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

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Listed:
  • Nikolas Anic
  • Andrea Barbon
  • Ralf Seiz
  • Carlo Zarattini

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.

Suggested Citation

  • Nikolas Anic & Andrea Barbon & Ralf Seiz & Carlo Zarattini, 2025. "ChatGPT in Systematic Investing -- Enhancing Risk-Adjusted Returns with LLMs," Papers 2510.26228, arXiv.org.
    • Handle: RePEc:arx:papers:2510.26228
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