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

Nicolás Magner, Pablo A. Henríquez and Aliro Sanhueza

Finance Research Letters, 2025, vol. 81, issue C

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.

Keywords: Textual analysis; Risk factors tone metrics; Artificial intelligence; TVP-VAR; QVAR (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612325007317
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:81:y:2025:i:c:s1544612325007317

DOI: 10.1016/j.frl.2025.107472

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().