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UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN BEDRIJFSKUNDE
ACADEMIEJAAR 2009 – 2010
The Influence of Venture Capitalist Reputation
and Experience on Valuation
Masterproef voorgedragen tot het bekomen van de graad van
Master in de Toegepaste Economische Wetenschappen
Jeroen Baert en Jan Dufourmont
onder leiding van
Prof. dr. ir. Sophie Manigart en Andy Heughebaert
II
III
UNIVERSITEIT GENT
FACULTEIT ECONOMIE EN BEDRIJFSKUNDE
ACADEMIEJAAR 2009 – 2010
The Influence of Venture Capitalist Reputation
and Experience on Valuation
Masterproef voorgedragen tot het bekomen van de graad van
Master in de Toegepaste Economische Wetenschappen
Jeroen Baert en Jan Dufourmont
onder leiding van
Prof. dr. ir. Sophie Manigart en Andy Heughebaert
IV
PERMISSION
Ondergetekenden verklaren dat de inhoud van deze masterproef mag geraadpleegd en/of
gereproduceerd worden, mits bronvermelding.
Jeroen Baert Jan Dufourmont
V
Preface
Writing a dissertation in order to obtain a master‟s degree in applied economics was a challenge and
in many ways a great experience. Working in pair required a setting of mutual understanding and
respect, predefined rules and a correct task distribution. If not for these elements, this project would
have been a lost cause.
We would like to thank our promoter Prof. dr. Sophie Manigart and Andy Heughebaert for letting
us use an extensive dataset, for the useful comments and continuous follow-up. A special word of
thanks goes out to Joy Van Poucke, Sanne Verbiese and our parents for being patient and
understanding for the duration of our master dissertation and without whose unconditional support
we would not have been able to complete this project. Also, we would like to express our thanks to
Jan Willems and Maarten Tollenaere, two fellow students writing a related master dissertation,
whose insights and feedback helped us avoid certain pitfalls. Further, our reviewers Katrien Baert,
Sebastiaan Dooms and Liselot Pausenberger are hereby greatly thanked. Finally, we are thankful to
Ghent University, for making available all the resources needed to extend our dataset.
VI
Inhoudsopgave
Abstract (Nederlands) ...................................................................................................................... 1
Abstract (English)............................................................................................................................ 2
1. Introduction ............................................................................................................................. 3
2. Theoretical Framework & Hypotheses ..................................................................................... 6
2.1 Reputation ......................................................................................................................... 6
2.1.1 Perceived Quality ....................................................................................................... 7
2.1.2 Prominence ................................................................................................................ 7
2.2 Experience ........................................................................................................................ 8
3. Data ....................................................................................................................................... 10
3.1 Sample and Data Sources ................................................................................................ 10
3.2 Measures ......................................................................................................................... 11
3.2.1 Dependent variable ................................................................................................... 11
3.2.2 Independent variables ............................................................................................... 12
3.2.3 Control variables ...................................................................................................... 14
4. Results ................................................................................................................................... 15
4.1 Descriptive statistics ........................................................................................................ 15
4.2 Method of analysis .......................................................................................................... 21
4.3 Dealing with a selection bias ........................................................................................... 25
5. Discussion, conclusions and limitations ................................................................................. 31
References ..................................................................................................................................... 34
VII
List of Abbreviations
VC = Venture Capitalist
VCs = Venture Capitalists
VCF = Venture Capital Firm
EVCA = European Venture Capital Association
CVC = Corporate Venture Capitalist
M&A = Mergers & Acquisitions
IPO = Initial Public Offering
NACE = Nomenclature statistique des Activités économique dans la Communauté Européenne
ICT = Information and Communication Technology
OLS = Ordinary Least Squares
VIII
List of tables and figures
Table 1. Descriptive statistics of the used variables…………………………………………….…..16
Figure 1. Average valuation per year………………………………………………………….……17
Table 2. Descriptive statistics at the company level……………………………………...…………19
Table 3. Univariate comparisons (based on Mann-Whitney tests)……………………………….…20
Table 4. Log-linear OLS regressions, with standard errors clustered on the lead VCF, from testing
the VC experience and reputation on pre-money valuation relationship..........................................22
Table 5. Probit regression modelling the probability of a company being selected by a more
reputable VCF (Heckman first step)………………………………………………………………..26
Table 6. Log-linear OLS regression, with standard errors clustered on the lead VCF, of pre-money
valuations while controlling for potential selection bias……………………………………………28
1
Abstract (Nederlands)
Het opzet van deze masterproef is het opbouwen en testen van hypothesen betreffende de graad
waarin de waardering van een portfolio onderneming beïnvloed wordt door de ervaring en reputatie
van de risicokapitaalinvesteerder. Het onderzoek is gebaseerd op een steekproef van 140 pre-money
waarderingen in de Belgische durfkapitaalmarkt. Deze masterproef toont dat
risicokapitaalinvesteerders1 met een ruimere ervaring lagere waarderingen zullen bieden aan
ondernemingen, terwijl de reputatie van de investeerder een dubbelzinnig effect blijkt te hebben op
de pre-money waardering. Er blijkt geen significant resultaat voor het “geobserveerde kwaliteit”-
aspect van reputatie. Uit de onderzochte data blijkt echter wel een positieve relatie tussen de
prominentie van een durfkapitalist en de waardering van een onderneming.
1 De term risicokapitaalinvesteerder is niet volledig juist, gezien elke financiering met aandelenkapitaal risicokapitaal is. Het
Nederlands voorziet echter niet in een betere vertaling voor „venture capitalist‟.
2
Abstract (English)
The goal of this master dissertation is to develop and test hypotheses concerning the degree to
which a portfolio company‟s valuation is affected by a venture capitalist‟s experience and
reputation. Our study is based on a sample of 140 pre-money valuations within the Belgian venture
capital market. The thesis of this paper is that venture capitalists with a higher experience tend to
value their ventures lower, whilst reputation seems to have an ambiguous effect on the pre-money
valuation. As for the perceived quality part of reputation, we find no significant result. The
prominence of a venture capitalist however, is positively related with a venture‟s valuation.
3
1. Introduction
A large number of academic papers discuss the importance of an entrepreneur‟s ability to mobilize
resources as a key factor to success for young start-ups. This is especially a key issue for high tech
entrepreneurs since their collateral has an intangible and knowledge-based nature. The bring-in‟s
specific nature of these young start-ups and the high information asymmetries between investors
and entrepreneurs often makes their search for external monetary or other resources an uphill battle.
Entrepreneurs can overcome this problem through internal and external options. An internal
solution could be bootstrap financing (Bhide, 1992), while externally business angels (Aernoudt,
1999; ColleWaert & Manigart, 2009) and venture capitalists provide a possible alternative.
Venture capital is a subsector of private equity where typically equity investments are made
in young high technology firms (Sahlman, 1990). Lately however, the attention of venture capital
investors has shifted towards more mature companies (Collewaert & Manigart, 2009). At the time
of investment the venture capitalist acquires shares in the investee company in return for a monetary
injection. Within this paper, we define a venture capitalist as an institutional investor in privately
held entrepreneurial firms, which actively participates in the management of his portfolio
companies and often is represented on the board of directors (Gompers & Lerner, 1999).
The risk will increase due to the lack of collateral in comparison with more traditional types
of investment. Hence the venture capitalist will require a higher return: this form of financing will
come at a cost. This cost is shown in the venture‟s valuation. The valuation reflects the percentage
of shares the VC gets in return for the committed funds. Since both the investor and the target are
affected by the valuation, it is a vital part of the investment process. The ultimate goal for a venture
capitalist is to maximize the difference between payoffs at an exit - such as M&A or IPO- and the
price they initially paid. Higher pre-money valuation may hence lead to reduced future gains for the
VC. Conversely it seems rational for the entrepreneur to give up the smallest possible ownership
stake in exchange for the maximum possible capital injection, since a lower valuation will lead to a
greater cost of capital for the portfolio company.
Valuation is driven by company characteristics, as well as VC characteristics, since it is the
result of a negotiation process. A sizeable part of firm valuations can be explained by accounting
information, as shown by Hand (2005). Seppä (2003) reasoned that early stage companies would
4
receive lower valuations due to the higher implied risk. Also, previous literature showed that higher
quality companies get higher valuations, since the VC may pay a price premium for the reduced risk
(Hsu, 2007). Contrary to a bank, venture capitalists are able to provide expertise and other value-
added services (Nahata, 2009). Target companies often are advised to deal with the more reputable
VC(s), since they will most likely add more value (Stuart, Hoang & Hybels, 1999; Greene, 1999;
Hallen, 2008). Since cooperating with more reputable partners confers these performance benefits,
this comes at a certain cost, again often reflected in the firm‟s valuation. As Hsu (2004)
demonstrated, ventures may opt to accept a lower valuation or give up a bigger ownership stake, in
order to affiliate with a more reputable partner and hence receive greater value-added services. This
is supported by Fairchild (2004) who stated that welfare can be maximized when the venture
capitalist has high value-adding capabilities.2 Sahlman mentions: “From whom you raise capital
often is more important than the terms” (Sahlman (1997, p.107)).
Not only can venture capitalists build on reputational capital, research has also shown that
their experience plays an important part. The literature concerning the influence of experience on
valuation contains two main lines of research. The first line of research states that more experienced
venture capitalists will offer a lower valuation (Hsu, 2004). Here the concept of experience is
closely related to reputation. Ventures may accept a discount in valuation in the belief that a more
experienced VC will most likely be more successful. Vanacker (2008) examined the relationship
between growth and experience and concluded that companies backed by more experienced VCs
achieve higher growth rates than companies backed by less experienced VCs3. The second line of
research suggests that more experienced venture capitalists do not necessarily offer a lower
valuation, since they are able to select better companies (the matching principle) and thereby
enhance their chances of positive returns (Sorensen, 2007).
It is clear that there exists a great heterogeneity among the different venture capitalists. Not
only may they differ in reputation and experience levels, there also exist several types of venture
capitalists. Even the type of VC (government related, open-ended, university related, …) influences
valuation (Heughebaert & Manigart, 2009). However different, they share a common broad goal,
2 Following Fairchild (2004) welfare is maximized when the venture capitalist has high value-adding capabilities, the market for
reputation is informationally efficient, and the manager has bargaining power. 3 This is true for the asset growth rates, however he finds no substantial evidence for the employment part of growth.
5
namely selling their participation after three to seven years (Black & Gilson, 1998) while
maximizing their returns.
This master dissertation is an attempt to capture the influence of both a venture capital firm‟s
reputation and its experience on a venture‟s valuation in venture capital backed deals in Belgium.
Our research relates measures of VC reputation and experience to the pre-money valuation of
companies whose funding took place between 1992 and 2009, while controlling for portfolio
company characteristics. Thus, the goal of this paper is twofold. We investigate a negative influence
of a venture capitalists‟ reputation on the venture‟s valuation, as well as an inverse relationship
between the VC‟s experience and the venture‟s valuation.
Recent literature distinguishes two distinct dimensions of reputation. The first dimension is
prominence and is concerned with the collective awareness and recognition (Rindova, Williamson,
Petkova & Sever, 2005). In order to measure prominence, we developed two proxies, MarketShare
and MediaCitations. The variable IndustryExperience measures the second dimension, perceived
quality, concerned with the perception of a VC‟s ability. We use several other variables to measure
experience, all based on previous literature. We introduce LnFundSize, OverallExperience,
IndustryExperience and VC_Age as four proxies to estimate experience. As for our controls, we
look at macro-economic data (a dummy is included to indicate whether the investment took place
during the bubble or not), and at the portfolio company characteristics, amongst others age and
balance total. We will elaborate on the variables in the next section of this dissertation.
This dissertation contributes to the literature by extending to the empirical research on
reputation and experience in the venture capital setting. By using a private setting, we try to fill the
gap in reputation literature that has largely ignored valuations of unquoted companies, although
reputation is probably more valuable in a private setting with high information asymmetries than in
a public setting. Sophisticated investors may have a larger influence on a venture‟s valuation in the
private, unquoted environment. We try to quantify the separate influences of a VC‟s experience,
respectively reputation, on the valuation he offers to his portfolio ventures. We combine Hsu‟s
(2004) method of analyzing reputation with Seppä‟s (2003) analysis of prominence, in order to
6
measure the impact of perceived quality respectively prominence as two different dimensions of
reputation4 on a venture‟s valuation.
The rest of the dissertation is organized as follows. In the next section, we develop a theoretical
framework of previous literature in order to build testable hypotheses. Secondly, we present our
data collection methods and our variables. We next present our descriptive statistics followed by
outlining our methods of analysis. The fourth section demonstrates and discusses the main findings
of our research. Finally, conclusions and limitations are discussed in the last section.
2. Theoretical Framework & Hypotheses
This section describes the construction of a theoretical framework on which we will base ourselves
to develop our hypotheses. We will first focus on the literature concerning the reputation of a
venture capitalist, after which we will continue by building the experience hypothesis.
2.1 Reputation
In this dissertation we define reputation as an economic good resulting from past experience and
performance that can generate future returns, especially when there are high information
asymmetries between actors (Hsu, 2004). We investigate equity valuations in a private setting. In
the latter only an absolute minimum of information is available, making reputation more valuable.
What makes a partner, or in this case a venture capitalist, reputable? Early reputation research
linked reputation with certification. Certification is the ability of a third party (here a VC) to reduce
uncertainty over other parties associated with them. Megginson & Weiss (1991) argued that in order
for the certification to be credible (and therefore have a good reputation), a couple of conditions
must be fulfilled, more specifically: the certifying party needs to have reputational capital at stake
and needs to be at risk of being adversely and materially affected if the certification proves false.
More recent reputation literature identifies two distinct dimensions of reputation, each concerned
4 As defined by Rindova et al., 2005
7
with different characteristics, namely: Perceived Quality and Prominence (Vanacker, 2009;
Rindova et al., 2005).
2.1.1 Perceived Quality
Perceived quality is concerned with the way in which stakeholders evaluate certain firm-specific
attributes. Stakeholders use signals to form expectations about unobservable firm characteristics,
and thus try to reduce uncertainty about the quality of the firm. Essentially, one of the attributes
stakeholders try to assess is the expected ability of the VC based on past experience (Vanacker,
2009). A higher perceived quality of the venture capitalist makes the VC more reputable. We
measure the perceived quality of the firm by looking at industry specific experience. More
specifically, we examine the total amount of deals in which the VC engaged in the eight year
preceding the investigated deal that are in the same industry as the investigated deal. Our measure
of perceived quality is hence tightly related with experience. In Hsu‟s (2004) closely related paper
he proposes that more reputable VCs offer a lower valuation and finds results supporting that
hypothesis. Contrary to this paper, Hsu does not make the distinction between the two dimensions
of reputation. He reasons that reputation and experience show a very high correlation and presumes
that proxies for experience can measure the construct reputation. Here, the perceived quality part of
reputation is largely based on past experience. Extending this line of reasoning and following Hsu
(2004) we argue:
H1: venture capitalists with higher perceived quality will offer lower valuations.
2.1.2 Prominence
Prominence, the second dimension of reputation, is related to the collective awareness and
recognition that a company has accumulated in its organizational field (Rindova et al., 2005). A
VC‟s reputational capital comprises the recognition, awareness and appreciation a VC has
accumulated during its activities. Prominence refers to this reputational capital, and is driven by
activity. Every time the VCF engages in an investment, it puts its reputational capital at stake. This
risk of losing reputational capital comes at a cost for the portfolio company. In the venture capital
industry, the compensation the VC gets for putting his „reputation on the line‟ is a potential discount
on the venture‟s valuation. This is in line with the criteria for reliable investor certification of
8
Megginson & Weiss (1991), which state that the certification must be costly and is an increasing
function of the degree and quality of the certification. In the venture capital deal-making, VCs can
be considered as certifying agents. Therefore, the more prominent a venture capitalist becomes, the
larger his discount on valuation will be. Also, since prominent VCs often are considered valuable to
their portfolio companies, these venture capitalists will have greater bargaining power5 over their
portfolio ventures and will as such enter deals on more favourable conditions. This is an important
difference between public and private equity valuation. While public equity valuation is mostly
done on a liquid trading market, private equity valuations suffer from a complete lack of an efficient
pricing mechanism. Hence, negotiation processes between investors and entrepreneurs will shape
most valuations in the private equity market (Cumming & Dai, 2008). Prominent venture capitalists
are likely to use their negotiation power to lower pre-money valuation and consequently increase
their expected returns (Seppä, 2003). We thus hypothesize:
H2: The more prominent a venture capitalists becomes, the lower pre-money valuations he
will offer.
2.2 Experience
The second part of this study is focused on the influence of the experience of venture capitalists on
the valuation they will offer to their portfolio ventures. Recent experience research has contested
the homogeneity of investors. Vanacker (2008) showed that ventures funded by venture capital
firms with a high experience, show higher growth rates ex-post than ventures funded by less
experienced VCFs6. Sorensen (2007) found that companies backed by more experienced VCs are
more likely to go public and Dimov & Shepherd (2005) argued that portfolio companies of venture
capital firms with specific human capital are less likely to fail.
Apart from Hsu (2004), experience literature has largely ignored the influence of experience
of a Venture Capitalist on its portfolio companies‟ valuations. Experience research mainly focused
on portfolio characteristics‟ influence on valuation (Hand, 2005; Armstrong, Davila & Foster, 2006;
5 For further research concerning the impact of bargaining power on valuation, we refer to Cumming & Dai (2008): “Fund Size,
Limited Attention and Valuation of Venture Capital Backed Firms”
6 This is true for the asset growth rates; however he finds no substantial evidence for the employment part of growth.
9
Hsu, 2007). Hsu (2004) showed that entrepreneurs are willing to pay a substantial amount for
reputable Venture Capital affiliation. He argued that reputation and experience are closely related,
and thus measures „reputation‟ based on experience proxies. We believe that this concerns the
perceived quality part of reputation. Experience is undeniably correlated with perceived quality,
however not the entirely the same; for an experienced employee often is, but not necessarily, a good
employee. We include proxies for experience, as described by Sörensen (2007) and Vanacker
(2008), in order to estimate as complete an image of experience as possible. Incorporating
LnFundSize as a proxy for experience, allows us to examine the possibilities for follow-up
financing, while VC_Age is related with the network density. Since Gompers, Kovner, Lerner &
Scharfstein (2009) demonstrated that there is in fact a difference to be noticed between specialized
and generalized venture capitalists on performance ex-post, we distinguish between industry
specific deal experience and overall deal experience.7 It is important to note that these variables are
not mutually exclusive for the constructs „perceived quality‟ and „experience‟.
Follow-up financing and network density are two advantages that an entrepreneur can obtain
from a more experienced VC. Again, these value-added services, and the possibility of a better
outcome as found by Dimov & Shepherd (2005), Sörensen (2007) and Vanacker (2008), come at
the cost, more often than not in the form of accepting a discount in valuation. Also, affiliation with
a more experienced partner is a signal of the portfolio company‟s quality to future investors
(Megginson & Weiss, 1991). Therefore, the venture may again accept a lower valuation, only to be
rewarded with higher future returns.
We hence argue that experience and valuation are inversely related. By affiliating with more
experienced venture capitalists, the target companies might give up a larger equity stake in
exchange for improved value-added services, which they believe, will make their remaining stake
more valuable. This price-experience trade-off leads us to the following hypothesis:
H3: More experienced venture capitalists will offer a lower valuation to portfolio companies
in venture capital-backed deals.
7 Also, Vanacker (2008) distinguishes between industry specific and overall experience.
10
3. Data
3.1 Sample and Data Sources
The hypotheses are tested on a unique hand-collected sample of Belgian VC backed companies that
received financing from a Belgian venture capitalist between 1988 and 2009. The primary sample
includes first round investments in 194 different investee companies. The uniqueness of the sample
arises from its richness in depth and quality. The sample has two important advantages compared to
previous VC valuation studies.
First, we retrieve the valuation data in the current research from the Belgian Law Gazette,
which reports official information on all capital increases in Belgian companies. This allows us to
put together an unbiased sample with high levels of reliability. Our dataset combines information
from several sources, including public and commercial databases with VC investments, annual
reports of VC firms and information from the Belgian Venturing Association. It therefore includes
investments from different types of VC investors, reducing the threat of biases induced by the use
of a single source of data. Second, unlike most U.S. studies, our sample includes unquoted firms,
regardless whether they later did an IPO. The sample hence includes successful as well as less
successful unquoted firms; that is firms that did an IPO, that failed, that were taken over or that are
still private. As such, any potential survivorship bias is eliminated.
Different sources of public information (press clippings, websites, annual reports of VC
companies), combined with the commercial databases of Thomson One (VentureXpert) and Zephyr
(i.e., a database of private equity deals similar to the Thomson ONE database, but with a special
focus on European transactions), are consulted to find the initial VC investment round in Belgian
firms between 1988 and 2009. The sample is limited to initial VC investments in firms younger
than ten years to ensure a focus on „pure‟ VC investments. All Belgian private firms are obliged by
law to announce capital increases in the official Belgian Law Gazette, ensuring a complete,
unbiased and reliable account of all initial equity investments in the VC portfolio companies. Based
on the total capital increase and as a result of this, the number of newly created shares, the value of
an investment round is calculated. The information provided by the Belgian Law Gazette further
allows to unambiguously identify all investors in each investment round in most cases.
11
When multiple VCFs invest in an investment round, the investment round is assigned to the
venture capital firm investing the largest amount, the lead VCF (Wright & Lockett, 2003). Lead
venture capital firms have more decision-making powers, more formal and informal contact with
investee management and receive more information compared to non-lead investors (Wright &
Lockett, 2003). Hence, it seems rational to suppose that lead investors will occupy a more
predominant role in bargaining the valuation with the entrepreneurial management team. We thus
focus on the lead investor. In 27 cases it is not unambiguous who the lead investor is, because all
syndicate venture capitalists invest the same amount or because the invested amount of each
individual VC is unknown. In these situations, we randomly assign an investor as the lead investor8.
In order to collect data concerning the experience and reputation of the lead VC providing initial
venture capital finance, we combined multiple sources including the Thomson ONE database,
Zephyr, Graydon, Belfirst, Mediargus, EVCA-guides and Amadeus. Deals with a corporate venture
capitalist are excluded from the primary sample, since they differ largely from other VCs9. Further,
deals with missing information, mostly because of the fact that the databases do not provide
information before 1990, are also left out. Eventually, deals with a missing pre-money valuation are
not incorporated in the analyses. Missing pre-money valuations result from portfolio companies
where the information necessary to calculate the pre-money valuation was not available. This leaves
us with a final sample of 140 deals.
3.2 Measures
3.2.1 Dependent variable
The unit of analysis is the first investment round. In line with previous research, we utilize the pre-
money valuation of the target company as dependent variable (Lerner, 1994; Gompers and Lerner,
8 Unreported sensitivity analyses were performed including another investor in the multivariate regressions. The results remained
robust.
9 Information asymmetries tend to be extra high for corporate investors. High quality ventures often are reluctant to share details
about their key technologies or intangible assets in order to avoid the „theft‟ of their ideas (Dushnitsky, 2004). Also, the
compensation of corporate VC fund managers often differs from that of independent venture capitalist fund managers, resulting in
less strong incentives for corporate VCs. In some cases investments are made with specific motives, e.g. a manager may want to
invest in new companies just to enlarge his firm. Finally, CVCs‟ focus may lie on strategic synergies, the economic effect of which
we cannot quantify. For all these reasons – among others- it is hard to compare corporate VC valuations with other venture capitalist
valuations.
12
2000; Seppä, 2003; Hand, 2005; Armstrong et al., 2006). In contrary to a post-money valuation, a
pre-money valuation is not directly dependent on the amount invested in the company during the
investment round (Lerner, 1994). Pre-money valuations are thus considered more appropriate than
post-money valuations since the amount invested may vary with many determinants, including the
fundraising environment. The pre-money valuation10
is calculated as the difference between the
post-money valuation and the invested amount. The post-money valuation represents the amount
that an investor would have paid to acquire 100% of the shares of the company and is calculated as
((investment) / (% of shares acquired)) * 100. As alternative, the pre-money valuation can be
measured by the total number of shares outstanding prior to the investment multiplied with the price
per share paid by venture capitalists in the focal investment round. All amounts used in the analyses
are inflation-adjusted. The mean pre-money valuation of the portfolio companies in the sample is
2.690.593,41 EUR, starting from a minimum valuation of 23.181,79 EUR going up to a maximum
valuation of 32.815.822,12 EUR (Table 1).
3.2.2 Independent variables
The key independent variables are correlates of venture capitalist prominence, perceived quality and
experience and are measured at the moment of investment. Venture capitalist prominence is
operationalized in two ways. First, we look at the absolute number of media citations of the venture
capitalist 8 years prior to investment (MediaCitations). The media tends to distribute information
more broadly than the opinions of average stakeholders, so they are likely to have a high degree of
influence on which organizations become more prominent in the minds of stakeholders (Rindova et
al., 2005). Only citations in „De Tijd‟, considered qualitatively the best financial newspaper in
Belgium, are taken into account. We make no distinction whether the article places the venture
capitalist in a „good‟ or „bad‟ daylight since Cook, Kieschnick and Van Ness (2006) showed that
the media provides non-negative coverage in 99% of the articles they studied in detail. Second, we
measure prominence from another perspective by considering the degree of presence on the venture
10 A simple example of a pre-money valuation: when an investor acquires 20% of the shares of a company for €100.000, the post-
and pre-money valuations are respectively equal to €500.000 and €400.000. So, the investor pays €100.000 to possess 20% of the
company shares after investment. No distinction is made between the acquired percentages of shares of different investors since this
doesn‟t affect the post-money valuation. In contrary, to come to the pre-money valuation, the total increase in capital is deducted
from the post-money valuation, irrespective if one or more venture capitalists were included in the deal.
13
capital market. This second measure of prominence is based on the VC‟s investment share in the
venture capital industry. MarketShare is operationalized as the quotient of the number of
investments by a certain venture capitalist two years prior to investment and the total number of
investments in the Belgian venture capital market in the same period. Hence, venture capitalists
with a higher investment share are likely to be more prominent since they are able to attract capital
from more limited partners, which contributes to a higher awareness concerning the VC.
Furthermore, a higher investment share seems more likely to positively contribute to the perception
of the unobserved qualities of the venture capitalist. Gompers (1996) used the venture capitalist‟s
age as an alternative measure of prominence; we however believe that age as such is not an
unambiguous estimator of reputation. After all, this would mean that the reputation building process
is automatic and linear without a connection to the performance of the VC (Seppä, 2003).
Venture capitalist perceived quality is operationalized as the absolute number of investments made
by the venture capitalist 8 years before investment in the same industry as the target company (4-
digit industry codes reclassified into EVCA-classification11
, using a conversion key12
).
IndustryExperience is in line with the concept of venture capitalist reputation as perceived quality
based on previous experience (Vanacker, 2009; Hsu, 2004). The successful development of
portfolio ventures is a powerful signal of the quality of the venture capitalist and a strong
contributor to the VC‟s perceived quality. Since the expertise needed to help portfolio companies
mature successfully grows with every investment, investment experience is a good proxy for
perceived quality. It is well known that people make judgements about certain entities based on past
observations and use these signals to form beliefs in predicting future performance (Weigelt &
Camerer, 1988).
Experience leads to knowledge accumulation. The accumulated competences are expected to have
11 The EVCA divides companies into 7 industry groups at baseline: “Agriculture, Chemicals and Materials”, “ICT”, “Business and
Industrial Products and Services”, “Consumer Products, Services and Retail”, “Energy and Environment”, “Financial Services” and
“Life Sciences”
12 The conversion key was found on the official EVCA-website:
http://www.evca.eu/uploadedFiles/Home/Knowledge_Center/EVCA_Research/Current_Surveys/sectoral_classification.pdf
14
an influence on the valuation. Venture capitalist experience is measured in four ways. First, we look
at the overall deal experience (Sorensen 2007; Vanacker 2008). The variable OverallExperience is
operationalized as the absolute number of completed venture capital investment rounds, eight years
before investment, in which the lead VC participated. Secondly, similar to the previous proxy the
industry specific deal experience is utilized to measure experience. IndustryExperience also only
focuses on a period of eight years before investment. Thus, in line with Vanacker (2008) we
distinguish between overall and specific experience. This distinction is supported by Gompers &
Lerner (2009) who showed that there are differences in performance between specialized and
generalized venture capitalists. As third proxy for experience we include the total funds committed
of the lead investor (Cumming & Dai, 2008). The higher the venture capitalist‟s fund size the more
commitments it has engaged with limited partners who are likely to select and invest in VC funds
with a greater experience, whose accumulated competences pump up the expected returns, implying
lower risks. The fund sizes in our dataset range from 924.780 EUR up to 1.129.029.510 EUR with
an average of 130.260.380 EUR. In order to reduce the spread of these values, we thought it
appropriate to perform a log transformation. Also, Cumming & Dai (2008) found that the fund size
of a VC is convexly related with the log transformed pre-money valuation. The log-transformed
variable LnFundSize helps us avoid non-linearities between the independent and dependent
variables, as is the case. Incorporating fund size allows us to indirectly examine the possibilities for
follow-up financing. Fourth, consistent with previous literature (Janney & Folta, 2006) we also
incorporate the age of the venture capitalist as a proxy of its investment experience (VC_Age). It
appears logical that the longer a VC has been in the venture capital industry, the more knowledge
and experience he or she has accumulated. Additionally, incorporating the venture capital firm‟s
age, allows us to indirectly measure the network density of the VC firm.
3.2.3 Control variables
Proxies that drive differences in the dependent variable other than those measuring reputation and
experience are included as control variables in order to capture the most representative effects of
reputation and experience. Our control variables represent a combination of variables tested in
15
univariate tests13
, variables used in previous literature and variables that intuitively seem
appropriate. Most of these control variables represent characteristics of the portfolio company.
Previous research has shown that valuations of risk-capital backed companies can be significantly
affected by company characteristics (Armstrong et al. 2006; Hand 2005). To overcome this
potential bias, we include a start-up dummy14
dividing the portfolio companies according to their
age at the time of investment (companies younger than two years, are labeled start-up and get a
value of 1; 0 otherwise). Second, two dummies15
(HIGH_TECH and LIFE_SCIENCES) are added
to control for the sector of the portfolio company. These dummies take on the value 1 if the
company is classified as a high-tech, respectively life sciences company, and 0 otherwise. Next, the
balance total of the company is incorporated to control for the size of the portfolio company at the
time of investment. Further, the number of investors engaged in the focal investment round and the
number of patent applications are inserted. We include number of investors because it is possible
that VCs will only share the best deals with their peers due to the fact that reputation is a very
fragile economic good. Since the number of patent applications at the time of investment is
generally low, we include a dummy variable taking on the value 1 when the company had applied
for patents prior to investment and 0 if it had not. Finally, valuations of unquoted ventures are
affected by valuations in the stock markets (Hand, 2005), hence we control for the timing of the
venture capital investment by a dummy variable taking on the value 1 if the investment took place
during the bubble period, i.e. 1999 up to 2001, and 0 otherwise.
4. Results
4.1 Descriptive statistics
Table 1 provides an overview of the mean, standard deviation, minimum and maximum of the used
variables. This table uncovers that 40% of the sample concerns start-up investments. 64% of the
13 These tests are presented later.
14 We ran another model replacing the start-up dummy by the actual age of the company as a robustness check and the results
remained qualitatively similar.
15 We ran another model replacing the two sector dummies by the EVCA sectoral classification as a robustness check. Apart from the
fact that the life sciences industry lost its significance, results stayed qualitatively unchanged.
16
companies‟ activities in the sample are classified as high-tech, while only 13% are categorized as
life sciences. Close to one third of the investments took place during the Internet bubble period, i.e.
between 1999 and 2001, signalling an appropriate distribution in terms of time of investments,
considering that more investments took place during the bubble. Further, only 12% of the studied
companies applied for at least one patent, prior to investment. Finally, 31 companies (22%)
received financing from more than one venture capitalist and the average portfolio company in the
sample has a balance total of 835,280 EUR.
Table 1. Descriptive statistics on the used variables
Variables Mean s.d. Min Max N
a) Dependent 1. Pre-money Valuation (000 EUR) 2,690.59 6,164.00 23.18 32,815.82 140
b) Independent
2. MarketShare 3.06 4.02 0.00 17.31 140
3. MediaCitations 154.32 396.93 0.00 2,269.00 140
4. IndustryDeals 6.95 9.35 0.00 50.00 140
5. OverallDeals 23.84 31.94 0.00 224.00 140
6. FundSize (000 EUR) 130,260.38 211,329.75 924.78 1,129,030.00 140
7. VC_Age 8.22 6.07 0.23 26.82 140
c) Control
8. PATENT 0.12 0.33 0.00 1.00 140
9. START-UP 0.40 0.49 0.00 1.00 140
10. HIGH-TECH 0.64 0.48 0.00 1.00 140
11. LIFE_SCIENCES 0.13 0.33 0.00 1.00 140
12. BUBBLE 0.29 0.46 0.00 1.00 140
13. NumberOfInvestors 1.38 0.87 1.00 7.00 140
14. BalanceTotal (000 EUR) 835.28 1,555.26 0.00 10,694.51 140 Table 1 shows an overview of the mean, standard deviation, minimum and maximum of the used variables
Our sample covers deals from February 1992 to April 2009. Figure 116
portrays the distribution of
the average pre-money valuation of the 140 deals over those 18 years and the curve is largely
parallel with numbers reported by the European Venture Capital Association. The high amount
invested in 2000 is explained by the millennium bubble or dot-com hype. Periodically, the
16 The minimum number of investments per year is set at one, so years without investments are excluded.
17
seemingly irrational behaviour of investors results in a speculative mania evidenced by
skyrocketing stock prices and exaggerated investor enthusiasm, until the bubble “bursts” (Lipton,
2003). Since valuations follow stock market prices (Hand, 2005), the remarkable drop in value after
2001 could be clarified by the effect of the burst. A Kruskal-Wallis-test17
indicates that there is a
significant difference in pre-money valuations among the different years. When looking at the graph
below we indeed see very high valuations during the bubble and remarkably low average valuations
afterwards.
Figure 1. Average valuation per year
Figure 1 plots the average valuation over the different years in our
sample
Finally, an unreported box plot concerning the pre-money valuations in the different industries
shows that companies in the financial services sector clearly obtain higher valuations although there
is no significant difference reported by the Kruskal-Wallis-test. This contradiction could be
attributed to the very small number of investments in this sector included in our sample.
Table 2 describes the sample at the company level. Panel A shows the number of
investments broken down by investment year. During the millennium bubble a higher number
17 The results of the test are not included in this research.
18
investments are completed. Apart from that, there are no significant concentrations in any given
year. Panel B exhibits the distribution of our sample according to the European Venture Capital
Association‟s sectoral classification. 4-digit NACE-Bel-codes are collected on the portfolio
companies and through an official conversion key –found on the EVCA website- we divide these
industry codes in the seven sectors the EVCA distinguishes at baseline. Noteworthy is that over
40% of the sample handles deals completed in the ICT industry. The segment „Business and
Industrial Products and Services‟ covers one fourth of the sample. Less important industries are
„Energy and Environment‟ and „Financial Services‟, both approximately 2% of the sample.
Companies are classified by age at time of investment in Panel C. Three distinct categories are
designed; firms less than two years, between two and five years and older than five years (Manigart
et al., 2002). We can see that our sample mostly includes young companies since the first category
represents about 40% of the sample. This is strengthened by a median value of three year. About
36% of the sample consists of companies older than five year. Panel D categorizes the companies
by their legal status at the beginning of 2009. More than half of the firms are still private, while
almost 27% failed. 10% have been acquired and only about 4% have done an IPO. Unfortunately,
there is a potential bias concerning private firms. Failures and IPOs are easy to track from public
information. Acquisitions however are not always that easy to identify. As a consequence, it might
be that some firms were acquired but are nevertheless improperly classified as still private firms in
this sample. Finally, Panel E splits the sample in two distinct groups; companies that have received
follow-up finance and companies that have not. This reveals that more than half of the companies
were able to attract at least a second investment round.
The average venture capital firm in this sample was 8.22 years old at the time of investment,
had done 24 deals and was cited 154 times in „De Tijd‟ prior to investment and had funds available
for a value of 130,260,000 EUR. Half of the examined venture capitalists had done 4 or more
investments in the same industry as the one their respective target company operated in. The
average market share of the venture capitalists in our sample is approximately equal to 3%.
19
Table 2. Descriptive Statistics at the company level
Panel A: Deals distributed by investment-year: actual number and cumulative percentage
1992 1993 1994 1995 1996 1997 1998 1999 2000
6 3 9 12 13 8 8 10 18
4.29% 6.43% 12.86% 21.43% 30.71% 36.43% 42.14% 49.29% 62.14%
2001 2002 2003 2004 2005 2006 2007 2008 2009
13 13 10 3 0 5 6 2 1
71.43% 80.71% 87.86% 90.00% 90.00% 93.57% 97.86% 99.29% 100.00%
Panel B: Sectoral classification of the companies
Sectors Distribution of companies
Agriculture, Chemicals and Materials 9 6.43%
ICT 59 42.14%
Business and Industrial Products and Services 37 26.43%
Consumer Products, Services and Retail 13 9.29%
Energy and Environment 3 2.14%
Financial Services 4 2.86%
Life Sciences 15 10.71%
Panel C: Number of firms classified by age
Age Distribution of companies
0-2 years 55 39.29%
2-5 years 34 24.29%
> 5 years 51 36.43%
Panel D: Number of firms classified by legal status
Legal Status Distribution of companies
Failure 38 27.14%
Private 82 58.57%
M&A 14 10.00%
IPO 6 4.29%
Panel E: Number of firms receiving follow-on finance
Follow-on Financing Distribution of companies
Yes 77 55.00%
No 63 45.00% Table 2 discloses descriptive statistics for the sample of 140 first round venture capital investments in 140
Belgian companies. Panel A distributes the number of investments by year. Panel B classifies the number of
target companies according to the sectoral classification of EVCA. In panel C, target companies are divided
into three groups; 0-2 years, 2-5 years and >5 years. Panel D distributes the companies by legal status. In
panel E, we split the sample by looking if the target company received follow-on finance or not.
20
Table 3. Univariate comparisons (based on Mann-Whitney tests)
Variables N Average Pre-money P-Value
BUBBLE 43 7,2967 0,001
NON_BUBBLE 101 6,3366
START-UP 56 6,7765 0,541
NON_START 84 6,5943
MULTIPLE_INVESTORS 31 7,1810 0,023
SINGLE_INVESTOR 109 6,4634
LIFE_SCIENCES 18 6,3460 0,365
NON_LIFE_SCIENCES 122 6,6630
HIGH_BALANCE_TOTAL 70 6,3372 0,029
LOW_BALANCE_TOTAL 70 6,9074
NON-PATENT 123 6,6062 0,743
PATENT 17 6,7388
HIGH-TECH 89 6,6571 0,72
NON_HIGH_TECH 51 6,5615 Table 3 splits each control variable in two groups and shows univariate
comparisons between the difference in average pre-money valuation
(ln(Premoney/1000)) of those groups. For BalanceTotal a dummy is created
taking on the value 1 if the observation exceeds the median and 0 otherwise.
Table 3 largely confirms our presumptions. The average valuation during the bubble is significantly
higher than in the post- and pre-bubble periods. Also when multiple investors18
participate in the
studied investment round, valuations are on average significantly higher than when only one
investor participates. We hence include the number of investors as a control variable in our final
model. We take a median split dummy of the balance total of the investee company to examine
whether higher balance total will lead to higher valuations. The latter is confirmed on the 5% level
by the results presented above. We find no significant results confirming that there is a difference in
average valuation for the high-tech dummy, the life-sciences dummy and the patent dummy.
However, we incorporate them in our final model based on previous literature. Also, these result do
not indicate a difference in average valuation when distinguishing between start-ups and more
mature companies. Nevertheless, we integrate the start-up dummy as a control variable since we
believe that the higher risk levels will lead to lower valuations when controlling for other factors.
18 We mentioned in part 3.2.3 why multiple investors might influence valuation.
21
.
4.2 Method of analysis
In the three previous tables we presented the main descriptive statistics of our data. While these
results are suggestive, they do not systematically control for a variety of factors. We therefore
analyze the relationship between correlates of reputation and experience and firm valuation in a
more systematic way using multivariate regressions. Using a log-linear OLS regression, we test our
hypotheses. The use of a log-linear model conforms to nearly every analysis of venture capital
valuation undertaken in the entrepreneurial finance literature (Gompers & Lerner, 1999; Collewaert
& Manigart, 2009; Cumming & Dai, 2008). The log-transformation‟s main advantages include its
capability to dramatically reduce the skewness of the dependent variable. Both the Kolmogorov-
Smirnov and the Shapiro-Wilkinson tests of normality show that the transformed dependent
variable follows a normal distribution, which is one of the conditions to execute an OLS. OLS
regression, a commonly used technique, allows us to estimate the unknown parameters in a linear
regression model. The regressions that we estimate characterize pre-money valuation as a function
of the major components of reputation and experience of a VC, while controlling for macro-
economic and portfolio firm characteristics. Finally, we cluster on the lead VC level in order to
obtain the level at which we have maximum randomness19
. The hypothesized model consists of the
following exogenous independent and control variables: Market Share, Industry Specific Deal
Experience, Overall Deal Experience, Fund Size, Media Citations, VC Age, balance total, number
of investors, and dummies for bubble, high-tech, life sciences, start-up and patent applications:
Ln [Premoney/1000]= ß0 +p
ß1 [MediaCitations] +n
ß2 [MarketShare]
+ j
ß3 [IndustryExperience] +k
ß4 [OverallExperience] +m
ß5 Ln [FundSize/1000] +i
ß6
[VC_Age] +i
ß6 [Controls] + ε0
19 In order to be able to correctly analyze our data, the correlation between the observations needs to be taken into account. Else, the
standard errors of the estimates will be wrong, making significance tests invalid. This occurs because the standard errors normally
assume perfect independency between all observations. The larger the correlation between observations, the less unique information
each observation contains.
22
Table 4. Log-linear OLS regressions, with standard errors clustered on the lead VCF, from testing the VC experience and reputation on pre-money valuation relationship
Model I Model II
MediaCitations 0,0010233 †
(0,0005347)
MarketShare 0,1498861 ** (0,0504728)
IndustryDeals 0,0142192 (0,0162205)
OverallDeals -0,0190733 * (0,0090765)
LnFundSize 0,0001092 (0,0006231)
AgeVC -0,0460678 † (0,0260473)
PATENT 0,3460964 0,4277831 (0,3659973) (0,3649221)
BUBBLE 1,091766 ** 1,478682 *** (0,3851997) (0,2871965)
START-UP -0,2436154 -0,5545467 † (0,3304477) (0,285154)
HIGH-TECH 0,2579318 0,1545695 (0,2713809) (0,2686488)
LIFE_SCIENCES -0,6974213 † -0,6400793 † (0,3853765) (0,3432336)
BalanceTotal 0,000319 *** 0,0003396 *** (0,0000794) (0,0000916)
NumberOfInvestors 0,4675226 † 0,221238 (0,3816242) (0,1577421)
_cons 5,303477 *** 5,492845 ***
(0,2717051) (0,3569295)
Number of Observations 140 140
F-value 5,65 6,02
R-squared 0,205 0,316
Adj. R-squared 0,163 0,246
Change in Adjusted R-squared 0,083
Change in F 2,32 *
Number of clusters (leadinvestor) 63 63
Table 3 presents the results of the log-linear OLS regression. Model 1 only contains the control variables
while model 2 also includes the reputation and experience variables.
P < 0,1 = †
P < 0,05 = *
P < 0,01 = **
P < 0,001 = ***
23
This log-linear model shows next to none heteroscedasticity in comparison with other, non-
transformed models. In an unreported analysis, the Variance Inflating Factors (VIF) for all
regressors show no severe collinearity problem. Table 4 examines the coefficients of the regressors
with standard errors clustered on the lead VC level.
The first model (Model I), containing only the control variables, explains 16.3% of the
variation in pre-money valuations in this sample and shows one significant firm characteristic: a
positive coefficient for the balance total variable (<0.001). The number of investors and the life
sciences dummy both are marginally significant. The negative sign of the life science dummy can
be explained by a higher perceived risk. Companies whose activities are classified as life sciences
are characterized by a higher volatility and thus risk, implying that when investing in a life sciences
portfolio company, a VC will require a higher expected rate of return, reflected in a lower valuation.
The coefficient of NumberOfInvestors is positive, indicating that a higher number of investors will
lead to a higher valuation. Indirectly, this confirms our presumption that the number of investors is
a sign of company quality. The bubble dummy‟s coefficient is also positive and significant (<0.01),
which is in concordance with Lipton (2003) who argued that stock prices skyrocket during the
bubble while valuations of unquoted ventures follow stock market prices (Hand, 2005). The high-
tech dummy, the start-up dummy and the patent dummy seem to have no influence whatsoever on
the pre-money valuations. This latter is in contrast with previous valuation studies (Hsu, 2004; Hsu,
2007; Collewaert & Manigart, 2009), in which the number of patents applications prior to
investment was a highly significant predictor of pre-money valuation. When introducing the control
variables in the full model (model II) the control variables remain largely unchanged. The bubble
dummy gains in significance, the number of investors loses its significance, while the negative
coefficient of the start-up dummy becomes marginally significant. The negative coefficient of the
start-up dummy, which receives the value one when the investment took place in the first two years
of the company‟s existence, is to be expected, since the younger the venture, the higher the implied
risk. As mentioned earlier, the higher the risk, the higher the cost, often in the form of a discount in
the valuation (Seppä, 2003).
In addition to the control variables, model II also contains the VC characteristics. This
model explains 24,6% of the variation in the pre-money valuation in this sample. Since the number
24
of observations is equal in the two models, both models can be compared and the change in R2 is
significant at the 0,05 level. Our proxies for VC experience and reputation hence explain a
significant part of the variation in the sample. The coefficients of the venture capitalist‟s
characteristics are not unambiguous. We find evidence to support our experience hypothesis.
However, not all experience proxies have a negative coefficient. Both LnFundSize and
IndustryExperience have a positive sign. Since both coefficients are not significant, we assume that
the negative and significant coefficient of OverallExperience (<0.05) and VC_Age (<0.10) is
sufficient empirical evidence to support the proposed hypothesis 3: VC experience is negatively
correlated with the pre-money valuation it offers to its portfolio ventures. Since VC_Age is largely
related with the density of the VC‟s network, this finding is consistent with Hsu (2007) who argued
that VCs‟ overall deal experience and network density may be more distinctive assets than their
functionally equivalent financial capital. Hence, venture capitalists who can count on a broader deal
experience and who can mobilize more resources through their more extensive network will be able
to demand a discount in valuation. We find no evidence to support our predicted relationships
between both dimensions of reputation and valuation. We find a positive and significant (<0,01)
coefficient for MarketShare and MediaCitations (<0,10), indicating the opposite of what was
hypothesized concerning prominent VCs. We hence reject hypothesis 2. The coefficient of our
perceived quality proxy is not significant which leads to rejection of hypothesis 1. Empirical
evidence of previous research showed that ventures backed by VCs with a higher perceived quality
show steeper growth curves (Vanacker, 2008) and are able to raise more follow-on equity
(Vanacker, 2009). We find no evidence to support the theory that entrepreneurs would be willing to
accept a discount in valuation in return for these improved prospects. Hence, our results do not
support Hsu‟s (2004) findings that more reputable20
venture capitalists will offer lower valuations to
its portfolio companies.21
20 Again, we have to point out that Hsu measured reputation based on past experience.
21 We hereby point out that in his paper “What do entrepreneurs pay for venture capital association”, Hsu made use of a very specific
dataset, only looking at startup companies, of which more than 80% received funding during the bubble.
25
The economic interpretation of the coefficients of a log-linear model differs slightly from an
ordinary OLS-regression. While a linear-linear model indicates absolute effects, a log-linear model
demonstrates relative effects, expressed in percentages. We next interpret the results of model II.
If the investment took place during the bubble, the pre-money valuation was on average
close to 150% higher than pre- or post-bubble investments. When the investment concerned a start-
up, valuation offered was on average 55% lower than a more mature venture. Finally, each
incremental thousand Euros on the balance total before investment, means an average incline of
0,03% in valuation. The valuation of a company classified under the life sciences category, will on
average be 64% lower than a non-life sciences company.
An incline in OverallDeals by one unit will cause a decline in the average offered valuation
of roughly 2%. However only marginally significant, each additional year of operations will lead to
a decrease of 4,6% in valuation offered. An incremental unit in media citations in the clustered
model leads to an increase of 0,10% in valuation. The small percentage is not remarkable, for it is
obvious that only one sole citation cannot have a large effect on valuation. An increase in
MarketShare by one unit – here one percent - leads to a predicted increase in the dependent value
pre-money valuation of approximately 15%, when all other regressors are held constant. The latter
results concerning investor prominence are in complete contradiction with Seppä (2003) and thus
have several implications.
Nahata (2008) found that more reputable VCs are able to select better quality deals and
hence are willing to pay a price premium for the reduced risk. Our model so far did not control for
selection. We next try to eliminate a possible selection bias, and thus investigate whether more
reputable venture capitalists are in fact better at selecting higher quality companies.
4.3 Dealing with a selection bias
The result that more prominent VCs offer higher valuations to their portfolio ventures might suffer
from endogeneity problems. Because of the likely matching between more reputable venture
capitalists and better quality companies, selection bias could arise. The higher valuation might thus
be a simple result of the portfolio company characteristics, instead of being related to prominence.
We will use a two-stage approach proposed by Heckman (1979) to overcome this problem. This
technique deals with endogeneity in two steps: first we estimate the likelihood of prominent venture
26
capitalists making an investment in a higher quality firm, after which we add an additional regressor
(the inverse Mills ratio) to estimate the pre-money valuation. A significant inverse Mills ratio‟s
coefficient suggests the possible existence selection bias. The results of the Heckman two-stage
approach are presented in tables 5 and 6 below.
Table 5. Probit regression modelling the probability of a company being selected by a more reputable VCF (Heckman first step)
Probit regression
Number of observations 140
Probability > Chi² 0,0366
PATENT_APPLICATIONS 0,0906347
(-0,3350082)
AmountInvested 1,67 **
(8,50E-08)
FOLLOW-UP 0,4896869 **
(0,2259162)
NumberOfInvestors -0,1446888
(0,1570836)
CompanyAge 0,0738899 **
(0,0368653)
_cons -0,5596181 *
(0,313871) Table 4 provides the results of a probit regression performed to investigate if
company characteristics influence the likelihood of being selected by a more
reputable VC.
P < 0,1 = †
P < 0,05 = *
P < 0,01 = **
P < 0,001 = ***
In the first step we run a probit regression in order to investigate whether company quality
characteristics influence the chance of being selected by a more prominent VC firm. A dummy that
takes the value 1 when both the VC‟s market share and media citations is above the median,
measures prominence. High growth companies usually have greater financing needs. Since different
VCs have different amounts of cash available to them, they might have to pass an otherwise good
opportunity due to financing constraints. Thus it is interesting to incorporate the amount invested in
27
the portfolio company as a company characteristic (Heughebaert & Manigart, 2009). As mentioned
earlier, less mature firms are riskier, which may lead to an increased required return on investment,
in itself leading to lower valuations (Wright & Lockett, 2001). Hence, CompanyAge is included as a
proxy for maturity. Since reputation is a very fragile economic good, it is possible that VCs will
only share the best deals with their peers. The number of investors is hence included to measure
company quality. For young high-growth firms, their intellectual property often is a very important
asset. The perfect way to protect these assets is by applying for a patent. Patent applications are
therefore incorporated as proxy for the venture‟s quality. We also include a dummy to investigate
whether the investee company receives any follow-on financing. A company able to attract follow-
on financing is likely to be a company that reached several milestones. Attaining a milestone – and
in extension next round financing- can hence be a signal of company quality. This process will
naturally be affected by the VC‟s input; however the effect of the portfolio venture‟s input will be
more important (Sapienza & Gupta, 1994).
The small probability of encountering a test statistic as extreme as the observed statistic
under the null hypothesis – i.e. all the regression coefficients are simultaneously equal to zero- leads
us to conclude that at least one of the coefficients is significantly different from zero. The positive
and significant sign of AmountInvested, CompanyAge and FOLLOW-UP mean that a rise in the
predictor variable will lead to an increase in the predicted probability of the dependent variable. The
constant amounts approximately -0,56, meaning that if all other variables are held constant at zero,
i.e. a “low” quality company, the foretold probability of a prominent VC investing in a company is
F(-0,56)= 0,2877. It is important to note that we are only able to control for a limited number of
company quality characteristics; for example we have no information whatsoever on the quality of
the company, which can be noticeable in ex-post information, nor on the quality of the management
team of the portfolio venture. However, in the past the latter has proven to significantly affect
valuation (Collewaert & Manigart, 2009; Hsu, 2007).
28
Table 6. Log-linear OLS regression, with standard errors clustered on the lead VCF, of pre-money valuations while controlling for potential selection bias
Model II
MediaCitations 0,0008184 †
(0,0004803)
MarketShare 0,1362237 ** (0,0499729)
IndustryDeals 0,0166497 (0,0139661)
OverallDeals -0,0197285 * (0,0084179)
LnFundSize -0,0002991 (0,0009851)
AgeVC -0,030879 (0,0248817)
PATENT 0,4967988 †
(0,3295656)
BUBBLE 1,558899 *** (0,2776064)
START-UP -0,3535733 (0,2976686)
HIGH-TECH 0,0872257 (0,2517956)
LIFE_SCIENCES -0,5680404 †
(0,3072157)
BalanceTotal 0,0002806 *** (0,0000772)
NumberOfInvestors 0,2012116 (0,1412481)
InverseMillsRatio -1,769177 ** (0,5420663)
_cons 6,88824 ***
(0,5515831)
Number of Observations 140
F-value 8,18
R-squared 0,3672
Adj. R-squared 0,2963
Number of clusters (leadinvestor) 63
Table 5 presents the results of the log-linear OLS regression. In addition to
table 3, the inverse mills ratio is included to control for a possible selection
bias.
P < 0,1 = †
P < 0,05 = *
P < 0,01 = **
P < 0,001 = ***
29
In the second step we include the inverse Mills ratio in our original model to control for a
possible selection bias. Table 6 presents the results. The coefficient of the inverse Mills ratio,
estimated from the first stage probit regression, is significant, indicating the existence of a selection
bias. More prominent venture capitalists will hence invest in different companies than their less
prominent peers; in this case „different‟ equals better quality (Cf. the selection model). After
controlling for selection bias the results remain fairly robust, that is, the sign of the coefficients does
not change. However, the coefficient of VC_Age loses its significance. The significant MarketShare
coefficient amounts approximately 0,14, meaning that if the market share of a VCF would increase
by one percent, the pre-money valuation would increase by 14%. The number of overall deals a
venture capitalist performs in the eight years preceding the date of investment is economically
significant at the 5% level. This indicates that every extra deal the VC engages in, causes the pre-
money valuation to decrease with approximately 2%. The control variables BUBBLE and
BalanceTotal still remain highly significant, with an economically significant increase in valuation,
amounting 156%, respectively 0,03%. The number of media citations stays marginally significant,
while the patent dummy becomes marginally significant, for an increase in valuation of
approximately 50% when the portfolio company has applied for at least one patent. The life
sciences dummy, indicating whether a firm operates in the life sciences sector, remains marginally
significant. The decrease in valuation of 56,8% is probably due to the fact that the life sciences
sector is perceived as one of the riskiest industries.22
The discount in valuation will hence serve as a
buffer to obtain higher returns ex-post.
We conclude that there in fact exists a selection bias influencing the results. When
controlling for selection our model explains close to 30 percent of the observed variation in the
sample. The age of the venture capitalist loses its significance and the patent dummy becomes
marginally significant, while the rest of the results only change in significance level, but largely
remain qualitatively unchanged. The coefficients of both our proxies measuring prominence remain
positive and significant, even after controlling for selection.
22 For a more elaborate description of the life sciences sector, we refer to Baeyens, Vanacker & Manigart (2006); Senker (1998) and
Brierley (2001)
30
We explain this result, based on previous literature. The set of investment opportunities is
likely to be larger for more reputable venture capitalists (Hochberg, Ljungqvist & Lu, 2007), since
companies want to affiliate with the „better‟ partner. This might leave their less reputable peers with
investment opportunities in younger and riskier ventures. Less reputable VCs will hence offer lower
valuations, explaining the positive relationship. In a private, unquoted setting where information
asymmetries are very high, it is in the VC‟s best interest to build a reputation as quickly as possible.
(Gompers & Lerner, 2000) Also, Gompers (1996) has shown that a venture capitalist that
underperforms the market will experience great difficulties raising new funds. Therefore, in order to
be able to raise new funds, generate greater returns ex post and thus a good reputation, less
reputable VCs will offer lower valuations. Also, MarketShare is, in contrast with previous studies
(Nahata, 2008), constructed using the absolute number of investments - due to data limitations -
instead of the accumulated amount. This measure is based on the idea that every investment is
equal, which is obviously not the case. Having a large market share does hence not necessarily
mean that the venture capitalist invested the most in absolute monetary terms. The total invested
amount in many smaller investments may be smaller than the amount invested in one large
investment. More reputable firms might invest in larger portfolio companies for larger amounts.
Thus it is possible that our MarketShare proxy for reputation might surface the exact opposite
results than market share proxies used in earlier studies using the invested amount. As for now, the
needed information is not available, but it might be an interesting topic for future research.
31
5. Discussion, conclusions and limitations
In addition to previous research, we separate experience from reputation to examine their respective
impact on pre-money valuations. Furthermore, we distinguish between two distinct dimensions of
venture capitalist reputation: prominence and perceived quality. We address the influence of these
two different dimensions on pre-money valuations within the venture capital industry. We base
ourselves on a unique dataset free of survivorship bias containing 140 Belgian venture capital-
backed companies. Results demonstrate that venture capitalists with a higher experience tend to
offer lower valuations to companies, whilst reputation shows an ambiguous effect on the pre-money
valuation. The perceived quality of a VCF seems to have no impact. However, more prominent VCs
appear to offer higher valuations.
Several valuable contributions are added to previous research through this study. First,
although prior studies have focused on the impact of different dimensions of firm reputation on the
ability to mobilize resources (Vanacker, 2009) and on the ability of firms to demand a premium in
order to affiliate with them (Rindova et al., 2005), to our knowledge this is one of the first studies
examining the influence of distinct dimensions of reputation on valuations within the venture
capital-setting. Second, in contrast with previous research (Hsu, 2004) this study also distinguishes
between experience and reputation as different venture capitalist characteristics. Third, using a
private setting in our study tries to fill a gap in the literature. Certification becomes more valuable in
this environment due to higher information asymmetries, so reputation and experience are likely to
have different –stronger- effects from those in a public setting.
This study offers a valuable insight for entrepreneurial companies. Greene (1999) argued that
entrepreneurial companies often are likely to accept financing from whoever is willing to offer it
since it can be complicated to find enough financing due to high growth ambitions. However, such
reasoning can stand in the way of a successful ex-post development, given that an investor of lower
reputation could be unable to mobilize sufficient resources other than providing capital (Vanacker,
2009). Furthermore, Vanacker (2008) demonstrated that companies backed by venture capitalists
32
with higher experience grow significantly faster23
. We offer further evidence that the heterogeneity
in reputation and experience between VCs has an impact on valuation and that it is hence not only
about the amount of money raised, nor about the minimization of the dilution of capital.
Several limitations of this study open windows for further research. First, the sample only considers
Belgian venture capital-backed companies, which restricts the external validity of the results. This
is especially the case for the more developed Anglo-Saxon venture capital industries. However, the
VC industry in most Continental Europe countries has a similar structure and analogous procedures
compared to the Belgian venture capital industry since most VCFs are independent with a closed-
end structure (Heughebaert & Manigart, 2009).
Second, the sample comprehends a period of about 20 years (1988-2009). Within this
timeframe, the Belgian venture capital industry did not remain static. It evolved from an emerging
industry during the late 80s to a booming industry in the late 90s. After the market correction in the
beginning of the 21st century, VC activity first dropped significantly and grew in later years at a
moderate pace (Heughebaert & Manigart, 2009). Furthermore, the pending financial crisis is
expected to have a relevant influence as well, as Hand (2005) points out the correlation between
company valuations and stock market performance. Our data comprises only three deals in the latest
crisis, which makes it hard to make solid predictions.
Third, the valuation a VC offers for a certain equity stake may not be the only factor that
matters when entrepreneurs select a venture capitalist, as term sheet covenants may not be „priced
in‟ to the offered valuation (Hsu, 2004). The investor may structure financial contracts in a manner
that minimizes principal agent problems (Kaplan & Stromberg, 2001). Our dataset does not cover
information concerning potential clauses incorporated in the negotiated contract, so it could be that
the interpretation of the studied valuations is not straightforward, since these clauses may impact
the entrepreneurial interpretation of a term sheet. It may well be that higher initial valuations go
hand in hand with tougher contractual clauses and it seems defensible that more experienced or
reputable investors are able to negotiate much tougher contract with entrepreneurial management
teams through accumulated knowledge on the subject.
23 This is true for the asset growth rates, he finds no evidence for the employment part of growth.
33
Further, variables on the venture capitalists‟ characteristics are undoubtedly no perfect
reflections of their reputation and experience. We measure perceived quality using a variable that
also proxies experience. It would have been of superior value if we were able to use a distinct
variable for perceived quality such as the quality of the venture capital firm‟s management team, a
ranking by peers or a ranking by portfolio management teams. More generally, in contrary with
Anglo-Saxon countries it could be that in Belgium there exists another perception of a venture
capital firm‟s reputation and experience. For example, it may be that entrepreneurial companies in
Belgium are more concerned with the broadness of a VC‟s network to form a perception of his
reputation.
Fifth, venture capitalists often valuate their potential investments in terms of the quality of
the entrepreneurial management team, customer acceptance and potential market share (Sörensen,
2007). We are, due to data limitations, not capable of researching these important proxies for
company quality. It would have been of superior value in our research.
Finally, we did not control for the unobserved characteristics of the portfolio companies‟
quality, which may be noticeable in ex-post information. This might influence the selection effect
even further. Also, we did not control for VC characteristics apart from reputation and experience.
Heughebaert & Manigart (2009) however, demonstrated that the type of investor drives valuation.
34
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