用大模型代理结合定性判断与定量因子，提升资产定价效果。
Empirical Asset Pricing with Large Language Model Agents
- 融合大模型的定性判断与人工精选的金融因子构建新定价模型。
- 投资组合优化的夏普比率提升10.6%，异常组合的|α|均值降低10.0%。
- 适用于量化金融研究者及对LLM应用于金融建模感兴趣的读者。
本研究提出一种基于大语言模型（LLM）代理的新型资产定价模型，将LLM代理提供的定性投资判断与人工精心筛选的定量金融经济因子相结合，旨在解释超额资产收益。实验结果表明，该方法在投资组合优化和资产定价误差方面均优于传统机器学习基线。值得注意的是，投资组合优化的夏普比率提升了10.6%，异常组合的|α|均值降低了10.0%。我们还对模型进行了全面消融研究，并深入分析了方法机制，进一步揭示了所提方法的有效性。结果充分证明了在实证资产定价中应用大语言模型的可行性。
原文摘要 · Abstract (English)
In this study, we introduce a novel asset pricing model leveraging the Large Language Model (LLM) agents, which integrates qualitative discretionary investment evaluations from LLM agents with quantitative financial economic factors manually curated, aiming to explain the excess asset returns. The experimental results demonstrate that our methodology surpasses traditional machine learning-based baselines in both portfolio optimization and asset pricing errors. Notably, the Sharpe ratio for portfolio optimization and the mean magnitude of $|α|$ for anomaly portfolios experienced substantial enhancements of 10.6\% and 10.0\% respectively. Moreover, we performed comprehensive ablation studies on our model and conducted a thorough analysis of the method to extract further insights into the proposed approach. Our results show effective evidence of the feasibility of applying LLMs in empirical asset pricing.
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