Venture Capital Communities

Abstract

While 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 three 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 locations. Machine learning tools classify communities into three groups roughly ordered by the age and reach. Community VC financing is associated with faster maturation and greater innovation especially for early-stage firms without an innovation history.

Keywords: Venture capital, syndication, community detection, social networks, boundaries of the firm, machine learning

JEL Classification: G20, G24