Presentation on theme: "Whom You Know Matters: Venture Capital Networks and Investment Performance YAEL HOCHBERG NORTHWESTERN UNIVERSITY ALEXANDER LJUNGQVIST NEW YORK UNIVERSITY."— Presentation transcript:
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 MODELLING PERFORMANCE (3) All results are robust to stopping in 1995 or including only the 1980s.
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
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
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%)
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
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 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 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 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.
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 …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