Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments \& Analysis]

Abstract:Prediction markets are markets for trading claims on universal future events (e.g., presidential elections). Fueled by a meteoric surge with over \$50 billion trading volume, they have emerged as a promising forecasting mechanism, where their prices provide continuously updated signals of collective beliefs. In decentralized platforms (e.g., Polymarket), the prediction market lifecycle include six stages: market creation, token registration, trading, oracle interaction, dispute, and final settlement. However, comprehensively tracking this complete pipeline remains a major challenge, as the underlying data are severely fragmented across heterogeneous on-chain smart contracts and off-chain sources. To fill this critical gap, we present the first continuously synchronized dataset suite for the full-lifecycle of decentralized prediction markets. To achieve large-scale cross-source integration, incomplete linkage, and continuous synchronization, we build a unified relational data system that integrates three canonical layers: i) market metadata, ii) fill-level trading records, iii) oracle-resolution events, through identifier resolution, on-chain recovery, and incremental updates. The resulting dataset spans from October 2020 to update-to-date and comprise more than 3.29 million market records, over 1.90 billion order execution records, and nearly 21 million oracle events. We describe the data model, collection pipeline, and consistency mechanisms that make the datasets reproducible and extensible. We further demonstrate its utility for multiple communities through NBA outcome calibration for sport traders, CPI expectation reconstruction for economists, and oracle-risk analysis for blockchain researchers. A public website with dataset access, interactive visualizations, and lightweight LLM-assisted exploration tools are publicly available at this https URL.

Machine Learning (cs.LG)

arXiv:2604.20421 [cs.LG]

(or arXiv:2604.20421v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.20421 Focus to learn more arXiv-issued DOI via DataCite

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