Hongjian Shi, Daojing Zhai · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.23517 · cited by 0 works (OpenAlex) · open-access PDF
Desenvolvemos um quadro para identificar e quantificar a arbitragem em contratos de previsão-mercado lógicamente relacionados. Demonstramos que os preços fora da casca convexa dos vetores de pagamento aceitáveis admitem arbitragem e identificam os portfólios de arbitragem ideais através da programação de inteiros. Usando metadados de local e análise semântica de critérios de resolução, recuperamos 1,9 milhões de relações lógicas e compilamos-as em sistemas de restrição que cobrem mais de 255.000 contratos. Reproduzimos livros de ordens Polymarket em profundidade total para simular um árbitro executando esses portfólios contra ordens em repouso. Entre os portfólios estabelecidos, um árbitro ideal que toma a profundidade de exibição completa de
Machine translation (Meta M2M100); the English original follows.
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, an ideal arbitrageur that takes the full displayed depth of every opportunity and reuses its capital at resolution earns a risk-free return of 3.5% a week after spreads and fees. We find that arbitrage opportunities persist longer and attract less observed participation where relation discovery and execution are more costly.
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Topics: Arbitrage, Profit (economics), Computer science, Stochastic game, Relation (database), Constraint (computer-aided design)
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