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 $|\alpha|$ 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.

翻译：本研究提出了一种新颖的资产定价模型，该模型利用大语言模型（LLM）代理，将来自LLM代理的定性自主投资评估与人工整理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，我们的方法在投资组合优化和资产定价误差方面均优于传统的基于机器学习的基准模型。值得注意的是，投资组合优化的夏普比率和异常投资组合的$|\alpha|$平均幅度分别实现了10.6%和10.0%的显著提升。此外，我们对模型进行了全面的消融研究，并对方法进行了深入分析，以进一步洞察所提出的方法。我们的结果为LLM在实证资产定价中应用的可行性提供了有力证据。