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Stress index strategy enhanced with financial news sentiment analysis for the equity markets

Author

Listed:
  • Baptiste Lefort
  • Eric Benhamou
  • Jean-Jacques Ohana
  • David Saltiel
  • Beatrice Guez
  • Thomas Jacquot

Abstract

This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts of market stress derived from volatility and credit spreads are enhanced when combined with the financial news sentiment derived from GPT-4. As a result, the strategy shows improved performance, evidenced by higher Sharpe ratio and reduced maximum drawdowns. The improved performance is consistent across the NASDAQ, the S&P 500 and the six major equity markets, indicating that the method generalises across equities markets.

Suggested Citation

  • Baptiste Lefort & Eric Benhamou & Jean-Jacques Ohana & David Saltiel & Beatrice Guez & Thomas Jacquot, 2024. "Stress index strategy enhanced with financial news sentiment analysis for the equity markets," Papers 2404.00012, arXiv.org.
    • Handle: RePEc:arx:papers:2404.00012
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    References listed on IDEAS

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    1. W. Brooke Elliott & Stephanie M. Grant & Frank D. Hodge, 2018. "Negative News and Investor Trust: The Role of $Firm and #CEO Twitter Use," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 56(5), pages 1483-1519, December.
    2. Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Oct 2025.
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    4. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    5. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    6. Ikizlerli, Deniz & Holmes, Phil & Anderson, Keith, 2019. "The response of different investor types to macroeconomic news," Journal of Multinational Financial Management, Elsevier, vol. 50(C), pages 13-28.
    7. Korinek, Anton, 2023. "Language Models and Cognitive Automation for Economic Research," CEPR Discussion Papers 17923, Centre for Economic Policy Research.
    8. Cheol-Won Yang, 2023. "Investment strategy via analyst report text mining," Journal of Derivatives and Quantitative Studies: 선물연구, Emerald Group Publishing Limited, vol. 31(2), pages 98-120, March.
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    Cited by:

    1. Baptiste Lefort & Eric Benhamou & Beatrice Guez & Jean-Jacques Ohana & Ethan Setrouk & Alban Etienne, 2025. "FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification," Papers 2507.22932, arXiv.org.
    2. Baptiste Lefort & Eric Benhamou & Jean-Jacques Ohana & David Saltiel & Beatrice Guez, 2024. "Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning," Papers 2409.11408, arXiv.org.

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