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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
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    References listed on IDEAS

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