IDEAS home Printed from https://ideas.repec.org/a/taf/hbhfxx/v21y2020i3p323-335.html
   My bibliography  Save this article

Numerosity: Forward and Reverse Stock Splits

Author

Listed:
  • Jessica West
  • Carol Azab
  • K. C. Ma
  • Michael Bitter

Abstract

Individuals have a tendency to fixate on large numbers and ignore other relevant information in their decision making process. The numerosity heuristic, a cognitive bias, is the first behavioral hypothesis to explain why investors prefer to receive more shares (rather than less shares) in a stock split even though the aggregate economic value is the same. For forward splits, after controlling for the positive signaling of improved earnings growth and liquidity from the split announcement, the stock price reacts positively to the larger number of shares issued. More importantly, the use of a dual class numerosity model can explain why most conventional hypotheses fail to explain the negative stock price reaction to reverse splits. Given a typical bearish outlook associated with a reverse stock split, investors’ cognitive resources have already been conditioned to derive a systematic conclusion to sell the stock at the higher price. Focusing only on large stock price numerosity, investors are incorrectly inferring a higher investment value. As the high numerosity encourages bearish investors to sell at the higher perceived investment value, the stock returns react more negatively to the higher post-reverse split price level. In both forward and reverse split cases, investors react to high numerosity.

Suggested Citation

  • Jessica West & Carol Azab & K. C. Ma & Michael Bitter, 2020. "Numerosity: Forward and Reverse Stock Splits," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(3), pages 323-335, July.
  • Handle: RePEc:taf:hbhfxx:v:21:y:2020:i:3:p:323-335
    DOI: 10.1080/15427560.2019.1672168
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/15427560.2019.1672168
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/15427560.2019.1672168?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. Borsboom, Charlotte & Füllbrunn, Sascha, 2021. "Stock Price Level Effect," MPRA Paper 109286, University Library of Munich, Germany.

    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:hbhfxx:v:21:y:2020:i:3:p:323-335. 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/hbhf .

    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.