Large Language Models and Futures Price Factors in China
利用GPT等大型语言模型为中国期货市场构建因子模型，通过40个因子设计投资组合，回测显示GPT生成的因子具有高夏普比率和年化收益，且优于IPCA基准。
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.