Whom You Know Matters in Venture Capital
- 1. Hochberg, Yael V., Alexander Ljungqvist, and Yang Lu, “Whom You Know Matters: Venture Capital Networks and Investment Performance.” Journal of Finance 62 (February 2007), pp. 251-301.
- 2. Background • Giving an academic answer to a well-known fact? – Hochberg, Ljungqvist and Lu’s article is fairly new (2007). It’s cited 631 times (June 2013), which is relatively well. – It was published in Journal of Finance, although it could have been published also in Journal of Venture Capital for example. – The title “Whom You Know Matters: Venture Capital Networks and Investment Performance” is quite self-evident fact for VC players. How could the role of contacts be negative? Either it matters or it has no effect. – This study gave an academic and reliable answer to this well- known hypothesis. However, the question still remains worth of reflecting.
- 3. Overview • The Content of the Article: 1. Abstract / Introduction (a long one) 2. Network Analysis Methodology 3. Sample and Data 4. Fund-Level Analysis 5. Company-Level Analysis 6. How Does Networking Affect Performance? 7. Further Robustness Tests 8. How Do VC Firms Become Networked? 9. Conclusions 10.Appendix: Network Analysis Example
- 4. Abstract / Objectives • Venture capital is a business of contacts: – “Many financial markets are characterized by strong relationships and networks, rather than arm’s-length, spot market transactions. We examine the performance consequences of this organizational structure in the context of relationships established when VCs syndicate portfolio company investments. – We find that better-networked VC firms experience significantly better fund performance, as measured by the proportion of investments that are successfully exited through an IPO or a sale to another company. – Similarly, the portfolio companies of better-networked VCs are significantly more likely to survive to subsequent financing and eventual exit. – We also provide initial evidence on the evolution of VC networks.”
- 5. Literature Review • Three reasons to expect that syndication networks improve the quality of deal flow: 1. VCs invite others to coinvest in their promising deals in the expectation of future reciprocity (Lerner (1994a)). 2. By checking each other’s willingness to invest in potentially promising deals, VCs can pool correlated signals and thereby select better investments in situations of often extreme uncertainty about the viability and return potential of investment proposals (Wilson (1968), Sah and Stiglitz (1986)). 3. Individual VCs tend to have investment expertise that is both sector- specific and location-specific. Syndication helps diffuse information across sector boundaries and expands the spatial radius of exchange, allowing VCs to diversify their portfolios (Stuart and Sorensen (2001)).
- 6. Network Analysis Methodology • Degree centrality – Degree: # of ties they have (i.e. syndication relationship); – Indegree: # of ties they receive (i.e., are invited to be syndicate members by many lead VCs); – Outdegree: # of ties an actor initiates (i.e., lead syndicates with many other VC members). • Closeness – Weighting an actor’s ties to others by the importance of the actors he is tied to; • Betweenness – The extent to which a VC may act as an intermediary by bringing together VCs with complementary skills or investment opportunities that lack a direct relationship between them.
- 7. Network Analysis Methodology – Mapping Relationships
- 8. Sample and Data • There is always hard to get data for VC / PE studies – Venture Capital: Thomson Financial’s Venture Economics; – Sample: 3,469 U.S. based VC funds managed by 1,974 VC firms which participate in 47,705 investment rounds involving 16,315 portfolio companies; – Time: 1980-2003; – Fund Characteristics: $64 million of committed capital, with a range from $0.1 million to $5 billion. (Fund size is unavailable for 364 of the 3,469 sample funds.) – Data on IRRs was available only for 188 of the 3,469 sample funds. – Company exit data no available.
- 9. Sample and Data - Definitions • No direct data available – indirect variables and measures – Measuring Fund Performance: fraction of the fund’s portfolio companies that have been successfully exited via an IPO or M&A transaction(Nov. 2003); – Company-level Performance Measures (2): 1. survival to another funding round as an interim signal of success; 2. successful exit as a final signal of the investment’s; – VC firm experience (4): 1. the age of the VC firm (the number of days since the VC firm’s first-ever investment) 2. the number of rounds the firm has participated in 3. the cumulative total amount it has invested 4. the number of portfolio companies it has backed – Network measures (5): degree, indegree, outdegree, eigenvector and betweenness.
- 10. Venture Capital Investments in Rounds The average VC fund writes off 75,3% of its investments. (Ljungqvist et al 2005)
- 11. The Effect of Firm Networks on Fund Performance • Having controlled for fund characteristics, competition for deal flow, investment opportunities, and parent firm experience, does a VC’s network centrality (measured over the prior 5 years) improve the performance of its fund (over the next 10 years)? The results, shown in Table III, indicate that it does. • Each specification in Table III suggests that better- networked VC firms are associated with significantly better fund performance, and the adjusted-R2 increases to around 19%.
- 12. How Does Networks Matter? • Eigenvectos, degree and indegree matter the most: – Of the five network measures, eigenvector has the largest economic effect, closely followed by degree and indegree. To illustrate, a one-standard deviation increase in these measures is associated with 2.4–2.5 percentage point increases in exit rates, all else equal. Thus, a VC benefits from having many ties (degree), especially when the ties involve other well-connected VCs (eigenvector), and from being invited into many syndicates (indegree). – Having the ability to act as a broker between other VCs (betweenness) has a smaller effect, with a one-standard-deviation increase in this centrality measure being associated with only a 1 percentage point increase in fund performance. Indirect relationships (those requiring intermediation) play a lesser role in the venture capital market. – Similarly, outdegree has a relatively small effect economically, consistent with the view that this measure captures a VC firm’s investment in future reciprocity, which takes some time to pay off. In other words, inviting many VCs into one’s syndicates today (i.e., high outdegree) will hopefully result in many coinvestment opportunities for one’s future funds (i.e., high future indegree).
- 13. Robustness - Reverse Causality and Performance Persistence • Are the results reliable and really true? – Are results driven simply by reverse causality, that is, a higher fund exit rate enables a VC to improve its network position, rather than the other way around? They construct the network centrality measures from syndication data for the 5 years before a fund is created. The fact that these data can help explain fund performance over the next 10 years suggests that networking truly affects performance. – Robustness to Alternative Explanations: It is possible that better-networked VCs are simply better at taking more marginal companies public, thus generating the appearance of better performance as measured by the VC’s exit rate or a portfolio company’s survival probability, but which would not be reflected in returns, if observed. To test this alternative hypothesis, they focused on two quality indicators, specifically whether the portfolio company had positive net earnings when it went public, and whether it survived the first 3 years of trading on the public markets. In conclusion, better-networked VCs do not appear to be associated with lower-quality IPO exits. – Location and Industry-Specific Networks: Findings are robust to using centrality measures derived from (1) industry-specific networks defined using the six broad Venture Economics industries, and (2) a network of Californian VC firms.
- 14. Conclusions • Fivefold contribution: 1. This is the first paper to examine the performance consequences of the VC industry’s predominant choice of organizational form: networks. Previous work focuses on describing the structure of syndication networks (Bygrave (1988), Stuart and Sorensen (2001)) and motivating the use of syndication (Lerner (1994a), Podolny (2001), Brander, Amit, and Antweiler (2002)). 2. The findings shed light on the industrial organization of the VC market. Like many financial markets, the VC market differs from the traditional arm’s-length spot markets of classical microeconomics. The high network returns documented suggest that enhancing one’s network position should be an important strategic consideration for an incumbent VC, while presenting a potential barrier to entry for new VCs. The results add nuance to Hsu’s (2004) finding that portfolio companies are willing to pay to be backed by brand-name VCs and suggest that there are real performance consequences to the contractual differences illustrated in Robinson and Stuart’s (2004) work on strategic alliances.
- 15. Conclusions (2) • Fivefold contribution: 3. The findings have ramifications for institutional investors choosing which VC funds to invest in, as better-networked VCs appear to perform better. 4. The analysis provides a deeper understanding of the possible drivers of cross-sectional performance of VC funds, and points to the importance of additional fundamentals beyond those previously documented in the academic literature. 5. It provides preliminary evidence regarding the evolution of a VC firm’s network position.
- 16. Discussion • Some thoughts to be discussed – Taking into account the nature of venture capital investing, in which more than half of the companies fail and only small section of portfolio companies appear to be great investments, enlarging your portfolio by syndicating and networking with other professionals makes sense. – What does it really mean that networks matter in VC industry? This theory is not comparable to for example portfolio theory, meaning that just adding contacts would automatically improve your performance. Are networks a primary or a secondary matter? What you buy, what you pay and how you exit are still the primary matters? – What are the most important networks for a VC company? Are other VCs the most important or is it actually institutional investors, banks, entrepreneurs, target companies, portfolio companies, service providers or what? Perhaps VC industry is an ecosystem of value-adding stakeholders?