ only concern. Baeyens et al. (2005)

suggest that the uncertainty risk for both the VC and the entrepreneur plays a more

9

dominant role in biotechnology companies. In perfect capital markets, investors are able to

eliminate part of the total risk. VCs typically invest in 10 to 20 companies per fund (Manigart

& Meuleman, 2004). Therefore, VCs should be able to eliminate the idiosyncratic risk, which

is the company or sector-specific risk, through diversification. The risk related to the overall

market depends on the covariance of its share price with movements in the overall market

and is measured by the beta (Rosenbaum & Pearl, 2013). This systematic risk cannot be

diversified. The existence of market imperfections implies however, that the idiosyncratic

investment risk and other investment characteristics may be as important as the market risk

in determining the required return (Manigart et al., 2002; Rea, 1989). Traditional finance

theory asserts that a higher risk involves a higher required rate of return. As a consequence,

VCs who operate in this highly risky environment will require a substantial rate of return to

compensate for this high risk.

3.1. Hypotheses

3.1.1. Early versus late

A closer look is taken at the positive relationship between the risk of an investment

and the return required by the investor. No two companies have the exact same risk profile,

and a company’s own risk profile will not remain the same during its entire life cycle. Elango,

Fried, Hisrich & Polonchek (1995) state that early stage ventures generally face considerable

management, market, and technological uncertainty. Furthermore, VCs feel that the risk of

loss of their investment is much higher for early stage investments. As information

asymmetries are significant in early stage and high technological companies, these

companies are likely to require close monitoring (Gompers, 1995). Older companies on the

other hand, have a more elaborate track record, which means more information can be

made available to potential investors. VCs obviously want to be compensated for their value-

adding abilities, monitoring and assistance, and hence will require high returns from early

stage companies.

Given the extensive coverage in the existing literature on the risk-return relation for

venture capital investments, some of which is mentioned above, it can be expected that the

returns required by VCs for early investments will be different compared to later

investments. This expectation is in line with findings from Elango et al. (1995) who state that

10

earlier stage investors seek ventures with higher potential returns, whereas later investors

require lower returns. Manigart et al. (2002) also found evidence that early VCs require

higher returns.

In short, the agency theory suggests that the higher the agency costs are, the higher

the required return of the VCs will be. The finance theory indicates that investments with a

higher risk profile require higher returns. Finally, previous literature indicates that earlier

stage investors expect higher returns than later investors. Assuming the risk profile and the

agency costs are higher at an earlier stage, the following hypothesis is suggested:

H1a: Early venture capital investors obtain higher returns than late venture capital

investors.

There are however reasons to believe early investors will not achieve higher returns

than late investors. First and foremost, biotechnology is perceived as one of the riskiest

industries in modern economy (Baeyens et al., 2005). Biotech companies are often

characterized by their long time-to-market and the difficulty of the used technology. More

importantly, it is known that biotech companies remain in the R&D phase until well after

going public (Lerner, 1994). This means that in biotech, an IPO is not necessarily undertaken

at a mature stage of development as is suggested in general venture capital literature. As a

result, VCs who invest later (or closer to IPO) still face very high risks. Given the positive risk-

return relationship, it can be assumed VCs would still have very high return requirements.

A second element involves the high capital requirements of biotech companies. As

explained in previous sections, biotech companies require significant amounts of capital to

fund the long development process. This high capital dependency puts biotech companies in

weaker positions to negotiate a next round of funding. Hence, the relative bargaining power

of VCs who provide financing at a late round could have a serious impact on the potential

returns. Although the relative bargaining power of VCs can vary greatly across different

financing rounds (Koskinen, Rebello, & Wang, 2014), it is known that later investors take the

liquidation rights of current investors as a floor and negotiate rights that are at least as

generous (Klausner & Venuto, 2013).

Given these elements, it is not unrealistic that later investors could obtain returns as

high, if not higher than early investors. Thus, an alternative hypothesis is presented:

11

H1b: Early venture capital investors do not obtain higher returns than late venture capital

investors.

3.1.2. Venture capitalist experience and reputation

As mentioned briefly in the first section, this study will also examine some of the

determinants of VC performance. The VC returns can be influenced by many factors,

including VC characteristics. Therefore, it is important to take into account these

characteristics when analyzing the VC returns. Several characteristics, such as experience or

reputation can impact the value-adding ability of the VC and even increase the likelihood of

exiting successfully (Fleming, 2004; Nahata, 2008). Companies highly value the experience of

a potential investor. Rosenstein, Bruno, Bygrave & Taylor (1993) found that experienced VCs

are perceived to add more value. Having an experienced partner would also send out a

strong quality signal (Stuart, Hoang, & Hybels, 1999). Ozmel, Robinson & Stuart (2013)

confirmed this by saying that biotech companies are more likely to look more favorable

when they receive “the stamp of approval” from experienced insiders.

On the other hand, the experienced VCs themselves will choose the investments with

the greatest potential return and try to minimize the risk by undertaking value-adding

activities (Fleming, 2004). In return for their value-adding activities they will be able to

negotiate favorable valuations (Cumming and Dai, 2010). Sapienza (1992) suggests that

entrepreneurs are willing to trade off valuation for the added value and reputation of the

VC. As a consequence, it is expected that prominent and experienced VCs will be in a

stronger position to negotiate lower valuations. Hsu (2004) found evidence for this, and

claims that entrepreneurs accept lower valuations from more reputable VCs.

As a result, more experienced VCs would achieve better performance (Hochberg,

Ljungqvist & Lu, 2007). Given the positive effects of VC reputation and experience

mentioned above, it is expected that these VCs achieve higher returns. Hence, the following

hypothesis is suggested:

H2: More experienced/reputed venture capital investors obtain higher returns than less

experienced/reputed venture capital investors.

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4. RESEARCH METHODOLOGY

4.1. Data collection

The entire database was computed manually. Two different sources were used to

obtain all necessary information:

1) Thomson One VentureXpert database: investment data tab and IPO data tab

2) EDGAR database from the US Securities and Exchange Commission (SEC)

First, the Thomson One VentureXpert database was used to find all venture capital

investments in US biotech companies that went public over the last 10 years (2004-2014).

The total equity amount invested per date per venture capital fund was extracted along with

other elements that will be used as control variables. Besides the relevant data found in the

investment tab, the IPO tab of the Thomson One database was used for all important

information associated with the IPOs. The following information was retrieved from the IPO

tab: the IPO date of each company, the offer price and the company’s operating stage at

IPO.

Second, the EDGAR database from the US Securities and Exchange Commission (SEC)

was used for the IPO prospectuses. In these prospectuses, the number of shares belonging

to each venture capital shareholder was extracted to calculate the returns of each VC. In the

prospectus, the amount of shares is listed per venture capital firm and not per venture

capital fund. The specific number of shares corresponding to each venture capital fund was

sometimes mentioned in the footnotes of the prospectuses, but only in a small number of

cases did the fund names from the prospectus correspond with the fund names in the

Thomson One database. To be consistent in the entire dataset, the investment data from

different venture capital funds affiliated with the same venture capital firm were taken

together. This is only done if different funds of the same VC firm invested in the same

biotech company. It should be noted that only the amount of shares of the >5%

shareholders are disclosed in the IPO prospectuses. As there was no information available

regarding the companies’ shareholder structure prior to the IPO, venture capital investors

who owned less than 5% at IPO could not be included in the analysis.

13

4.2. Sample description

In total, the Thomson One database recorded 175 venture capital-backed US

biotechnology companies that went public between 2004 and 2014. Of these 175 IPO

companies, only 106 companies could be included in the dataset. Because of errors and

inconsistencies between the two different data sources 69 IPO companies could not be used.

These companies were deleted to avoid research bias. The list of specific reasons why 69

companies were excluded can be consulted in the appendices in Table 7.

Each of the 106 IPO companies was backed by one or more venture capital investors.

Of these VCs, the internal rate of returns and money multiples at IPO were calculated.

However, some of the VCs could not be added to the dataset for the following reasons:

1. Not all equity amounts invested by the VC were disclosed.

2. The number of shares held by the VC was not disclosed in the IPO prospectus. This is due

to the fact that the VC had less than 5% company ownership at IPO or because the VC

exited entirely prior to the IPO.

3. The name of the VC was not disclosed in the Thomson One database, which means the

amount invested by that VC could not be attributed.

The final dataset includes 167 different venture capital investors. These VCs invested

at least in one of the 106 IPOs. From these VCs, 402 unique internal rates of return and

money multiples were calculated. This means that the average VC invested in 3,79 of the

biotech IPO companies. The unit of analysis is set at the level of the returns (N=402). Advent

Venture Partners LLP (VC) for example, invested in two IPO companies: Conatus

Pharmaceuticals and Versartis. The VC obtained an internal rate of return of 22% and a

money multiple of 2.53 from Conatus Pharmaceuticals, and an internal rate of return of

101% and a money multiple of 1.29 from Versartis. Each of these 2 returns is a separate unit

in the dataset.

14

4.3. Measures

4.3.1. Dependent variables

Two distinct performance measures are used to assess the returns of the venture

capital investors. The internal rate of return2 (IRR) and money multiple3 (MM). The IRR is a

frequently used method to calculate the returns of VCs (Bienz & Leite, 2008; Chen, Baierl &

Kaplan, 2002; Cochrane, 2005). It is important however to examine both performance

measures (Fleming, 2004). The MM is a useful alternative measure of VC returns. While the

internal rate of return takes into account the dates at which investments are made, the

money multiple does not. Incorporating investment dates can lead to very high IRRs in cases

when a VC invests shortly before the company goes public. Evidence of this phenomenon is

found in the dataset. For example, Fidelity Investments (VC) invested in Dermira (IPO

company) two months before to IPO and obtained an IRR of 11,207% but a money multiple

of ”only” 1.77. To decrease the probability that such extreme IRRs would drive the results,

the natural logarithm of the IRR was taken as a normalizing transformation. Before

transformation, +1 was added to each IRR to account for negative IRRs. The money multiple

will be used as the alternative measure in all models to assess the robustness of the results.

This measure will also be transformed by calculating the natural logarithm to correct for

positive skewness.

4.3.2. Independent variables

Five key independent variables are used to assess the timing of the VC investments

(4) and the experience and reputation of the VCs (1).

1. VC investment timing variables (H1)

These 4 independent variables incorporate the timing of the initial investment by a VC in an

IPO company. The variables indicate whether an initial venture capital investment is made

2 The IRR calculates the rate of return at which cash flows are discounted so that the net present value

amounts to zero. The calculation requires the amounts and dates of the cash flows that occurred (Burgel,

2000). The IRRs were calculated in Microsoft Excel using the XIRR function.

3 The money multiple is calculated as follows: (IPO offer price (€) * Number of shares held by the VC at IPO) /

(Sum of the investments made by the VC (€)

15

relatively early or late. To be perfectly clear: each of these 4 time indicators will be used

separately in the models. The goal of using 4 different time indicators is to obtain

unambiguous and robust results.

Early versus Late 1: ratio with IPO founding date

The first independent variable is a continuous variable calculated as follows:

The variable compares the date of the initial investment made by VCi with the founding date

of the IPOj company. This time period is than compared with the time period between the

IPO date and the founding date of the IPO company. In other words, in the numerator the

IPO company’s founding date is deducted from the venture capitalist’s first investment date,

in the denominator that founding date is deducted from the IPO date.

Consider the following example: Company BioSolution Inc. is founded on 01/01/2000. VC

Biovest Capital LLC firmly believes BioSolution inc. is a promising new venture and makes an

initial investment on 01/01/2002. On 01/01/2010, BioSolution Inc. files for IPO and is listed

on the NASDAQ.

When the formula is applied: (01/01/2002 – 01/01/2000) / (01/01/2010 – 01/01/2000), it

results in a value of 0.2. Using this formula, a continuous variable between 0 and 1 is

constructed. A value of 0.1 will correspond to a relatively early investment, whereas a value

of 0.9 will correspond to a relatively late investment.

First investment

Biovest Capital LLC

01/01/2002

IPO date

BioSolution Inc

01/01/2010

Founding date

BioSolution Inc

01/01/2000

16

Early versus Late 2: ratio with date first investment ever

The second time variable is very similar. The only difference is the replacement of the

founding date of the IPO company with the date of the first investment received by any VC,

which is not necessarily the VC whose investment timing is being determined. If the specific

VC whose returns are calculated invested in the first round, the numerator will be 0, as the

two dates will be the same.

The reason for including this second similar variable is simple. After observing the data, it

became clear that using the founding date of the IPO company was not always a good

measure to analyze the relative timing of venture capital investments. Some companies in

the dataset only received a first venture capital investment after several years of existence.

ZS Pharma for example, received its first venture capital investment almost 5 years after

founding and went public after 6.5 years. Using the first time variable, we would consider

that Alta Partners, who invested in ZS Pharma in the first round, was a relatively late

investor, with a score of 0.74 (0-1). However, Alta Partners could also be considered as an

early investor as it invested in the first of the 3 rounds of financing that took place before

the IPO. Remember that in biotechnology companies specifically, an IPO is not necessarily a

good indication of the company’s stage in its lifecycle4 (Lerner, 1994). To take into account

this discrepancy this second variable will also be used.

Early versus Late 3: investment time to IPO

The third time variable calculates the difference between the IPO date and the date of the

initial investment by the VC. The natural logarithm of this variable is used to take into

account the time dimension of this variable relative to the time-related internal rate of

return. To facilitate the reasoning process and to enable the comparison with the other

independent variables, this third variable is transformed by multiplying it with -1. A higher

4 Lerner (1994) states that in the biotechnology industry each financing round is accompanied by a formal

review of the firm’s status, and each round involves an explicit decision to go public or remain private

17

number in the two first independent variables indicates a later investment time, whereas a

higher number in the third variable indicates a longer period between the investment and

the IPO, and thus, an earlier investment timing. The transformation permits analysis of the

independent variables in the same direction.

Early versus Late 4: Number of the investment round

The fourth time variable indicates the investment round in which the VC makes an initial

investment in the IPO company. To deal with investment dates that are listed very close to

one another, the assumption was made that all investments within one year are seen as one

single economic investment round. For example, a company that received funds on

01/04/2000, 05/05/2003, 06/07/2003 and 02/12/2006 had 3 economic investment rounds.

2. VC reputation-experience variable (H2)

The last independent variable measures the level of experience and reputation of the VC.

This variable is calculated as the number of biotech IPOs in which the VC participated in the

10 years prior to the IPO company’s IPO date. This measure incorporates both industry-

experience and reputation. As companies funded by more experienced VCs are more likely

to go public (Sorensen, 2007), and companies backed by more reputable VCs will access

public markets faster and are more likely to exit successfully (Nahata, 2008), the number of

prior biotech IPOs in which a VC invested is a good indication of its reputation and

experience.

4.3.3. Control variables

Next to the independent variables, other factors could also influence the venture

capital returns. To take these factors into account, several control variables are included.

These control variables can be divided in two categories: VC control variables and IPO

company control variables.

18

VC control variables

VC size is the first important variable to control for. Elango et al. (1995) state that

large venture capital firms invest over half their funds in late stage investments, but still

remain an important source of early stage financing. Smaller firms would focus more on the

earlier stages, as smaller amounts of funding are required at an earlier stage (Gompers,

1995). Kaplan & Schoar (2005) found a positive but concave relationship between venture

capital fund size and performance. Hochberg et al. (2007) even found that a company’s

survival rate is positively related to the size of the leading venture capital investor. Given the

impact VC size can have on the performance, it is controlled for by taking the natural

logarithm of the total equity amount invested in all companies by the VC. The second control

variable is VC age, measured as the difference between the year of the VC’s initial

investment in the specific IPO company and the founding year of the VC.

The effects of VC syndication are also incorporated as a control variable. Dimov and

Milanov (2009) found that more than 73% of VC investments in the US are syndicated.

Several authors claim syndication has a positive influence on the company and VC

performance (Giot & Schwienbacher, 2005; Hege, Palomino & Schwienbacher, 2003;

Hohberg et al., 2007). Ozmel et al. (2012) even predicted that biotech start-up companies

who attract funding from VCs with central positions in the VC syndicate network are more

likely to undergo an IPO or trade sale. The syndication variable is measured as the total

number of distinct VCs who invested in the IPO company divided by the total number of

investment rounds in the IPO company. A higher number means that the average number of

distinct VCs involved per investment round is higher, and thus indicates a higher level of

syndication.

The fourth control variable deals with the possible effect of the recent crisis on VC

performance. Lerner (1994) discusses the ability of VCs to time IPOs when markets are high

in order to obtain higher returns. Furthermore, Lerner (1994) has shown that the exit

climate is highly cyclical and depends on the state of stock markets. More recently, Lazonick

& Tulum (2011) discussed the impact of financial crisis of 2008 on the biotechnology

industry. As the 2008 financial crisis lies in the middle of the time frame, a dummy variable is

used to incorporate the economic climate effects pre and post crisis.

The fifth control variable is the VC type. Manigart et al. (2002) suggested that

different types of VCs could have different incentives and thus different required returns. To

19

account for the various types of VCs, dummy variables are included. The Thomson One

VentureXpert database distinguishes 6 different VC types: independent VC firms, corporate

VCs, pension/endowment/foundation funds, banks, governments and investment

management firms. In the sample, several categories are underrepresented. The bank

(n=11), government (n=1), pension/endowment/foundation (n=1), and investment

management firm (n=2) categories are taken together in the new category “other VC”. The

resulting categories are: independent VCs (n=132), corporate VCs (n=20) and other VCs

(n=15). Two dummy variables are included to indicate whether the investment is made by a

corporate VC or other VC type.

IPO company control variables

The following IPO company characteristics will be included as control variables: size,

age at exit and operating stage. IPO company size, measured as the natural logarithm of the

total funds received, is included as a control variable based on the premise that companies

of different sizes will have different public offering expectations. The age of the IPO

company at exit is also added as a control variable and is measured by the difference

between the company’s IPO date and founding date. Giot & Schwienbacher (2005) found

that biotech and internet companies have the fastest IPO exits. More interestingly however,

they found that: “as time flows, biotech companies first inhibit an increased likelihood of

exciting to an IPO, but after having reached a plateau, the probability of an IPO exit

decreases”. This suggests that the best IPO candidates tend to be selected relatively quickly

(Giot & Schwienbacher, 2005). Although this paper only focusses on companies that actually

did go through a public offering, the effectiveness of the IPO with respect to the achieved

offer price and target amount raised could still be jeopardized if the company does not

achieve a public listing fast enough.

Lastly, the company’s operating stage at IPO is included as a control variable. VCs

returns can be affected by the operational stage of the company at IPO. Lerner (1994) claims

that biotechnology companies are often still in the R&D phase after IPO, but early signs of

commercial viability or even profitability could definitely affect the exit performance in a

positive way. The operating stage is obtained directly from the Thomson One database. The

4 defined operating stages will be coded from 1 to 4: Beta (1), clinical trials (2), shipping

product or providing services (3) and profitable (4).

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Table 1: Summary all measures

SUMMARY DESCRIPTION MEASURES

Dependent variables Description - Calculation

Internal Rate of Return (LN) Rate of return at which cash flows are discounted so that the net present value amounts to zero

Money multiple (LN) ((Number of share held by VC at IPO * Share Price (€)) / (Sum of Investments made (€))

Independent variables

Early VS Late 1 (Date first investment by VCi in IPOj - Founding date IPOj) / (Filing date IPOj - Founding date IPOj)

Early VS Late 2 (Date first investment by VCi in IPOj - Date first investment by any VC in IPOj) /

(Filing date IPOj - Date first investment by any VC in IPOj)

Early VS Late 3 (LN)*-1 Filing date IPOj - Date first investment by VCi in IPOj

Early VS Late 4 Number of the investment round at which the first investment by VCi is made in IPOj

VC experience/reputation Number of biotech IPO companies in which the VC invested in the last 10 years

Control variables

VC size (LN) Total equity invested in all companies (million €)

VC age Year first investment in IPOj - Founding year VC

VC syndication Total number of investment rounds in IPO company / Total number of distinct VCs IPO company

Corporate VC type (dummy) 1 = Corporate VC type, 0 = Independent VC type or Other VC type

Other VC Type (dummy) 1 = Other VC type, 0 = Independent VC type or Corporate VC type

Total amount invested by VC (LN) Total amount (million €) invested by VC in IPO company

VC crisis (dummy) 1 = Post-crisis (>2008), 0 = Pre-crisis

IPO company size (LN) Total funds (million €) received by all VC investors

IPO company age IPO date - Founding date IPO company

IPO company operating stage Beta = 1, Clinical trials = 2, Shipping product or Providing services = 3, Profitable = 4

21

5. ANALYSIS

5.1. Summary statistics

An overview of the means, medians, standard deviations, minima and maxima of all

variables is reported in Table 2. No transformation (natural logarithm) of the variables was

computed for this overview to facilitate interpretation of the summary statistics. The

correlation matrix can be consulted in the appendices (Table 10). The matrix shows a

significant positive correlation (0.56) between the two dependent variables IRR and MM,

which was expected. This is consistent with previous findings (Fleming, 2004). Correlations

between independent variables and control variables are mostly low. VC size and VC

experience/reputation are moderately correlated, which is expected since bigger VCs tend to

have more experience. This is based on the fact that VCs ability to raise new and bigger

funds depends on their track record and past performance (Lerner, 1994). Besides the

Pearson correlation coefficients, variance inflation factor (VIF) values were analyzed to

assess potential multicollinearity issues, but all values remain below 5 and hence do not

indicate problematic multicollinearity.

The average obtained IRR in the sample is 175%, which is much higher than median

IRR of 18%. The data shows some extreme observations on the upside IRRs with a maximum

of 20,215% and the upper 1 percent IRRs all above 3,160% annualized returns. As explained

before in the measures section, the natural logarithm of the IRR was calculated as a

normalizing transformation, to decrease the probability that such extreme observations

would drive the results. The same transformation is done for the money multiple variable,

which also displays some extreme results (maximum=56.11).

Interestingly, a significant part of the IRRs are negative and money multiples below 1,

which implies VCs obtained negative returns. So even if public offerings are only available for

the most successful companies (Lerner, 1994; Cumming & Macintosh, 2001; Schwienbacher,

2004), an IPO exit does not guarantee extraordinary returns for the VCs. According to the

data, 88 of the 402 observations have money multiples below 1. This means in

approximately 22% of the cases, the returns at IPO (offer price * number of shares held by

the VC) do not cover the aggregate amount invested.

22

Table 2: Summary statistics: means, medians, standard deviations, minima and maxima

DESCRIPTIVE STATISTICS

Dependent variables N Mean Median Std. Dev. Min Max

Internal Rate of Return 402 1.75 0.18 12.34 -0.46 202.15

Money multiple 402 2.58 1.65 4.29 0.19 56.21

Independent variables

Early VS Late 1 402 0.39 0.35 0.31 0.00 0.98

Early VS Late 2 402 0.28 0.14 0.32 0.00 0.98

Early VS Late 3 402 5.15 4.84 3.17 0.12 14.65

Early VS Late 4 402 2.28 2.00 1.65 1.00 8.00

VC experience/reputation 402 4.55 4.00 3.56 0.00 14.00

Control variables

VC size (in million €) 402 1,528.66 887.04 2,896.06 5.08 36,982.90

VC age 402 16.45 13.00 11.10 0.005 47.00

VC syndication 402 2.87 2.50 1.52 0.67 9.00

Corporate VC type (dummy) 402 0.09 0.00 0.29 0.00 1.00

Other VC Type (dummy) 402 0.06 0.00 0.25 0.00 1.00

VC crisis (dummy) 402 0.43 0.00 0.50 0.00 1.00

IPO company size (in million €) 402 107.82 97.67 50.75 5.90 270.40

IPO company age 402 8.97 8.10 4.30 1.20 25.34

IPO company operating stage 402 2.49 2.00 0.55 1.00 4.00

The first investment timing variable (Early VS Late1) can be interpreted as follows:

the mean of 0.39 indicates that the average VC made an initial investment 3.90 years after a

company’s inception in a company that went public after 10 years (3.90/10=0.39). In a

company that went public after 5 years, this mean indicates that the average VC had

invested 1.95 years after the company was founded (1.95/5=0.39). In a company that goes

public after 20 years, the average VC invested after 7.80 years (7.80/20=0.39). The minimum

of 0 implies that the VC investment is made at the company’s founding date. The second

investment timing variable can be interpreted similarly. The third independent variable

reflects the number of years before the IPO in which the VC made its first investment in the

biotech company. Hence, the average first VC investment is made 5.15 years prior to IPO.

The fourth variable measures the economic investment round in which VCs make their initial

investment in a biotech company. The average VCs invest in the second or third economic

investment round.

5 The age of the VC is measured as the difference between the year in which the VC made the initial investment

in the IPO company and its founding year. Thus, an age of 0 is possible, when the year in which the initial

investment is made, is the same year is the VC founding year.

23

As explained in the measures section, the level of experience/reputation is measured

as the number of biotech IPOs in which the VC invested in the 10 years before the IPO date

of the biotech company. In Table 2 can be seen that the average VC invested in 4.55 biotech

companies that went public before. The table further indicates that there are some VCs with

no biotech IPO-experience at all (min=0). The dataset (not included) shows that 5 VCs

haven’t been involved in a biotech IPO before and 101 VCs did participate in exactly 1

biotech IPO before. Figure 3 (appendices) gives an overview of the distribution of the

experience and reputation level in the dataset. In 51% of the cases, the VC has invested in

more than 3 biotech companies that went public. Moreover, in almost 9% of the times, the

VC has built up experience through investments in more than 10 biotech companies that

went public. It will be interesting to see whether VCs can leverage this reputation obtained

into higher returns.

Table 8 (appendices) shows the means and medians of the returns per VC type,

because the means, medians, maxima and minima of these dummy variables are not

relevant for interpretation. Next, The VC syndication variable shows that on average the IPO

company has between 2 and 3 distinct VCs per economic investment round. This does not

necessarily mean VCs are distributed evenly across the investment rounds, because staged

financing is frequently applied. This entails that VCs do not invest the total amount in one

shot, but spread the total amount of funding across several rounds of financing. An overview

of the average syndication level per IPO company is given in Figure 4 (appendices).

At last, the distribution of the IPO companies’ operating stage is illustrated in figure 5

(appendices). Exactly half of the IPO companies in the dataset were still in the beta stage or

in the clinical trial stage. This confirms what Lerner (1994) already suggested earlier, that

many biotech companies are still in the R&D phase at IPO.

5.2. Regression models

To test the hypotheses built up in section 2, 8 models are constructed using the OLS

method. The IRR (LN+1) is used as dependent variable in the first 4 models and the MM (LN)

is used as dependent variable in the last 4 models. The 4 independent investment timing

variables are included one at a time with each of the dependent variables. Moreover, all

control variables discussed in section 3.4 are included to control for other factors that may

influence the obtained VC returns. Additionally, a robust variance estimator is used to

24

correct for clustered VC returns within the same IPO company, as VC returns of the same IPO

company were found to be similar in several cases.

LN(IRRij+1) = α + β1 (Early VS Lateij 1,2,3 or 4) + β2 (VCi experience/reputation) + β3 LN(VCi

size) + β4 (VCi age) + β5 (VC syndication) + β6 (Corporate VC type) + β7 (Other VC type) + β8

(VCi crisis) + β9 LN(IPOj company size) + β10 (IPOj company age) + β11 (IPOj company

operating stage) + ε

LN(MMij+1) = α + β1 (Early VS Lateij 1,2,3 or 4) + β2 (VCi experience/reputation) + β3 LN(VCi

size) + β4 (VCi age) + β5 (VC syndication) + β6 (Corporate VC type) + β7 (Other VC type) + β8

(VCi crisis) + β9 LN(IPOj company size) + β10 (IPOj company age) + β11 (IPOj company

operating stage) + ε

5.3. Results

Table 3 presents the estimates of the models when the internal rate of return is used

as dependent variable, Table 4 shows the estimates with the money multiple as the

dependent variable. Model C1 and C2 from Table 3 and 4 respectively do not include the

investment timing variables. These models are added to observe the change in the R-

squared when the investment timing variables are included. Although, the level of the R-

squared does not guarantee that the estimated regression line is a good fit, this coefficient

of determination will indicate which proportionate amount of variation in the response

variable is explained by the investment timing variables. The models with the investment

timing variables (models 1 to 8) provide a bigger explanatory value than models C1 and C2.

The increase in the R-squared is especially high in the models 2 and 3 with the IRR as a

dependent variable, reaching values of 0.33 and 0.47 compared to 0.10 in model C1. The

models with the money multiple as dependent variable have less variance explanatory

power, and the increase in the R-squared when including the time variables is also less

significant than in the first 4 models. The adjusted R-squared, which corrects for the number

of independent variables, is also included and attains similar values.

25

Table 3: Regression models C1,1,2,3,4 with IRR as dependent variable

Model C1 Model 1 Model 2 Model 3 Model 4

Dependent variable IRR IRR IRR IRR IRR

Independent variables

Early VS Late 1 0.90***

(0.18)

Early VS Late 2 1.08***

(0.18)

Early VC Late 3 0.57***

(0.09)

Early VS Late 4 0.14***

(0.03)

VC experience/reputation 0.03* 0.02* 0.01 0.01 0.02*

(0.01) (0.01) (0.01) (0.01) (0.01)

Control variables

VC size -0.09* -0.05 -0.02 -0.00 -0.04

(0.04) (0.03) (0.03) (0.02) (0.03)

VC age 0.01* 0.01 0.00 0.00 0.01

(0.00) (0.00) (0.00) (0.00) (0.00)

VC syndication 0.07 0.06 0.07* 0.02 0.10*

(0.04) (0.03) (0.03) (0.03) (0.04)

Corporate VC type 0.04 -0.09 -0.11 -0.09 -0.07

(0.10) (0.11) (0.09) (0.09) (0.09)

Other VC type -0.00 -0.15 -0.16 -0.22 -0.06

(0.20) (0.19) (0.17) (0.15) (0.19)

VC crisis 0.08 0.09 0.09 0.07 0.08

(0.08) (0.07) (0.06) (0.06) (0.07)

IPO company size -0.08 -0.02 -0.08 -0.00 -0.15

(0.11) (0.10) (0.10) (0.08) (0.10)

IPO company age -0.03** -0.04*** -0.04*** 0.01 -0.04***

(0.01) (0.01) (0.01) (0.01) (0.01)

IPO company operating stage -0.01 -0.03 -0.03 -0.00 -0.06

(0.08) (0.07) (0.07) (0.06) (0.08)

Constant 1.04* 0.43 0.66 0.96* 0.98

(0.55) (0.45) (0.45) (0.41) (0.54)

Number of observations 402 402 402 402 402

F-statistic 3.53 7.18 10.61 13.35 5.79

Prob > F 0.000 0.000 0.000 0.000 0.000

R-squared 0.10 0.24 0.33 0.47 0.18

Adjusted R-squared 0.08 0.22 0.31 0.46 0.16

Levels of significance: *p < 0.05; **p < 0.01; ***p < 0.001 Robust standard errors between parentheses.

26

Table 4: Regression models C2,5,6,7,8 with MM as dependent variable

Model C2 Model 5 Model 6 Model 7 Model 8

Dependent variable MM MM MM MM MM

Independent variables

Early VS Late 1 0.51***

(0.13)

Early VS Late 2 0.71***

(0.15)

Early VC Late 3 0.26***

(0.06)

Early VS Late 4 0.12***

(0.03)

VC experience/reputation -0.00 -0.01 -0.01 -0.01 -0.01

(0.01) (0.01) (0.01) (0.01) (0.01)

Control variables

VC size -0.03 -0.01 0.01 0.00 0.01

(0.04) (0.03) (0.03) (0.03) (0.04)

VC age 0.00 -0.01 -0.00 -0.00 -0.00

(0.00) (0.00) (0.00) (0.00) (0.00)

VC syndication 0.04 0.03 0.04 0.01 0.06

(0.03) (0.03) (0.03) (0.03) (0.03)

Corporate VC type 0.24 0.18 0.14 0.18 0.14

(0.16) (0.17) (0.15) (0.16) (0.15)

Other VC type -0.30* -0.38* -0.40** -0.40** -0.35*

(0.14) (0.15) (0.14) (0.14) (0.14)

VC crisis 0.13 0.14 0.14 0.13 0.14

(0.08) (0.08) (0.08) (0.08) (0.08)

IPO company size -0.23* -0.19 -0.23* -0.20* -0.29**

(0.10) (0.10) (0.10) (0.10) (0.10)

IPO company age -0.04* -0.05** -0.05** -0.02 -0.05**

(0.02) (0.02) (0.01) (0.02) (0.02)

IPO company operating stage 0.03 0.01 0.01 0.03 -0.02

(0.10) (0.10) (0.09) (0.09) (0.10)

Constant 1.96*** 1.62** 1.72** 1.93*** 1.90**

(0.53) (0.52) (0.51) (0.52) (0.54)

Number of observations 402 402 402 402 402

F-statistic 3.36 4.40 5.72 5.57 4

Prob > F 0.001 0.000 0.000 0.000 0.000

R-squared 0.11 0.14 0.18 0.16 0.16

Adjusted R-squared 0.08 0.12 0.16 0.14 0.14

Levels of significance: *p < 0.05; **p < 0.01; ***p < 0.001 Robust standard errors between parentheses.

27

5.3.1. Early versus late VCs

The coefficients of the independent investment timing variables (Early VS Late 1 to 4)

are positive and significant in all 8 models at the 0.001 significance level. This positive

relationship implies that late VCs higher returns than early VCs. Surprisingly, this result

contradicts the predictions made following the agency theory and the traditional finance

theory. As a result, Hypothesis 1a, which states that early VCs obtain higher returns than late

VCs, is rejected. Hence, Hypothesis 1b is supported.

An illustration of the economic significance of each investment timing variable (Early

VS Late 1 to 4) is presented in Figure 1 and Figure 2. In the graphs, the expected values of

the returns (IRR and MM) are simulated by changing the time variable, while all other

variables are held constant at their mean level. The specific method used is illustrated in

Table 9 (appendices).

Figure 1: Economic significance of investment timing estimates in models 1 to 4 (IRR)

28

Figure 2: Economic significance of investment timing estimates in models 5 to 8 (MM)

5.3.2. VC experience/reputation

Evidence is found showing that VC experience/reputation is positively related to VC

returns. In model C1 of Table 3, the coefficient of the VC experience/reputation is positive

and significant (β = 0.03, p < 0.05). This model does not incorporate the investment timing

variables however. When the timing variables are included, model 2 and 3 do not

demonstrate significant coefficients with respect to the VC experience/reputation, but

model 1 and 4 do show positive and significant coefficients (β = 0.02, p < 0.05) and (β = 0.02,

p < 0.05) for the VC experience/reputation. Interestingly however, this positive and

significant relationship is not found when the money multiple is used as a dependent

variable. Even though not all models confirm the positive significant effects, it can still be

assumed a certain level of experience will have a positive influence on the VC returns.

29

5.3.3. Early versus late VCs & VC experience/reputation

To further analyze the impact of VC experience/reputation on the internal rate of

return, an interaction term is included in the models where the effect of VC

experience/reputation was positive and significant (model 1 and 4 from Table 3). The goal is

to examine whether VC experience/reputation has a stronger or weaker influence

depending on the timing of the initial VC investment, early versus late. This leads to a new

regression equation:

LN(IRRij+1) = α + β1 (Early VS Lateij 1 or 4* VCi experience/reputation) + β2 (Early VS Lateij 1

or 4) + β3 (VCi experience/reputation) + β4 LN(VCi size) + β5 (VCi age) + β6 (VC syndication) +

β7 (Corporate VC type) + β8 (Other VC type) + β9 (VCi crisis) + β10 LN(IPOj company size) + β11

(IPOj company age) + β12 (IPOj company operating stage) + ε

The estimates of the models with the interaction term are presented in Table 5 on

the next page. Interaction term Early VS Late 1*VC experience/reputation is tested in model

I1, while model I2 tests interaction term Early VS Late 4*VC experience/reputation. The

interaction terms in both models are not significant at the 0.05 significance level (β = 0.08; p

= 0.188 and β = 0.01; p = 0.129 respectively). Moreover, in model I2 (Table 5) the beta

coefficient of the VC experience/reputation-variable is not significant once the interaction

term is included. The investment timing variables still remain positive and significant in both

models (β = 0.61; p < 0.05 in model I1 and β = 0.09; p < 0.01 in model I2). As a consequence,

it can be concluded that VCs who invest later obtain higher returns, irrespective of their level

of experience or reputation.

30

Table 5: Model I1 and I2; interaction Early VS Late and VC experience/reputation

Model I1 Model I2

Dependent variable IRR Dependent variable IRR

Independent variables Independent variables

Early VS Late 1 * VC exp/rep 0.08 Early VS Late 4 * VC exp/rep 0.01

(0.06) (0.01)

Early VC Late 1 0.61* Early VC Late 4 0.09**

(0.28) (0.03)

VC experience/reputation 0.02* VC experience/reputation 0.00

(0.01) (0.01)

Control variables Control variables

VC size -0.03 VC size -0.03

(0.03) (0.03)

VC age 0.01 VC age 0.01

(0.00) (0.00)

VC syndication 0.07* VC syndication 0.10*

(0.04) (0.04)

Corporate VC type -0.09 Corporate VC type -0.06

(0.12) (0.08)

Other VC type -0.13 Other VC type -0.05

(0.19) (0.19)

VC crisis 0.09 VC crisis 0.09

(0.07) (0.07)

IPO company size -0.05 IPO company size -0.15

(0.08) (0.10)

IPO company age -0.04*** IPO company age -0.04***

(0.01) (0.01)

IPO company operating stage -0.04 IPO company operating stage -0.06

(0.07) (0.08)

Constant 0.58 Constant 1.05*

(0.42) (0.53)

Number of observations 402 Number of observations 402

F-statistic 6.69 F-statistic 5.35

Prob > F 0.000 Prob > F 0.000

R-squared 0.25 R-squared 0.19

Adjusted R-squared 0.22 Adjusted R-squared 0.17

Levels of significance: *p < 0.05; **p < 0.01; ***p < 0.001

Robust standard errors between parentheses.

31

5.3.4. Control variables

The estimates of the Other VC type category are negative and significant across all models

where the money multiple is used as the dependent variable (Table 4). However, no

significant effects are found for the Other VC type category in the models using the IRR as

the dependent variable (Table 3). Hence, the Other VC type category which includes banks,

foundation/endowment/pension funds, governments and investment management firms

achieves lower money multiples than independent VCs. No significant estimates are found

with respect to the Corporate VCs.

VC size and VC age are only significant in model C1 (Table 3). Surprisingly, the size of

the VC in model C1 is negatively related to the IRR (β = -0.09; p < 0.05). VC age on the other

hand is positively related to the IRR (β = -0.09; p < 0.05). This suggests that older VCs will

obtain higher internal rate of returns. However, when the investment timing variables are

included, no significant estimates remain. The VC crisis dummy does not show any significant

results as well, despite the affirmations of Lazonick & Tulum (2011).

VC syndication, measured as the average number of distinct VCs per investment

round, is positive and significant in model 2 (β = 0.07; p < 0.05) as well as in model 4 (β =

0.10; p < 0.05) from Table 3. This result suggests that a higher syndication level results in

higher internal rates of return at IPO. Intuitively though, one could argue this relationship

will not be positive for very high values. Several authors found that VC syndication has a

positive influence on performance (Hohberg, Ljungqvist & Lu, 2007; Giot & Schwienbacher,

2005; Hege, Palonmino & Schwienbacher, 2003). Nonetheless, a very high level of

syndication could be counterproductive. The more VCs are involved, the more it will be

difficult to align the different interests and the higher the probability of arising conflicts

could be. Moreover, the more parties are involved, the more people have to agree when

decisions have to be made. To test this suspicion, the squared syndication variable is

included in the regression models next to the original syndication variable. The adapted

regression equations are presented on the next page. The estimates of the regression

models are presented in Table 6. Interestingly, model S1 and S2 from Table 6 show that the

original syndication variables have more explanatory power than in the original models6. The

6 The models in which the original VC syndication variable is significant are model 2 and model 4 from Table 3.

32

coefficients confirm the positive influence of syndication on the VC returns. The syndication

coefficients are higher and significant at a higher level (β = 0.29 ; p < 0.01 in model S1 and β

= 0.40 ; p < 0.001 in model S2). Furthermore, as intuitively expected, the VC syndication level

squared is significant and negative. This suggests that there is a negative quadratic (or

inverse U) relationship between the level of VC syndication and the IRR. In other words, a

higher syndication level is beneficial to a certain extent. A level of syndication above 57,

meaning more than 5 distinct VCs per economic investment round involved will influence

the returns negatively, all else held constant. 10 of the 106 IPO companies in the dataset

have a syndication level above 5.

LN(IRRij+1) = α + β1 (Early VS Lateij 2 or 4) + β2 (VCi experience/reputation) + β3 LN(VCi size) +

β4 (VCi age) + β5 (VC syndication) + β6 (VC syndication^2) + β7 (Corporate VC type) + β8

(Other VC type) + β9 (VCi crisis) + β10 LN(IPOj company size) + β11 (IPOj company age) + β12

(IPOj company operating stage) + ε

Finally, there are some interesting results regarding the IPO company control

variables, seen in Table 3 and 4, as well as Table 5 and 6. The operating stage of the

company at IPO does not appear to have an influence on the obtained VC returns. Further,

the company size appears to have a negative and significant effect in some of the regression

models. The negative effect is only significant in the models where the money multiple acts

as the dependent variable. The most powerful effect though is the negative relationship

between the age of the IPO company and the returns. This indicates that obtained VC

returns are higher if the biotech company is younger at IPO.

7 The top of the quadratic function is calculated by -(0.29)/2*(-0.03)= 4.833 ≈ 5 in model S1 and by –(0.40)/2*(-

0.04) = 5 in model S2. (As –b/2a is the top in ax2 + bx + c)

33

Table 6: Model S1 and S2; VC syndication squared

Model S1 Model S2

Dependent variable IRR Dependent variable IRR

Independent variables Independent variables

Early VC Late 2 1.079*** Early VC Late 4 0.15***

(0.18) (0.03)

VC experience/reputation 0.15 VC experience/reputation 0.02*

(0.01) (0.01)

Control variables Control variables

VC size -0.02 VC size -0.03

(0.03) (0.03)

VC age 0.00 VC age 0.01

(0.00) (0.00)

VC syndication 0.29** VC syndication 0.40***

(0.10) (0.11)

VC syndication^2 -0.03* VC syndication^2 -0.04**

(0.01) (0.01)

Corporate VC type -0.11 Corporate VC type -0.07

(0.01) (0.09)

Other VC type -0.20 Other VC type -0.12

(0.17) (0.19)

VC crisis 0.07 VC crisis 0.09

(0.06) (0.07)

IPO company size -0.12 IPO company size -0.21*

(0.10) (0.10)

IPO company age -0.04*** IPO company age -0.05***

(0.01) (0.01)

IPO company operating stage -0.03 IPO company operating stage -0.07

(0.07) (0.08)

Constant 0.51 Constant 0.78

(0.44) (0.53)

Number of observations 402 Number of observations 402

F-statistic 10.97 F-statistic 6.92

Prob > F 0.000 Prob > F 0.000

R-squared 0.34 R-squared 0.21

Adjusted R-squared 0.32 Adjusted R-squared 0.19

Levels of significance: *p < 0.05; **p < 0.01; ***p < 0.001

Robust standard errors between parentheses.

34

6. DISCUSSION

6.1. Conclusion

This master thesis contributes to the existing literature on venture capital in several

ways. The findings suggest a remarkable relationship between the timing of the investment

made by the VC and the obtained returns at IPO. The majority of the existing literature

discusses the required returns by VCs following the traditional finance theory and the agency

theory, which were also used in the theoretical section of this study. Several works

confirmed that early investments generated higher returns than late investments (Bygrave

and Timmons, 1992; Espenlaub, Khurshed, & Mohamed, 2014; Seppä & Laamanen, 2001).

The results of this study however, completely oppose to what is suggested by the

conventional theories and what is found in previous works. All used models in this research

indicate that late VCs obtain higher returns than early VCs. The robustness of these results is

tested in multiple ways. To begin with, 4 different independent variables reflecting the

investment timing, early versus late, were used to test the robustness of the relationship. All

regression models confirm that later investors obtained higher results. Moreover, 2 different

dependent variables were used to assess the relationship between the timing of the initial

VC investment, early versus late, and the returns at exit. While the internal rate of return

(IRR) takes into account the time held of the investment, the money multiple (MM)

calculates the cash-on-cash return. The relationship found between the investment timing

variables, early versus late, and both dependent variables was consistent in all models.

Additionally, all regression models displayed in Tables 3 and 4 were run with an adapted

dataset in which the upper and lower 1% IRR and MM values are left out. The results were

also consistent with upper and lower 5% cutoffs.

A second contribution is the analysis of several VC characteristics that can have an

influence on the returns. This study attempts to find which characteristic has a distinct

influence on early versus late VC returns. The impact of VC experience and reputation on the

performance is frequently discussed in previous research. In short, more experienced VCs

have higher value-adding abilities (Fleming, 2004; Rosenstein et al., 1993), more reputed VCs

send a strong quality signal to the public markets (Cumming & MacIntosh 2003; Stuart, et al.

1999), and more reputed or experienced VCs have greater bargaining power to negotiate

lower valuations (Cumming and Dai, 2011; Hsu, 2004). Evidence in this study confirms the

35

positive influence of VC experience/reputation on the returns, but only in the models where

the IRR acts as dependent variable. However, no significant effect was found for the

interaction between investment timing, early versus late, and the level of experience and

reputation. This means the positive impact of experience/reputation is irrespective of the

timing of the investment.

The level of VC syndication was closely examined as well in this study. The results

show an inverse U-shape relationship between the level of syndication and the internal rate

of return. This indicates that a higher level of syndication can have positive influence on VC

returns, but a very high level of syndication will influence the returns negatively.

6.2. Limitations and directions for further research

Several limitations of this study should be addressed. The first limitation is the

existing survivorship bias, which occurs when failing companies are not taken into account.

As this study only includes companies that went through an IPO, VC investments from

companies that failed are not included. Given the results however, it is expected that

including failed VC investments would only fortify the positive relationship between late

investments and returns. The explanation is that the failure rate of younger companies is

higher than the failure rate of older companies (Hall & Hofer, 1993). As a result, failed

companies include more early investors than late investors. Thus, including failed companies

would increase the significance of the results.

A second limitation concerns the external validity, as this study only examines US

biotech companies that went public. To begin with, an IPO is not the only nor the most

frequently used exit strategy, but it is regarded as the most important exit strategy along

with a trade sale. Trade sales, which are more common, and other exit possibilities such as

secondary sales or even liquidations differ in their allocation of issuing proceeds and the

provision of incentives (Bienz & Leite, 2008). Klausner & Venuto (2013) claim that in trade

sales a later-stage investor is able to negotiate an initial liquidation preference that is senior

to the preferences of earlier investors. This could indicate that late VCs obtain higher results

in trade sales as well. However, it remains difficult to generalize the results found in this

study to all types of exit-strategies. To continue, only US biotech companies are analyzed.

Even though previous studies indicate that findings of the US market are transferable to

other countries (Black & Gilson, 1998; Jeng & Wells, 2000), exit strategies differ across

36

countries and are also different based on legal and institutional environment. At last, this

study only covers the biotechnological sector. As a consequence, this also has its

implications on generalizing the results towards other sectors.

A third limitation concerns limited representation of VCs that have small equity

stakes in the biotech companies. As mentioned in the data collection section, the VCs who

did not own 5% of a biotech company at IPO were not disclosed in the IPO prospectuses.

Consequently, those VCs with small equity stakes could not be included in the dataset.

Given the limitations mentioned above, some directions can be given for future

research. First, the overall generalization of the results could be improved by including other

exit-strategies such as trade sales, since they are the most commonly used exit-strategy in

venture capital. Moreover, other industries could be included to test whether the results are

consistent across different sectors. Third and finally, a closer look should be taken at

potential underlying reasons why early investors in the end are not compensated for the

higher risk taken at an earlier stage. Several elements have been investigated in this work,

not all but at least they lead to a defendable conclusion.

VII

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XI

8. APPENDICES

Table 7: Description deleted IPOs

Table 8: Mean and median returns per VC type

IRR MM

Mean Median Mean Median

Independent VC type 1.59 0.18 2.52 1.65

Corporate VC type 1.03 0.37 3.84 1.96

Other VC type 4.89 0.12 1.66 1.35

XII

Figure 3: VC experience/reputation distribution per VC

Figure 4: VC syndication level distribution per IPO company

XIII

Figure 5: Distribution of the biotech companies operating stage at IPO

Table 9: Method calculation economic significance (illustration for model 1)

The specific method is illustrated by using the values of model 1 in Table 3

Betas Means Betas*Means

Early VS Late 1 0.90 (varying values) 0.1* 0.09

VC experience/reputation 0.02 4.55 0.09

VC size (LN) -0.05 7.33 -0.37

VC age 0.01 16.45 0.16

VC syndication 0.06 2.87 0.17

Corporate VC type -0.09 0.09 -0.01

Other VC type -0.15 0.06 -0.01

VC crisis 0.09 0.43 0.04

IPO company size (LN) -0.02 4.68 -0.09

IPO company age -0.04 8.97 -0.36

IPO company operating stage -0.03 2.49 -0.07

Constant 0.43

sum Betas*Means -0.35

+ intercept 0.43

= 0.0756

exp(0.075583)-1 0.078513

IRR ≈ 8%

0.1*

This calculation is also done for 0.2; 0.3; 0.4; 0.5; 0.6; 0.7; 0.8; 0.9; 1

XIV

Table 10: Correlation matrix

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 Internal rate of return (LN) 1.00

2 Money multiple (LN) 0.56 1.00

3 Early VS Late 1 0.35 0.15 1.00

4 Early VS Late 2 0.46 0.23 0.71 1.00

5 Early VS Late 3 0.67 0.32 0.57 0.69 1.00

6 Early VS Late 4 0.21 0.14 0.52 0.76 0.37 1.00

7 Corporated VC type 0.04 0.11 0.19 0.18 0.10 0.22 1.00

8 VC experience/reputation 0.05 -0.05 -0.07 -0.05 -0.01 -0.11 -0.14 1.00

9 VC size (LN) -0.06 -0.10 -0.19 -0.17 -0.13 -0.24 -0.22 0.52 1.00

10 VC age 0.13 -0.01 0.04 0.13 0.19 0.07 0.06 0.03 0.31 1.00

11 VC syndication 0.14 0.03 0.00 -0.02 0.14 -0.18 -0.05 -0.03 0.09 0.06 1.00

12 Investment in crisis 0.07 0.10 -0.01 -0.00 0.01 0.01 0.08 0.08 -0.02 -0.01 -0.02 1.00

13 IPO company size (LN) -0.08 -0.19 -0.12 -0.03 -0.15 0.08 -0.07 0.10 0.26 0.05 0.24 -0.03 1.00

14 IPO company age -0.19 -0.23 0.21 0.18 -0.39 0.29 0.07 -0.04 -0.05 -0.08 -0.05 0.01 0.18 1.00

15 IPO company operating stage -0.03 0.01 0.05 0.03 -0.03 0.12 -0.01 -0.13 0.01 -0.04 0.03 0.04 -0.02 0.03 1.00

Bold: Correlations significant at the 0.05 level

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