Arbitrage in Political Prediction Markets

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Published: Sep 23, 2020
DOI: https://doi.org/10.5750/jpm.v14i1.1796
Keywords:
prediction market political prediction election forecasting arbitrage market efficiency binary option

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Andrew Stershic
Sandia National Laboratories, 7011 East Ave., Livermore, CA 94550, U.S.A.
Kritee Gujral
University of Washington, The CHOICE Institute, H375 Health Science Building, Seattle, Washington 98195-7630, U.S.A.

Abstract

Online prediction markets are a powerful tool for aggregating information and show promise as predictive tools for uncertain outcomes, from sporting events to election results. However, these markets only serve as effective prediction tools so long as the market pricing remains efficient. We analyze the potential arbitrage profits derived from such mispricings in two leading American political prediction markets, PredictIt (for the 2016 and 2020 elections) and the Iowa Electronic Markets (for the 2016 election), to quantify the degree of mispricing and to show how market design can contribute to price distortion. We show that contracts hosted by PredictIt, compared to the IEM, are chronically mispriced, with large arbitrage profits in the 2016 election markets and non-negligible profits for the 2020 markets. We discuss the role of profit fees and contract limits, the primary differences between the PredictIt and IEM, in distorting pricing on PredictIt by limiting the ability of traders to capture arbitrage profits. Additionally, we examine the association between arbitrage and margin-linking, increased liquidity, and the number of unique contracts PredictIt's markets. This research provides cautionary evidence of potential inefficiencies in prediction markets with the intention of improving market implementation and enhancing market predictiveness.

Article Details

Issue
Vol. 14 No. 1 (2020)
Section
Articles
Author Biographies

Andrew Stershic, Sandia National Laboratories, 7011 East Ave., Livermore, CA 94550, U.S.A.

Andrew Stershic is a senior mechanical engineer for the U.S. Department of Energy at Sandia National Laboratories in Livermore, CA. Dr. Stershic earned degrees in Economics and Civil & Environmental Engineering from the University of Maryland in 2011 and completed his Ph.D. in Civil Engineering from Duke University in 2016. In 2012, he was named a Department of Energy Computational Science Graduate Fellow. Dr. Stershic’s current research focuses on numerical methods to characterize large-deformation plasticity and fracture of ductile materials.

Kritee Gujral, University of Washington, The CHOICE Institute, H375 Health Science Building, Seattle, Washington 98195-7630, U.S.A.

Kritee Gujral is a senior postdoctoral fellow at the CHOICE Institute, University of Washington, Seattle, Washington. Dr. Gujral earned degrees in Mathematics and Economics from the University of Florida in 2010 and completed her Ph.D. in Economics from University of Florida in 2016. Her research fields are applied microeconomics, health economics, public policy, and online markets.

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