Whom You Know Matters:Venture Capital Networks and

Investment Performance YAEL HOCHBERG

NORTHWESTERN UNIVERSITY

ALEXANDER LJUNGQVISTNEW YORK UNIVERSITY

YANG LUNEW YORK UNIVERSITY

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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?

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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

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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

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

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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)

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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

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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

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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

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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)

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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

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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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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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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%)

18Figure 3.

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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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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

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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

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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

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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.

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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?

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…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