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How to Talk When a Machine is Listening?: Corporate Disclosure in the Age of AI

Author

Listed:
  • Sean Cao
  • Wei Jiang
  • Baozhong Yang
  • Alan L. Zhang

Abstract

Growing AI readership, proxied by expected machine downloads, motivates firms to prepare filings that are friendlier to machine parsing and processing. Firms avoid words that are perceived as negative by computational algorithms, as compared to those deemed negative only by dictionaries meant for human readers. The publication of Loughran and McDonald (2011) serves as an instrumental event attributing the difference-in-differences in the measured sentiment to machine readership. High machine-readership firms also exhibit speech emotion assessed as embodying more positivity and excitement by audio processors. This is the first study exploring the feedback effect on corporate disclosure in response to technology.

Suggested Citation

  • Sean Cao & Wei Jiang & Baozhong Yang & Alan L. Zhang, 2020. "How to Talk When a Machine is Listening?: Corporate Disclosure in the Age of AI," NBER Working Papers 27950, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:27950
    Note: AP CF LE
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    Citations

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    Cited by:

    1. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    2. Klockmann, Victor & von Schenk, Alicia & Villeval, Marie Claire, 2022. "Artificial intelligence, ethics, and intergenerational responsibility," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 284-317.
    3. Pungaliya, Raunaq S. & Wang, Yanbo, 2023. "Machine invasion: Automation in information acquisition and the cross-section of stock returns," Journal of Financial Markets, Elsevier, vol. 64(C).
    4. Wenting Song & Samuel Stern, 2022. "Firm Inattention and the Efficacy of Monetary Policy: A Text-Based Approach," Staff Working Papers 22-3, Bank of Canada.
    5. Rong Liu & Jujun Huang & Zhongju Zhang, 2023. "Tracking disclosure change trajectories for financial fraud detection," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 584-602, February.
    6. Sanjiv Das & Xin Huang & Soji Adeshina & Patrick Yang & Leonardo Bachega, 2023. "Credit Risk Modeling with Graph Machine Learning," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 197-217, October.
    7. Agam Shah & Arnav Hiray & Pratvi Shah & Arkaprabha Banerjee & Anushka Singh & Dheeraj Eidnani & Sahasra Chava & Bhaskar Chaudhury & Sudheer Chava, 2024. "Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis," Papers 2402.11728, arXiv.org, revised Oct 2024.
    8. Brückbauer, Frank & Cezanne, Thibault, 2022. "Bank manager sentiment, loan growth and bank risk," ZEW Discussion Papers 22-066, ZEW - Leibniz Centre for European Economic Research.
    9. Simone Vannuccini & Ekaterina Prytkova, 2021. "Artificial Intelligence’s New Clothes? From General Purpose Technology to Large Technical System," SPRU Working Paper Series 2021-02, SPRU - Science Policy Research Unit, University of Sussex Business School.

    More about this item

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General

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