IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/31222.html
   My bibliography  Save this paper

Generative AI and Firm Values

Author

Listed:
  • Andrea L. Eisfeldt
  • Gregor Schubert
  • Miao Ben Zhang

Abstract

What are the effects of recent advances in Generative AI on the value of firms? Our study offers a quantitative answer to this question for U.S. publicly traded companies based on the exposures of their workforce to Generative AI. Our novel firm-level measure of workforce exposure to Generative AI is validated by data from earnings calls, and has intuitive relationships with firm and industry-level characteristics. Using Artificial Minus Human portfolios that are long firms with higher exposures and short firms with lower exposures, we show that higher-exposure firms earned excess returns that are 0.4% higher on a daily basis than returns of firms with lower exposures following the release of ChatGPT. Although this release was generally received by investors as good news for more exposed firms, there is wide variation across and within industries, consistent with the substantive disruptive potential of Generative AI technologies.

Suggested Citation

  • Andrea L. Eisfeldt & Gregor Schubert & Miao Ben Zhang, 2023. "Generative AI and Firm Values," NBER Working Papers 31222, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31222
    Note: AP CF EFG
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w31222.pdf
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Samir Huseynov, 2023. "ChatGPT and the Labor Market: Unraveling the Effect of AI Discussions on Students' Earnings Expectations," Papers 2305.11900, arXiv.org, revised Aug 2023.
    2. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    3. Claudia Biancotti & Carolina Camassa, 2023. "Loquacity and visible emotion: ChatGPT as a policy advisor," Questioni di Economia e Finanza (Occasional Papers) 814, Bank of Italy, Economic Research and International Relations Area.
    4. Ozge Demirci & Jonas Hannane & Xinrong Zhu, 2024. "Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms," CESifo Working Paper Series 11276, CESifo.
    5. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023. "From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI," Papers 2310.17721, arXiv.org.

    More about this item

    JEL classification:

    • E0 - Macroeconomics and Monetary Economics - - General
    • G0 - Financial Economics - - General

    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:nbr:nberwo:31222. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.