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

Ivo Blohm; Torben Antretter; Charlotta Sirén; Dietmar Grichnik; Joakim Wincent

doi:10.1177/1042258720945206

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

Ivo Blohm¹; Torben Antretter¹; Charlotta Sirén^1,2; Dietmar Grichnik¹; Joakim Wincent^1,3

¹ University of St.Gallen, St.Gallen, Switzerland · ² University of Queensland, UQ Business School, Brisbane Qld, Australia · ³ Hanken School of Economics Helsinki Finland

Entrepreneurship Theory and Practice 2022

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.

DOI: 10.1177/1042258720945206
Volume: 46 (4)
Pages: 1054-1091
Language: en
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