In this study, we propose a novel asset pricing approach, LLM Agent-based Asset Pricing Models (AAPM), which fuses qualitative discretionary investment analysis from LLM agents and quantitative manual financial economic factors to predict excess asset returns. The experimental results show that our approach outperforms machine learning-based asset pricing baselines in portfolio optimization and asset pricing errors. Specifically, the Sharpe ratio and average $|\alpha|$ for anomaly portfolios improved significantly by 9.6\% and 10.8\% respectively. In addition, we conducted extensive ablation studies on our model and analysis of the data to reveal further insights into the proposed method.

翻译：本研究提出了一种新颖的资产定价方法——基于大语言模型智能体的资产定价模型（AAPM），该方法融合了来自大语言模型智能体的定性自主投资分析与定量的人工金融经济因子，以预测资产超额收益。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基于机器学习的资产定价基线模型。具体而言，异象投资组合的夏普比率和平均$|\alpha|$分别显著提升了9.6%和10.8%。此外，我们对模型进行了广泛的消融研究并对数据进行了深入分析，以进一步揭示所提方法的深层机制。