Published online by Cambridge University Press: 11 January 2019
Although venture capitalists (VCs) can choose from thousands of potential syndicate partners, many co-syndicate with small groups of preferred partners. We term these groups “VC communities.” We apply computational methods from the physical sciences to 3 decades of syndication data to identify these communities. We find that communities comprise VCs that are similar in age, connectedness, and functional style but undifferentiated in spatial location. Machine-learning tools classify communities into 3 groups roughly ordered by their age and reach. Community VC financing is associated with faster maturation and greater innovation, especially for early-stage firms without an innovation history.
We are very grateful to Jarrad Harford (the editor) and an anonymous referee for extensive and thoughtful feedback. We thank Alexandre Baptista, David Feldman, Jiekun Huang, Ozgur Ince, Vladimir Ivanov, Pete Kyle, Josh Lerner, Laura Lindsey, Vojislav Maksimovic, Robert Marquez, Manju Puri, Krishna Ramaswamy, Rajdeep Singh, Richard Smith, Anjan Thakor, Susan Woodward, and Bernard Yeung for their comments. We also thank participants at the 2011 ISB CAF, 2011 Financial Intermediation Research Society, 2013 Midwest Finance Association, 2011 World Private Equity, and 2012 T. A. Pai Management Institute (TAPMI) conferences for their helpful comments; and we thank the seminar participants at BlackRock Inc., University of Florida, George Washington University, Georgia State University, Georgia Tech University, the Indian School of Business, Northwestern University, the University of Maryland, College Park, National University of Singapore, the Bay Area R User Group, Rutgers University, Tulane University, the University of New South Wales, and Villanova University for helpful comments.
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