Combinatorial Arbitrage in Prediction Markets
Hongjian Shi, Daojing Zhai
Abstract
We develop a framework to identify and quantify arbitrage across logically related prediction-market contracts. We prove that prices outside the convex hull of admissible payoff vectors admit arbitrage and identify optimal arbitrage portfolios through integer programming. Using venue metadata and semantic analysis of resolution criteria, we recover 1.9 million logical relations and compile them into constraint systems covering more than 255,000 contracts. We replay full-depth Polymarket order books to simulate an arbitrageur executing these portfolios against resting orders. Among settled portfolios, guaranteed profit after spreads and fees equals 2.2% of committed capital, while profit at resolution reaches 3.2%. We find that arbitrage opportunities persist longer and attract less observed participation where relation discovery and execution are more costly.
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