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Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text

Author

Listed:
  • Leland Bybee
  • Bryan Kelly
  • Yinan Su
  • Tarun Ramadorai

Abstract

We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

Suggested Citation

  • 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.
    • Handle: RePEc:oup:rfinst:v:36:y:2023:i:12:p:4759-4787.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhad042
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    Citations

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    Cited by:

    1. Libo Yin & Jier Zhang & Hong Cao & Ying Li, 2025. "The Role of Policy on Commodity Markets: From the Perspective of Narrative News Based on NLP," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(11), pages 1871-1910, November.
    2. Chenghao Huang & Siyang Tian, 2026. "Actual share repurchases, private information, and stock price crash risk: Evidence from China’s reformed open market repurchase program," Review of Quantitative Finance and Accounting, Springer, vol. 66(2), pages 831-866, February.
    3. Jian Chen & Guohao Tang & Guofu Zhou & Wu Zhu, 2025. "ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?," Papers 2502.10008, arXiv.org.
    4. Yin, Libo & Zhang, Jier & Li, Ying, 2025. "Firm financialization: The role of policy inconsistency," Finance Research Letters, Elsevier, vol. 80(C).
    5. Enmao Liu & Cong Sui, 2026. "Does Sentiment Measured Through Language Models Encompass a Broader Expanse of Information From the Options Market?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(3), pages 489-510, March.
    6. Chen, Xing & Huang, Rui & Wu, Chongfeng, 2026. "Quantile auto-encode narrative asset pricing model in the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 96(C).
    7. Jia, Yuecheng & Liu, Yuzheng & Wu, Yangru & Yan, Shu, 2024. "Information spillover and cross-predictability of currency returns: An analysis via Machine Learning," Journal of Banking & Finance, Elsevier, vol. 169(C).
    8. Huang, Rui & Chen, Xing & Wu, Chongfeng, 2025. "The textual similarity of news content and stock return synchronicity," Emerging Markets Review, Elsevier, vol. 67(C).
    9. Biktimirov, Ernest N. & Sokolyk, Tatyana & Ayanso, Anteneh, 2024. "Unpacking the relation between media sentiment and house prices: A topic modeling approach," Journal of Housing Economics, Elsevier, vol. 66(C).
    10. Kim Ristolainen, 2026. "Quantifying Minsky Cycles," Discussion Papers 173, Aboa Centre for Economics.
    11. Linying Lv, 2025. "Do Sell-side Analyst Reports Have Investment Value?," Papers 2502.20489, arXiv.org, revised Aug 2025.
    12. Giuseppe Matera, 2025. "Corporate Earnings Calls and Analyst Beliefs," Papers 2511.15214, arXiv.org, revised Nov 2025.
    13. Yin, Libo & Zhu, Xiaoye & Li, Jingtian, 2025. "Does exposure to biodiversity risk drive firms’ digital transformation?," Finance Research Letters, Elsevier, vol. 82(C).
    14. Liu, Qingfu & Tse, Yiuman & Wang, Chuanjie & Yang, Jiaer, 2026. "Hedging financial turbulence risk with textual analysis," Pacific-Basin Finance Journal, Elsevier, vol. 96(C).
    15. Songrun He, 2026. "Interpretable Systematic Risk around the Clock," Papers 2604.13458, arXiv.org.
    16. Gang Li & Dandan Qiao & Mingxuan Zheng, 2025. "Structured Event Representation and Stock Return Predictability," Papers 2512.19484, arXiv.org.
    17. Changeun Kim & Younwoo Jeong & Bong-Gyu Jang, 2025. "Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model," Papers 2512.16251, arXiv.org, revised Apr 2026.
    18. Ke Wu & Baozhong Yang & Zhenkun Ying & Dexin Zhou, 2025. "Anonymization and Information Loss," Papers 2511.15364, arXiv.org.
    19. Dinggao Liu & Robert 'Slepaczuk & Zhenpeng Tang, 2025. "EXFormer: A Multi-Scale Trend-Aware Transformer with Dynamic Variable Selection for Foreign Exchange Returns Prediction," Papers 2512.12727, arXiv.org, revised Jan 2026.
    20. Jesús Villota, 2025. "Predicting Market Reactions to News: An LLM-Based Approach Using Spanish Business Articles," Working Papers wp2025_2501, CEMFI.
    21. Jun Nakayama & Daisuke Yokouchi, 2025. "Relationship Between Japanese Stock Market Behavior and Category-Based News," Risks, MDPI, vol. 13(3), pages 1-29, March.

    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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