arXiv Machine Learning By Huaiyu Jia, Luofeng Zhou, Wentao Zhang, Lin William Cong, Siguang Li, Shuo Sun

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

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The paper introduces a continuously synchronized dataset suite covering the entire lifecycle of decentralized prediction markets, from market creation to final settlement. It integrates market metadata, fill-level trading records, and oracle-resolution events into a unified relational system, providing over 3.29 million market records, 1.90 billion order executions, and 21 million oracle events from October 2020 to the present. The authors detail the data model, collection pipeline, and consistency mechanisms, and demonstrate the dataset’s usefulness for sports betting, economic forecasting, and blockchain research, with public access via a website and interactive tools.

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arXiv Machine Learning
Aug 13

TradingMoE: Routing the Right Experts in Evolving Markets

arXiv:2608. 11785v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions.

By Chang Zhou, Xingtong Yu, Minbin Huang, Zhennan Wu, Yuan Fang, Hong Cheng, Xinming Zhang