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

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Presentation on theme: "Whom You Know Matters: Venture Capital Networks and Investment Performance YAEL HOCHBERG NORTHWESTERN UNIVERSITY ALEXANDER LJUNGQVIST NEW YORK UNIVERSITY."— Presentation transcript:

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 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 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 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 5 Figure 1. Network of biotech VC firms,

6 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 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 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 9 SAMPLE AND DATA Thomson Venture Economics  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 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 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 12 MODELLING PERFORMANCE (3) All results are robust to stopping in 1995 or including only the 1980s.

13 13 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

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15 15 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

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17 17 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%)

18 18 Figure 3.

19 19 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

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23 23 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

24 24 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

25 25 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: 2 nd 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

26 26 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 centrality i,t = f(exits i,t-1, follow-on rounds i,t-1, experience i,t-1, IPO underpricing i,t-1, log # new funds t, centrality i,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.

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28 28 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?

29 29 …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


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