1
Local Bias in the VC industry: the extent that foreign equity market
valuations influence cross-border venture capital investments
MSc thesis in Finance
Jonathan Pothuis s1842129
Supervisor: Dr. Peter Smid Word Count: 11,494
Abstract
The persistence by venture capital firms to invest local has been demonstrated in both theoretical and empirical work. This paper studies the relation of perceived foreign investment opportunities and international venture capital firm investment activity between 1981 and 2007. We find that venture capital firms with more experience react more strongly to changes in foreign equity market valuations. Reaction to foreign investment opportunities is also found to be larger for venture capital firms with more country experience. These results suggest that venture capital firms take up the additional risk of investing abroad given to attractive investment opportunities, which are perceived to occur by the investor’s observation of changes in valuations of equity markets. We also find that the maturity level of the investment opportunity has no correlation with venture capital investment activity.
JEL Classification: G24; G32
Keywords: Venture capital, Local bias
1. Introduction
2 new firms searching for financing are thus faced with a selection mechanism whereby they have to compete in order to bring their ideas to the market. Yet, the source of funding seems to have been local, up until recently. One of the reasons for the persistence of local bias in the venture capital industry is due to VC investors lacking information about new investment opportunities. It has been suggested that changes in public equity market valuations reflect changes in perceived investment opportunities (Lev and Thiagarajan, 1993). The authors document the relation between fundamentals such as earnings and stock prices.
This paper’s aim is to understand whether foreign public equity market valuations are indicative to international venture capital investment activity. We analyze the extent to which changes in foreign public equity market valuations determines VC firm investment activity. Do changes in foreign public equity market valuations influence cross-border VC firm investment activity? Are then VC firms with more experienced willing to pursue foreign investment opportunities? Will these VC firms be willing to invest in portfolio companies of every maturity level, be it a seed or an expansion investment? The study extends the VC literature by further analyzing the persistence of VC firms to remain local. To do so, we start by considering the unique human capital of a VC firm (VC firm characteristics, experience). The resource-based view (RBV) infers that the costs involved in internationalizing can be reduced by a VC firm’s resource superiority (Rugman and Verbeke 2002). In other words, VC firms gather professional experience, create a vast network of contacts, and build a reputation throughout the investment cycle. Having experience in a foreign market entails that investors are more capable to react accordingly in case of a negative shock, such as an economic downturn or local entrepreneurs’ negative expectations of investment opportunities. The nature of venture capital investments encompasses the unearthing of new ideas that involve a particular level of uncertainty. It follows that local bias is observed in the VC industry because VC firms rely on local expertise and networks in order to avoid risk (Gompers and Lerner 2004; Sahlman 1990). The risk that VC firms endure compared to other financial institutions such as banks is based on the fact that VC firms do not have the possibility to rely on past quantitative and qualitative information about the investment. In other words, the information problem faced by VC firms is a result of the fact that company information of new investment opportunities is either private or only known to the entrepreneur, creating a problem when valuing the attractiveness of the investment. The lack of information regarding investment opportunities is one of the reasons for VC firms to remain local (Coval and Moskowitz, 1999, 2001; French and Poterba, 1991). Hence, companies seeking financing may find that they lack the necessary foreign investors who carry the expertise to manage and finance their project (Baygan and Freudenberg 2000).
The resilience of VC investors to cross borders is found in additional elements involving the internationalization of these organizations. By studying VC syndicates consisting of local and foreign VC firms, Mäkelä and Maula (2005) find that downward changes in market expectations have larger adverse effects on commitment levels of foreign VC firms given to their geographic distance to the investment. Differences in business ethics and values between investor and investee are also argued to create conflict in ventures by inducing mistrust and discouraging risk sharing (Giannetti and Yafeh 2012), causing resilience on VC firms to fund ventures in culturally distant countries. However, Schertler and Tykvová (2011) find that cross-border investments account for 33.4 percent of total venture capital deals.
3 1998), as well as by the amount of investments made by VC firms in portfolio companies (Gompers and Lerner, 2004). The understanding is that VC firms perceive changes in public equity market valuations as an upsurge of new investment opportunities.
Two views explain the association of public equity market valuations to new investment opportunities. The first view argues that the volatility of the VC industry is caused by investors overreacting to changes in public equity market valuations. The “overreaction view” states that changes in investment activity may be due to a behavioral bias of investors. The view contends that VC firms perceive changes in public equity market valuations and invest out of a concern of obtaining a negative reputation when not following the crowd. The second, the “fundamentals view” argues that the volatility of the VC industry is caused by a response of VC firms to new investment opportunities. That is, simply, a sudden increase in the demand for funds causes a supply reaction by VC firms (Scharfstein and Stein, 1990). Gompers et al. (2008) contest these two views and find that the volatility in the VC industry is attributed to the “fundamentals view”. Their results suggest that public equity markets provide the necessary information to VC firms concerning new investments opportunities and in turn increases VC investment activity.
This study is conducted at the VC firm-country-level using data provided by Thomson Reuters SDC Premium database. We begin by selecting all VC firm investments made across countries between 1981 and 2007. The dependent variable is the number of investments by VC firms f in country k in year t. Our measure of foreign equity market valuations or foreign investment opportunities is Tobin's q (country-Q). Our venture capital firm characteristics are prior experience, country experience, and specialization. We employ a cross-sectional and time series empirical analysis. We find that venture capital firms invest more in periods where country-Q is higher than when it is lower. Additionally, we find that VC firms with more experience in the dataset are more sensitive to changes in country-Q than less experienced VC firms. Finally, we find that the maturity level of the portfolio companies where VC firms invest is not a significant factor determining VC firm investment activity.
The remainder of this paper will be structured as follows: section two reviews the related theory. Section three present previous empirical findings and based on these builds the hypotheses to the tested. Section four describes the data and methodology to be used. In section five we show the main results and discuss. Finally, section six concludes.
2. Review of related theory
2.1
The Internationalization of the Venture Capital IndustryVenture capital firms who internationalize are those that invest in portfolio companies outside the market in which they are headquartered, as well as raise funds from an outside market. Local bias in the venture capital industry is consistent in both theoretical (e.g. Kanniainen and Keuschnigg, 2003, 2004; Schwienbacher, 2007) and empirical work (e.g., Bruton et al., 2005; Davila et al., 2003; Engel and Keilbach, 2007; Jääskeläinen et al., 2006; Lerner, 1995; Manigart et al., 2000, 2001, 2002, 2006; Mäkelä and Maula, 2007; Meuleman and Wright, 2007; Sapienza, 1992; Sapienza et al., 1996, 2004; Tian, 2007). It is argued that the lack of available information related to new investment opportunities has brought VC firms to resort to spatial proximity in order to reduce the possibility of financial risk (Coval and Moskowitz, 1999, 2001; French and Poterba, 1991). However, as mentioned earlier, over one-third of venture-backed companies receive their funding from a foreign VC firm.
4 to limited domestic investment opportunities. Another view of the internationalization of the VC industry is that VC managers working in a foreign country repatriated to find new business opportunities in their country’s VC industry (Kenney et al. 2002). The literature on the internationalization of VC firms can be summarized into two main theoretical bodies (Wright et al. 2005). The first being the study of cross-country factors related to the industry. The second body examines the determinants of VC crossing-borders. For the purpose of this paper, we remain focused on the crossing borders literature.
2.2 Agency and Institutional Theory
Previous research has relied on agency theory in order to understand how VC firms operate. The most widely applied view to the problem of the uncertainty in international investments has passed through the scope of agency theory. For the venture capital literature, this dominant agency-theoretic view remains the primary paradigm (Cornelius & Persson 2006). The “Anglo-American” view (Wright et al., 2005) suggests that venture capitalist reduce information gaps by investing in structural and contractual mechanisms so to carefully monitor their venture. Thus, causing investors to be more inclined to seek new business opportunities within geographic proximity.1 However, by
focusing on agency theory prior research has not been able to fully explain VC behavior because it views the VC firm’s manager chief executive relationship as purely economic (Arthurs & Buzenitz, 2003). It would be even more questionable in an international setting to focus on any theory that is based on an assumption of purely economically driven human behavior. Internationally, countries differ from one another in a variety of ways (Wright, Lockett, & Pruthi, 2002).
Bruton et al. (2005) extend the VC literature beyond agency theory by providing a more affluent theoretical perspective on VC internationally. The authors employ an institutional perspective across countries in order to examine the relation between institutional differences and the spread of VC from on country to another. The weight that institutional differences have on international investments has been extensively investigated within institutional theory (Bruton & Ahlstrom, 2003; Bruton et al., 1999; Zacharakis et al., 2003). However, Scott (1995) argues that theoretical perspectives whose scope emphasized particular institutional forces drove previous research. Scott’s argument comes as a result of the arduous process of accounting for all institutional forces in financial research. To that end, we turn to understand instead how specific VC firm characteristics are related to cross-border VC firm investments.
2.3 The Resource-Based View
In their extensive work Wright et al. (2002) provide a unification of international business (IB) literature with venture capital. The authors argue that IB can complement venture capital by helping understand the behavior of investors in an international setting. The resource-based view (RBV), a complementary theory to transaction cost economics2, is focused on path dependency and VC
firm-specific resources to understand the internationalization of venture capital. The RBV is prominent in international venture capital behavior in offering both an equilibrium and dynamic conceptualization of the phenomenon (Lockett and Thompson 2001). In equilibrium, the RBV gathers that the costs involved in internationalizing can be reduced by the VC firm’s resource superiority (Rugman and Verbeke 2002). Additionally, in equilibrium the availability of resources influences the diversity of markets and firm behavior in international investments (Teece et al. 1997). And more importantly, the RBV emphasizes that success in foreign markets is not possible if the assets that firms possess are not widely available and can be disseminated at no cost (Combs and Ketchen 1999). Dynamically, the RBV focuses on the response of venture capital firms that internationalized to different
1 Sahlman (1990) provides extensive evidence on venture capital firms mechanisms used to reduce agency
costs.
5 environments, the path-dependent process of knowledge absorption followed by resource recombination, and diffusion of knowledge.
2.4 Factors influence international VC investments
In this section, we turn to the factors that influence VC firm investment activity internationally. We do so to understand how VC firm-specific characteristics influence cross-border investment activity when there is a change in public equity market valuations. We start by explaining the motivation of VC firms to invest followed by several factors that may affect their motivation. We finalize the section by describing several strategies that VC firms use to reduce foreign risk.
2.4.1 Economic driven motivation
We begin by investigating the motivation for VC firms to cross-borders. The initial motivation for a VC firm to cross-borders may, in fact, be purely economic. Baygan and Freudenber (2000) argue that the international spread of VC firms relates to higher expected returns, innovative technologies, growing economies, and increasing entrepreneurship in foreign countries. During the periods of 1969-1972, 1981-1983, and 1998-2000 the venture capital industry saw a boom in investments, with a focus on upcoming markets and technologies. For example, VC firms invested heavily in personal computer manufacturers in the 80’s and on Internet and telecommunications in the late 90’s. Periods like these are in contrast to times where companies struggle to find VC financing. Industry observers attribute the observed volatility in the VC industry to an overreaction of VC firms to perceived investment opportunities (Gupta 2000). Hong and Stein (1999) provide a theoretical framework for understanding the volatility of the VC industry. In their model shocks are slow diffusing news about future fundamentals. The authors argue that if there is ever a short-run under-reaction to the news by investors, in the long-run there will ultimately be an overreaction. Hence, VC firms invest when they perceive new business opportunities.
2.4.2 Physical and cultural distance
Venture capital firms gather their investment, organizational and managerial experience throughout the investment cycle. The resource-based view contends that the human capital developed throughout the investment cycle constitutes the unique asset position of the VC firm. That is, VC investors gain experience, construct networks of contacts and build a reputation that may in turn facilitate in future ventures. Hence, knowledge of foreign markets, cultures, and business practices may have significant effects on the willingness of investors to cross borders. Additionally, the capability to adapt to foreign markets is crucial to the success or failure of cross-border ventures (Bruton et al., 2003). Differences in business ethics and values in countries have been argued to create conflict in ventures by inducing mistrust and discouraging risk sharing (Giannetti and Yafeh 2012). The notion of cultural distance has encouraged VC firms to recur to isomorphic adaptation by replicating the structures and strategies of local VC firms (Zalan 2004). On the other hand, VC firms may attempt to export their domestic conduct and structure of operation to the foreign market and maintain their distinctive factor of differentiation. Also, geographic proximity is a significant feature determining the willingness of VC firms to invest abroad since a large distance between the investor an investee affects the ease with which managers can physically travel and monitor the venture (Cumming and Dai 2010, Davila et al., 2003).
2.4.3 Forms of Entry
6 geographic regions and stages of investment. The three models of Dixit and Jayaraman accentuate the importance of a VC firm’s perception of their resources when approaching a foreign market. A VC may be more inclined to invest via the specialized fund model when it has already developed a human capital base in some geographic location. Alternatively, assuming that a VC firm has little or no experience in a particular foreign country the affiliate model would be the most appropriate internationalization model for this firm.
2.4.4 Network of contacts and Syndicates
VC firms compete in the market of new and innovative investment opportunities characterized by their early stage of development. Information about such ventures is rarely available, so a VC firm seeking to internationalize may find it difficult to identify deals in a foreign market. Local bias is consistent in the industry given that VC firms recur to their local network of contacts to gain knowledge of new investment opportunities. In order to overcome the information problem, VC firms internationalized into foreign markets by having a stronger physical presence in the foreign country. Hence, VC firms recur to having a stronger presence in foreign markets in order to gain access to networks of local intermediaries and entrepreneurs who may prove necessary to gain knowledge of possible early-stage investments. Additionally, Mäkelä and Maula (2005) and Maula and Mäkelä (2003) argue that foreign VC firms reduce the information problem by jointly investing with a syndicate containing local VC firms. The idea is that the local VC firms may provide the necessary expertise to aids the foreign VC firm with dealing with local market operations, legal requirements and also provide access to deal flow.
2.4.5 Portfolio Company Maturity Level
Staged financing is observed in the venture capital industry within agency models of asymmetric information. Neher (1999) argues that VC firms stage their payments to the entrepreneur in order to reduce their bargaining power. They do this because entrepreneurs threaten to hold up the VC firms by reneging on investments. Landier (2002) claims that when bankruptcy laws are lenient staging is one way for VC firms to protect themselves from the risk of the entrepreneur having a high exit option. The view is that VC firms reduce moral hazard by staging their investments. They do so in order to have the option of ending projects that have low early returns. For example, if the projects early output turns out to be low, both for the VC firm and entrepreneurs it would be more efficient to discontinue work and collect their respective outside options. More importantly, the fact that entrepreneurs receive funds in stages and that VC firms are able to join funds that are investing either on a later stage project or early stage project entails that a VC firms has the option to assume either highly risky projects (early stage) or less risky projects (later stage).
3. Empirical findings and Hypothesis development
In this section, we elaborate on the relevant empirical findings of previous academic research in order to construct our hypotheses to be tested. In a similar structure as the previous section, we begin by presenting empirical findings on economic motivation, followed by cultural and geographic distance, market experience, and finally we show the academic findings concerning syndication.
7 findings suggest that an inherent change in fundamentals is the primary cause of an increase in investment activity and that experience in the industry plays a significant role on the willingness of VCs to invest. As a result, we construct our first hypothesis (H1) and present it below.
Second, we have discussed that other factors influence the willingness of VC firms to invest abroad. Meyer and Shao (1995) find that the likelihood of a VC-firm crossing borders may be hampered by culture and spatial proximity. A study conducted on foreign VC investing in China has shown that in order to address the cultural differences, organizations have found it in their favor to adapt to local market conditions (Zhang 2002). Bruton et al. (2005) find that distinctive institutional and cultural differences play a significant role on VCs investment behavior. The authors show that organizational features such as codes of conduct and family control have a significant impact on how VC firms from East Asia and the West invest. Black and Gilson (1998) find that institutional factors are a significant determinant of the willingness of individuals to take risks. Jeng and Wells (2000) and Megginson (2004) find that government policies are a major determinant of VC development since governments can have a substantial impact through setting the regulatory framework. In their study of 15 countries covering a 13-year period, Armour and Cumming (2004) find that government involvement can hamper the growth of private equity. Hence, investment behavior is not only determined by economic factors; investors encompass in their investment decision factors such as culture, institutional, and geographic distance in order to avoid possible future risk.
The different forms of market entry of VC firms reflect the weight that experience has on investment activity. Pruthi (2004) find that UK VC firms have taken several forms of market presence when internationalizing, such as arm’s length investing, ventures, acquisitions, and even franchising. More importantly, the authors find that informal control used to coordinate operations internationally rate more highly than formal controls. These results provide evidence that the provision of knowledge of foreign markets and the know-how necessary to devise a strategy is most likely not only based on direct reporting by investment executives in international operations. But also on informal controls, such as frequent overseas trips and visits where investors can have hands-on chands-ontact with the culture. Hence, a VC firm entering a foreign market is chands-oncerned with its executive’s expertise of the market.
Mäkelä and Maula (2005) and Maula and Mäkelä (2003) argue that syndicates of VC firms aid the foreign investor tap the human capital necessary to overcome domestic difficulties. The authors provide evidence that by having acquired experience in the market previously, VC firms may find it easier to operate in the foreign market, deal with legal requirements, and have access to deal flow. Hall and Tu (2003) argue that there is a negative correlation between the age of the VC firm and its willingness to invest overseas. Interestingly, the authors find a positive correlation between the size of the VC firm, the investment stage of the investee, and the willingness of the VC firm. Given these results, one might assume that young VC firms are least aware of the complexity of investing overseas. And that older firms are resilient to invest in foreign early stage venture given their risk. In a study conducted by Beaverstock (2004), legal firms who internationalize not only provide services by expatriating their local staff but hire locals who have knowledge of the countries jurisdiction law. Leeds and Sunderland (2003) conduct a survey of VC firms who internationalize and find evidence of the need for culturally attuned staff. Wright et al., (2002) find that in the Indian VC industry 91% of the executives of foreign VC firms are Indian nationals and 33% have had previous experience in a foreign VC market. Evidence shows that managers continue to rely on their local network of contacts in order to gain knowledge of new business opportunities (Mäkelä and Maula, 2005).
8 and Maula 2005). In a case study of Finish VCs, Mäkelä and Maula (2005) find that with changing expectations foreign VC firms are more likely to maintain commitment in the country when their involvement is higher. They define involvement by having co-invested with the same investee or by having gained experience in the local market via other investments. Additionally, foreign VC firms may be reluctant to withdraw from the venture in the case of a shock when involved in a syndicate because this may create a negative impact on the firm’s reputation. Thus in line with the argument that the resource base allows the VC firm overcome these additional factors our second and third hypotheses are:
H1: A change in perceived foreign investment opportunities will increase VC firm investment activity.
H2: Experienced VC firms have a stronger reaction to changes in foreign investment opportunities.
H3: Experienced VC firms make more investments in early stage and seed companies than less experienced VC firms.
4. Data and Methodology
In what follows we present our main regression equation with a description of the variables and prefixes. The model used in this study is based on the work of Wright et al. (2005). In section 4.1 we describe from where and how we construct our sample. In section 4.2 we explain how we measure our variables in detail. Section 4.3 presents the descriptive statistics of all the variables, a correlation table for our main variables, and two graphs describing the relation between VC firm investments and country-Q.
𝐿𝑜𝑔#𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑡𝑠𝑓𝑘𝑡
= 𝜇𝑘+ 𝑣𝑡+ 𝛽1𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1+ 𝛽2𝐿𝑜𝑔𝐴𝑑𝑗𝑃𝐸𝑓𝑡+ 𝛽3𝐿𝑜𝑔𝐴𝑑𝑗𝐶𝐸𝑓𝑘𝑡+ 𝛽4𝑆𝑝𝑧𝑓𝑘𝑡 + 𝛽5𝐿𝑜𝑔𝐴𝑑𝑗𝑃𝐸𝑓𝑡∗ 𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1+ 𝛽6𝐿𝑜𝑔𝐴𝑑𝑗𝐶𝐸𝑓𝑘𝑡∗ 𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1 + 𝛽7𝑆𝑝𝑧𝑓𝑘𝑡∗ 𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1+ 𝛽8𝐿𝑜𝑔#𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑠𝑓𝑘𝑡−1+ 𝛽9𝐷𝑢𝑚𝑚𝑦𝑓𝑡+ 𝜖𝑖𝑡 Where 𝐿𝑜𝑔#𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑡𝑛𝑠𝑓𝑘𝑡 is the number of investments made by VC firm f in country k in year t. The terms μk and vt stand for country fixed effects and time fixed effects. The 𝛽 terms are the coefficient estimates. 𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1 is the foreign investment opportunities, or Q in country k in t-1. 𝐿𝑜𝑔𝐴𝑑𝑗𝑃𝐸𝑓𝑡 stands for the log adjusted variable of VC firm’s f prior experience before the investment in question. Similarly, 𝐿𝑜𝑔𝐴𝑑𝑗𝐶𝐸𝑓𝑘𝑡 stands for the log adjusted variable of VC firm’s f prior country specific experience before the investment in question. And 𝑆𝑝𝑧𝑓𝑘𝑡, stands for specialization which is the fraction of the number of investment VC firm f made in country k before the investment in question divided by all previous investments that VC firm f made before the investment in question. Log#Investmentsfkt−1 is the lagged dependent variable. Dummyft is our dummy variable account for the maturity of the company where VC firm f is investing. And ϵit is the error term.
4.1 Sample Construction
In order to obtain the necessary information on the venture capital industry, we turn to Thompson Reuter’s SDC Platinum database. In the VentureExpert section of the database, one can find information about the VC industry at the firm, fund, and portfolio company level. Our primary variable of interest is the venture capital firm. The study will be conducted between the years 1981 and 2007. The time frame is chosen because of the availability of data.
9 period. However, a significant portion of these investments consists of follow-up investments made by VC firms in the same portfolio company. Since the primary interest of this paper is to understand a VC firm’s motivation to invest abroad, VC firm investment is defined as the first time a VC firm takes part in funding a particular foreign portfolio company. Thus, we drop any observations of a VC firm’s follow-up investments in the same portfolio company to ensure that we only capture new investments. And finally, we restrict the dataset to VC firms who have previously invested in at least three companies in order to only account for genuine VC firms. This step, however, may introduce a survivorship bias to the study if only the best firms can make more than one investment.
By following the chosen boundaries imposed on the dataset, we are left with 3066 venture capital firms who invested in 11586 portfolio companies. That leaves us with a sample of 17924 observations between 1981 and 2007. The first panel of Table 1 shows the distribution of the sample. We see that there are more observations than companies in our sample. This is because it can be that there is more than one VC firm investing in the same portfolio company.
4.2 Critical measures
The initial challenge is the measurement of foreign investment opportunities. Ideally, we would like to observe the pool of foreign private firms that form the true potential investment opportunities in a country. Since information on private companies is unavailable, we turn to the use of public equity market valuations. We do this based on the view that VC firms acquire the necessary information about new investment opportunities via changes in public equity market valuations. Our measure of perceived foreign investment opportunities is the country’s Tobin’s Q (country-Q).
Country-Q is based on the work of Tobin and Brainar (1968) who argued that the firm will invest if Tobin’s q, the ratio of the market valuation of a firm’s capital stock to its replacement value, exceeds one. In practice the most common way to measure Tobin’s q is by calculating the ratio of the firm’s market value of equity plus the book value of liabilities divided by the book value of equity plus the book value of liabilities. Given the availability of information in the SDC database we construct country-Q by the ratio of the firm’s market value of equity plus the book value of assets minus the book value of equity divided by the book value of assets. All the financials are dated to the last day of the previous year. As mentioned above we do not have information of private firms, so we amount to all firms in the Global Issues section of the SDC Platinum database that have gone public. We construct country-Q by equally weighting all public companies Q in country k in year t.
The final challenge is to measure the characteristics of the venture capital firms in the sample. Our first measure of firm characteristics is prior experience (PriorE). This variable is measured by calculating the number of investments made by VC firm f prior to the investment in question. The second variable of VC firm characteristics is country experience (CountryE). This variable is measured by calculating the number of investments made by VC firm f in country k prior to the investment in question. The third characteristic, VC firm specialization (Spz) is the fraction of the number of investment VC firm f made in country k before the investment in question divided by all previous investments that VC firm f made before the investment in question.
10 adjusted prior experience (LogAdjPE) in this case is the log of the number of investments made by VC firm f prior to year t minus the log of the average number of investments made all VC firms prior to year t. Likewise, log adjusted country experience (LogAdjCE) is the log of the number of investments made by VC firm f in country k prior to year t minus the log of the average number of investments made by all VC firms in country k prior to year t. Finally, in order to account for the stage of the portfolio company we construct a dummy variable, where the dummy is equal to one if the company where the VC firm f is investing is at a seed, start-up, or early stage and 0 if otherwise.
4.3 Descriptive statistics
The descriptive statistics for all our explanatory variables are presented in Panel B of Table 1. The analysis is conducted at the venture capital deal level, that is, at the level of the investment made by the venture capital firm. We could not find any extreme outliers in the dataset. We see that the average VC firm in the sample had 32.44 times prior investments, of which 14.35 were in the country of the investment in question. The resulting country experience is not surprising since the observations in our sample are investments in countries where the venture capital firm had chosen participate. We would like to include in the dataset an observation of no participation for every VC firm in order to balance the panel. That is, a zero for every country in which a firm has not invested. However, given the amount of countries and VC firms in the dataset this procedure is not possible. These figures also show that the average prior experience of VC firms is nearly two times the average level of country experience indicating that more experienced VC firms are also making more investments.
In Panel C of Table 1, we present the sample characteristics of our experience measures for four specific years of the period, at the venture capital firm-country-year level. We show these four periods in order to demonstrate how our experience measure grow over time and justify the fact that we adjust the experience measure as explained in the previous section. These are 1981, 1990, 2000, and 2004. We can see an increase in the number of active VC firms between 1981 and 1990 and between 1990 and 2000. However, the number of active VC firms decreases after 2000. The change in the number of active VC firms may be caused by the volatility in the VC industry that was mentioned above, 2000 saw the dotcom bubble where many VC firms invested in upcoming internet companies. Furthermore, we see that our experience measures increase over time. As compared to 1981 a VC firm investing in 1990 had previously invested around 10 more before the investment in question. However, in 2000 the average experience of a VC firm is lower than that of 1990. This result is caused by the amount of active VC firms in 2000 as compared to 1990.
Table 1
Sample Characteristics Panel A: sample by country
Countries Companies Observations
152 11586 17924
Panel B: Sample Characteristics
Jarque-Bera Kurtosis Skew Mean s.d. N
#Investments 42,556,875 240.716 10.889 1.6457 2.078 17924
Country-Q 103,171 14.222 -1.747 0.0647 0.065 17924
PriorE 379,474 23.933 4.180 32.447 65.987 17924
CountryE 882,410 35.809 5.126 14.359 33.852 17924
11 AdjPE 49,691 9.452 2.495 0.5693 0.943 17924 AdjCE 2,257,035 56.508 6.305 1.008 2.618 17924 LogAdjPE 616 2.251 0.257 -0.544 0.683 17924 LogAdjCE 1,591 2.688 0.713 -0.501 0.608 17924 LogCountry-Q 7,480 6.163 -0.050 1.173 0.163 17924 Log#Investments 19,423 5.428 2.2424 0.1184 0.233 17924
Panel C: Sample Characteristics by Year 1981 PriorE 3.767 4.994 30 CountryE 3.600 4.994 30 AdjPE 0.664 1.208 30 AdjCE 0.722 1.387 30 Spz 0.953 0.118 30 1990 PriorE 30.914 38.137 128 CountryE 24.461 37.639 128 AdjPE 1.411 1.804 128 AdjCE 0.959 1.539 128 Spz 0.745 0.354 128 2000 PriorE 26.317 56.302 3497 CountryE 9.643 26.701 3497 AdjPE 0.952 2.118 3497 AdjCE 0.896 2.769 3497 Spz 0.589 0.364 3497 2004 PriorE 57.754 114.253 1960 CountryE 15.534 38.182 1960 AdjPE 1.833 0.366 1960 AdjCE 0.936 3.682 1960 Spz 0.513 2.458 1960
Panel A shows the distribution of the dataset. There are 152 countries in the sample with 11586 portfolio companies and
12 number of investments made by VC firm f in country k prior to year t minus the log of the average number of investments made by all VC firms in country k prior to year t. Country-Q is the average of all public companies Tobin’s Q in country K in year t. LogCountry-Q is the natural logarithm of Country-Q
Table 2 examines the correlation between our primary variables. We can see that prior experience and country experience are highly correlated. When we test for multicollinearity for the log adjusted prior and country experience variables we also find that they remain extremely highly correlated. However, the experience variables are not correlated with the specialization variable. We find that specialization has a negative correlation with prior experience. This may be given to the fact that less experienced VC firms are less likely to have invested in numerous countries, and because specialization entails that VC firms limit the pool of investments from which they can choose from.
Table 2
Correlations Between Firm Characteristics Variables
(N=16910) PriorE CountryE Spz LogAdjPE LogAdjCE
PriorE 1.00
CountryE 0.75 1.00
Spz -0.06 0.27 1.00
LogAdjPE 0.75 0.62 -0.10 1.00
LogAdjCE 0.70 0.73 0.28 0.78 1.00
Table 2 shows the correlation between prior experience (PriorE), country experience (CountryE), and Specialization (Spz).
The table also displays the correlation between log adjusted prior experience (LogAdjPE) and log adjusted country experience (LogAdjCE). All the results remain significant at the 5% level.
Fig 1 shows the relation between our measure of foreign investment opportunities and VC firm investment activity, that is, the amount of investments made by foreign VC firms in country k in year t. We choose two countries out of the dataset in order to show that the relation between VC firm investment and country-Q may be stronger for some countries than for other. In Japan, one can see that investments peaked in 1988, 1996, and 2000. And Q has a similar trend around those same years. In comparison, the correlation between the two variables in Brazil appears to be lower. As mentioned earlier, in theory, investors may be less inclined to invest in countries given to factors such as different business ethics and values, cultural distance, and geographic distance
Figure 1. Number of investments made for selected countries. The left y-axis shows the number of
investments made. The right y-axis shows Q. The x-axis displays the year.
0 0.2 0.4 0.6 0.8 1 1.2 1.4 0 20 40 60 80 100 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 Q In ve stm e tn s
Number of Investments and Q:
Brazil
In v 1.2 1.25 1.3 1.35 1.4 1.45 1.5 0 50 100 150 200 250 300 350 400 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05 Q Inves tm et n sNumber of Investments and Q:
Japan
13
5. Analysis
We now turn to understand the relation between perceived foreign investment opportunities (country-Q) and VC firm investment. In order to confirm the use of country-Q as a proxy for perceived foreign investment opportunities, we estimate a regression that shows the relation between country-Q and the total number of investments made by VC firms in a country. Thus, the dependent variable is the logarithm of the total number of investments made by VC firms in country K in year Y. We take the logarithm of the dependent variable and Country-Q because as can be seen in Panel B of Table 1 both variables are not normally distributed. In the first specification of Table 3, we run the regression for LogCountry-Q and find that this variable is not statistically significant. We argue that it probably takes time for the information of foreign investment opportunities to reach the VC firm. In the second specification of Table 3, we lag country-Q. We now find that the Lagged LogCountry-Q variable is statistically significant, indicating that our measure for perceived foreign investment opportunities is valid and that it does take time for the information of perceived investment opportunities to reach the VC firm. In the first and second columns, we estimate a simple pooled regression. In the third column we estimate a panel analysis specifying country and year fixed effects within-group correlation. We include an AR(1) term of the dependent variable (LaggedD) in all the specifications of Table 3 out of concern for serial correlation. The coefficient estimate of Lagged LogCountry-Q in the third column implies that an increase in country-Q from the lowest level to the highest in the sample boasts VC investment in a country by almost 40%.
Table 3
𝐿𝑜𝑔#𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑡𝑠𝑘𝑡= 𝜇𝑘+ 𝑣𝑡+ 𝛽1𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1+ 𝛽2𝐿𝑜𝑔#𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑠𝑘𝑡−1+ 𝜖𝑖𝑡 VC firm Investment and Foreign Investment Opportunities
Dependent variable (1) Country Investments (2) Country Investments (3) Country Investments
Model Simple Poole Simple Pooled Panel
LogCountry-Q 0.3126 (1.49) Lagged LogCountry-Q 0.3724 (1.92)** 0.03519 (7.345)*** LaggedD 0.0005 (10.33)*** 0.0005 (10.31)*** 0.0001 (3.55)***
Year Fixed Effects No No Yes
Country Fixed Effects No No Yes
Adj. R-squared 0.7546
N 435 435 435
Table 3 presents a regression analysis of the relation between country-Q and foreign investment opportunities. The
14 second column presents the results again for a simple pooled regression, however, in this case specifying a lagged LogCountry-Q variable. The third column presents the results for a panel regression. *, **, ***, show the statistical significance at 10%, 5%, and 1% level, respectively.
In Table 4 we present our results depicting the relation between country-Q, our experience variables, and venture capital firm investments. The analysis is conducted at the venture capital firm-country-year level. The dependent variable is the log of the number of investments made by venture capital firm f in country k in year t. The foreign market investment opportunities variable (lagged country-Q) is the average Tobin’s Q of public companies in country k in year t-1. Our variable for prior experience is the log adjusted prior experience measure that was introduced in the critical measures section of the paper. We use this variable in order to adjust for the time trend of VC firm experience. We take the logarithm of the variable for two reasons. One is to normalize the sample and the second is because and investment made by a less experienced firm might increase the experience of this VC firm relatively more than for a more experienced firm. In other words, when a VC firm with very little prior experience invests it will learn very much from this experience. A more mature VC firm to an extent will not learn as much given to the fact that it has already gained the knowledge in previous investments. The more experienced VC firms are in a higher position of a marginally decreasing learning curve so to speak. Log adjusted prior experience is the logarithm of VC firm’s f prior experience minus the log of the average experience of all VC firms in the year of the investment. Likewise, our measure for country experience is the log adjusted country experience variable. For every specification shown in Table 4, we include the lagged dependent variable (LaggedD) out of concern for serial correlation. The lagged dependent variable is VC firm’s f number of investments made in country k in year t-1. This variable is interesting since it stand for VC firm’s f most recent level of experience. For every specification shown in Table 4, we include country and year fixed effects. We do so since we reject the null hypothesis of the Hausman test. Furthermore, the t-statistics of the regression are presented underneath the coefficient estimates in italics.
Table 4
𝐿𝑜𝑔#𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑡𝑠𝑓𝑘𝑡
= 𝜇𝑘 + 𝑣𝑡+ 𝛽1𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1+ 𝛽2𝐿𝑜𝑔𝐴𝑑𝑗𝑃𝐸𝑓𝑡+ 𝛽3𝐿𝑜𝑔𝐴𝑑𝑗𝐶𝐸𝑓𝑘𝑡+ 𝛽4𝑆𝑝𝑧𝑓𝑘𝑡 + 𝛽5𝐷𝑢𝑚𝑚𝑦𝑓𝑡+ 𝛽6𝐿𝑜𝑔#𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑠𝑓𝑘𝑡−1+ 𝜖𝑖𝑡
Investment patterns with no interaction
15 Portfolio Firm Dummy 0.0015 (1.02) LaggedD 0.0346 (38.39)*** 0.0321 (33.98)*** 0.0267 (26.10)*** 0.0341 (37.66)** 0.0317 (33.31)*** 0.0321 (33.10)** Country Fixed Effects
Yes Yes Yes Yes Yes Yes
Year
Fixed Effects
Yes Yes Yes Yes Yes Yes
Adjusted R-squared
63.52% 63.95% 64.49% 63.79% 64.81% 61.50%
N 17924 17924 17924 17924 17924 17924
Table 4 presents the results of the main regression analysis of the paper. The unit of observation is the venture capital firm
f in country k in year t. The dependent variable is the log of the number of investments made by venture capital firm f in country k in year t. The foreign investment opportunities variable is lagged log country Q. We construct Q by equally weighting all companies that have gone public in a particular country and year. LogAdjPE is the log of the number of investments made by firm f prior to year t minus the log of the average number of investments made all VC firms prior to year t. LogAdjCE is log of the number of investments made by VC firm f in country k prior to year t minus the log of the average number of investments made by all VC firms in country k prior to year t. Spz isthe fraction of the number of investment VC firm f made in country k before the investment in question divided by all previous investments that VC firm f made before the investment in question. Portfolio firm Dummy is one for seed and early stage investments and zero otherwise. LaggedD is the AR(1) term of the dependent variable. For all specializations, we include country and year fixed effects. Below the coefficient estimates we present the t-statistic in italics. *, **, ***, show the statistical significance at 10%, 5%, and 1% level, respectively.
16 same time specialized VC firms are influenced by foreign public equity market valuations. However, the LogCountry-Q variable remains insignificant. In the last column of Table 4, we present our results from the regression including the dummy variable for portfolio firm stage level. We find no significant results, indicating that venture capital firms invest regardless of the maturity of the portfolio company.
Table 5 shows the results of how VC firms with differing characteristics react to foreign investment opportunities. We add to the regressions three new variables that interact our country-Q measure with our VC firm characteristics variables. For the interpretation of the interaction terms, we have constructed a table depicting the main variables by quartiles (see Appendix).
Table 5
𝐿𝑜𝑔#𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑡𝑠𝑓𝑘𝑡
= 𝜇𝑘+ 𝑣𝑡+ 𝛽1𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1+ 𝛽2𝐿𝑜𝑔𝐴𝑑𝑗𝑃𝐸𝑓𝑡+ 𝛽3𝐿𝑜𝑔𝐴𝑑𝑗𝐶𝐸𝑓𝑘𝑡+ 𝛽4𝑆𝑝𝑧𝑓𝑘𝑡 + 𝛽5𝐿𝑜𝑔𝐴𝑑𝑗𝑃𝐸𝑓𝑡∗ 𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1+ 𝛽6𝐿𝑜𝑔𝐴𝑑𝑗𝐶𝐸𝑓𝑘𝑡∗ 𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1 + 𝛽7𝑆𝑝𝑧𝑓𝑘𝑡∗ 𝐿𝑜𝑔𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑄𝑘𝑡−1+ 𝛽8𝐿𝑎𝑔𝑔𝑒𝑑𝐷𝑓𝑘𝑡−1+ 𝜖𝑖𝑡
Investment patterns (with interactions)
Dependent variable (1)Firm-Country Investment (2)Firm-Country Investment (3)Firm-Country Investment Country-Q 0.0460 (2.75)*** 0.0466 (2.41)*** 0.0031 (0.19) LogAdjPE -0.0026 (-0.73) LogAdjCE -0.1564 (-3.31)*** Spz -0.0019 (-1.05) LogAdjPE*LogCountry-Q 0.0451 (6.15)*** LogAdjCE*LogCountry-Q 0.1637 (4.04)*** Spz*LogCountry-Q 0.1098 (12.28)*** LaggedD 0.0321 (33.97)*** 0.0339 (37.52)*** 0.0268 (25.97)***
Fixed Effects Yes Yes Yes
17
N 17924 17924 17924
Table 5 presents the results of the panel regression including three interaction variables between our Q measure and our main variables. The unit of observation is the venture capital firm f in country k in year t. The dependent variable is the log of the number of investments made by venture capital firm f in country k in year t. The foreign investment opportunities variable is lagged log country Q.We construct Q by equally weighting all companies that have gone public in a particular country and year. LogAdjPE is the log of the number of investments made by firm f prior to year t minus the log of the average number of investments made all VC firms prior to year t. LogAdjCE is log of the number of investments made by VC firm f in country k prior to year t minus the log of the average number of investments made by all VC firms in country k prior to year t. Spz isthe fraction of the number of investment VC firm f made in country k before the investment in question divided by all previous investments that VC firm f made before the investment in question. LaggedD is the dependent variable with AR(1) specification. For all specializations, we include country and year fixed effects. Below the coefficient estimates we present the t-statistic in italics. *, **, ***, show the statistical significance at 10%, 5%, and 1% level, respectively.
In order to interpret what happens to VC firm’s investment activity given a change in country-Q we take the first derivative of the regression with respect to country-Q in order to obtain the marginal effect as a composite coefficient estimate. In what follows when we refer either to high or low levels of country-Q we are speaking of the top and lowest quartiles of country-Q in our dataset respectively. Likewise, when we refer to high or low levels of our experience variables we are speaking of the highest and lowest quartiles of the variables respectively. In the first specification of Table 5 we see that the interaction term LogAdjPE*Country-Q is statistically significant. The result suggests that experienced VC firms are more sensitive to changes in the level of country-Q than less experienced VC firms. Experienced VC firms invest 20% when country-Q is high and 11% when country-Q is low. In comparison, less experienced VC firms invest only 3.2% when country-Q is high and 1.7% when country-Q is low. In the second specification of Table 5 we see that the interaction term LogAdjCE*Country-Q is also statistically significant. The result suggests that VC firms with country experience are more sensitive to changes in the level of country-Q than less country experienced VC firms. We find that in high levels of country-Q country experienced VC firms invest 59% as compared to 32% when country-Q is low. In comparison, in high levels of country-Q less experienced VC firms invest 26% as compared to 14% when country-Q is low. In the final specification when see again that the interaction term is significant. As in the previous table, the lagged country-Q term becomes insignificant. However, in this case, we are not able to assume that this happens because of omitted variables or because of the inclusion of the interaction term. The estimated coefficient of the interaction term Spz*Country-Q tells us that when country-Q is high specialized firms invest 22% as compared to only 12% when country-Q is low. In comparison, less specialized firms invest 4.9% when country-Q is high and 2.7% when country-Q is low. We see in this case that specialized VC firms are less sensitive to changes in country-Q than less specialized VC firms. As in the previous regression, we argue that specialized firms are probably more aware of the situation in the country and do not react to changes in country-Q. In comparison, less specialized VC firms rely more on foreign public equity market valuations given the lack of knowledge of the situation in a country.
Conclusion
18 opportunities. We also test whether the maturity of the investment opportunities has any correlation with investment activity.
The study is conducted between 1981 and 2007. We analyze almost 18000 cross-border VC firm-country investments during this period. We find that VC firms do react to changes in foreign public equity market valuations. We find that by contrast to less experienced, experienced VC firms have stronger reactions to changes in public equity market valuation. We also find that VC firms with country-specific experience increase their investments in countries and years when perceived foreign investment opportunities are high. These results suggest that market valuations provide the necessary information to VC firms of investment opportunities in a country. However, we see that in order to take advantage of these investment opportunities VC firms need a level of human capital to overcome the risk associated with investing overseas. Finally, we find that portfolio company maturity level has no significant correlation with venture capital investment activity.
The results of this study are in line with the results of Wright et al. (2005) who find that in years where public equity market valuations in a particular industry is high experienced venture capital investors are more inclined to invest than less experienced venture capital investors. We find several limitations to this study. First, given that true investment opportunities are hard to observe, we measure these using a proxy. In future studies, researchers may apply other proxies for foreign investment opportunities that may come closer to the reality. Second, we were unable to balance the panel dataset. In further research, one might test for the significance of this study by using fewer countries and years. As discussed earlier, we proxy a VC firm’s resource base by calculating its experience. In future research on might include other proxies to account for the VC firm’s network of contacts and reputation. However, given the limitations of this study we can say that there is a significant correlation between the rise of new investment opportunities and the spread of venture capital organizations. Moreover, that the resource base, in this case, measured as the level of experience of a VC firms, plays a significant role in this association. In a professional perspective, this study may be used to understand how important it is for an organization to have the necessary human capital to invest abroad.
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Appendix
Table 6
Firm Characteristics and Country-Q by Quartiles
Country-Q LogAdjPE LogAdjCE Specialization
Quartiles
1 1.065 -1.376 -1.104 0.200
2 1.156 -0.870 -1.017 0.571
3 1.284 -0.377 -0.555 1.000
4 1.967 1.233 1.548 1.000
Table 6 describes the dependent variable Country-Q, and the firms characteristic variables used in the main regression
equation by their respective quartiles.
Table 7
Summary of Symbols
Log#Investments The natural logarithm of the number of investments made by venture capital firm f in country k in year t.
PriorE The total number of investments made by a venture capital firm prior to the time of the investment in question.
CountryE The total number of investments made by a venture capital firm in a specific country prior to the time of the investment in question.
Spz The fraction of the number of investment VC firm f made in country k before the investment in question divided by all previous investments that VC firm f made before the investment in question.
AdjPE The number of investments that the venture capital firm made prior to year t divided by the average number of investments that active venture firms made prior to year t.
22 venture firms made in country k prior to year t.
LogAdjPE The log of the number of investments made by VC firm f prior to year t minus the log of the average number of investments made all VC firms prior to year t.
LogAdjCE The log of the number of investments made by VC firm f in country k prior to year t minus the log of the average number of investments made by all VC firms in country k prior to year t.
Country-Q The average Tobin’s Q of public companies in a particular country and year. LaggedD Is the dependent variable specifying an AR(1) term.
