Empirical Asset Pricing with Large Language Model Agents
This addresses the problem of improving asset pricing accuracy for financial analysts and investors, representing an incremental advance by combining existing methods.
The study tackled asset pricing by integrating qualitative evaluations from Large Language Model agents with quantitative financial factors, resulting in a 10.6% improvement in Sharpe ratio for portfolio optimization and a 10.0% reduction in asset pricing errors.
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.