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Man versus machine: on artificial intelligence and hedge funds performance

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  • Klaus Grobys
  • James W. Kolari
  • Joachim Niang

Abstract

Employing partially hand-collected data, sample hedge funds are formed into four categories depending on their level of automation. We find that hedge funds with the highest level of automation outperform other hedge funds with more reliance on human involvement. Also, we find that a man versus machine zero-cost strategy that is long hedge funds portfolio with highest level of automation and short those with highest level of human involvement yields a highly significant spread of at least 50 basis points per month. We conclude that automation plays an important role in the profitability of the hedge fund industry.

Suggested Citation

  • Klaus Grobys & James W. Kolari & Joachim Niang, 2022. "Man versus machine: on artificial intelligence and hedge funds performance," Applied Economics, Taylor & Francis Journals, vol. 54(40), pages 4632-4646, August.
    • Handle: RePEc:taf:applec:v:54:y:2022:i:40:p:4632-4646
    DOI: 10.1080/00036846.2022.2032585
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    Cited by:

    1. Axelsson, Birger & Song, Han-Suck, 2023. "Univariate Forecasting for REITs with Deep Learning: A Comparative Analysis with an ARIMA Model," Working Paper Series 23/10, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance, revised 14 Nov 2023.
    2. Zhao, Jingyou & Hu, Enhua & Han, Mingyan & Jiang, Keshen & Shan, Hongmei, 2023. "That honey, my arsenic: The influence of advanced technologies on service employees’ organizational deviance," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    3. Zoran Stoiljkovic, 2023. "Applying Reinforcement Learning to Option Pricing and Hedging," Papers 2310.04336, arXiv.org.

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