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

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.

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

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 , where is the AI adoption share, the algorithmic signal correlation, the performative feedback intensity, and the endogenous effective price impact. Because is decreasing in , the coupling is convex in adoption, implying that the systemic risk multiplier 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 ). The model implies tail-loss amplification of 18--54%, economically significant relative to Basel III countercyclical buffers.

Paper Structure

This paper contains 57 sections, 15 theorems, 25 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Three questions.
  3. Main results.
  4. Motivation.
  5. Distinction from existing amplification mechanisms.
  6. Empirical approach.
  7. Outline.
  8. Literature Review
  9. Algorithmic Convergence and Monoculture Risk.
  10. Performativity and Self-Fulfilling Prophecies.
  11. Volatility Clustering and Algorithmic Amplification.
  12. Cognitive Dependency and Automation Bias.
  13. Literature Gap.
  14. On Structural Novelty Relative to the Microstructure Literature.
  15. Theoretical Model
  16. ...and 42 more sections

Key Result

Proposition 3.1

For any $(\phi, \rho, \beta) \in [0,1] \times [0,1] \times [0,1)$ satisfying $\phi\rho\beta/N < \lambda(\phi)$, there exists a unique linear equilibrium price impact $\lambda'(\phi) = \lambda(\phi) + \phi\rho\beta/N > 0$. The mapping $\phi \mapsto \lambda'(\phi)$ is continuous, strictly positive, an

Figures (6)

  • Figure 1: Conceptual framework: Three mutually reinforcing risk channels. The systemic risk multiplier $\mathcal{M} = (1 - \phi\rho\beta/\lambda')^{-1}$ is superlinear in the product of channel intensities. The bottom panel illustrates the diversified equilibrium (left) and monoculture equilibrium (right), with hysteresis barriers creating path-dependent transitions.
  • Figure 2: Systemic risk multiplier $\mathcal{M}(\phi, \rho, \beta)$ as a function of $\phi$ for varying $\rho$ and $\beta$, exhibiting superlinear growth near the stability boundary.
  • Figure 3: Hysteresis in the adoption--risk relationship. The gap between the tipping point $\phi^*$ and recovery point $\phi^{**}$ widens with the atrophy rate $\kappa$ and duration of the monoculture phase.
  • Figure 4: Synthesis of empirical findings. (a) Portfolio convergence increases $+12\%$ post-2016 (real 13F data, $p = 0.002$). (b) Cumulative abnormal returns during algorithmic amplification episodes. (c) AI-related language in SEC filings shows $>$50$\times$ growth (EDGAR EFTS, 2013--2024). (d) Cross-sectional relationship between AI adoption intensity and portfolio convergence.
  • Figure 5: Tail risk amplification. (a) Heatmap showing superlinear growth. (b) The "calm before the storm" paradox: lower unconditional volatility coexists with higher conditional tail losses.
  • ...and 1 more figures

Theorems & Definitions (17)

  • Proposition 3.1: Equilibrium Existence and Uniqueness
  • Proposition 3.2: Excess Volatility from Algorithmic Homogeneity
  • Lemma 3.3: Endogenous Fragility: Convexity of the Equilibrium Coupling
  • Lemma 3.4: Impossibility of Endogenous Fragility Without Performative Feedback
  • Proposition 3.5: Monoculture Trap
  • Proposition 3.6: Tail Risk Amplification
  • Proposition 3.7: Calm Before the Storm
  • Corollary 3.8: Distinguishing the AI Monoculture CBS from Leverage-Cycle CBS
  • Remark 3.9: Distinction from Leverage-Cycle and Crowded-Trade Amplification
  • Remark 3.10: Testable Predictions
  • ...and 7 more