ChatGPT in Systematic Investing -- Enhancing Risk-Adjusted Returns with LLMs
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914124127010816 |
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| author | Anic, Nikolas Barbon, Andrea Seiz, Ralf Zarattini, Carlo |
| author_facet | Anic, Nikolas Barbon, Andrea Seiz, Ralf Zarattini, Carlo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChatGPT in Systematic Investing -- Enhancing Risk-Adjusted Returns with LLMs Anic, Nikolas Barbon, Andrea Seiz, Ralf Zarattini, Carlo Portfolio Management Pricing of Securities 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. |
| title | ChatGPT in Systematic Investing -- Enhancing Risk-Adjusted Returns with LLMs |
| topic | Portfolio Management Pricing of Securities |
| url | https://arxiv.org/abs/2510.26228 |