IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v20y2014i7-9p637-656.html
   My bibliography  Save this article

Information-based stock trading and managerial incentives: evidence from China's stock market

Author

Listed:
  • Michael Firth
  • Man Jin
  • Yuanyuan Zhang

Abstract

This paper uses stock price informativeness, or information-based stock trading, to help explain the pay-performance sensitivity (PPS) of chief executive officer (CEO) compensation in China's listed firms. We argue that higher stock price informativeness, which we measure by the probability of informed trading, helps and encourages shareholders to incentivize the top management team based on stock market performance. The regression results support our argument and show that a higher level of stock price informativeness is associated with higher CEO PPSs. Moreover, the impact of stock price informativeness on CEO incentives is stronger for privately controlled listed firms than it is for state-controlled listed firms. The results also hold when information asymmetry is approximated by the accuracy and dispersion of the earnings forecasts made by financial analysts.

Suggested Citation

  • Michael Firth & Man Jin & Yuanyuan Zhang, 2014. "Information-based stock trading and managerial incentives: evidence from China's stock market," The European Journal of Finance, Taylor & Francis Journals, vol. 20(7-9), pages 637-656, September.
  • Handle: RePEc:taf:eurjfi:v:20:y:2014:i:7-9:p:637-656
    DOI: 10.1080/1351847X.2012.672441
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1351847X.2012.672441
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1351847X.2012.672441?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. Man Jin & Shunan Zhao & Subal C. Kumbhakar, 2020. "Information asymmetry and leverage adjustments: a semiparametric varying‐coefficient approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 581-605, February.
    2. Man Jin & Huiting Tian & Subal C. Kumbhakar, 2020. "How to survive and compete: the impact of information asymmetry on productivity," Journal of Productivity Analysis, Springer, vol. 53(1), pages 107-123, February.

    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:eurjfi:v:20:y:2014:i:7-9:p:637-656. 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/REJF20 .

    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.