Newest papers

What Prediction-Market Contracts Reveal at Launch: Trading Volume and Dispute Risk

Ahmed Mahrous, Roberto Di Pietro · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29578 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

使用2023年1月至2026年5月之间在PolyMarket上列出的802 863个解决的市场近普查,并且只有在上市时可用的信息(市场标题和描述,命名为解决源和合同元数据),我们可以预测终身交易量,以及结算是否被争议。合同信息设计预测交易量较小,但可测量的改善(R^2 = 0.747美元的日志量),而且大多数信号是可解释的:市场主题提供了最大的收益($ +0.289美元R^2美元在0.220美元的合同功能基线上),定义和解决规则设计增加了更精确的交易量(R^2 = 0.747美元的日志量组合),并且神经文本模型不太过于可以解释的特征。这种表现不会存活在一个

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

Abstract

Using a near-census of 802,863 resolved markets listed on Polymarket between January 2023 and May 2026, and only information available at listing (the market's title and description, named resolution source, and contract metadata), we predict lifetime trading volume and whether settlement is later disputed. Contract information predicts trading volume well (out-of-sample $R^2 = 0.747$ for log volume), and most of the signal is interpretable: market topic provides the largest gain ($+0.289$ $R^2$ over a $0.220$ contract-feature baseline), wording and resolution-rule design add smaller but measurable improvements ($+0.027$ combined), and neural text models add little beyond the interpretable features. This performance does not survive a period of platform change coinciding with a shift in listing formats: accuracy in predicting whether a market reaches \$1,000 falls from AUC $0.940$ under same-period evaluation to $0.577$ when training on past quarters and testing on later ones. The loss concentrates in markets whose question forms were absent from training (76.9% of test markets; on familiar forms the model still reaches $0.778$), and a decomposition attributes more of it to a changed relationship between contract characteristics and demand than to an outdated mapping from text to features. Dispute predictors are more stable but less precise: launch-time features rank dispute risk far better than chance (AUC $0.78$), yet at a 0.31% base rate precision is too low for case-by-case prediction (average precision $0.012$), so the scores suit ranking groups of markets rather than forecasting individual disputes.

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Topics: Settlement (finance), Ranking (information retrieval), Volume (thermodynamics), Dispute resolution, Listing (finance), Rank (graph theory)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange