IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v36y2023i9p3603-3642..html
   My bibliography  Save this article

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

Author

Listed:
  • Sean Cao Robert H. Smith
  • Wei Jiang
  • Baozhong Yang J. Mack Robinson
  • Alan L Zhang
  • Tarun Ramadorai

Abstract

Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.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

  • Sean Cao Robert H. Smith & Wei Jiang & Baozhong Yang J. Mack Robinson & Alan L Zhang & Tarun Ramadorai, 2023. "How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI," The Review of Financial Studies, Society for Financial Studies, vol. 36(9), pages 3603-3642.
  • Handle: RePEc:oup:rfinst:v:36:y:2023:i:9:p:3603-3642.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhad021
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Cao, Sean & Jiang, Wei & Wang, Junbo & Yang, Baozhong, 2024. "From Man vs. Machine to Man + Machine: The art and AI of stock analyses," Journal of Financial Economics, Elsevier, vol. 160(C).
    4. Hunter Ng, 2024. "Strategic Control of Facial Expressions by the Fed Chair," Papers 2410.20214, arXiv.org.
    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. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. Ding, Rui & Guo, Jintong & Zhang, Min, 2024. "Practice a poker face: Manager emotion and investor sentiment," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General

    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:oup:rfinst:v:36:y:2023:i:9:p:3603-3642.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.html .

    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.