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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.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhad021
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    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

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