Whom You Know Matters: Venture Capital Networks and Investment Performance

1. Whom You Know Matters:Venture Capital Networks and Investment Performance YAEL HOCHBERG NORTHWESTERN UNIVERSITY ALEXANDER LJUNGQVIST NEW YORK UNIVERSITY YANG LU NEW YORK UNIVERSITY

2. MOTIVATION • Networks feature prominently in the venture capital industry • VCs tend to syndicate investments, rather than investing alone (Lerner (1994)) • VCs draw on their networks of service providers to help companies succeed (Gorman and Sahlman (1989), Sahlman (1990)) • Capital comes from small set of investors with whom VCs have long-standing relationships (Lerner and Schoar (2005)) Performance consequences of this organizational choice remain unknown • Some VCs should have better networks and relationships • Implies differences in clout, opportunity sets, information access • Structure of syndication networks, motivations for use have been looked at, but not performance implications Do these differences help explain the cross-section of VC investment performance?

3. FOCUS ON SYNDICATION • Syndication relationships are a natural starting point • Good reasons to believe they are vital to VC performance 1. Ability to source high-quality deal flow • Invite others to co-invest in expectation of future reciprocity (Lerner (1994)) • Better investment decisions through pooling of correlated signals (Sah and Stiglitz (1986)) • Diffuse information across sector boundaries and widen spatial radius of exchange (Stuart and Sorensen (2001)) 2. Ability to nurture investments • Facilitate sharing of information, contacts and resources (Bygrave (1988)) • Improve chances of securing follow-on funding, widen capital pool • Indirectly gain access to other VCs’ relationships with service providers

4. THE PUNCHLINE • YES – NETWORKS DO MATTER • Funds raised by better-networked VCs have better performance • Portfolio companies of better-networked VCs are more likely to survive • To exit • To future funding rounds • Effects flow through both deal flow access and value-added

5. Figure 1. Network of biotech VC firms, 1990-1994

6. MEASURING HOW ‘NETWORKED’ A VC IS • Borrow from mathematical discipline of graph theory • Tools for describing networks at a macro level • Tools for measuring relative importance, or ‘centrality’, of each VC in the network • Access to and control over resources or information are particularly well-suited to measurement by these concepts (Knoke and Burt (1983)) • Used before in economics literature: Robinson and Stuart (2004), Stuart, Hoang and Hybels (1999) Network is represented by a square “adjacency matrix” • Cells represent ties between the VCs • Undirected: ties matter, but not who originated them • Directed: distinguish between originator (lead VC in syndicate) and receiver of ties (non-lead syndicate member)

7. NETWORK ANALYSIS METHODOLOGY Networks are not static • New entry of VCs, changes in relationships, exit of VCs • Relationships get stale • Construct adjacency matrices over trailing five-year windows • Network measures, lead VC designations change over time • All measures ‘normalized’ (based on network size) Five measures of centrality: • Degree: no. of relationships  proxy for access to information, deal flow, expertise, contacts, and pools of capital • Indegree: no. of syndicate invitations   access to resources and investment opportunity set • Outdegree: no. of syndicate  investment in future reciprocity • Eigenvector: recursive degree  access to the best-connected VCs • Betweenness: economic broker

8. MEASURING PERFORMANCE • Performance at the fund level • Ideally, would like to use returns, but data not available • Measure indirectly: Exit rates • Relate to IRRs provided in FOIA requests • Performance at the portfolio company level • Again, data availability prevents us from computing returns • Survival from round to round • Achieving exit (IPO or sale) • Time to exit

9. SAMPLE AND DATA Thomson Venture Economics • 1980-1999 vintage year funds • Venture investments only, by U.S. based VCs • 47,705 investment rounds in 16,315 portfolio companies made by 3,469 VC funds managed by 1,974 VC firms • Distinguish between funds, firms, and companies • Most funds organized as ten-year limited partnerships • First three to four years spent selecting investments • Middle years spent nurturing and making follow-on investments • Exit occurs in second half of fund life: IPO, M&A • Funds raised in sequence

10. MODELLING PERFORMANCE (1) • Fund performance = f (fund characteristics, competition for deal flow, investment opportunities, parent experience, network centrality) • Fund characteristics (benchmark model) • Committed capital (fund size) • Fund sequence number • Vintage year • Industry specialization • Stage focus (seed/early stage, later stage) • Competition for deal flow • “Money chasing deals” (Gompers and Lerner (2000)), proxied using aggregate VC fund inflows • Investment opportunities • Investment opportunities proxied using industry average B/M or P/E ratio Kaplan and Schoar (2004)

11. MODELLING PERFORMANCE (2) • Fund performance = f (fund characteristics, competition for deal flow, investment opportunities, parent experience, network centrality) • Parent experience Persistence of returns (Kaplan and Schoar (2004))  importance of experience • Length of investment history since inception • Number of completed rounds since inception • Total $$ invested since inception • Number of portfolio companies since inception Network centrality • degree, outdegree, indegree • eigenvector • betweenness

12. FUND-LEVEL RESULTS (1) Benchmark determinants of fund performance • Replicate Kaplan and Schoar’s fund performance model • Positive, concave relationship between size and performance • First time funds have worse performance • “Money chasing deals” has expected negative effect • Better investment opportunities has expected positive effect • More experienced VC parent firms enjoy better performance Controlling for these effects, network measures are positively and significantly related to fund performance

13. FUND-LEVEL RESULTS (2) Performance persistence • There is considerable performance persistence in exit rates as well as IRRs • Maybe better-networked VCs are simply the ones with better past performance • Re-estimate with additional control for the exit rate of most recent past fund • Three of the five network measures continue to be positively and significantly related to fund performance; similar economic significance Reverse causality • Could argue that superior performance enables VCs to improve their network positions, rather than vice versa • Timeline should mitigate concerns of reverse causality • ‘Network centrality’ measured prior to fund vintage • Results are robust when controlling for past performance • Find no evidence of this when we model evolution of network

14. FUND-LEVEL RESULTS (3) Exit rates and internal rates of return • Sample of fund IRRs recently disclosed by limited partners (LPs) under FOIA • Available for 188 of the 3,469 funds in our sample • Exit rates are a useful but noisy proxy (correlation = 0.42) Re-estimate models using sub-sample for which we have IRRs • indegree and eigenvector remain significant; very large economic effects Regress IRRs on exit rates • Estimated relation is nearly one-to-one (point estimate = 1.046) • If we assume relation remains one-to-one in overall sample, implies we can translate economic effect on exit rates into IRR gains on same basis • 2 pct point increase in exit rate roughly equivalent to 2 pct point increase in IRR (from mean of 15%)

15. Figure 3.

16. COMPANY-LEVEL RESULTS Round-by-round survival models • Network measures significantly and positively related to company survival • Experience measures lose significance Pooled panel survival models • Network measures significantly and positively related to company survival • Experience measures have negative effect Time-to-exit models • Controlling for state of exit markets, network measures significantly and negatively related to time-to-exit

17. ROBUSTNESS Exit rates, survival probabilities may only reflect a better-networked VC’s ability to “push out” even poor quality portfolio companies • Look at M&A and IPOs separately • Look at financials of companies at time of IPO (positive net earnings) • Look at delisting probability post-IPO Results don’t support this alternative hypothesis • Similar results for M&A rates alone • Portfolio companies of well-networked VCs more likely to be in the black at IPO • Portfolio companies of well-networked VCs less likely to delist post-IPO Syndication vs. Networking • Robust to controlling for whether deal is syndicated • Result remains in the sub-sample of non-syndicated deals

18. LOCATION/INDUSTRY SPECIFIC NETWORKS So far, network measures assumed each VC in U.S. potentially syndicates with every other U.S. VC • If VCs geographically concentrated, or industry focused, we may underestimate a VC’s network centrality • e.g., biotech VC may be central in network of biotech VCs, but lack connections to non-biotech VCs • e.g., Silicon Valley VC may be well connected in CA but not in network that includes East Coast VCs Repeat the analysis for • Six industry-specific networks • California VCs Same positive and significant effect; larger economic magnitude

19. HOW DOES NETWORKING EFFECT PERFORMANCE? Deal flow is important, but networking also positively affects ability to provide value-added: 1. Proxy and control for deal flow access • Classify firms as above or below median indegree, interact with other networking measures • For eigenvector, degree - effects are stronger when indegree is lower: Networking boosts performance precisely when the VC does not have good access to deals 2. Networking with “value-added” (corporate) VCs • Construct separate measures of centrality based on networking with CVCs • Reduce effect of deal flow access: 2nd round deals, lead managed by new VCs, with no CVCs involved • Companies financed by new VCs that are well-networked to CVCs are more likely to survive to next round

20. EVOLUTION OF NETWORK POSITIONS If being networked has such high pay-off, how do you become networked? • Emerging track record  more desirable syndication partner in future • For a rookie VC, a track record consists of exits and arm’s-length follow-on rounds Network centralityi,t = f(exitsi,t-1, follow-on roundsi,t-1, experiencei,t-1, IPO underpricingi,t-1, log # new fundst, centralityi,t-1) Results Controlling for persistence and unobserved VC-specific heterogeneity, VC firms improve their network position, … • …the more experience they become • … the more arm’s-length follow-on rounds they achieve • … the more eye-catching their IPOs were Lagged number of exits has no effect except for outdegree.

21. TAKE-AWAYS • First look into the importance of networks as a choice of organizational form in the VC industry • Shed light on industrial organization of the VC market • Ramifications for LPs choosing a VC fund • Deeper understanding of the possible drivers of VC cross-sectional performance • Raises interesting questions: • How do these networks arise? • What determines the choice of whether or not to network? • What are the costs?

22. …AND NEXT PAPER • Large academic literatures on networks and collusion/competition and on market entry • Look at whether macro-level networking in a VC market presents a barrier to new entry: It does! • Define markets by natural combination of state and industry • More networked VC markets experience less entry by outside VCs • The more networked a market, the less likely a potential entrant is to enter • But networking can also help a VC overcome this barrier to entry • Previous experience lead-managing deals in which an incumbent was an investor (in another market) not only mitigates the entry problem, but can actually overcome it • Previously investing along with an incumbent as a non-lead doesn’t have nearly as strong an effect • Not surprisingly, barriers to entry also affect pricing • Valuations are lower in more networked markets, and higher where entrants manage to get more market share • Deepens understanding of how VCs get benefits from being networked