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

Frank Ecker, Xitong Li (), Yilan Li and Fan Wu
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Frank Ecker: Frankfurt School of Finance & Management
Xitong Li: HEC Paris
Yilan Li: ESSEC Business School
Fan Wu: The Chinese University of Hong Kong (CUHK)

No 1563, HEC Research Papers Series from HEC Paris

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.

Keywords: Generative AI; investors; information acquisition; information processing (search for similar items in EconPapers)
JEL-codes: D83 G11 G14 G53 M41 (search for similar items in EconPapers)
Pages: 72 pages
Date: 2025-05-06
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1563

DOI: 10.2139/ssrn.5224596

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