IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v101y2025ics1059056025003168.html
   My bibliography  Save this article

The pricing ability of factor model based on machine learning: Evidence from high-frequency data in China

Author

Listed:
  • Zhang, Ailian
  • Pan, Mengmeng
  • Zhang, Xuan

Abstract

The existing literature mainly documents the asset pricing models estimated on low-frequency data, lacking the empirical evidence for exploring the “right” systematic factors based on high-frequency (HF) level. This study develops a revised HF factor model and evaluates the asset pricing performance. Using machine learning algorithms, we find that HF factor model includes three very persistent systematic factors, well-approximated by a portfolio of market, finance, and information. Sharpe ratios and out-of-sample tests prove that the HF revised factor model has the best explanatory power compared to the CAPM, Fama-French three-factor and five-factor models. The findings contribute to an in-depth understanding of the characteristics and mechanisms of risk and return from an HF perspective in the Chinese stock market.

Suggested Citation

  • Zhang, Ailian & Pan, Mengmeng & Zhang, Xuan, 2025. "The pricing ability of factor model based on machine learning: Evidence from high-frequency data in China," International Review of Economics & Finance, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:reveco:v:101:y:2025:i:c:s1059056025003168
    DOI: 10.1016/j.iref.2025.104153
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056025003168
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.iref.2025.104153?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.

    More about this item

    Keywords

    Asset pricing; Machine learning; High-frequency; Chinese stock market;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:reveco:v:101:y:2025:i:c:s1059056025003168. 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/inca/620165 .

    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.