Artificial Intelligence (AI) Framework for Venture Capital Industry: How to Predict Startups’ Success in Primary Market


Why this research is significant?

  • Find a right startup to invest and exist is key to survival of VCs

Background and existed research

  • Empirical studies lack because of quantitative data lacks.
  • Some factors which have effects on startups’ success are studied. However, the perspective stands at startups rather than venture capitals who focus on exists.
  • Although venture capitals have some general rules to do investment, it is not easy to do decision when investors face a specific case.

Method

  • Independent variables: to be extended
    • Macroeconomics environment
    • Basics
    • Macroeconomics environment
    • Funding history
    • Team
    • Media exposure
    • Operation
    • Endorsement by VCs
  • Dependent variables: come to next round, investors exist
    • Whether come to next round
  • Regression model: find key factors (significance test)
  • Prediction: Deep Learning, Neural Networks

Dataset

  • 200,000+ TMT (Telecom, Media, Technology) companies

Reference

  • Gompers, Paul, and Josh Lerner. “The venture capital revolution.” The journal of economic perspectives 15.2 (2001): 145-168.
  • Gompers, Paul, et al. “Venture capital investment cycles: The impact of public markets.” Journal of Financial Economics 87.1 (2008): 1-23.
  • Gompers, Paul, et al. How Do Venture Capitalists Make Decisions?. No. w22587. National Bureau of Economic Research, 2016.
  • Kaplan, Steven N., and Josh Lerner. “Venture capital data: Opportunities and challenges.” Measuring Entrepreneurial Businesses: Current Knowledge and Challenges. University of Chicago Press, 2016.
  • Song, Michael, et al. “Success factors in new ventures: A meta‐analysis.” Journal of product innovation management 25.1 (2008): 7-27.