ChatGPT in Systematic Investing -- Enhancing Risk-Adjusted Returns with LLMs

TL;DR

This paper demonstrates that large language models like ChatGPT can enhance momentum investing strategies by interpreting financial news to improve risk-adjusted returns, outperforming traditional benchmarks in various conditions.

Contribution

It introduces a novel approach of using prompt-engineered queries to leverage LLMs for real-time news interpretation in stock selection, showing improved investment performance.

Findings

LLM-enhanced strategies outperform standard momentum benchmarks.

Gains are robust to transaction costs and portfolio constraints.

High-conviction portfolios benefit most from LLM integration.

Abstract

This paper investigates whether large language models (LLMs) can improve cross-sectional momentum strategies by extracting predictive signals from firm-specific news. We combine daily U.S. equity returns for S&P 500 constituents with high-frequency news data and use prompt-engineered queries to ChatGPT that inform the model when a stock is about to enter a momentum portfolio. The LLM evaluates whether recent news supports a continuation of past returns, producing scores that condition both stock selection and portfolio weights. An LLM-enhanced momentum strategy outperforms a standard long-only momentum benchmark, delivering higher Sharpe and Sortino ratios both in-sample and in a truly out-of-sample period after the model's pre-training cut-off. These gains are robust to transaction costs, prompt design, and portfolio constraints, and are strongest for concentrated, high-conviction portfolios. The results suggest that LLMs can serve as effective real-time interpreters of financial news, adding incremental value to established factor-based investment strategies.