IDEAS home Printed from https://ideas.repec.org/a/bla/scotjp/v72y2025i4ne70007.html
   My bibliography  Save this article

Partisan Sentiment and Returns From Online Political Betting Markets in the 2020 US Presidential Election

Author

Listed:
  • Mary Becker
  • Zachary McGurk
  • Marc LoGrasso

Abstract

In this study, we estimate the role of daily partisan sentiment in predicting the returns from political betting markets on the PredictIt platform for 10 of the most competitive states in the 2020 US presidential election. We utilize a textual analysis approach (multinomial inverse regression method) to measure partisan sentiment for market participants through message board posts on each market's web page. Our results suggest that estimated partisan sentiment may play a role in the mispricing of political betting markets. Results are strongest for Republican assets and are robust to different specifications.

Suggested Citation

  • Mary Becker & Zachary McGurk & Marc LoGrasso, 2025. "Partisan Sentiment and Returns From Online Political Betting Markets in the 2020 US Presidential Election," Scottish Journal of Political Economy, Scottish Economic Society, vol. 72(4), September.
  • Handle: RePEc:bla:scotjp:v:72:y:2025:i:4:n:e70007
    DOI: 10.1111/sjpe.70007
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/sjpe.70007
    Download Restriction: no

    File URL: https://libkey.io/10.1111/sjpe.70007?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
    ---><---

    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:bla:scotjp:v:72:y:2025:i:4:n:e70007. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/sesssea.html .

    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.