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

Nikolas Anic, Andrea Barbon, Ralf Seiz and Carlo Zarattini
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Nikolas Anic: Swiss Finance Institute - University of Zurich; Finreon
Andrea Barbon: University of St. Gallen; University of St.Gallen
Ralf Seiz: University of St.Gallen; Finreon
Carlo Zarattini: Concretum Group

No 25-94, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 longonly 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 cutoff. 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.

Keywords: Large Language Models; Momentum Investing; Textual Analysis; News Sentiment; Artificial Intelligence (search for similar items in EconPapers)
Pages: 28 pages
Date: 2025-10
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-fmk, nep-for and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2594

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