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AI Democratization and Trading Inequality

ANNE YANRU CHANG¹; Xi Dong¹; Xiumin Martin²; Changyun Zhou³

¹ Zicklin School of Business Baruch College · ² Olin Business School, Washington University in St. Louis · ³ School of Accounting, Big Data Laboratory on Financial Security and Behavior (Laboratory of Philosophy and Social Sciences, Ministry of Education) Southwestern University of Finance and Economics

ABSTRACT We are among the first to investigate how Generative AI (GenAI) shapes investors' trading activities. Using an AI‐sentiment measure extracted from earnings‐call transcripts to proxy for textual signals, we find notable shifts in trading behaviors around earnings calls. Before the wide deployment of ChatGPT, short selling was aligned with AI‐sentiment, whereas retail trading was not. However, following ChatGPT's deployment, the alignment of retail traders with AI‐sentiment significantly increases, while the alignment of short sellers weakens, albeit insignificantly. Stocks with higher information processing costs exhibit a more pronounced increase in retail trading alignment, scenarios where retail investors are likely to benefit more from AI. Using retail‐AI alignment as a proxy for the extent to which retail investors trade based on AI signals, we show that information asymmetry declines and retail investors' trading profitability improves, whereas short sale profitability declines in high retail‐AI alignment stocks. Exogenous outages reduce the alignment between retail trading and AI‐sentiment, allowing us to draw causal inferences. Collectively, this study suggests that AI is a promising technology for narrowing the information gap in the trading of complex textual financial disclosures between investor classes with clear disparities in the ability to process public disclosures.

DOI

10.1111/1475-679x.70063

Volume

64 (3)

Pages

1287-1331

Language

en

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