Investment Strategies of Venture Capital Funds

Bachelor Thesis, 2019

58 Pages, Grade: 1,3

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Table of Contents

List of Tables

List of Attachments

List of Abbreviations

1 Introduction

2 Overview of Venture Capital
2.1 Definition of Venture Capital
2.2 Differentiation of Venture Capital to Other Forms of Equity Financing
2.3 Organizational Structure of Venture Capital Funds

3 Review Framework and Method

4 Results of the Review
4.1 Definition and Dimensions of Investment Strategies
4.2 Analysis of Methodology of Reviewed Literature
4.2.1 Measures of diversification and specialization
4.2.2 Measures of performance
4.3 Theoretical Background
4.3.1 The neoclassical theory of finance
4.3.2 The resource-based theory
4.3.3 The agency theory
4.4 The Investment Strategy–Performance Relationship
4.4.1 Stage Theoretical considerations Performance implications
4.4.2 Industry Theoretical considerations Performance implications
4.4.3 Geography Theoretical considerations Performance implications
4.4.4 Influencing and moderating factors
4.5 Synthesis of Findings and Critical Assessment of Data Sources

5 Discussion
5.1 Interpretation of Results
5.2 Limitations
5.3 Future Research



List of Tables

Table 1: Characteristics of Independent Venture Capital, Captive Venture Capital and Informal Venture Capital

List of Attachments

Appendix 1: Stages of venture capital financing

Appendix 2: Keywords Used for the Database Search

Appendix 3: Analysis of the Methodology of Reviewed Studies

Appendix 4: Studies Analyzing the Investment Strategy–Performance Relationship

Appendix 5: Discussion of Risk-Return Considerations for Investment Strategy Dimensions

List of Abbreviations

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1 Introduction

The international economy and society at large are undergoing a massive transformation fueled by technological changes. Advancing and stimulating innovation has become a major goal for government leaders all around the globe as advancements in technology and business models boost productivity and efficiency and ultimately result in economic growth (DRI-WEFA, 2002). The most important vehicle for generating technological innovations are young and small-sized companies (start-ups) who face the challenge of operating in fast-growing and highly volatile industries. Venture capital (VC) as a financing form has developed as a critical provider of financial support, market expertise, and industry contacts to realize the potential of start-ups (Gompers, 2008). Many of today’s industry leaders were backed by VC: Google, Alibaba, WhatsApp, Twitter, Facebook, Snapchat, or Dropbox are just the most prominent examples with countless more having received VC financing (CB Information Services, 2019). In 2018 alone, the value of first-time financing deals into VC-backed companies worldwide amounted to 17 billion U.S. dollars (KPMG, 2019) whereas the value generated from startup exit deals totaled 219 billion U.S. dollars (MTB, 2018). These numbers showcase the significance of VC, and the positive impact of this form of financing on the growth of an economy is now undisputed (Audretsch, Lehmann, & Keilbach, 2006; DRI-WEFA, 2002; Kortum & Lerner, 2000).

The investment decisions of venture capitalists1 have thus been the subject of numerous theoretical and empirical studies. VC firms2 typically do not invest in one project only, but rather combine several investments in a portfolio. Some firms diversify their investments across different industries, development stages, or geographical regions, while others specialize in a few. In light of the above, the main purpose of this thesis is to develop an understanding of the investment strategies of VC funds. With the choice of the fund’s investment strategy, the VC firm determines which ventures3 to invest in and how these investments are combined. The result of the VC fund’s investment activities is a portfolio characterized by expected rate of return and associated risk (Chiampou & Kallett, 1989). The task of the VC fund managers is to control or manage this risk, keeping it in general alignment with the fund’s (i.e., the LPs’) risk preferences. The investment strategy is the fund manager’s means to carry out this task, and in doing so, he or she has two general options to control risk: diversification and specialization (Clercq, Goulet, Kumpulainen, & Mäkelä, 2001). In general, diversification refers to a high degree of variation of the constituents of a portfolio with regard to the nature of the projects held (Brealey, Myers, & Allen, 2017) and in the view of traditional financial theories, diversification is understood to reduce risks that are associated with a particular investment, which results in overall lower portfolio risk (Lockett & Wright, 2001). A VC fund is said to follow a diversification strategy if it holds a portfolio of companies that are heterogeneous in nature and characteristics. However, the investment decisions of venture capitalists are not solely determined by risk/return considerations (Knill, 2009). This view implicitly assumes that the venture capitalists are not able to influence PC risk (Lockett & Wright, 2001). An equally important aspect that VC fund managers need to consider is the “scope of the conglomerate those decisions create” (Knill, 2009, p. 442). When VCs are seen as providers of non-financial support, closely monitoring and assisting their PCs, the resource-based theory suggests that specialization results in the development of unique experience, skills, and expertise. This “unique bundle of heterogeneous resources” (Cressy, Malipiero, & Munari, 2014, p. 145) can then be applied across the fund’s multiple PCs to add value to these ventures (Dimov & Clercq, 2006). Thereby, a VC fund follows a specialization strategy when choosing to invest in ventures that are similar in nature and characteristics (Knill, 2009).

This thesis will review the research conducted on the investment strategies pursued by VC funds and the dimensions among which these are formulated. Specifically, this thesis seeks to answer the following research questions: Which portfolio strategies do venture capitalists pursue in practice? How can these be explained from a theoretical point of view? What factors influence the choice of investment strategy? What are the implications of the investment strategies for VC fund performance?

The remainder of this thesis is structured as follows. The first section provides a general overview of VC. The next section briefly describes the method and framework of the review. The results of the review are presented in the following section, including a definition of investment strategies and a description of the dimensions along which they are formulated. This is followed by an analysis of the methodology of the reviewed studies. The section continues with a summary of relevant theories, an examination of the relationship between investment strategy and performance and concludes with a synthesis of the findings. The last section links the empirical findings with the theory reviewed in the previous section and discusses implications for practice. Finally, some limitations of this thesis and avenues for further research are identified.

2 Overview of Venture Capital

This section provides a general overview of VC. First, this form of financing is defined. Secondly, VC is differentiated from other forms of equity financing. Finally, the organizational structure of VC funds is described.

2.1 Definition of Venture Capital

As Landström (2007) remarks, “it is not an easy task to provide a generally accepted definition of institutional venture capital (also called ‘formal venture capital’) – the number of definitions is almost as great as the number of authors writing articles on the subject” (p.5). Accordingly, some often-cited definitions of the term venture capital are provided to outline the characteristics of this form of financing instead of adopting one single definition. In their present yearbook, the American National Venture Capital Association (2019) describes VC as "a segment of the private equity industry which focuses on investing in new companies with high growth potential and accompanying high risk” (p. 59). Gompers and Lerner (2001) provide another often-cited definition: VC consists of “independent, professionally managed, dedicated pools of capital that focus on equity or equity-linked investments in privately held, high growth companies” (p. 146). These definitions focus on new companies and the characteristics associated with newness whereas Sahlman (1990) sees VC as “a professionally managed pool of capital that is invested in equity-linked securities of private ventures at various stages in their development [emphasis added]” (p. 473). To summarize the defining elements of the previous characterizations of VC, Brander, Amit, and Antweiler (2002) identified four core characteristics that can be subsumed under the term VC: (1) “financing for privately held companies (2) in the form of equity” (p. 428) with the venture capitalist (3) serving “as an intermediary between the investors and the entrepreneurs” (p. 428) while he is (4) “actively involved in the management of young companies” (p. 428). This definition also highlights the now generally recognized active involvement of venture capitalists which has been found to contribute to the success of the PCs (Bottazzi, Darin, & Hellmann, 2008; Hellmann & Puri, 2002).

2.2 Differentiation of Venture Capital to Other Forms of Equity Financing

The term VC is often used differently in the literature, which is why the term is first differentiated it from the often synonymously used concept of private equity (PE). Academics in the United States (US) commonly categorize VC as a segment of the broader asset class of private equity (e.g., Gompers & Lerner, 2004; Metrick & Yasuda, 2011). This categorization differs from that of European researchers who use both VC and PE somewhat interchangeably. Following the US categorization, the other three main classes of PE are mezzanine capital, buyout transactions, and distressed investing (Metrick & Yasuda, 2011). PE differs from VC in that PE investments are typically made in companies with a proven track record and existing cash flows. Private equity funds also often take publicly listed companies private (Tykvová, 2018). In contrast, venture capitalists usually invest much earlier into innovative, young companies that are not publicly listed, potentially realizing significantly higher rates of returns, but naturally risking the loss of their whole investment in any given venture (Drover et al., 2017). Another significant difference is that private equity funds often employ substantial amounts of leverage in their investment activities. VC firms tend to employ only equity for their investments (Tykvová, 2018). The third distinction lies in the fact that, as active financial intermediaries, venture capitalists not only provide young companies with capital, but are actively involved as consultants on management issues (see, e.g., Amit, Brander, & Zott, 1998; Barry, 1994; Sahlman, 1990; Timmons & Harry J. Sapienza, 1992). Their involvement can be classified into strategic roles, networking roles, and social/supportive roles (Timmons & Harry J. Sapienza, 1992).

Since the term VC has been used very differently in the literature reviewed, the perspective of VC in its broadest sense will be adopted to refer to early-stage, expansion-stage, and later-stage financing4. This allows for an evaluation and analysis of a broader range of relevant research. Moreover, it is imperative to note that several sources of outside equity financing for entrepreneurs exist. Next to institutional VC, informal VC is the second major form of equity-linked source of capital for high-growth ventures (Wong, 2010). In a narrow sense (Landström, 2007), the informal VC market is comprised of so-called business angels. Freear, Sohl, and Wetzel (1994) define business angels as “[accredited], high net worth individuals (. . .) who invest a portion of their assets in high-risk, high-return entrepreneurial ventures” (p. 109). Apart from informal VC, the broader segment of institutional VC is made up of independent VC funds and captive VC funds, the latter of which can be differentiated according to their respective institutional affiliation (Tykvová, 2018). Independent VC is the traditional form of VC and characterized by being wholly owned by the venture capitalist professionals who are not affiliated with any institution (Drover et al., 2017). Captive VC funds, on the other hand, are funded and controlled by a parent organization (Landström, 2007). Two of the better-researched types of captive VC funds are governmental venture capital (GVC) funds and corporate venture capital (CVC) funds. GVC funds are affiliated with governmental institutions (Douglas Cumming et al., 2007) whereas CVC funds are managed by established corporations such as larger, non-financial companies, banks, or insurance companies (Röhm, 2018). Table 1 – For a detailed overview of the stages of VC financing, please refer to Appendix 1. Characteristics of independent venture capital, captive venture capital and informal venture capital details the differences of independent VC, captive VC, and informal VC in terms of fund structure, source of funds, objectives, and investment strategy. Throughout the remainder of this thesis, the term VC will refer to the traditional form of independent VC and not to any forms of captive VC.

Table 1: Characteristics of Independent Venture Capital, Captive Venture Capital and Informal Venture Capital

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Source: Based on Douglas Cumming et al. (2007) and Landström (2007) with modifications from Drover et al. (2017), Siegel, Siegel, and MacMillan (1988), Mayer, Schoors, and Yafeh (2005).

2.3 Organizational Structure of Venture Capital Funds

The venture capital limited partnership (VCLP) between the general partner (GP) and the limited partners (LP) is the dominant organizational form in the VC industry (Sahlman, 1990). The GP in the VCLP is the VC firm itself, managed by investment professionals (commonly referred to as venture capitalists or general partners) who make the investment decisions for the company. The venture capitalists are the legal owners of the VC firm and thus bear unlimited liability (Sahlman, 1990). Usually, they participate in the VC funds with their capital for reasons of incentive alignment. The capital commitment of the venture capitalists commonly does not exceed more than one percent of the overall capital of the VC fund, however. LPs are the investors that provide the lion’s share of capital for the fund (Tykvová, 2018). They are mainly institutional investors, such as state and private pension funds, banks, and insurance companies (Gompers & Lerner, 1999). The VCLP is the contractual agreement governing the relationship between LPs and GPs over the whole lifetime of the fund (Gompers & Lerner, 2004). After the formation of the VCLP, the LPs only have limited access to the fund’s assets and are prohibited from withdrawing their commitments. Besides, they may not participate in day-to-day operations if their liability is supposed to be limited to the capital they committed to the fund (Sahlman, 1990). The outcome of this agreement “is important because it is the crucial mechanism for limiting the behavior of venture capitalists” (Gompers & Lerner, 2004, p. 29). Most funds have a finite, fixed life of around ten years, with the option of extension of one to three years to accommodate companies seeking follow-up funding (Douglas Cumming et al., 2007). After all portfolio investments have been liquidated or after the fund is dissolved, the proceeds are distributed to the LPs and the GP (i.e., the VC firm) according to the terms set in the VCLP. Generally, venture capitalists receive a fixed management fee for running the VC firm as well as a performance-based percentage of the fund proceeds (Douglas Cumming et al., 2007). The LPs obtain the rest of the proceeds from the fund investments (Tykvová, 2018).

3 Review Framework and Method

This review covers articles published in English international journals from 1980 onwards. The three databases that were searched via eAccess of the Technical University of Munich are Thomson Reuters’/Clarivate Analytics’ Web of Science, Elsevier’s Scopus, and EBSCO’s Business Source Complete. Based on preliminary searches using Google Scholar, combinations of keywords5 from two categories, ‘venture capital’ and ‘investment strategy’, were identified. These were then used to search the title, abstract, and keyword/subject categories of the articles in the databases. A screen of the title and abstract (and introduction if needed) served as the initial inclusion criteria to determine whether the article was relevant to the topic of this thesis. Further, articles from journals which did not meet at least the B rating of the VHB-JOURQUAL3 classification were excluded from the review. The Venture Capital Journal was an exception from this rule as some highly relevant articles are contained in this journal. A significant number of relevant articles were also found by reviewing the reference lists of the articles identified from the database search.

4 Results of the Review

The review is organized as follows. First, a definition and overview of the dimensions of investment strategies are provided6. Next, the methodology of the reviewed studies is analyzed followed by a summary of relevant theories. After that, the relationship between investment strategy and performance is examined along the three dimensions of investment strategies. The section concludes with a synthesis of the findings and critical assessment of used data sources.

4.1 Definition and Dimensions of Investment Strategies

There is a large volume of published studies investigating VC fund’s diversification and specialization strategies. In his early empirical work, Robinson (1987) reported the emergence of “strategic groups” (p. 53) in the VC industry. Among other things, these groups are differentiating themselves from each other based on their focus on the different developmental stages of investee companies. In addition to confirming Robinson’s (1987) results, Sahlman (1990) noted two other dimensions of differentiation in his seminal article: industry and geographic focus. “Some [VC] firms focus on computer-related companies, others on biotechnology or specialty retailers. Some will invest only in early-stage deals, whereas others concentrate on later-stage financings. Many firms also limit their geographic scope” (p. 489). Similarly, Barry (1994) concluded that venture capitalists focus on specific development stages or industries. Elango, Fried, Hisrich, and Polonchek (1995) examined further potential opportunities for differentiation and found that VC firms differed with regard to their size, amount of support provided and preferred investment stage. From these analyses, it can be concluded that “’generalist’ funds with broadly spread investments coexist with ‘specialist’ funds which are more focused on specific industries, [development stages] and geographical areas” (Cressy et al., 2014, p. 140).

The term investment strategy in this thesis refers to the diversification or specialization choices of a VC fund along three dimensions: (1) industry focus, (2) developmental stage of investee companies and (3) geographic scope. The first dimension corresponds to the industry or sector in which the PCs operate in. While some VC funds seek to invest in a range of industries to limit their exposure to unsystematic risk (Norton & Tenenbaum, 1993), a VC fund can also have a specific industry focus, such as software, biotechnology, energy, or consumer goods (NVCA, 2019), to build specialized expertise and knowledge to better assist the PC (Clercq, 2003). The second dimension corresponds to the developmental stage (i.e., early, expansion, late-stage) of the PC at the time of investment. Again, the VC firm can choose to either specialize in investments at a particular stage which enables them to take advantage of specialized knowledge. Alternatively, the fund could be diversified across several/all stages, for instance, to have access to a much greater number of potential companies to invest in (Clercq et al., 2001). The third and final dimension corresponds to the geographic location of the respective PCs. Being located in close geographic proximity to the investee company may facilitate the relationship between venture capitalist and entrepreneur. In contrast, a wider geographic investment scope might enable higher returns but could limit the options for involvement by the venture capitalists (Cressy et al., 2014).

4.2 Analysis of Methodology of Reviewed Literature

This section analyzes the methodology of the reviewed literature. Specifically, the measures of diversification and specialization as well as of VC fund performance7 are reviewed.

4.2.1 Measures of diversification and specialization.

Moving on to the analysis of diversification and specialization measures, a range of operationalizations have been developed: binary, categorical, and continuous variables. Binary variables were mostly adopted to measure fund or firm specialization on certain investment stages, usually operationalized as the percentage of investments into one or multiple specific stages (e.g., Bartkus & Hassan, 2009; Elango et al., 1995; Patzelt, Knyphausen-Aufseß, & Fischer, 2009). Another way of measuring the investment strategies of VC funds are categorical variables (e.g., Hege, Palomino, & Schwienbacher, 2003; Lockett & Wright, 2001; Norton & Tenenbaum, 1993; Patzelt, Knyphausen-Aufseß, & Fischer, 2009). These are based on some form of classification of the stages, industries, or geographic regions, although there is no consistent way of doing so among the studies reviewed. Alternatively, the classifications can be used in the form of a continuous variable. The most straightforward and probably most intuitive way is to count the number of industries, stages, or geographic regions (e.g., Gupta & Sapienza, 1992; Humphery-Jenner, 2013; Knill, 2009) or the percentage of investments (e.g., Ljungqvist & Richardson, 2003; Patzelt, Knyphausen-Aufseß, & Habib, 2009) to assess the level of diversification or specialization. However, one of the disadvantages of this approach is, for example, that “taking into account only the number of different industries does not allow differentiation between VCFs with a high versus low total number of portfolio companies spread over a given number of industries” (Clercq et al., 2001, p. 50). The HHI and entropy measures have been developed to overcome this; they assess the relative degree of specialization or diversification (Cressy et al., 2014). The more refined results of these measures can also be utilized in conjunction with a greater variety of analytical techniques. Besides, the classification is (more) standardized, thereby making it easier to replicate the research and build upon the results (Robins & Wiersema, 1995). Example studies that employed the HHI include Clercq (2003), Dimov and Clercq (2006), Gompers, Kovner, and Lerner (2009), Han (2009) and Buchner, Mohamed, and Schwienbacher (2017). Cressy et al. (2014) used the Complement of Herfindahl index (a variation of the HHI) to measure fund diversification for industries and geography. Other, more exotic measures include the Index of Competitive Advantage (Cressy, Munari, & Malipiero, 2007) and the Focus Concentration Ratio (Han, 2009). Lastly, Clercq et al. (2001) developed their own, more refined measure to better compare the relative size of investments into different industries, stages, or geographic locations. Likewise, Knill (2009) used self-developed measures.

4.2.2 Measures of performance.

Turning to the second important unit of analysis, two common approaches to measuring fund performance are widely used: IRR and successful exit outcomes. Especially in the studies reviewed that belong to the strand of PE research, the IRR has been the dominant measure of fund performance (e.g., Buchner et al., 2017; Humphery-Jenner, 2012; 2013; Ljungqvist & Richardson, 2003). Moreover, the databases differ in how IRRs are reported (Buchner et al., 2017; Rin, Hellmann, & Puri, 2013). Gross returns quantify the total return of a VC investment without considering any compensation fees, whereas net returns do take these into account. Round level investment data is thus sufficient to calculate gross returns, but for net returns, the data needs to be on the fund level (Rin et al., 2013). There are a number of drawbacks associated with IRR as a measure of fund performance. Firstly, it is often not clear whether gross or net returns are used, making it difficult to compare the analyses from studies (Cressy et al., 2014). Secondly, the databases that include return data are based on the voluntarily provision of this information by VC firms and LPs since neither of them are legally required to report this kind of data (Rin et al., 2013). It is thus very difficult to obtain information about IRRs in the first place. Also, self-reporting introduces reporting/selection bias, “i.e. the fact that reporting is likely to be (positively) correlated with performance” (Rin et al., 2013, p. 621). Thirdly, the data on returns available in commercial databases “is based on unrealized as well as realized investments, which introduces noise and potential biases due to subjective accounting treatment” (Cressy et al., 2014, p. 148).

Therefore, an alternative approach is approximating investment performance as successful exit outcomes through IPO or M&A of the PCs (e.g., Bartkus & Hassan, 2009; Clercq, 2003; Cressy et al., 2014; Han, 2009; Hege et al., 2003; Matusik & Fitza, 2012). This assumes that successful exits result in superior returns over non-exited investment, as confirmed by empirical data (Gompers et al., 2009). Still, a successful exit is only a proxy of investment performance that does not quantify the returns earned. Another issue is that the returns of IPO vary considerably (McKenzie & Janeway, 2011). This suggests that conclusions drawn from studies using this measure must be interpreted with caution. Furthermore, Rin et al. (2013) note that exits are measured inadequately in most databases. Some studies drew on alternative measures. To disentangle the effects of a diversification strategy on the VC firm and the PCs, Knill (2009) used VC firm growth to assess the impact on the VC firms whereas time to IPO and M&A exit were used to assess the impact on the PCs. To measure the performance of PCs, operating profitability has also been utilized (Cressy et al., 2007). Lastly, Dimov and Clercq (2006) used the failure rate of the portfolio to quantify the effects of a specialization strategy.

4.3 Theoretical Background

This chapter provides an overview of theories relevant to explaining the choice of investment strategy: the neoclassical theory of finance, the resource-based theory, and the agency theory.

4.3.1 The neoclassical theory of finance.

Modern portfolio theory, the core of neoclassical finance8, provides a first theoretical argument for the choice of venture capitalists to diversify their VC fund portfolios. Generally, investors are faced with the decision of how to allocate their capital among different asset classes or securities, the “portfolio selection decision” (Constantinides & Malliaris, 1995, p. 1), which involves decision-making under uncertainty (Ruhnka & Young, 1991). Associated with that are the probabilistic concepts of expected return and risk, which is defined as “the variability in asset returns” (Norton & Tenenbaum, 1993, p. 433)). Markowitz’ (1952) seminal work on portfolio selection serves as the basis of modern portfolio theory with Tobin (1958) providing extensions regarding liquidity preferences (Constantinides & Malliaris, 1995).

VC funds are portfolios consisting of securities, which are the shares of investee companies. In order to solve the portfolio problem, Markowitz (1952) accounts for risk through the M-V rule9 where M is the mean expected return and V is the variance of the returns (Constantinides & Malliaris, 1995). The expected return M corresponds to the weighted sum of the individual securities, but Markowitz (1952) important insight lies in the fact that the variance of returns V is not merely the weighted sum of the individual securities. Instead, V is expressed in terms of the stochastic concept of co-variance. This concept considers not only the unique characteristics of the security but also how the security co-varies with the other securities of the portfolio (Elton & Gruber, 1997). The works of Markowitz (1952) and others are of great significance for portfolio selection and formation. If the returns of the individual investments are uncorrelated, increasing returns with falling variance can be realized up to a certain point. In the case of fully negatively correlated returns (i.e., high returns for one investment are associated with low returns for another investment), investors can achieve positive expected returns with variance decreasing down to zero using a portfolio mix. In contrast, no diversification effect can be achieved if the expected returns are fully positively correlated (Elton, Gruber, Brown, & Goetzmann, 2014). Portfolio selection theory provides crucial practical implications for the portfolio selection of VC funds. Venture capitalists can reduce the overall risk of their portfolio without reducing expected overall returns with an appropriate mix of investments into PCs, which differ in their unique risks. At a minimum, this requires the returns of the individual investments not to be entirely positively correlated, however (Constantinides & Malliaris, 1995; Lockett & Wright, 2001; Manigart et al., 2006).

Sharpe (1964), Lintner (1965) and Mossin (1966) built upon portfolio selection theory and developed the capital asset pricing model (CAPM). The CAPM is fundamentally based on the same principles as portfolio selection theory provides further arguments for a diversified portfolio. However, the model also introduces some additional rigid assumptions10. Many of these assumptions are unrealistic in practice, but some very relevant conclusions can still be made (Sharpe, 1964). Investors will choose to own the so-called market portfolio, which is made up of all risk assets and contains those in proportion to their relative shares in the overall markets. Together with the risk-free asset, the market portfolio forms the so-called efficient frontier (Elton et al., 2014). Risk-averse investors then choose a combination of risk-free and risky assets to adjust the risk of the market portfolio according to their individual risk-return preferences (Elton et al., 2014; Mossin, 1966; Perridon et al., 2014). Applying this to VC, fund portfolios will then be constituted of investee companies fraught with differing risk levels depending on the VC investors’ individual preferences for risk and return. The CAPM divides the total risk associated with any investment into two types: (1) systematic (market) risk and non-systematic (company or unique) risk (Lockett & Wright, 2001). Norton and Tenenbaum (1993) refer to systematic risk as “the effects of market or economy-wide influences on the returns to each asset” and to non-systematic risk as “firm, industry, or other asset-specific effects” (p.433). The market risk is denoted by beta ((3) in the CAPM and can be calculated from the covariance between the expected return of the individual investment and that of the market portfolio, divided by the variance of the expected return of the market portfolio (Perridon et al., 2014).

Several studies11 (Fiet, 1995; Kaplan & Strömberg, 2004; Ruhnka & Young, 1991; Tyebjee & Bruno, 1984) have investigated risk factors in the context of VC investing as VC investors need to deal with high levels of unique risk in their investee companies (Norton & Tenenbaum, 1993). It can be concluded from these empirical studies that VC firms are generally faced with high unsystematic risk by investing in new ventures (Norton & Tenenbaum, 1993) because those companies are characterized by “a high level of uncertainty as well as a high risk of failure” (Ruhnka & Young, 1991, p. 115). Hence, the investment outcomes tend to vary a lot bi-directionally, which results in the returns of VC fund portfolios to fluctuate a lot, too (Ruhnka & Young, 1991). As portfolio selection theory has shown, the VC managers can largely diversify all risk except for the market risk by including investments with negatively correlated return expectations in his or her portfolio (Copeland et al., 1988). It follows from this that there should be a return premium for systematic risk only and that venture capitalists can only attract investors if they can eliminate non-systematic risk to a large extent (Norton & Tenenbaum, 1993). This implies that the returns of a portfolio of investment companies specialized in specific industries, stages, or geographic markets will co-vary to a certain extent. Thus, the non-systematic risk is not reduced to insignificant levels, and the resulting higher risk requires higher returns. In contrast, a well-diversified portfolio of investments should then require lower returns as risk is not as high (Manigart et al., 2002).

However, the conclusions drawn from the CAPM must be considered with caution since the rigid assumptions as well as the implications, e.g., investors holding all risky assets (Levy, 1991), do not hold in practice12. As with any financing transaction (Merton, 1987), a central problem of VC investing is the procurement and processing of information about the investment object and the associated costs of assessing and evaluating this information (e.g., Constantinides & Grundy, 1989; Eisenhardt, 1989; Manigart et al., 2002). The generalized version of the CAPM (GCAPM) addresses this by taking incomplete availability of information for the investor into account and will be described in the following.

The GCAPM is the result of the works of Levy (1978, 1991) and Merton (1987), who acknowledges that “financial models based on frictionless markets and complete information are often inadequate to capture the complexity of rationality in action” (p. 484). The GCAPM predicts the formation of different segments in the capital market as the existence of fixed costs to attaining information are considered13. Levy (1978) proposes that investors realistically need to deal with variable as well as fixed transaction costs. Variable transaction costs do not influence the results of the CAPM, but fixed transaction costs do. These are mainly connected to developments and events within the investee company. The fixed transaction costs prevent a fully diversified portfolio by setting an upper bound to the number of different investments in a portfolio (Levy, 1978, 1991; Norton & Tenenbaum, 1993). This conclusion is highly relevant for the portfolio formation of VC funds, considering that the procurement and processing of information about the PC is a central problem for VC investments. Similarly, Merton (1987) relaxes the premise of perfect markets. He does not implicitly include transaction costs in his model, but instead starts from the premise that financial actors only have information on a subset of the market portfolio. Further, he assumes that information acquisition about investment opportunities not within the investor's area of expertise is associated with high fixed costs. Consequently, investors exclusively invest in those assets on which they already possess informational advantages or at least adequate amounts of information. Also, this results in investors only investing in those instances where they can acquire information at low costs (Levy, 1991; Merton, 1987; Norton & Tenenbaum, 1993).

However, the neoclassical view has been criticized in the context of VC. The VC market is not perfect due to unequal access to information and the illiquidity of VC investments (Manigart et al., 2002). Also, diversification is difficult to obtain in practice with limited fund volume and deal flow (Cressy et al., 2014; Manigart et al., 2002). Further, the VC investors are actively involved in the companies, violating the assumption of known and fixed returns (Cressy et al., 2014). Benefits to specialized expertise, such as learning curve effects, and information sharing are not considered either (Manigart et al., 2002; Norton & Tenenbaum, 1993) as well individual differences between venture capitalists (Cressy et al., 2007).

4.3.2 The resource-based theory.

While the risk considerations of a VC fund’s portfolio of investee companies were central to the neoclassical theory of finance, the resource-based theory treats companies from the perspective of “a unique bundle of heterogeneous resources” (Cressy et al., 2014, p. 145). The resource-based view of the firm (RBV)14 is a collection of theories which are all based around the two premises of resource heterogeneity and resource immobility across firms. By applying and leveraging their strategic resources, which include knowledge and capabilities, firms can gain a competitive advantage (Miller, 2016). This notion is captured in a later extension of the RBV by Barney (1991), who defines firm resources as “all assets, capabilities, organizational processes, firm attributes, information, knowledge, etc. controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness” (p. 101). Resources can be both tangible and intangible (Manigart et al., 2002) and form the basis for a “sustained competitive advantage” (Barney, 1991, p. 99) provided they fulfill the VRIN criteria15 (Barney, 1991). A VC firm’s primary tangible resource is financial capital. Examples for intangible resources of venture capitalists mainly include their human capital, such as managerial, market, and product knowledge, and their networks through which they can provide further access to required resources for PCs (Baum & Silverman, 2004; Matusik & Fitza, 2012).

Two concepts that arise when considering the utilization of the resource of firms are economies of scope and economies of scale16 (Panzar & Willig, 1981). Both are relevant to VC financing. As VC funds are usually comprised of several investments due to the previously outlined advantages of diversification, the VC investor can benefit from economies of scale and scope using his tangible and intangible resources across investee companies with related needs (Bernile, Cumming, & Lyandres, 2007; Humphery-Jenner, 2012). Like any other company, VC firms have certain fixed costs (e.g., rent, accounting, legal) (Humphery-Jenner, 2012; Sahlman, 1990). When the VC firm’s fund capital increases, it can be expected that the associated unit costs do not increase linearly as they are spread over a larger number of investments (Sahlman, 1990). Similarly, the venture capitalists know-how can be easily applied across multiple firms without a considerable increase in cost and effort (Jääskelainen, Maula, & Seppa, 2006). However, there is an upper limit to these economies of scope because VC fund managers only have so much time to allocate to each PC (Kanniainen & Keuschnigg, 2003).

An extension of the RBV is provided with the knowledge-based view, which treats knowledge as a company’s most important strategic resource (Eisenhardt & Santos, 2006). The transfer, acquisition, and integration of knowledge and capabilities form the basis for competitive advantage and are, thus, the differentiating factor for explaining performance differences across firms. The literature on the knowledge-based view generally distinguishes between two distinct types of knowledge: tacit and explicit17 (Eisenhardt & Santos, 2006). Explicit knowledge can be easily communicated and resembles information that is a public good in the traditional economic sense that, once it is available on the market, everyone can use it freely. Tacit knowledge is hard, slow, and costly to communicate because it can only be gained by practical experience and observation (Grant, 1996). Only by applying tacit knowledge can it be transformed into messages (i.e., information) and then communicated, but this is still distinct from the concept of explicit knowledge (Eisenhardt & Santos, 2006). Explicit knowledge, although transferable, also contains a component based on experience. Moreover, explicit knowledge is also often based on tacit knowledge, so both types of knowledge are inseparably connected, and both are essential for a firm’s success. The primary source of competitive advantage arises from tacit knowledge’s unique and immobile nature (Eisenhardt & Santos, 2006). VC researchers have made a similar distinction to tacit and explicit knowledge in differentiating between general and specific knowledge18 (Dimov & Shepherd, 2005; Jensen & Meckling, 1995; Sapienza, Manigart, & Vermeir, 1996). General knowledge comprises the basic know-how that every venture capitalist is assumed to have about the specifics of founding, financing, and actively adding value to new ventures19 (Jungwirth & Moog, 2004). On the other hand, specific knowledge is related to specialized know-how about some particular industry, technology, or developmental stage (Clercq, 2003). Because of general knowledge being (close to) costless to transfer among actors in the same market, it is central to the venture capitalist’s role as a financial intermediary between LPs and investee companies. In contrast, specific knowledge is costly to transfer, which results in it being heterogeneously distributed among VC firms. Additionally, specific know-how provides an opportunity for differentiation and competitive advantage relative to other VC firms (Jungwirth & Moog, 2004).

Closely connected with knowledge are organizational learning20 and learning curve effects (Eisenhardt & Santos, 2006). A firm’s ability to absorb and implement newly created knowledge largely depends on its existing knowledge base, which is comprised of the individual members’ knowledge. When this existing stock of knowledge is diverse, extensive, and, most importantly, related to the new knowledge, competitive advantages to the firm can accrue (Cohen & Levinthal, 1990). VC firms accumulate knowledge from their prior investments and re-apply it to aiding existing investments (Sapienza & Clercq, 2000) and when selecting or evaluating new investments (Clercq & Dimov, 2008). On a larger scale, the probability of PCs failing is dependent on the VC firm’s ability to acquire and integrate new knowledge about the PC (Dimov & Shepherd, 2005). Learning is also relevant for improved access to a wider range of investment opportunities. Over time, the venture capitalists develop an expanded network of partnerships with third-party resource suppliers, other VC firms and entrepreneurs (Sapienza & Clercq, 2000), which can facilitate faster learning about investment opportunities and access to more investments (Sahlman, 1990). These partnerships can also serve to contribute to missing know-how and expertise (Clercq & Dimov, 2008).

4.3.3 The agency theory.

By relaxing the assumption of perfect markets, the neo-institutional theory of finance analyses institutions with respect to behavioral incentives in the context of corporate governance that result from risk considerations. The basis of all neo-institutionalist approaches are the exchange relations between two or more parties. The focus will be on the incentive approach, especially the principal-agent problem (Perridon et al., 2014). The starting point for the incentive approach is the “separation of ownership and control” (Fama & Jensen, 1983, p. 301) that is typically present in most types of organizations in the form of executives who run the firm and owners who are the majority shareholders. More precisely, agency relationships21 are situations in which one or multiple individuals (the principal (s)) contracts another individual (the agent) to execute a task for them (Eisenhardt, 1989; Jensen & Meckling, 1976). This way, the principal transfers the control over decisions to the agent (Jensen & Meckling, 1976) and also sets the reward for the agent depending on his or her performance22 (Eisenhardt, 1989). Principal-agent relationships are fundamentally characterized by asymmetries in information (Eisenhardt, 1989), usually in favor of the agent (Arrow, 1984). This informational advantage of the agent is of concern to the principal if the agent uses this advantage to his or her benefit (Arrow, 1984). The principal can counteract the divergent behavior of the agent by setting up appropriate incentive structures and by penalizing the agent (Jensen & Meckling, 1976). Still, in many situations, the principal is unable to observe the actions taken by the agent and unable to judge whether these increased the principal’s welfare or rather the agent’s (Arrow, 1984). The result is that the principal incurs costs related to the structuring and monitoring of contracts while the agent incurs costs when choosing to limit his scope of action. These costs are termed agency costs (Jensen & Meckling, 1976). Agency theory suggests contracts that detail the task and rights of the agent as well as the compensation scheme as the solution to these goal conflicts and the ensuing agency costs (Eisenhardt, 1989; Fama & Jensen, 1983). However, contracts are not a perfect mechanism to resolve the problems associated with information asymmetries. This leads to the agency problem where the two parties have conflicting goals, and the principal cannot monitor the behavior of the agent fully.

Besides, there is the problem of risk sharing when the risk attitudes of the parties differ, and the agent chooses to act in alignment with his or her risk attitude instead of the principal’s preference (Eisenhardt, 1989).

The agency framework is especially relevant for VC (Amit et al., 1998). For one, the process of venture capital financing is a typical example of the agency problem. The VC investor, as the principal, supplies financial and other intangible resources to the entrepreneur who, as the agent, needs these resources to grow his venture (Kaplan & Strömberg, 2001). The VC investor’s primary goal is to maximize the returns from the investment. While the entrepreneur might partially share this goal, he or she is also very likely to aspire to a whole range of other personal or organizational goals that conflict with the goal of maximum returns (Sapienza & Clercq, 2000). Alternatively, the relationship can be analyzed from the reverse perspective. Apart from funding, the entrepreneur employs the venture capitalist for managerial support. The venture capitalist does not necessarily view this goal as most relevant, so he or she might not dedicate as much time to nurturing the company or seek to take the company public prematurely (Sapienza & Clercq, 2000).

The literature further distinguishes among two types of information asymmetry: hidden action and hidden information. Hidden information refers to situations in which one has information that is unavailable to the other (Amit et al., 1998). To take advantage of this, the agent might be inclined to exaggerate his or her abilities; a phenomenon called adverse selection (Eisenhardt, 1989). In VC financing, entrepreneurs naturally have much better information about the quality of their companies. The entrepreneur might take advantage of this by misrepresenting the worth of his or her company to increase the chance of receiving funding (Amit et al., 1998). Hidden action is said to occur when the principal cannot fully observe the actions taken by the agent or when it is costly to do so (Perridon et al., 2014). This leads to the problem of moral hazard when “the agent may simply not put forth the agreed-upon effort” (Eisenhardt, 1989, p. 61). In the context of the venture capitalist-entrepreneur relationship, the investor can only allocate limited attention to control the effort of the entrepreneur and verify the decisions he or she is making for maximal company value (Amit et al., 1998). Thus, entrepreneurs might be incentivized to continue to run their business even though it is not profitable because it offers them private gains (Gompers, 1995).

The relationship between entrepreneurs and venture capitalists is especially fraught with high levels of uncertainty (Sapienza & Clercq, 2000). Thus, venture capital has developed a number of mechanisms to cope with agency problems (Douglas J. Cumming, 2005; Gompers, 1995; Kaplan & Strömberg, 2001, 2004). Mechanisms to limit adverse selection include the pre-investment screening and due diligence process, which aim at reducing informational asymmetries. Contracts based on outcomes are intended to align the interests of entrepreneurs and VC investors. Mechanisms to cope with moral hazard include contract clauses to replace the company’s management and staging of investments. Staging involves the successive provision of the full capital to the venture based on pre-defined milestones and company performance. Finally, the VC fund managers actively monitor and oversee the PCs to limit the opportunistic behavior of entrepreneurs, e.g., via a seat on the board, via regular interactions, or via consulting and assistance in decision-making.

This section has reviewed the neoclassical theory of finance, which provides some valuable insights for the investment strategies of VC funds about the risk-return relationship. The resource-based theory further considered both tangible and intangible assets of firms. The findings from the analysis of the agency theory shed light on the relationship between entrepreneurs and venture capitalists.

4.4 The Investment Strategy – Performance Relationship

This part of the thesis begins by exploring the theoretical rationales for the varying levels of diversification or specialization of VC funds. This is followed by a review of the respective performance implications23 of these strategies as well as a discussion of influencing and moderating factors. The section concludes with a critical assessment of the data sources and with a synthesis of the findings.

4.4.1 Stage. Theoretical considerations.

As pointed out in the previous chapter on the neoclassical theory of finance, diversification serves the purpose of eliminating the non-systematic risk that is associated with specific firms. Risk can be spread by diversifying investments across stages or reduced by specializing in specific stages and building up stage-specific knowledge. Provided the VC fund is able to maintain a well-diversified portfolio, it is then only susceptible to systematic, market-wide fluctuations (Cressy et al., 2014). Based on the varying risk-return profiles of investee companies in different stages, Buchner et al. (2017) propose that VC firms can cap their downside risk by investing in later stage firms. More interestingly, they formulate a “Risk Hypothesis” where the average risk of the portfolio is then lower if the VC fund employs a diversification. This permits investments into early-stage firms that are riskier but offer higher potential returns. In total, a diversification strategy should then offer higher returns on average. Another aspect of non-systematic risk is the liquidity risk that is associated with exiting investments during unfavorable market conditions. The timing of an exit might fall into a period of less liquid (“cold”) markets where the returns for the VC fund are lower whereas in “hot” market periods the returns would be much higher. By diversifying the fund portfolio through investing at differing developmental stages, the venture capitalist can decrease the likelihood of having to exit investments during unfavorable market periods, thus lowering liquidity risk (Norton & Tenenbaum, 1993). Another advantage of investing in multiple stages is improved deal-flow, i.e., an increased amount of investment opportunities. With more investment opportunities, the VC fund can be more selective in choosing companies that offer higher returns (Clercq et al., 2001). Makarevich (2018) also considers knowledge spillover effects to be advantageous. A stage-diversified fund is likely to benefit from transferring and drawing on expertise that is the result of having to monitor and advise companies from different stages.

However, diversification is not without tradeoffs. The more the VC fund invests at different financing stages, the less control will the venture capitalists have over the investee firm managers and the less advice will they be able to provide (Bartkus & Hassan, 2009; Clercq et al., 2001). This is because the variety of the PCs requires the venture capitalists to grow their general management skills instead of stage-specific know-how that would enable them to better add value to the companies (Clercq et al., 2001). Also, being actively involved in the management of the PCs is associated with time commitments and expenses. Thus, the more VC investors are involved in the ventures and the more ventures they need to monitor, the less they are able to add value and monitor the other PCs (Knill, 2009).

Whereas stage diversification lowered the monitoring intensity and value-added of venture capitalists, specializing in specific company stages increases the control over the PC managers and value-adding capabilities (Clercq et al., 2001; Cressy et al., 2014). By focusing only on some stages, the venture capitalists can build up stage-specific knowledge, capabilities (e.g., regarding the special challenges and needs of early-stage ventures), and networks of experts to better understand and manage company-specific risks and uncertainties as well as agency risks (Clercq et al., 2001; Manigart et al., 2002) if it is assumed that investee companies in similar growth stages require similar expertise and capabilities from the investors (Carter & van Auken, 1994). For instance, the venture capitalist can manage the agency problems of moral hazard and adverse selection more effectively since the extensive industry knowledge of the venture capitalists makes it much harder for entrepreneurs to conceal critical information on firm performance or to cover up management failures (Clercq, 2003). As the venture capitalists have specialized knowledge about the complexities and operational challenges of one or multiple company development stages, most of the fund’s portfolio will consist of firms along these stages, which enables the investors to transfer and share knowledge across these firms more easily (Clercq et al., 2001) as well as to re-apply the monitoring know-how on similar investee firms (Dimov & Clercq, 2006). This specialized knowledge is also likely to put the fund managers in a better position when screening and evaluating potential investment opportunities since the venture capitalists will possess more relevant knowledge on stage-specific complexities and problems (Clercq, 2003; Matusik & Fitza, 2012). Moreover, it is conceivable that VC funds specializing on certain financing stages will be able to build up better networks of resource suppliers (e.g., investment bankers in preparation of IPOs of more mature firms or law firms for companies in the founding stages) that the companies in the respective stages require (Clercq, 2003). The accumulated knowledge might also serve to reduce the chance of investee companies failing if venture capitalists have an improved understanding of the success factors that are central to some, especially the early, stages (Dimov & Clercq, 2006).

However, stage specialization is not without disadvantages. A specialized VC fund might struggle to find adequate investment opportunities and could even end up investing in ventures with negative return expectations (Gompers et al., 2009). The downside of having only specialized stage knowledge is that, in case of environmental changes or downturns associated with the stage domains the VC fund is focused on, the venture capitalists might not be able to adequately respond to these issues due to having only limited expertise to draw from (Makarevich, 2018). Performance implications.

Having discussed the theoretical rationales for stage diversification and specialization, this section summarizes the empirical evidence regarding the performance implications of the respective strategies.

Clercq (2003) reported that venture capitalists’ specialized stage knowledge positively impacts their number of IPO and M&A exits, although the impact is rather weak. Likewise, Dimov and Clercq (2006) demonstrated that VC firms’ knowledge and expertise regarding specific venture stages reduces the likelihood of the PC defaulting. Finally, Han (2009) found stage specialization to positively influence VC fund performance for both the proportion of IPO and M&A exits. Hege et al. (2003), however, found that there were less IPO and M&A exits with increasing rates of investments into early-stage ventures. Lastly, Cressy et al.

(2007) did not obtain a statistically significant result for their hypothesis that specialization of PE investors leads to improved operating profitability of the PCs.

Turning to the evidence for stage diversification, Bartkus and Hassan (2009) found that VC managers are more successful in taking their PCs public or selling them by way of M&A when they diversify across stages. Similarly, Buchner et al. (2017) obtained evidence that stage diversification increases VC fund performance (calculated with the internal rate of return (IRR)). In her in-depth study, Knill (2009) reported mixed results on the performance. A diversification by stage positively influences the growth of VC firms themselves but, when considering the time to IPO exit of the PCs, the influence is negative. In line with this, stage diversification is positively related to the probability that the portfolio will remain privately held. The results on time to exit by mergers & acquisitions (M&A) are not statistically significant.

4.4.2 Industry. Theoretical considerations.

Similarly to stage diversification, diversifying across a variety of industries allows the VC fund to lower the portfolio’s industry-specific risks such as those related to the technology and market (Cressy et al., 2014; Norton & Tenenbaum, 1993; Patzelt, Knyphausen-Aufseß, & Fischer, 2009). This is highly relevant for the business of VC because the firms the VC funds invest in often launch their products into new markets that are very unpredictable (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). By spreading their investments across industries, such uncertainties can be reduced to a minimum. Also, based on the “Risk Hypothesis”, if non-systematic risks are largely eliminated, riskier investments can be included in the fund portfolio that carry both greater upside and downside potential (Buchner et al., 2017). In the context of industries, high tech is considered to be a high-risk industry that offers the potential for high returns. Overall, a diversification strategy by industries should then increase fund returns. Likewise to stage diversification, industry diversification offers the VC fund a greater amount of potential investment opportunities to select from, thus permitting the VC fund to be more selective in choosing companies that offer higher returns (Clercq et al., 2001). Also, more investment opportunities mean that the possibility of achieving higher as well as lower returns increases (Cressy et al., 2014). Matusik and Fitza (2012) also consider the effects of broad industry knowledge on adaptability and flexibility. With investments into multiple industries, diversified VC firms possess a broader stock of knowledge that enables the fund managers to “guide an entrepreneurial firm down an alternate evolutionary trajectory, shepherding it toward the market that is a strong fit for the capabilities of the PC, rather than trying to guide the company’s development to fit with where the VC is specialized” (p. 410). A broad knowledge stock further provides VC firms with a general stock of knowledge that can be variably applied across many areas (knowledge spillover), and that can be used to find solutions by combining different perspectives (Makarevich, 2018). In addition to the advantages of industry diversification, it is important to take the possible drawbacks into account, too. If a VC fund spreads its investments across a wide range of industries, its portfolio will be more heterogeneous in nature. Consequently, the fund managers will need to grow their general management skills instead of industry-specific know-how that would enable them to add value to the PCs better and monitor them more thoroughly. This leads to the venture capitalists having less control over the ventures, which increases the possibility for agency problems (Clercq et al., 2001). Matusik and Fitza (2012) also argue that with increasing levels of diversification, a VC firm’s knowledge stocks become more diverse, i.e., the venture capitalists become less knowledgeable about industry specifics. Thus, the VC investors will not be able to judge investment opportunities as competently and manage them as well, subsequently. Additionally, it might be too costly for VC firms to attain sufficient know-how regarding individual industries if the fund is invested in too many unrelated industries (Bartkus & Hassan, 2009).

Moving to industry specialization, a VC fund can build up specialized industry know-how and expertise by solely investing in firms from those industries (Cressy et al., 2014; Matusik & Fitza, 2012). Makarevich (2018) subsume “an understanding of market domain trends, technology, regulation, human resources, competitive landscape, [and] successful product development strategies used by other companies in a domain” (p. 154) under the term industry knowledge. With this knowledge, investors can better evaluate and control for the peculiarities of certain sectors (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). With in-depth industry know-how, the venture capitalist can also offer more appropriate advice (Cressy et al., 2014) and increased added value since it enables the venture capitalists to “become more directly involved in the key decision making processes of their PCs as well as to better evaluate environmental resources and constraints affecting their actual and potential portfolio company investments” (Clercq et al., 2001, p. 45). Overall, this results in higher quality investments (Gompers et al., 2009). Besides, potential agency problems can be actively mitigated since the extensive industry knowledge acquired by the venture capitalists over time (Cressy et al., 2007) makes it much harder for entrepreneurs to take advantage of the agency problems of adverse selection and moral hazard (Clercq, 2003). In this way, the non-systematic risk of the fund is reduced (Clercq et al., 2001). Moreover, operating within a narrow range of industries allows the VC firms to build up valuable networks of “accountants, patent lawyers, marketing consultants and suppliers that aid in the growth of the [investee] firm” (Bartkus & Hassan, 2009, p. 135). Sapienza and Clercq (2000) also highlight the synergistic effects of specialization with regard to the management and marketing know-how of venture capital investors. Especially the monitoring of high-tech firms entails significant monitoring costs, but if the learnings and knowledge from ventures in the same or related industries can be re-applied to existing and future investments (learning curve effects, Clercq, 2003) the VC firms can take advantage of the synergies that arise from “evaluating and developing ideas related to these investments and in helping portfolio companies that operate within that area” (Matusik & Fitza, 2012, pp. 409–410). The accumulated knowledge might also serve to reduce the chance of investee companies failing if venture capitalists have an improved understanding of the competitive environment in certain sectors (Dimov & Clercq, 2006).

Nevertheless, industry knowledge is expensive to acquire, build up, and maintain (Bartkus & Hassan, 2009; Norton & Tenenbaum, 1993). As pointed out previously, VC firms with a narrow industry focus might find it difficult to find adequate as well as sufficient amounts of investment opportunities in the industries they are specialized in (Gompers et al., 2009). Additionally, VC funds focused on particular industries are especially liable to industry downturns, and in extreme cases, the know-how can become practically useless when the entire industry breaks down (Kang, Burton, & Mitchell, 2011). Performance implications.

Turning to the performance implications of an industry diversification or specialization strategy, Clercq (2003) found that VC firms who specialize by industry achieve a higher number of successful IPO and M&A exits. In like manner, Cressy et al. (2007) observed industry specialization by the VC firms to positively impact the operating profitability of PCs. Gompers et al. (2009) obtained similar results. The authors differentiated between specialization at the VC firm level and at the individual VC manager level. Specialization of the venture capitalists has a strong positive effect on investment performance, as measured by IPO and M&A exits or registration for an IPO. However, the specialization of the VC managers negatively influences performance when the VC firm itself is already specialized, whereas the effect is positive for non-specialized VC firms. The effect of increased specialization at the level of the VC firm is only very limited. Han’s (2009) study confirms these results by documenting a positive relationship between industry specialization and performance in terms of IPO and M&A exit. Similarly, Matusik and Fitza (2012) showed that VC firms benefit from specialization with more IPO exits. In contrast to these studies, no statistically significant effect of industry specialization on the rate of failure in VC firms’ portfolio was observed by Dimov and Clercq (2006). While there seem to be definitive benefits to industry specialization, the results for diversification are not as clear-cut. In her seminal article, Knill (2009) observed industry diversification to impact the growth of VC firms positively. However, diversification across industries prolongs the time to exit by IPO as well as M&A for the PCs. In line with this, the likelihood of the PCs remaining privately held is higher with increasing levels of diversification, too (Knill, 2009). Next, both Humphery-Jenner (2012) and Humphery-Jenner (2013) found that industry diversification increases both fund IRRs and liquidation multiples. Matusik and Fitza (2012) further showed that high levels of diversification are associated with higher IPO rates of the PCs, whereas moderate levels had an inferior result. Lastly, both Ljungqvist and Richardson (2003) and Bartkus and Hassan (2009) found no statistically significant effects for an industry diversification strategy.

4.4.3 Geography. Theoretical considerations.

A diversification strategy in terms of geographic scope yields similar advantages to that of stage or industry diversification. Firstly, risks that are specific to certain regions can be widely eliminated when the VC fund has a broad geographic scope (Clercq et al., 2001; Mason, 2007). Secondly, the fund will have a greater number of investment opportunities to choose from when not limiting investments into specific geographic locations (Clercq et al., 2001). This increases the possibility of achieving higher as well as lower returns (Cressy et al., 2014). Finally, Tarrade (2012) proposes that cross-border investments might be part of the VC firm’s business strategy to “develop or gain new capabilities through a learning process, realizing economies of scale by repeatedly investing internationally” (p. 29).

Geographic diversification with vast physical distances between investors and PCs comes at a cost, however, due to cultural differences and contrasting legal and institutional environments (Buchner, Espenlaub, Khurshed, & Mohamed, 2018). With increasing geographic distance, agency problems might become worse (Tarrade, 2012) because the direct communication (a much more productive means of communication which cannot be achieved with electronic means, such as mail or phone (Gupta & Sapienza, 1992)) between VC managers and entrepreneurs is likely reduced. This then lowers the possibility of managing (Cressy et al., 2014) and monitoring the investments (Clercq et al., 2001), thus increasing agency costs for the VC firm (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). The VC investors might also have a harder time evaluating companies in more distant locations because they have a harder time acquiring sufficient information on the ventures and the market. This leads to increased market risk for the VC fund (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). Another disadvantage of large distances between VC investor and entrepreneur is that the support quality provided by the venture capitalist might deteriorate. This could lead to the PC performing worse, which, in turn, decreases the VC fund returns (Tarrade, 2012).

Focusing investments into single or few geographic locations enables the VC investors to obtain a deeper understanding of the location-specific technological, market, and competitive environment. This is because close physical proximity promotes face-to-face interactions, which are vital for both the quality and amount of support that VC investors provide (Mason, 2007). Further, specialized knowledge aids in properly evaluating potential investment opportunities. For instance, venture capitalists can more easily scrutinize and verify an entrepreneur’s qualities if they are from the same city or state because then the venture capitalists can leverage their ‘local’ knowledge and tap into their local connections (Mason, 2007). In addition, the VC investors become more capable of performing their value-added activities and can monitor their investments more effectively (Cressy et al., 2014). The VC firm can also benefit from knowledge spillover effects when the knowledge and expertise accrued over time from many investments spills over to other projects of the VC fund or firm (Kang et al., 2011). Another important aspect of close physical proximity to PCs are the social networks which are so essential to the supporting activities of the venture capitalists. When not located far away from the PCs, the VC managers can leverage this network more easily and besides, the quality of information from a local network is likely to be higher. Maybe even more importantly, the VC firm might only be able to build up a tight social network in the first place when it restricts its investments into few geographic locations. High-quality referrals for investment opportunities usually result from more local networks, too (Mason, 2007). Finally, the value of chance encounters shouldn’t be discounted: “there are real advantages that accrue to firms and venture capitalists to being ‘on the scene’ – unplanned encounters at restaurants or coffee shops, opportunities to confer in the grandstands during Little League baseball games or at soccer matches, or news about a seminar or presentation all happen routinely in such settings” (Powell, Koput, Bowie, & Smith-Doerr, 2002, p. 294).

However, a narrow geographic scope might turn into a liability when the whole VC fund portfolio in some geographic location fails for unforeseen reasons (Kang et al., 2011), e.g., due to political instability or economic downturns. Moreover, as discussed previously, the acquisition of specialized knowledge and expertise and maintaining a social network is both costly in terms of money and time. Lastly, the problem of finding sufficient amounts of investment opportunities in the geographical area of focus is another drawback of geographic specialization (Gompers et al., 2009). Performance implications.

Moving to the empirical evidence, only Han (2009) found a weak positive correlation between domestic geographic specialization and the fraction of investments being exited by way of IPO or M&A. However, when only IPO exits were considered, no statistically significant effect could be shown.

Turning to the evidence on geographical diversification, Knill (2009) revealed domestic and international diversification to be positively related to VC firm growth. But similarly to stage and industry diversification, the time to M&A exit is prolonged by both forms of geographic diversification, although the effect for is only marginal for international diversification. For time to IPO exit, domestic diversification had a negative impact, too, whereas no statistically significant effect for international diversification was identified. Humphery-Jenner (2012) identified a positive effect of domestic geographic diversification on both fund IRRs as well as liquidation multiples. Another study of the same author only revealed a positive effect of international diversification on the IRRs of seed funds (Humphery-Jenner, 2013). Buchner et al. (2017) found that fund IRRs were not affected by international diversification.

4.4.4 Influencing and moderating factors.

The following section reviews influencing and moderating factors of the relationship between investment strategy and performance. Expertise and experience have been identified to play a significant role in determining a VC fund’s investment strategy. In general, higher levels of experience are associated with higher levels of diversification across stages (Clercq et al., 2001). Dimov, Shepherd, and Sutcliffe (2007) revealed that more finance expertise leads to fewer investments into early-stage ventures. This relationship was further amplified for VC firms that had higher status while it was lessened for low-reputation VC firms. In contrast, science and engineering expertise, as well as entrepreneurial experience, is associated with early-stage specialization (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). Entrepreneurial, as well as international experience, is correlated with international geographic diversification. Besides, management expertise increases diversification across industries (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). Knill (2009) discovered that the previously insignificant effect of stage diversification on M&A exit becomes significant for average and expert venture capitalists. Further, little experience of venture capitalists increases the probability of PC failure with increasing stage, industry, and domestic geographic diversification, whereas average levels of expertise decrease the probability. High levels of expertise do not have an effect (Knill, 2009). The analyses by Buchner et al. (2017) revealed that industry and stage diversification positively impacted VC fund IRRs for experienced VC firms whereas for less experienced firms, there is no relationship between diversification and performance. Besides, when the venture capitalists diversified into stages they did not invest into in the past, fund IRRs were not significantly affected. However, when they mostly diversified into industries they did not invest into in the past, fund performance was negatively affected (Buchner et al., 2017).

Also, the investment strategies were revealed to have complex interactions with each other. The pioneering work of Gupta and Sapienza (1992) revealed that early-stage specialization is associated with more industry and domestic geographic specialization, which was confirmed by Clercq et al. (2001). Norton and Tenenbaum (1993) also found that specialization on seed investments leads to less diversification by industry and number of firms. Dimov and Clercq (2006) also posited that a specific industry focus leads to more deals coming from a particular stage. With higher grades of international geographic diversification, stage diversification was found to increase (Clercq et al., 2001). Han (2009) obtained similar results, concluding that “the focus in one dimension tends to imply that the VC funds are more focused in the other two dimensions” (Han, 2009, p. 27). For early-stage investors specifically, high degrees of industry diversification lead to more IPO exits, whereas, for late-stage investors, this effect becomes less pronounced (Matusik & Fitza, 2012). Also, international geographic diversification is higher for late-stage investors in comparison to early-stage investors (Cressy et al., 2014). The study by Humphery-Jenner (2013) revealed additional interactions. More industry-diversified funds were found to invest proportionally more into ventures in the seed stage and especially an international geographic diversification strategy of PEs funds investing into ventures in the seed stage had a positive impact on fund IRRs. Too much industry diversification in combination with international geographic diversification hurt fund IRRs because the venture capitalists are then too distributed across PCs. When the fund managers in prior fund’s diversified across industries, the fund performance in subsequent funds was increased as well. Moreover, the likelihood of specializing in the high-tech is reduced by domestic geographic specialization as found by Jungwirth and Moog (2004).

Fund size plays a role as well. Larger PE funds were found to be more diversified in terms of industries and geographic regions (Gupta & Sapienza, 1992), which is in line with the results obtained by Humphery-Jenner (2012). Han (2009) similarly observed VC fund size and age of the VC firm to negatively influence the degree of specialization in terms of stage, industry, and US regions. Another finding pertains to the negative influence of high levels of carry on industry diversification (Humphery-Jenner, 2013). Finally, Clercq et al. (2001) identified some more influencing factors. The relationship between average ownership and geographic or stage diversification was negative. Besides that, both dimensions of diversification were positively associated with the number of companies in investors’ portfolios.

Another moderating factor is the syndication behavior of the VC firms. The fewer co-investors a VC firm had, the more significant was the U-shaped relationship between diversification and the number of IPO exits. However, the U-shape was not moderated by low to moderate co-investing partners (Matusik & Fitza, 2012). Also, originating in a syndication was found to further increase the detrimental impact of diversification on time to exit of PCs (Knill, 2009).

4.5 Synthesis of Findings and Critical Assessment of Data Sources

Overall, the results imply that neither stage diversification nor specialization is a “better” investment strategy regarding VC fund performance. For stage specialization, three studies reported a positive effect, one an adverse effect and another one no effect. Turning to the evidence for stage diversification, two studies report positive results, and one found mixed results. These findings suggest that both strategies are viable. The study results further imply that both industry specialization and diversification can be used to achieve high VC fund performance. Five studies reported a positive effect of specializing in specific industries; one could not establish statistical significance. For industry diversification, three studies outlined positive effects, and two a negative effect. Two more studies revealed mixed implications for performance. No statistically significant was found in the final two studies. These results imply that specialization is overall the more consistent strategy for increasing fund performance. Finally, the empirical data favors no specific strategy in terms of geographic scope as the evidence is limited and mixed. Only one study reported a positive impact of domestic specialization on performance, and the results are partially statistically insignificant. For international diversification, two studies found a positive impact, while one did not obtain statistically significant results. Finally, negative results were reported for both international and domestic diversification in one study.

Moving on to a critical assessment of the data sources24, the use of a variety of data, such as commercial databases, hand-collected data, and proprietary industry data was observed. A general issue is that many studies focus on US VC firms. As Tykvová (2018) notes, “researchers, practitioners and policy­makers would profit from more evidence from outside the US, because there are distinct differences between the characteristics of the US and those of other regions, which may influence the way in which the VC and PE industry operates” (p. 353). She further cautions that “extreme care should be paid when analyzing the European or Asian situation in light of empirical findings based on US samples” (p. 353). The majority of researchers used Thomson Financial’s VentureXpert database (formerly Venture Economics) as the main source of data for their studies due to its relative accessibility and comprehensiveness (Gompers & Lerner, 2004). However, a potentially huge disadvantage is that “these data are largely self-reported by the venture capitalists and/or by the companies in which they invest” (Kaplan, Strömberg, & Sensoy, 2002, p. 1), making selection bias a concern. Other drawbacks include missing financing rounds (about 15%), oversampling of both California companies and larger financing rounds as well as noisy measures of the amount of financing and valuations (Kaplan et al., 2002). Some other utilized databases include PREQIN (e.g., Humphery-Jenner, 2013), with data on fundraising and returns for a wide range of investment funds (Rin et al., 2013), Pratt's Guide to Venture Capital Sources (e.g., Gupta & Sapienza, 1992), Galante’s Private Equity and Venture Capital Directory, Thomson Financial’s SDC Platinum (e.g., Knill, 2009), FAME (e.g., Cressy et al., 2007), or CEPRES (e.g., Buchner et al., 2017), which documents cash flows between individual PCs and VC funds (Rin et al., 2013). Overall, it must be noted that not all the information possibly needed for research are contained in these databases and that the researchers do not control how the samples are selected (Rin et al., 2013). A number of researchers therefore hand-collected their own survey data (e.g., Clercq et al., 2001; Elango et al., 1995; Hege et al., 2003; Lockett & Wright, 2001; Manigart et al., 2002; Norton & Tenenbaum, 1993) to maintain control over the selected sample. Additionally, this method gives insights about data specific to individual that is not included in commercial databases. However, due to the costliness of hand-collecting data, the sample size and, thus, the scope of the study, is sometimes restricted (Rin et al., 2013). Two other sources were proprietary industry data, as employed by Ljungqvist and Richardson (2003) who had access to the records of one of the largest US institutional investors, and searching the web pages of the VC firms, as done by Patzelt, Knyphausen-Aufseß, and Fischer (2009) and Patzelt, Knyphausen-Aufseß, and Habib (2009).

5 Discussion

The purpose of this final section is to link the empirical findings with the theory reviewed in the previous section. Some limitations of this thesis and avenues for further research are identified as well.

5.1 Interpretation of Results

The largely positive evidence for stage specialization, and even more so for industry specialization, support the notion of Clercq et al. (2001) and Manigart et al. (2002) that specialization allows for the build-up and acquisition of firm- and industry-specific knowledge and capabilities. In combination with a network of experts, this allows the VC fund managers to better understand and manage the company-/stage-specific risks and uncertainties more effectively. Furthermore, the value-added services of the venture capitalists are likely to be of higher quality. Though, Gompers et al. (2009) caution that “it is difficult to determine whether the superior performance of specialists is driven by their ability to better select investments or whether specialists are also better able to add value to those investments” (p. 843). The specialized expertise is only of value when it is relevant to the context of the PC (Clercq, 2003). Further, it should be noted that specialization on early-stage ventures entails significant risk (Hege et al., 2003). Due to the very limited analyses of geographic specialization, a conclusive interpretation seems premature, but the data indicates stronger levels of geographic specialization with high levels of specialization on the other two dimensions.

For stage, industry and geographic diversification, the risk-reduction effect, which allows for investments into high-risk ventures (Buchner et al., 2017), seems to be the most likely explanation for the positive relationship with VC fund performance. Perhaps, the potentially higher amount of investment opportunities (Buchner et al., 2017; Clercq et al., 2001) that come with a diversification strategy play a role as well. However, the negative empirical results also suggest that the drawbacks of high levels of diversification, such as unfocused and costly knowledge stocks (Bartkus & Hassan, 2009; Matusik & Fitza, 2012), less control over investee firms (Bartkus & Hassan, 2009; Clercq et al., 2001), and limited attention (Knill, 2009), need not be neglected, especially for the PCs.

5.2 Limitations

This thesis is not without limitations. Firstly, the number of empirical studies focusing on the investment strategies of institutional VC turned out to be limited and to be biased towards US investors so that conclusions drawn in this review must be viewed as preliminary and interpreted with caution.

Secondly, this thesis did not differentiate between investment strategy at the VC fund level and VC firm level.

Thirdly, there are some limitations owing to the limited scope of this thesis. For one, the included studies were not individually evaluated regarding validity or reliability and the different methodology, measures, and data sources make direct comparisons hard to realize. Also, the syndication strategy of the VC fund, and the involvement strategy of the VC fund managers, were not considered for the review. Both can be argued to be further dimensions of the investment strategy of VC funds and have been found to have significant positive as well as negative effects on fund performance. Further, several studies on cross-border investments and the internationalization of VC, which might have shed more light on the geographic dimension, could not be considered because the investment strategy was not measured in a standardized way. This is not meant to imply that the results are not meaningful, but that a detailed analysis of these was beyond the scope of this thesis. Finally, other drivers of VC fund performance, apart from the identified investment strategies, have been identified. These are not necessarily part of a deliberate investment strategy but do influence fund performance, nevertheless. The implications for performance were beyond the scope of this thesis, thus, only a brief enumeration of further drivers can be included: fund size, human capital, experience and composition of the investors as well as macroeconomic and environmental factors such as institutional context, level of competition, market cyclicality, or fund inflows all displayed a significant influence on fund performance.

Another limitation of this thesis pertains to the fact that papers, which studied PE funds (according to the narrow definition of VC), were included for the review. Due to the differences between PE and VC, such as investment focus (early vs. late stage), organizational structure, and investment size, it is likely that PE funds differ from VC funds in terms of investment strategy. A pure focus on VC in the narrow sense might have drawn a clearer picture of the investment strategies of VC funds.

5.3 Future Research

As for specific variables, several studies already demonstrated, the experience (or skills/talent) often moderates the relationship between investment strategy and performance. Thus, future researchers should always take this variable into account for their analyses. Moreover, as Knill’s (2009) seminal work demonstrated, future analyses of the performance implications of venture capitalist’s investment strategies should aim at differentiating between experience at the level of the individual investment managers and the level of the VC firm as a whole. Moreover, research that investigates the performance implications should always aim at multiple measures of performance since none of the reviewed measures is without drawbacks.

Further, making use of new data sources apart from the commercial databases might provide more detailed and accurate data. These might include hand-collected data, such as from surveys, or proprietary data. Besides, other data sources are required to shift the geographical attention from the US towards other countries, especially Asia and Europe, which will allow for a deeper understanding of VC investment strategies on a global level.

While conducting this review, it has occurred to the author that the literature on the investment strategies of VC funds seems to be rather disparate and segmented at times, so that results from one discipline were not taken into account by researchers from other disciplines. This notion is confirmed by Douglas J. Cumming and Vismara (2017). Future researchers might benefit from reviewing all the relevant findings across different literature strands and fields so that these can be appropriately incorporated for both theory-building and empirical analyses.

Appendix Appendix 1: Stages of venture capital financing

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Appendix 2: Keywords Used for the Database Search

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Appendix 3: Keywords Used for the Database Search

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Appendix 4: Studies Analyzing the Investment Strategy – Performance Relationship

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Appendix 5: Discussion of Risk-Return Considerations for Investment Strategy Dimensions

The financing stage is a critical factor of a VC fund’s investment strategy. In his seminal survey study on VC firms, Robinson (1987) reports that there exists extensive variation in the distribution of investments across different PC stages. Carter and van Auken (1994) hypothesized that venture capitalists’ preferences for certain financing stages are a function of their risk preferences. From a return perspective, it is most advantageous to invest in ventures as early as possible because the investee company is then not yet valued highly. This allows the fund to buy large shares of the ventures at modest prices so that it then owns large amounts of shares worth multiples of the initial price when the investment is exited (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). Turning to the combined risk-return mix, early-stage investments offer both high upside and downside potential (Buchner et al., 2017) and require the investors to be involved with management issues (Carter & van Auken, 1994). In contrast, later-stage investments offer reduced upside potential, but also less downside risk (Buchner et al., 2017). Generally, it can be said that the earlier the investment stage, the higher the level of uncertainty and associated risks (Gupta & Sapienza, 1992; Mason & Harrison, 2004; Matusik & Fitza, 2012), and the higher the assistance and monitoring intensity required by venture capitalists (Gupta & Sapienza, 1992). Early-stage investments involve particularly high levels of non-systematic risk because the ventures suffer from the liabilities of newness, such as technological uncertainty, high market risks, or inexperienced management (Gupta & Sapienza, 1992; Mason & Harrison, 2004). Additionally, agency asymmetries and problems are most pronounced at this stage because the venture capitalists and entrepreneurs are likely to disagree on the company’s future development as it is yet highly uncertain (Clercq, 2003; Patzelt, Knyphausen-Aufseß, & Fischer, 2009). In later company stages, on the other hand, these agency costs should be less noticeable (Gompers, 1995) and agency risk thus more comfortable to manage (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). Also, an established track record provides the venture capitalist with better information, reducing informational asymmetries (Amit et al., 1998). However, later-stage companies tend to receive larger investments due to higher capital requirements (Gompers, 1995), so in case the venture fails, the loss for the VC firm is higher as well (Robinson, 1987). Then again, Ruhnka and Young (1991) found that investors perceive the likelihood of late-stage ventures failing as significantly lower than for early-stage ventures.

Next to company stages, VC funds diversify across or specialize in certain industries. Ventures from the software, pharmaceutical and biotech sectors receive disproportionally large amounts of VC funding (NVCA, 2019) than would be expected by the relative shares of these industries of the total economic output (Amit et al., 1998; Gompers, 1995). More generally, these industries can be categorized as high technology. These types of ventures present special risks and challenges for both entrepreneurs and investors with unusually high levels of information asymmetries for both sides as well as extensive research & development efforts which result in assets that are industry- and firm-specific (Sapienza & Clercq, 2000). Amit et al. (1998) and Gompers et al. (2009) posit that informational asymmetries are most pronounced in high-technology firms and that venture capitalists as financial intermediaries specialize in monitoring such ventures. More conventional firms, on the other hand, are much easier to monitor, but risky nevertheless. As Sapienza and Clercq (2000) note, research on VC has mostly focused on high-technology firms, and as such, they might be “over-represented in both the popular and the academic literature” (p. 58). As a result, VC firms require high returns on investments for high-technology ventures, similar to investments in early-stage firms (Sapienza & Clercq, 2000). Also, in comparison with low-tech ventures, these ventures receive more total VC money (Gompers et al., 2009). Low-tech investments offer significantly lower returns on average, and the competitive environment is much more developed, too (Jungwirth & Moog, 2004).

Moving to the third dimension, VC investments are far from being uniformly distributed in terms of geography; instead they are often narrowly focused in certain locations. In the US, for instance, most of the VC flows into startups in the states of California, New York, and Massachusetts, which together make up around 79% of the invested capital (NVCA, 2019). Similarly, VC investments tend to be concentrated in a few states and cities in Europe as has been found from studies in countries such as the UK, France, and Germany (Mason, 2007). More generally, VC funds can be said to invest in only one or very few geographic areas whereas others have a much broader investment scope at an international or even global level (Patzelt, Knyphausen-Aufseß, & Fischer, 2009). Whether the VC fund has a diversified or specialized geographic scope depends on a) where the VC firm itself is headquartered and b) into which geographic locations most of the funds flow. This decision determines how close or far away the venture capitalist will be from the PC, thereby impacting “the possibility of maintaining a tight relationship with the entrepreneurial team” (Cressy et al., 2014, p. 142). The geographic proximity to the investee companies also influences the investor’s monitoring and involvement (Gupta & Sapienza, 1992). Uncertainty and risk can be maximally reduced by limiting the investment scope to local firms because then the venture capitalists are better able to identify and evaluate potential investee companies while maximizing their value-added services (Mason, 2007). However, investments into firms that are located in other geographical markets may generate higher returns (Cressy et al., 2014; Tarrade, 2012).


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1 Venture capitalists are the investment professionals who run the VC fund and make investment decisions on behalf of the fund (Douglas Cumming, Fleming, & Schwienbacher, 2007). Also referred to as VC investors and VC managers in this thesis.

2 VC firm refers to “the collection of funds that comprise a venture capital organization” (Douglas Cumming, Fleming, & Schwienbacher, 2007, p. 162) which needs to be distinguished from the VC fund, “a single fund that is part of a venture capital firm” (Douglas Cumming, Fleming, & Schwienbacher, 2007, p. 162). Both terms are used interchangeably throughout this thesis, however.

3 From the point of view of the VC firm, the companies as capital recipients are referred to as portfolio companies (PC) or investee companies.

4 Please refer to Appendix 2 for a list of the keywords used.

5 Please refer to Appendix 2 for a list of the keywords used.

6 Due to page restrictions, a brief discussion of the respective risk-return implications of the investment strategy dimensions was deferred to Appendix 5 as it is not strictly relevant to the research questions of this thesis, but still worthwhile to share.

7 Please see Appendix 3 for a complete overview of the diversification/specialization measures, and Appendix 4 for a complete overview of the performance measures.

8 The neoclassical theory of finance assumes that the agents in the market are rational (they maximize their expected utility over time), have homogenous return expectations, are risk-averse, and have no influence on prices. Moreover, the capital markets are assumed to be perfect (no taxes, regulations or other impeding restrictions), frictionless (freely available, costless information) and liquid (assets can be traded at any time and are infinitely divisible) Bondt, Werner F. M. De, Muradoglu, Shefrin, and Staikouras (2008); Mossin (1966); Lintner (1965); Sharpe (1964); Tobin (1958); Markowitz (1952); Merton (1987), Levy (1991, 1978).

9 The rule presumes investors to have quadratic utility functions Perridon, Steiner, and Rathgeber (2014). Further, investors are risk-averse, implying that, given a level of expected return M, they prefer an investment with lower variance V to one with greater variance. Also, investors are expected to prefer a higher expected return to a lower return. Under these assumptions, the investor will choose the portfolio that maximizes his or her preferred risk/return rate. This portfolio is called risk-efficient if there is no alternative portfolio with either (1) the same M and lower V, or (2) the same V and lower M, or (3) both higher M and lower V Sharpe (1964).

10 (1) Investors have homogenous expectations regarding the return of assets, which have a joint normal distribution (Sharpe, 1964; Mossin, 1966), and are not able to influence these returns (Copeland, Weston, & Shastri, 1988). (2) Further, they can limitlessly borrow and lend capital at the risk-free rate (Sharpe, 1964; Lintner, 1965). (3) All assets are marketable, i.e. can be freely sold and bought. (4) Also, all assets are infinitely divisible (Sharpe, 1964; Mossin, 1966). (5) The capital markets are frictionless, so there are no transaction costs and information are freely available (Copeland, Weston, & Shastri, 1988). (6) Besides, there are no taxes and regulations that restrict securities trading in any way (Sharpe, 1964). (7) Finally, the capital market is in a state of equilibrium, thus investors have no incentives to restructure their portfolios (Perridon, Steiner, & Rathgeber, 2014; Mossin, 1966).

11 The first systematic study of venture capitalist’s risk perceptions was carried out by Tyebjee and Bruno in 1984. They report that VC firms evaluate investment deals in along five dimensions: (1) market attractiveness, (2) product differentiation, (3) managerial capabilities, (4) environmental threat resistance, and (5) cash-out potential. Apart from managerial capabilities, each of these dimensions refers to market risk. In the same vein, Ruhnka and Young (1991) identified the major risks as perceived by venture capitalists across the different development phases of the investee companies. They classified the risks into development, i.e. non-systematic, risk (e.g., feasibility of business idea or technology, incompetent management) and external, i.e. systematic, risk (e.g., better/unforeseen competition, unfavorable market conditions) and concluded that the early financing stages are characterized by high total risk, most of which is non-systematic risk. By contrast later stages involve less total risk and risk factors unrelated to the venture make up a large part of that. Fiet (1995) examined differences in the risk evaluation approaches of both informal and formal venture capitalists. His findings suggest that institutional VC investors assign greater importance to market risk, such as substitute products/services or existing and upcoming competitors, because they perceive themselves more able to control this type of risk by using contracts that allow them to replace managers. Finally, Kaplan and Strömberg (2004) compiled a diverse range of factors that venture capitalists viewed as investments risks. They subdivide non-systematic risk into management quality, performance to date, downside risk, influence of other investors, VC investment fit and monitoring costs, and valuation.

12 As a result, various modifications and extensions of the CAPM have been developed with more realistic or relaxed assumptions, such as the existence of personal taxes, heterogenous investor expectations, or assets that cannot be readily traded on the market (Copeland, Weston, & Shastri, 1988; Elton, Gruber, Brown, & Goetzmann, 2014; Perridon, Steiner, & Rathgeber, 2014).

13 In the case of complete information (zero fixed costs), the GCAPM reduces to the previously discussed CAPM (Merton, 1987; Levy, 1978; Norton & Tenenbaum, 1993).

14 The RBV dates back to the pioneering work of Penrose (1959) on the growth of firms and was first termed as such by Wernerfelt in 1984.

15 The VRIN criteria are: (1) valuable with regard to improving the firm’s efficiency and effectiveness, (2) rare, meaning that none or not many other firms possess this resource, (3) imperfectly imitable in the sense that the resources are not easy to use for competitors, and (4) non-substitutable, i.e., the resource cannot be substituted with another resource

16 In explaining the existence of the multiproduct organization, Teece (1980) describes that economies of scope “exist when for all outputs y1 and y2, the cost of joint production is less than the cost of producing each output separately” (p. 224). Resources or inputs for production are generally not perfectly divisible and as a result, firms have excess capacity of resources. For economies of scope to occur, the resource is required to be sharable so that once it has been used to produce one output, some part of it at least is still available for producing another output (Panzar & Willig, 1981). Economies of scale are cost savings in production that arise from an increased scale of the firm (Panzar & Willig, 1977, 1981).

17 Whereas explicit knowledge refers to facts and theories (“know what”, Grant, 1996) and tends to be obtained through formal education (Dimov & Shepherd, 2005), tacit knowledge is about “knowing how” (Grant, 1996) and is obtained through personal practical experience in specific fields (Dimov & Shepherd, 2005).

18 According to Jensen and Meckling (1995), who first established this distinction, “specific knowledge is knowledge that is costly to transfer among agents and general knowledge is knowledge that is inexpensive to transmit” (p. 4).

19 This knowledge can be categorized as general in the context of VC since it is specifically required for the task of a venture capitalist and, thus, a basic qualification that practically every venture capitalist is presumed to possess (Jungwirth & Moog, 2004).

20 On the level of the individual, learning refers to “the process by which new information is incorporated into the behavior of agents, changing their patterns of behavior and possibly, but not always, leading to better outcomes” (Eisenhardt & Santos, 2006, pp. 143–144). On the organizational level, learning forms the foundation for the creation of new knowledge in firms (Penrose, 1959).

21 Eisenhardt (1989) summarizes the assumptions underlying agency theory regarding actors, organizations, and information. The agents are presumed to be boundedly rational, risk-averse and to act to their own interest and benefit. With regard to organizations, there is a partial goal conflict among agents, as well as some information asymmetry between principal and agent. Finally, it is assumed that information is a commodity that can be bought.

22 Decision-making and risk-bearing are thus separate from each other in agency arrangements, and Fama and Jensen (1983) argue that this is because there are advantages associated with one party specializing in management and risk-bearing.

23 Please see Appendix 4 for a detailed analysis of the studies reviewing the investment strategy-performance relationship.

24 Please see Appendix 3 for an overview of the data sources of the reviewed studies.

58 of 58 pages


Investment Strategies of Venture Capital Funds
Technical University of Munich  (Chair of Entrepreneurial Finance)
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venture capital, investment strategies, investment strategy performance
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Kilian Kollmuß (Author), 2019, Investment Strategies of Venture Capital Funds, Munich, GRIN Verlag,


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