IDEAS home Printed from https://ideas.repec.org/p/ebg/heccah/1563.html
   My bibliography  Save this paper

How Stock Market Participants Use Generative Artificial Intelligence: Evidence from User-Platform Interaction Data

Author

Listed:
  • Ecker, Frank

    (Frankfurt School of Finance & Management)

  • Li, Xitong

    (HEC Paris)

  • Li, Yilan

    (ESSEC Business School)

  • Wu, Fan

    (The Chinese University of Hong Kong (CUHK))

Abstract

We systematically delineate how stock market participants use Generative Artificial Intelligence (GenAI) to aid their processing of investment-related information. Drawing on a comprehensive dataset of user-platform interactions from one of China's largest GenAI service providers, we identify over 1.7 million stock-related queries submitted during the first half of 2024, spanning a wide range of topics and information-processing tasks. We find that firm size, short-term performance, and media coverage are key correlates of query volume. Moreover, user activity increases on days with financial disclosures, but these increases largely parallel media coverage. In addition, we find suggestive evidence of a substitutive relationship between informative management disclosures and GenAI usage. At the answer level, user fixed effects explain most variation in answer attributes, with more accurate trading signals linked to positive feedback and continued engagement. Finally, GenAI usage is associated with more informed trading, lower liquidity, and aggregated answer sentiment correlates with same-day abnormal returns. Overall, we provide comprehensive descriptive evidence on how users rely on GenAI to acquire information for stock market investment, offering practical insights for GenAI providers, firms, and regulators into how to cater to the informational demands of (retail) investors.

Suggested Citation

  • Ecker, Frank & Li, Xitong & Li, Yilan & Wu, Fan, 2025. "How Stock Market Participants Use Generative Artificial Intelligence: Evidence from User-Platform Interaction Data," HEC Research Papers Series 1563, HEC Paris.
    • Handle: RePEc:ebg:heccah:1563
    DOI: 10.2139/ssrn.5224596
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Keywords

    Generative AI; investors; information acquisition; information processing;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G53 - Financial Economics - - Household Finance - - - Financial Literacy
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

    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:ebg:heccah:1563. 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: Antoine Haldemann (email available below). General contact details of provider: https://edirc.repec.org/data/hecpafr.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.