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It’s a Peoples Game, Isn’t It?! A Comparison Between the Investment Returns of Business Angels and Machine Learning Algorithms

Author

Listed:
  • Ivo Blohm
  • Torben Antretter
  • Charlotta Sirén
  • Dietmar Grichnik
  • Joakim Wincent

Abstract

Investors increasingly use machine learning (ML) algorithms to support their early stage investment decisions. However, it remains unclear if algorithms can make better investment decisions and if so, why. Building on behavioral decision theory, our study compares the investment returns of an algorithm with those of 255 business angels (BAs) investing via an angel investment platform. We explore the influence of human biases and experience on BAs’ returns and find that investors only outperformed the algorithm when they had extensive investment experience and managed to suppress their cognitive biases. These results offer novel insights into the role of cognitive limitations, experience, and the use of algorithms in early stage investing.

Suggested Citation

  • Ivo Blohm & Torben Antretter & Charlotta Sirén & Dietmar Grichnik & Joakim Wincent, 2022. "It’s a Peoples Game, Isn’t It?! A Comparison Between the Investment Returns of Business Angels and Machine Learning Algorithms," Entrepreneurship Theory and Practice, , vol. 46(4), pages 1054-1091, July.
    • Handle: RePEc:sae:entthe:v:46:y:2022:i:4:p:1054-1091
    DOI: 10.1177/1042258720945206
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