IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v82y2023ics0927538x23002494.html
   My bibliography  Save this article

Momentum in machine learning: Evidence from the Taiwan stock market

Author

Listed:
  • Bui, Dien Giau
  • Kong, De-Rong
  • Lin, Chih-Yung
  • Lin, Tse-Chun

Abstract

We revisit 86 asset pricing anomalies in the Taiwan stock market and find that long-short portfolio strategies based on machine-learning methods bring substantial benefits. For example, neural networks and partial least squares generate long-short returns ranging from 1.20% to 1.50% per month. More importantly, five of the top 20 influential return predictors are momentum-related variables. This result provides novel evidence to the momentum literature given that the Taiwan stock market is one of the few exceptions to the momentum anomaly. In contrast with this conventional view, we show that momentum contributes to stock return predictability when adopting machine-learning models.

Suggested Citation

  • Bui, Dien Giau & Kong, De-Rong & Lin, Chih-Yung & Lin, Tse-Chun, 2023. "Momentum in machine learning: Evidence from the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:pacfin:v:82:y:2023:i:c:s0927538x23002494
    DOI: 10.1016/j.pacfin.2023.102178
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X23002494
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.pacfin.2023.102178?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. Hsiao-Peng Fu & Shu-Fan Hsieh, 2024. "Seasonality, Monetary Supply and Taiwanese Momentum," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(2), pages 1-2.

    More about this item

    Keywords

    Momentum; Asset pricing anomalies; Stock return predictability; Machine learning; Variable importance;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G35 - Financial Economics - - Corporate Finance and Governance - - - Payout Policy
    • G40 - Financial Economics - - Behavioral Finance - - - General

    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:eee:pacfin:v:82:y:2023:i:c:s0927538x23002494. 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/pacfin .

    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.