Narrative Ambiguity Matters

By extracting information from economic news articles, this paper proposes a novel measure of narrative-based ambiguity that captures investors’ attitudes toward narrative uncertainty and exhibits strong in- and out-of-sample predictive power for the stock market returns. It reveals general ambiguity aversion except in high loss probability scenarios in which ambiguity tolerance emerges. Further tests confirm the significant pricing power and distinct information of the narrative ambiguity on top of existing ambiguity measures, such as survey- and return-based ambiguity. By aligning industry-specific narrative ambiguity, we construct a superior predictor for market returns with the predictability predominantly driven by the ambiguity from consumption-, energy-, and technology-related sectors. Our findings also carry broad implications as the predictability remains significant across international markets and other asset classes.

This paper has been accepted by Will Cong for the Virtual Special Issue on Digital Finance.

Funding: Q. Zhang acknowledges research support from the National Natural Science Foundation of China [Grant 72573098], the National Natural Science Foundation of China Excellent Young Researcher Project [Grant 72222013], and the National Key R&D Program of China [Grant 2023YFA1009200]. X. Wei acknowledges research support from the Capital University of Economics and Business Scientific Research Project [Grant XRZ2025010].

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.02735.