Empirical Asset Pricing with Large Language Model Agents

TL;DR

This paper presents a new asset pricing model using Large Language Model agents that combines qualitative and quantitative data, outperforming traditional methods in portfolio optimization and anomaly detection.

Contribution

Introduces a novel empirical asset pricing approach integrating LLM-based qualitative assessments with quantitative factors, demonstrating improved performance over existing models.

Findings

Sharpe ratio increased by 10.6% in portfolio optimization

Mean magnitude of alpha improved by 10.0% in anomaly portfolios

Model outperforms traditional machine learning baselines

Abstract

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.