IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v54y2022i7p774-792.html
   My bibliography  Save this article

Media-based investor sentiment and stock returns: a textual analysis based on newspapers

Author

Listed:
  • Yu He
  • Linshan Qu
  • Ran Wei
  • Xuankai Zhao

Abstract

This study constructs a media-based investor sentiment index based on a textual analysis of China’s leading financial newspapers. We employ both the Word2Vec technique and the dictionary method to measure the aggregate textual tone in the media news. Based on a sample of publicly listed A-share firms in China, we document that media-based sentiment is positively (negatively) related to the cross-section of stock returns over a short (long) horizon. Further analysis shows that higher information quality, measured by more analyst coverage, better audit opinions and non-governmental ownership could mitigate the effects of sentiment. Overall, our findings imply that media news contains important information for measuring the overall investor sentiment that drives the future stock price up in the short term and then down in the long term.

Suggested Citation

  • Yu He & Linshan Qu & Ran Wei & Xuankai Zhao, 2022. "Media-based investor sentiment and stock returns: a textual analysis based on newspapers," Applied Economics, Taylor & Francis Journals, vol. 54(7), pages 774-792, February.
    • Handle: RePEc:taf:applec:v:54:y:2022:i:7:p:774-792
    DOI: 10.1080/00036846.2021.1966369
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2021.1966369
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2021.1966369?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

    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:taf:applec:v:54:y:2022:i:7:p:774-792. 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/RAEC20 .

    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.