IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2609.05485.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2609.05485
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    2. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    3. Breeden, Douglas T., 1979. "An intertemporal asset pricing model with stochastic consumption and investment opportunities," Journal of Financial Economics, Elsevier, vol. 7(3), pages 265-296, September.
    4. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    5. Lubo? P?tor & Pietro Veronesi, 2009. "Technological Revolutions and Stock Prices," American Economic Review, American Economic Association, vol. 99(4), pages 1451-1483, September.
    6. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    7. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    8. Dyer, Travis & Lang, Mark & Stice-Lawrence, Lorien, 2017. "The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation," Journal of Accounting and Economics, Elsevier, vol. 64(2), pages 221-245.
    9. Kathleen Weiss Hanley & Gerard Hoberg, 2019. "Dynamic Interpretation of Emerging Risks in the Financial Sector," The Review of Financial Studies, Society for Financial Studies, vol. 32(12), pages 4543-4603.
    10. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    11. Hong, Harrison & Kacperczyk, Marcin, 2009. "The price of sin: The effects of social norms on markets," Journal of Financial Economics, Elsevier, vol. 93(1), pages 15-36, July.
    12. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    13. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    14. Shumway, Tyler, 1997. "The Delisting Bias in CRSP Data," Journal of Finance, American Finance Association, vol. 52(1), pages 327-340, March.
    15. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    16. Pástor, Ľuboš & Stambaugh, Robert F. & Taylor, Lucian A., 2022. "Dissecting green returns," Journal of Financial Economics, Elsevier, vol. 146(2), pages 403-424.
    17. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    18. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    19. Pástor, Ľuboš & Veronesi, Pietro, 2013. "Political uncertainty and risk premia," Journal of Financial Economics, Elsevier, vol. 110(3), pages 520-545.
    20. Bali, Turan G. & Brown, Stephen J. & Tang, Yi, 2017. "Is economic uncertainty priced in the cross-section of stock returns?," Journal of Financial Economics, Elsevier, vol. 126(3), pages 471-489.
    21. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
    22. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    23. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2019. "Information, Trading, and Volatility: Evidence from Firm-Specific News," The Review of Financial Studies, Society for Financial Studies, vol. 32(3), pages 992-1033.
    24. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    25. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    26. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    27. Faccini, Renato & Matin, Rastin & Skiadopoulos, George, 2023. "Dissecting climate risks: Are they reflected in stock prices?," Journal of Banking & Finance, Elsevier, vol. 155(C).
    28. Po‐Hsuan Hsu & Kai Li & Chi‐Yang Tsou, 2023. "The Pollution Premium," Journal of Finance, American Finance Association, vol. 78(3), pages 1343-1392, June.
    29. Jonathan Brogaard & Andrew Detzel, 2015. "The Asset-Pricing Implications of Government Economic Policy Uncertainty," Management Science, INFORMS, vol. 61(1), pages 3-18, January.
    30. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    31. Huseyin Gulen & Mihai Ion, 2016. "Editor's Choice Policy Uncertainty and Corporate Investment," The Review of Financial Studies, Society for Financial Studies, vol. 29(3), pages 523-564.
    32. Leland Bybee & Bryan Kelly & Yinan Su & Tarun Ramadorai, 2023. "Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text," The Review of Financial Studies, Society for Financial Studies, vol. 36(12), pages 4759-4787.
    33. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Faccini, Renato & Matin, Rastin & Skiadopoulos, George, 2023. "Dissecting climate risks: Are they reflected in stock prices?," Journal of Banking & Finance, Elsevier, vol. 155(C).
    2. Budras, Oliver & Dierkes, Maik & Schroen, Sebastian, 2026. "Text-implied uncertainty in 10-K filings: Do investors get the message?," The Quarterly Review of Economics and Finance, Elsevier, vol. 106(C).
    3. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    4. Lin, Lei & Tan, Jing, 2025. "Monetary policy uncertainty and ambiguity premium from news," Research in International Business and Finance, Elsevier, vol. 80(C).
    5. Lin, Jianhao & Fan, Jiacheng & Zhang, Yifan, 2025. "Information Dissemination and the Monetary Policy Uncertainty Premium: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 171(C).
    6. Atilgan, Yigit & Demirtas, K. Ozgur & Gunaydin, A. Doruk, 2026. "Pollution premium: Further evidence," Global Finance Journal, Elsevier, vol. 71(C).
    7. Hollstein, Fabian & Prokopczuk, Marcel & Wese Simen, Chardin, 2020. "Beta uncertainty," Journal of Banking & Finance, Elsevier, vol. 116(C).
    8. Cakici, Nusret & Zaremba, Adam, 2022. "Salience theory and the cross-section of stock returns: International and further evidence," Journal of Financial Economics, Elsevier, vol. 146(2), pages 689-725.
    9. Peng, Ya & Zhang, Xueyong, 2024. "Economic policy uncertainty in OFDI host countries and the cross-section of stock returns," Journal of International Money and Finance, Elsevier, vol. 149(C).
    10. Xu, Zhiwei & Gou, Xinyi & Zhang, Teng, 2025. "Have the Chinese crude oil futures prices made a progress towards becoming the regional oil pricing benchmark? Empirical analysis from the asset pricing perspective," Energy Economics, Elsevier, vol. 145(C).
    11. Şahin, Baki Cem & Danışoğlu, Seza, 2022. "Ambiguity and asset pricing: An empirical investigation for an emerging market," International Review of Financial Analysis, Elsevier, vol. 84(C).
    12. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224, December.
    13. Cakici, Nusret & Zaremba, Adam, 2023. "Recency bias and the cross-section of international stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    14. Xu, Zhongxiang & Chevapatrakul, Thanaset & Li, Xiafei, 2019. "Return asymmetry and the cross section of stock returns," Journal of International Money and Finance, Elsevier, vol. 97(C), pages 93-110.
    15. Le, Anh Tuan & Nguyen, Harvey & Nguyen, Cuong, 2025. "Regret to reward: Investor regret and the cross-sectional stock returns in the Chinese market," Global Finance Journal, Elsevier, vol. 68(C).
    16. Ming Gu & Minxing Sun & Yangru Wu & Weike Xu, 2021. "Economic policy uncertainty and momentum," Financial Management, Financial Management Association International, vol. 50(1), pages 237-259, March.
    17. Bai, Jennie & Bali, Turan G. & Wen, Quan, 2021. "Is there a risk-return tradeoff in the corporate bond market? Time-series and cross-sectional evidence," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1017-1037.
    18. Wei, Xin & Liu, Xi & Zhang, Xueyong, 2022. "Shadow banking and the cross-section of stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    19. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2017. "Research in finance: A review of influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 188-199.
    20. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble Learning for Sector Rotation: A Gradient-Free Framework," Papers 2304.09947, arXiv.org, revised Nov 2025.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2609.05485. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.