Combinatorial Arbitrage in Prediction Markets: Identification Framework and Empirical Replay
ORIGINAL / Combinatorial Arbitrage in Prediction Markets
Develops a framework to identify and quantify arbitrage across logically related prediction-market contracts, using a convex-hull condition for arbitrage existence and integer programming for optimal portfolios, with order-book replay on Polymarket to quantify net profits and persistence.
01 ABSTRACT
The study builds a framework proving that prices outside the convex hull of admissible payoff vectors admit arbitrage and uses integer programming to identify optimal arbitrage portfolios. Using venue metadata and semantic analysis of resolution criteria, the authors recover about 1.9 million logical relations covering more than 255,000 contracts and replay full-depth Polymarket order books to simulate execution. Among settled portfolios, guaranteed profit after spreads and fees equals 2.2% of committed capital, while profit at resolution reaches 3.2%; arbitrage opportunities persist longer and attract less observed participation where relation discovery and execution are more costly.
02 KEY FINDINGS
- Proves that prices outside the convex hull of admissible payoff vectors admit arbitrage and solves optimal portfolios via integer programming.
- Recovers about 1.9 million logical relations from metadata and semantic analysis of resolution criteria, compiling constraint systems covering over 255,000 contracts.
- Replaying Polymarket order books shows guaranteed profit after spreads and fees of 2.2% of committed capital for settled portfolios, with profit at resolution of 3.2%.
AI GENERATED SUMMARY / DISCOVERED BY ARXIV Q-FIN