Artificial Intelligence and Systemic Risk: A Unified Model of Performative Prediction, Algorithmic Herding, and Cognitive Dependency in Financial Markets
For financial regulators and market participants, this provides a formal framework linking AI adoption to systemic risk, with quantitative estimates of tail-loss amplification.
This paper develops a unified model showing that AI adoption in financial markets generates systemic risk through performative prediction, algorithmic herding, and cognitive dependency, with a convex coupling leading to superlinear growth in systemic risk. Empirical validation using SEC 13F filings (99.5 million holdings, 2013-2024) implies tail-loss amplification of 18-54%.
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 $r(Ï) = ÏÏβ/λ'(Ï)$, where $Ï$ is the AI adoption share, $Ï$ the algorithmic signal correlation, $β$ the performative feedback intensity, and $λ'(Ï)$ the endogenous effective price impact. Because $λ'(Ï)$ is decreasing in $Ï$, 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 adoption game produces a saddle-node bifurcation into an algorithmic monoculture. Third, cognitive dependency as an endogenous state variable yields an impossibility theorem (hysteresis requires dynamics beyond static frameworks) and a channel necessity theorem (each channel is individually necessary). Empirical validation uses the complete universe of SEC Form 13F filings (99.5 million holdings, 10,957 institutional managers, 2013--2024) with a Bartik shift-share instrument (first-stage $F = 22.7$). The model implies tail-loss amplification of 18--54%, economically significant relative to Basel III countercyclical buffers.