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Large Language Models and Futures Price Factors in China

Author

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  • Yuhan Cheng
  • Yanchu Liu
  • Heyang Zhou

Abstract

We leverage the capacity of large language models such as Generative Pre‐trained Transformer (GPT) in constructing factor models for Chinese futures markets. We successfully obtained 40 factors to design single‐factor and multi‐factor portfolios through long‐short and long‐only strategies, conducting backtests during the in‐sample and out‐of‐sample periods. Comprehensive empirical analysis reveals that GPT‐generated factors deliver remarkable Sharpe ratios and annualized returns while maintaining acceptable maximum drawdowns. Notably, the GPT‐based factor models also achieve significant alphas over the IPCA benchmark. Moreover, these factors demonstrate significant performance across extensive robustness tests, particularly excelling after the cutoff date of GPT's training data.

Suggested Citation

  • Yuhan Cheng & Yanchu Liu & Heyang Zhou, 2026. "Large Language Models and Futures Price Factors in China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(2), pages 262-282, February.
    • Handle: RePEc:wly:jfutmk:v:46:y:2026:i:2:p:262-282
    DOI: 10.1002/fut.70061
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    References listed on IDEAS

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    Cited by:

    1. Allen Yikuan Huang & Zheqi Fan, 2026. "Beyond Prompting: An Autonomous Framework for Systematic Factor Investing via Agentic AI," Papers 2603.14288, arXiv.org, revised Apr 2026.

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