[2604.03272] Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets

Quantitative Finance > Computational Finance arXiv:2604.03272 (q-fin) [Submitted on 23 Mar 2026] Title:Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets Authors:Shuchen Meng, Xupeng Chen View a PDF of the paper titled Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets, by Shuchen Meng and 1 other authors View PDF HTML (experimental) Abstract:We develop a unified model in which AI adoption in financial markets generates systemic risk through three mutually reinforcing channels: performative prediction, algorithmic herding, and cognitive dependency. Within an extended rational expectations framework with endogenous adoption, we derive an equilibrium systemic risk coupling $r(\phi) = \phi\rho\beta/\lambda'(\phi)$, where $\phi$ is the AI adoption share, $\rho$ the algorithmic signal correlation, $\beta$ the performative feedback intensity, and $\lambda'(\phi)$ the endogenous effective price impact. Because $\lambda'(\phi)$ is decreasing in $\phi$, the coupling is convex in adoption, implying that the systemic risk multiplier $M = (1 - r)^{-1}$ grows superlinearly as AI penetration increases. The model is developed in three layers. First, endogenous fragility: market depth is decreasing and convex in AI adoption. Second, embedding the convex coupling within a supermodular ad...