IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v54y2022i40p4632-4646.html
   My bibliography  Save this article

Man versus machine: on artificial intelligence and hedge funds performance

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2022.2032585
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2022.2032585?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Junsheng & Peng, Zezhi & Zeng, Yamin & Yang, Haisheng, 2023. "Do big data mutual funds outperform?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    2. 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.
    3. 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).
    4. Zoran Stoiljkovic, 2023. "Applying Reinforcement Learning to Option Pricing and Hedging," Papers 2310.04336, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:54:y:2022:i:40:p:4632-4646. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.