用大模型生成40个期货因子，收益稳定且超越基准。
Large Language Models and Futures Price Factors in China
- 用GPT构建中国期货市场因子，通过多策略回测验证
- 生成因子年化收益高，夏普比率优异，最大回撤可控
- 在GPT训练数据截止后表现更优，适合量化研究者
我们利用生成式预训练模型（如GPT）在构建中国期货市场的因子模型方面的能力。通过长短期和纯多头策略，成功生成40个因子并设计单因子与多因子投资组合，对样本内和样本外时间段进行了回测。综合实证分析表明，GPT生成的因子表现出显著的夏普比率和年化收益率，同时保持可接受的最大回撤水平。值得注意的是，基于GPT的因子模型在与IPCA基准比较时获得显著阿尔法收益。此外，这些因子在广泛的稳健性测试中表现突出，尤其在GPT训练数据截止日期之后表现更为优异。
原文摘要 · Abstract (English)
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 obtain 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 period. 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.
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