Researchers publish paper on arbitrage framework for prediction market portfolios
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Hongjian Shi and Daojing Zhai have published a research paper proposing a framework for identifying and quantifying arbitrage opportunities between logically related prediction market contracts: arbitrage space exists when prices fall outside the convex hull of feasible return vectors, and the optimal arbitrage portfolio can be solved via integer programming. The study recovered 1.9 million logical relationships through semantic analysis of platform metadata and settlement rules, constructing a constraint system covering more than 255,000 contracts. The paper is based on a backtesting simulation of Polymarket's full-depth order book. Settled portfolios guarantee a profit of 2.2% of invested capital after deducting spreads and fees, with profits reaching 3.2% at settlement.
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- arXiv q-finPortfolio Arbitrage in Prediction Markets: Empirical Analysis Based on the Polymarket Order Book
This study proposes a framework for identifying and quantifying arbitrage opportunities between logically related prediction market contracts, demonstrating that arbitrage exists when prices fall outside the convex hull of feasible return vectors, and solving for the optimal arbitrage portfolio via integer programming. The study recovered 1.9 million logical relationships through semantic analysis of platform metadata and settlement rules, constructing a constraint system covering more than 255,000 contracts.
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