IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v11y2025i1d10.1186_s40854-025-00774-z.html
   My bibliography  Save this article

Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market

Author

Listed:
  • Zhong‑Qiang Zhou

    (Guizhou University of Finance and Economics
    Digital Economy Research Institute of Humanities and Social Sciences Research Base)

  • Jiajia Wu

    (Guizhou University of Finance and Economics
    Digital Economy Research Institute of Humanities and Social Sciences Research Base)

  • Ping Huang

    (Guizhou University of Commerce)

  • Xiong Xiong

    (Tianjin University
    Laboratory of Computation and Analytics of Complex Management Systems (CACMS))

Abstract

This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models. Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression, we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market. Our results demonstrate that conditional models significantly outperform their unconditional counterparts. Notably, investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period. Additionally, it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs. We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.

Suggested Citation

  • Zhong‑Qiang Zhou & Jiajia Wu & Ping Huang & Xiong Xiong, 2025. "Cross-sectional anomalies and conditional asset pricing models based on investor sentiment: evidence from the Chinese stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-24, December.
    • Handle: RePEc:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-025-00774-z
    DOI: 10.1186/s40854-025-00774-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-025-00774-z
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-025-00774-z?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
    ---><---

    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:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-025-00774-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.