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Are AI Risks Priced in the U.S. Stock Market? Evidence from Financial News Factors

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  • Yanhui Shen

Abstract

This paper asks whether firms' exposures to news about different types of AI risk are priced in U.S. stock returns. Using AI and risk keywords, I identify 7,787 Wall Street Journal articles from January 2016 to December 2025. I combine latent Dirichlet allocation (LDA) with the Domain Taxonomy in the MIT AI Risk Repository to construct four news-based systematic risk factors. I estimate betas to factor innovations and test pricing with univariate portfolio analysis and Fama-MacBeth regressions. Only the taxonomy-mapped Misinformation factor (D3) is robustly priced. Its high-minus-low beta portfolio earns monthly alphas of 0.49%-0.57%, and the estimated D3 price of risk is positive and statistically significant across beta-estimation windows, conventional factor and industry controls, alternative innovation models, and the pre-ChatGPT subsample. The other factors are not reliably priced, indicating that AI-risk pricing is domain-specific.

Suggested Citation

  • Yanhui Shen, 2026. "Are AI Risks Priced in the U.S. Stock Market? Evidence from Financial News Factors," Papers 2609.05485, arXiv.org.
    • Handle: RePEc:arx:papers:2609.05485
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