Paper: arXiv 2604.03272

Authors: Shuchen Meng, Xupeng Chen

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 $r(φ) = φρβ/λ’(φ)$, where $φ$ is the AI adoption share, $ρ$ the algorithmic signal correlation, $β$ the performative feedback intensity, and $λ’(φ)$ the endogenous effective price impact. Because $λ’(φ)$ is decreasing in $φ$, 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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a strong theoretical framework combining extended rational expectations, game-theoretic bifurcations, and state-dependent cognitive hysteresis to model AI-driven systemic risk. It backs up its complex mathematical modeling with robust empirical validation using a large SEC Form 13F dataset and a Bartik shift-share instrument to address endogeneity. Overall, it provides significant novelty and structural rigor, bridging advanced financial theory with large-scale empirical data.

Research Flowchart

  flowchart TD
    A[Research Goal: Model AI-Driven Systemic Risk in Financial Markets] --> B[Theoretical Framework: Unified REE Model<br>1. Performative Prediction<br>2. Algorithmic Herding<br>3. Cognitive Dependency]
    B --> C[Derive Risk Coupling & Multiplier<br>r = φρβ / λ'φ  ==>  M = 1 / 1-r]
    D[Empirical Inputs: SEC Form 13F Filings<br>99.5M Holdings, 10.9K Managers, 2013–2024] --> E[Causal Estimation: Bartik Shift-Share Instrument<br>First-Stage F = 22.7]
    C --> E
    E --> F[Outcomes: Algorithmic Monoculture Bifurcation<br>& 18–54% Tail-Loss Amplification]