Venture Capital Communities

54 Pages Posted: 19 Mar 2011 Last revised: 16 Mar 2018

See all articles by Amit Bubna

Amit Bubna

Indian School of Business

Sanjiv Ranjan Das

Santa Clara University - Leavey School of Business

Nagpurnanand Prabhala

The Johns Hopkins Carey Business School

Multiple version iconThere are 2 versions of this paper

Date Written: March 11, 2018

Abstract

While VCs can choose from thousands of potential syndicate partners, many prefer to co-syndicate with partners drawn from small groups of preferred partners, whom we term "VC communities." Using computational methods from the physical sciences, we identify communities from three decades of syndication data and characterize their number, size, composition, and economic activities. Communities comprise VCs similar in age, connectedness, and functional style and undifferentiated spatial locations. Machine learning tools classify communities into three groups roughly ordered by VC age and reach. Community VC financing is associated with faster maturation and greater innovation especially for early-stage firms without innovation histories.

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

JEL Classification: G20, G24

Suggested Citation

Bubna, Amit and Das, Sanjiv Ranjan and Prabhala, Nagpurnanand, Venture Capital Communities (March 11, 2018). Available at SSRN: https://ssrn.com/abstract=1787412 or http://dx.doi.org/10.2139/ssrn.1787412

Amit Bubna (Contact Author)

Indian School of Business ( email )

Hyderabad, Gachibowli 500 019
India

Sanjiv Ranjan Das

Santa Clara University - Leavey School of Business ( email )

Department of Finance
316M Lucas Hall
Santa Clara, CA 95053
United States

HOME PAGE: http://algo.scu.edu/~sanjivdas/

Nagpurnanand Prabhala

The Johns Hopkins Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States
+1 410 234 4532 (Phone)

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