Venture Capital Communities
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Transcript of Venture Capital Communities
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Venture Capital Communities
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Sanjiv DasSanta Clara University
Full Paper: http://algo.scu.edu/~sanjivdas/vccomm.pdf
(Joint work with Amit Bubna, Indian School of Business, and N.R. Prabhala, Univ. of Maryland)
R User Group: Dec 2011
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Communities: Multi-Disciplinary Applications
Biologyo Metabolic networks of cellular organisms (Duch and Arenas, 2005)o Community structure of the human brain (Wu et al, 2011)o Compartmentalization of food chain webs (Dunne, 2006)
Political Science o Political preferences through voting patterns (Porter et al, 2007)
Social interactiono Mobile phone and online networks (Porter et al, 2009)o Collaboration between scientists (Newman, 2001)
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Syndication
The VC Marketo 56,000 deals, $146 billion from 1980-1999o 39,002 deals, $316 billion from 2000-2010
Syndicationo 44% of # dealso 66% of amount invested
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Choosing Syndication Partners
If partners are chosen at random oSpatially diffuse VC network
If VCs have preferred partners oSpatial clustering of VC networks
We term spatial clusters as VC communities.
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Example: J. P. Morgan
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Why are Communities Important? Syndication: acquiring or improving skills
How does familiarity help?o Familiar norms, processes, and people [Gertler (1995), Porter (2000)]o Flow of informal knowledgeo Mitigates incomplete contracting problems, builds trust and enhances reciprocity [Guiso, Sapienza
and Zingales (2004); Bottazzi, Da Rin and Hellmann (2011)]
Pure transaction cost effecto Less administrative overheads and paperworko Behavioral affinity for the familiar
Knowledge spillovers through repeated interactionso Acquiring or improving skillso Learning facilitated through familiar norms, processes, and people
Resource sharing without burden of organizational inflexibility
Economics literature on clusteringo Krugman (1991), Porter (1998): new organizational paradigm to capture benefits of externalities.
o Lindsey (2007): VCs blur boundaries between portfolio firms. Communities similarly blur lines between VCs. 7
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Detecting Communities
Community identification shouldo Accommodate large number of players o Not pre-specify the # of communitieso Allow for VC communities of varying sizes o Permit fuzzy boundaries between communities
This is a computationally hard clustering problem.
Modularity optimizationo Modularity – strength of internal ties compared to ties outside
(Girvan and Newman, 2003)o We implement an agglomerative algorithm
o “Walktrap” algorithm (Pons and Latapy, 2005)
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CommunityMathematical Construct
Adjacency matrix of a graph A o A [i,j] = nij o nij = # syndicates involving VC i and VC j.
Partition, P, divides A into collections of nodes, P = (P1, P2, … Pn )o mutually exclusive and collectively exhaustive
The best community structure maximizes in-community deals relative to the predicted in-community deals, or the modularity
where, ki = # syndicates involving VC i and m = # deals in Pn 9
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Example
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Quick R
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Communities• Group-focused concept• Members learn-by-doing
through social interactions.
Centrality• Hub focused concept• Resources and skill of
central players.
Community v. Centrality
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Data
Sources: SDC o VentureExpert database (VE) - 1980-1999o Exits data - IPO, M&A: 1980-2010
Level of observation in the VE database:o Company × Round × Investor
Community identification using VE database:o Not Individuals, Management or Undisclosed
Filters used in exit analysis:o U.S. investmentso Investment is not at "Buyout/Acquisition" stageo Not “Angel or individual” investors 13
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Conclusion
• COMMUNITIES: a new way of looking at networks and social interactions in finance.
• VCs form communities which tend to be homophilous.
• Communities facilitate learning amongst VCs, which have important economic effects for portfolio communities.
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