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 and Joakim Wincent

Entrepreneurship Theory and Practice, 2022, vol. 46, issue 4, 1054-1091

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.

Keywords: business angels; artificial intelligence; machine learning; biases; investment experience; decision making (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1042258720945206 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:entthe:v:46:y:2022:i:4:p:1054-1091

DOI: 10.1177/1042258720945206

Access Statistics for this article

More articles in Entrepreneurship Theory and Practice
Bibliographic data for series maintained by SAGE Publications ().