IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v58y2023ipas1544612323006578.html
   My bibliography  Save this article

Stop-loss adjusted labels for machine learning-based trading of risky assets

Author

Listed:
  • Hwang, Yoontae
  • Park, Junpyo
  • Lee, Yongjae
  • Lim, Dong-Young

Abstract

Since the rise of ML/AI, many researchers and practitioners have been trying to predict future stock price movements. In actual implementations, however, stop-loss is widely adopted to manage risks, which sells an asset if its price goes below a predetermined level. Hence, some buy signals from prediction models could be wasted if stop-loss is triggered. In this study, we propose a stop-loss adjusted labeling scheme to reduce the discrepancy between prediction and decision making. It can be easily incorporated to any ML/AI prediction models. Experimental results on U.S. futures and cryptocurrencies show that this simple tweak significantly reduces risk.

Suggested Citation

  • Hwang, Yoontae & Park, Junpyo & Lee, Yongjae & Lim, Dong-Young, 2023. "Stop-loss adjusted labels for machine learning-based trading of risky assets," Finance Research Letters, Elsevier, vol. 58(PA).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pa:s1544612323006578
    DOI: 10.1016/j.frl.2023.104285
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323006578
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.104285?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.

    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:eee:finlet:v:58:y:2023:i:pa:s1544612323006578. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.