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arXiv · 2510.26228

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

Nikolas Anic·Andrea Barbon·Ralf Seiz·Carlo Zarattini
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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.

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Nikolas Anic, Andrea Barbon, Ralf Seiz, Carlo Zarattini. 2025-10-30. ChatGPT in Systematic Investing -- Enhancing Risk-Adjusted Returns with LLMs. https://arxiv.org/abs/2510.26228

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