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Venture Capital Communities

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  • Bubna, Amit
  • Das, Sanjiv R.
  • Prabhala, Nagpurnanand

Abstract

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.

Suggested Citation

  • Bubna, Amit & Das, Sanjiv R. & Prabhala, Nagpurnanand, 2020. "Venture Capital Communities," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(2), pages 621-651, March.
    • Handle: RePEc:cup:jfinqa:v:55:y:2020:i:2:p:621-651_8
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    Citations

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    Cited by:

    1. Dimitris Christopoulos & Stefan Koeppl & Monika Köppl-Turyna, 2022. "Syndication networks and company survival: evidence from European venture capital deals," Venture Capital, Taylor & Francis Journals, vol. 24(2), pages 105-135, April.
    2. Nguyen, Giang & Vu, Le, 2021. "Does venture capital syndication affect mergers and acquisitions?," Journal of Corporate Finance, Elsevier, vol. 67(C).
    3. Amini, Shahram & Elmore, Ryan & Öztekin, Özde & Strauss, Jack, 2021. "Can machines learn capital structure dynamics?," Journal of Corporate Finance, Elsevier, vol. 70(C).
    4. Zhiyi Qiu & Bingyi Liu & Ye Yang, 2023. "Like Performance, Perfect Match: Role of Past Performance in Venture Capital Syndication," SAGE Open, , vol. 13(4), pages 21582440231, December.
    5. Fabien Guimtrandy & Thierry Burger-Helmchen, 2022. "The Pitch: Some Face-to-Face Minutes to Build Trust," Administrative Sciences, MDPI, vol. 12(2), pages 1-14, April.
    6. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    7. Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021. "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers 2108.00480, arXiv.org, revised Mar 2023.
    8. Gu, Jing & Zhang, Fujuan & Xu, Xun & Xue, Chaokai, 2023. "Stay or switch? The impact of venture capitalists' movement across network communities on enterprises’ innovation performance," Technovation, Elsevier, vol. 125(C).

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