[论文解读] Semantic Non-Fungibility and Violations of the Law of One Price in Prediction Markets

Jonas Gebele, Florian Matthes|arXiv (Cornell University)|Jan 5, 2026

Sports Analytics and Performance被引用 0

一句话总结

论文开发了一个语义对齐框架，用于识别跨平台的等价性和子集关系，以预测市场事件之间的跨平台等效性，表明语义不可替代性导致跨平台价格分歧和套利机会。

ABSTRACT

Prediction markets are designed to aggregate dispersed information about future events, yet today's ecosystem is fragmented across heterogeneous operator-run platforms and blockchain-based protocols that independently list economically identical events. In the absence of a shared notion of event identity, liquidity fails to pool across venues, arbitrage becomes capital-intensive or unenforceable, and prices systematically violate the Law of One Price. As a result, market prices reflect platform-local beliefs rather than a single, globally aggregated probability, undermining the core information-aggregation function of prediction markets. We address this gap by introducing a semantic alignment framework that makes cross-platform event identity explicit through joint analysis of natural-language descriptions, resolution semantics, and temporal scope. Applying this framework, we construct the first human-validated, cross-platform dataset of aligned prediction markets, covering over 100 000 events across ten major venues from 2018 to 2025. Using this dataset, we show that roughly 6% of all events are concurrently listed across platforms and that semantically equivalent markets exhibit persistent execution-aware price deviations of 2-4% on average, even in highly liquid and information-rich settings. These mispricings give rise to persistent cross-platform arbitrage opportunities driven by structural frictions rather than informational disagreement. Overall, our results demonstrate that semantic non-fungibility is a fundamental barrier to price convergence, and that resolving event identity is a prerequisite for prediction markets to aggregate information at a global scale.

研究动机与目标

• 为跨预测市场平台的语义碎片化问题提供动机并形式化描述。
• 开发一个可扩展的框架，用于在不同场景中识别语义等价或子集相关的市场。
• 构建一个经过人工验证的跨平台对齐预测市场事件数据集。
• 量化跨平台价格分歧并描述由语义不可替代性引发的套利机会。

提出的方法

• 定义一个统一的二元条件认领模型和含执行摩擦的均衡约束。
• 开发一个语义匹配流水线，结合结构过滤、基于嵌入的检索以及基于LLM的逻辑验证，以推断事件等价性和子集关系。
• 用分辨函数r_m来表示每个市场在原子结果空间Omega上的分辨，并定义YES区域f(m)。
• 使用跨平台的均衡条件来识别同一市场内、跨市场和跨平台的套利机会。
• 构建并分析Omega的跨平台划分，以识别跨平台的负风险套利。
• 用人工标注评估流水线，并报道阶段性召回率和假阳性率。

实验结果

研究问题

• RQ1 如何在异质化的预测市场平台之间可靠地识别语义等价或子集相关的市场？
• RQ2 当前预测市场生态系统中跨平台的语义碎片化程度如何？
• RQ3 语义不可替代性如何促成跨平台价格分歧和套利机会？
• RQ4 有哪些数据集和方法论能够实现对预测市场事件身份的生态系统级分析？

主要发现

• 在超过102,275个事件中，大约有6%在不同平台之间存在语义关联，约占总事件天数的10%。
• 语义等价的市场在流动性良好的情境下也存在2–4%的执行相关价格偏差。
• 跨平台套利源于结构性摩擦，而非信息分歧。
• 作者构建了1,501个等价类、1,645个子集相关事件集以及跨市场的1,123个负风险构建。
• 基于嵌入的检索能够在前20个邻居中捕获99.9%的经验证的等价与子集关系，且LLM提供高精度的验证。
• 数据集包含来自十个平台的超过10万条唯一事件，将对公众公开发布。