IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v56y2020i4p820-839.html
   My bibliography  Save this article

Sentiment Dispersion and Asset Pricing Error: Evidence from the Chinese Stock Market

Author

Listed:
  • Xiong Xiong
  • Jiatong Han
  • Xu Feng
  • Yahui An

Abstract

Previous studies have suggested that the impact of investor sentiment on asset pricing error is determined by the difference between the aggregate sentiment of optimistic and pessimistic investors. This article has found the influence of the in-group sentiment dispersion of optimistic and pessimistic investors on pricing error. We established a two-period model of heterogeneous investors and described the sentiment dispersion of the optimistic and pessimistic groups with the variance of sentiment bias. The results suggested that when the sentiment dispersion of the two groups are identical, the pricing error depends on the aggregate sentiments of the optimistic and pessimistic groups. Conversely, when the two groups have different sentiment dispersion, the pricing error is determined by both the sentiment dispersion ratio and the aggregate sentiment ratio. Finally, data from the Chinese stock market are generated to verify the above conclusions.

Suggested Citation

  • Xiong Xiong & Jiatong Han & Xu Feng & Yahui An, 2020. "Sentiment Dispersion and Asset Pricing Error: Evidence from the Chinese Stock Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(4), pages 820-839, March.
  • Handle: RePEc:mes:emfitr:v:56:y:2020:i:4:p:820-839
    DOI: 10.1080/1540496X.2019.1570128
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2019.1570128
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1540496X.2019.1570128?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. Xu, Wenhao & Chen, Taoqin, 2024. "Mutual fund value creation: Insights from the residual income model," Finance Research Letters, Elsevier, vol. 62(PB).
    2. Suardi, Sandy & Rasel, Atiqur Rahman & Liu, Bin, 2022. "On the predictive power of tweet sentiments and attention on bitcoin," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 289-301.

    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:mes:emfitr:v:56:y:2020:i:4:p:820-839. 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/MREE20 .

    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.