Newest papers

Combinatorial Arbitrage in Prediction Markets

Hongjian Shi, Daojing Zhai · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.23517 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

我们开发了一个框架来识别和量化在逻辑上相关的预测市场合约中的仲裁。我们证明,可接受的报酬向量体外的价格允许仲裁,并通过整数编程识别最佳的仲裁组合。使用场地元数据和决策标准的语义分析,我们恢复了190万个逻辑关系,并将它们编译成覆盖25万多个合约的制约系统。我们重新播放全面深度的Polymarket订单书籍,以模拟一个仲裁者执行这些组合对待休息订单。在固定组合中,一个理想的仲裁者采取每个机会的完整显示深度,并在决策时重复使用其资本,在分布和费用之后每周获得3.5%的无风险回报。我们发现仲裁机会持续更长时间,并在发现

Machine translation (Meta M2M100); the English original follows.

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, 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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