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

Item Type Journal paper
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.
Authors Blohm, Ivo; Antretter, Torben; Siren, Charlotta; Wincent, Joakim & Grichnik, Dietmar
Research Team IWI6, Crowdsourcing, CCC
Projects Blohm, Prof. Dr. Ivo; Wincent, Prof. Ph.D Joakim & Malmström, Malin (2019) Learning Algorithms for Discrimination Free Innovation Funding Activities [fundamental research project]
Journal or Publication Title Entrepreneurship Theory and Practice
Language English
Subjects business studies
information management
HSG Classification contribution to scientific community
HSG Profile Area Global Center for Entrepreneurship + Innovation
Refereed Yes
Date July 2022
Publisher Wiley-Blackwell SSH
Number of Pages 38
ISSN 1042-2587
Publisher DOI https://doi.org/10.1177/1042258720945206
Depositing User Corinne Metzger-Wyder
Date Deposited 06 Oct 2020 11:51
Last Modified 08 Nov 2022 15:10
URI: https://www.alexandria.unisg.ch/publications/261136

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Citation

Blohm, Ivo; Antretter, Torben; Siren, Charlotta; Wincent, Joakim & Grichnik, Dietmar (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, ISSN 1042-2587

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https://www.alexandria.unisg.ch/id/eprint/261136
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