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

Forskningsoutput: TidskriftsbidragArtikelPeer review

3 Citeringar (Scopus)

Sammanfattning

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.
OriginalspråkEngelska
Referentgranskad vetenskaplig tidskriftEntrepreneurship Theory and Practice
ISSN1042-2587
DOI
StatusPublicerad - 14.09.2020
MoE-publikationstypA1 Originalartikel i en vetenskaplig tidskrift

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  • 512 Företagsekonomi

Styrkeområden och områden med hög potential (AoS och AoHP)

  • AoHP: Strategisk och entreprenörsk praxis
  • AoS: Ansvarsfull organisering

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