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Decoding risk sentiment in 10-K filings: Predictability for U.S. stock indices

Author

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  • Magner, Nicolás
  • Henríquez, Pablo A.
  • Sanhueza, Aliro

Abstract

This study demonstrates that the tone of the risk factors section in the 10-K reports of U.S. public companies predicts returns on major U.S. stock indices. We created five tone indicators using text mining, the Loughran-McDonald dictionary, and AI-calibrated alternatives (GPT-3.5-turbo-0125, GPT-4, GPT-4o, and GPT-4o-mini). These indicators showed significant predictive power for weekly returns, with optimism correlated with higher returns. Tone measurements based on GPT-4 outperformed the others in terms of predictive accuracy. We analyzed the Loughran-McDonald dictionary’s utility and highlighted the underexplored risk factors section, offering novel insights into sentiment analysis and financial forecasting.

Suggested Citation

  • Magner, Nicolás & Henríquez, Pablo A. & Sanhueza, Aliro, 2025. "Decoding risk sentiment in 10-K filings: Predictability for U.S. stock indices," Finance Research Letters, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:finlet:v:81:y:2025:i:c:s1544612325007317
    DOI: 10.1016/j.frl.2025.107472
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