IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/d5yx2_v1.html
   My bibliography  Save this paper

Prediction Markets? The Accuracy and Efficiency of $2.4 Billion in the 2024 Presidential Election

Author

Listed:
  • Clinton, Joshua D.

    (Vanderbilt University)

  • Huang, TzuFeng

Abstract

Political prediction markets have exploded in size and influence, moving billions of dollars and shaping how journalists, donors, and voters interpret electoral odds. If these prices truly capture rational expectations, they should efficiently aggregate information about political outcomes. But do they? We analyze more than 2,500 political prediction markets traded across the Iowa Electronic Markets, Kalshi, PredictIt, and Polymarket during the final five weeks of the 2024 U.S. presidential campaign involving more than than two billion dollars in transactions to assess whether prices accurately and efficiently aggregate political information. While 93% of PredictIt markets correctly predicted outcomes better than chance, accuracy fell to 78% on Kalshi and 67% on Polymarket. Even the most accurate markets showed little evidence of efficiency: prices for identical contracts diverged across exchanges, daily price changes were weakly correlated or negatively autocorrelated, and arbitrage opportunities peaked in the final two weeks before Election Day. Together, these findings challenge the view that prediction markets necessarily efficiently and accurately aggregate information about political outcomes.

Suggested Citation

  • Clinton, Joshua D. & Huang, TzuFeng, 2025. "Prediction Markets? The Accuracy and Efficiency of $2.4 Billion in the 2024 Presidential Election," SocArXiv d5yx2_v1, Center for Open Science.
  • Handle: RePEc:osf:socarx:d5yx2_v1
    DOI: 10.31219/osf.io/d5yx2_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/692db7dd37baa6ac72b550fb/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/d5yx2_v1?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:osf:socarx:d5yx2_v1. 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: OSF (email available below). General contact details of provider: https://arabixiv.org .

    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.