The impact of generative AI on institutional efficiency: Regulatory and trading evidence from financial markets

Abstract

This paper examines how generative artificial intelligence (GenAI) affects institutional efficiency in financial markets through the combined roles of regulatory and trading institutions. While AI adoption in finance has expanded rapidly, most studies emphasize predictive accuracy and trading performance, leaving the institutional mechanisms through which GenAI influences markets less explored. We conceptualize GenAI diffusion as an institutional shock that reshapes information production, monitoring capacity, and enforcement quality, thereby affecting market efficiency through both regulatory and trading channels.

Using a cross-market panel dataset and a novel proxy for GenAI adoption, we analyze its relationship with institutional efficiency and market outcomes. Our empirical approach employs fixed effects regressions, interaction models, instrumental variable estimation, and difference in differences designs to address endogeneity concerns. The results indicate that GenAI adoption significantly improves institutional efficiency, particularly in markets with strong governance and regulatory capacity. Institutional quality acts as a key moderating factor, amplifying efficiency gains while limiting the benefits in weaker environments. We also find asymmetric effects: in low governance markets, GenAI may intensify informational imbalances. Gains in institutional efficiency further transmit GenAI’s impact to trading dynamics by increasing liquidity and stabilizing volatility. Overall, the results underscore the importance of institutional context in shaping the economic consequences of generative AI.