IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v103y2025ics1059056025005866.html
   My bibliography  Save this article

Why do investors trade more following high returns?

Author

Listed:
  • Chuang, Wen-I
  • Lee, Yun-Huan
  • Lee, Hsiu-Chuan
  • Susmel, Rauli

Abstract

We investigate investors’ trading behavior in response to gains and losses at the stock, style, and market levels by testing the various implications of seven trading theories. Using all U.S. stocks from July 1963 to June 2021 as a sample, we obtain several important stylized facts. First, investors trade more actively following high returns at various levels. Second, investors trade more frequently subsequent to high returns during high market-uncertainty periods than during low market-uncertainty periods. Third, investors increase their trading drastically after observing positive returns, but decrease their trading only mildly after observing negative returns. Fourth, individual investors trade more actively following positive returns than institutional investors. Fifth, investors are less motivated to trade following high returns in the recent period after the exogenous events, such as the reductions in the minimum tick size. Overall, these stylized facts are consistent with the theoretical predictions of disposition effects and overconfidence.

Suggested Citation

  • Chuang, Wen-I & Lee, Yun-Huan & Lee, Hsiu-Chuan & Susmel, Rauli, 2025. "Why do investors trade more following high returns?," International Review of Economics & Finance, Elsevier, vol. 103(C).
  • Handle: RePEc:eee:reveco:v:103:y:2025:i:c:s1059056025005866
    DOI: 10.1016/j.iref.2025.104423
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056025005866
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2025.104423?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:103:y:2025:i:c:s1059056025005866. 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.