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

Joshua D. Clinton and TzuFeng Huang
Additional contact information
Joshua D. Clinton: Vanderbilt University

No d5yx2_v1, SocArXiv from Center for Open Science

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.

Date: 2025-12-01
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/692db7dd37baa6ac72b550fb/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:d5yx2_v1

DOI: 10.31219/osf.io/d5yx2_v1

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().