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

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Peer-reviewed scientific journalEntrepreneurship Theory and Practice
ISSN1042-2587
DOIs
Publication statusPublished - 14.09.2020
MoE publication typeA1 Journal article - refereed

Keywords

  • 512 Business and Management
  • business angels
  • artificial intelligence
  • machine learning
  • biases
  • investment experience
  • decision making

Areas of Strength and Areas of High Potential (AoS and AoHP)

  • AoHP: Strategic and entrepreneurial praxis
  • AoS: Responsible organising

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