q-fin.TRApr 14, 2026

Representation Homogeneity and Systemic Instability in AI-Dominated Financial Markets: A Structural Approach

Authors: Yimeng Qiu, Qiwei Han

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Abstract

This paper investigates how similarity in the informational representation of market states among Artificial Intelligence (AI) trading agents can generate systemic instability in financial markets. We construct a structural multi-agent market model calibrated using high-frequency microstructural moments. AI agents are modeled through a two-layer decision architecture consisting of a nonlinear representation layer and an adaptive linear readout layer. The representation layer maps raw market states into high-dimensional feature vectors, while the readout layer generates return forecasts that feed into a risk-controlled trading rule. This representation-based microfoundation separates two objects that are often conflated in the literature: representation homogeneity (the degree to which agents encode market states into similar feature spaces) and forecast overlap (the degree to which agents produce similar return predictions). We show theoretically that these two concepts are related but not equivalent, and that representation homogeneity can compress the effective space of forecast disagreement under stress even when predictions appear diverse in normal times. Through controlled factorial experiments that vary representation homogeneity while conditioning on alternative risk-aversion and learning-rate distributions, we hypothesize that increasing representation similarity amplifies synchronization in beliefs and positions, leading to volatility clustering, liquidity stress, and elevated tail risk. Our structural mechanisms suggest that low perceived volatility regimes can endogenously accumulate hidden leverage through position stickiness, which subsequently collapses when shocks trigger synchronized deleveraging. The results provide a structural foundation for macroprudential policies aimed at monitoring and preserving diversity in how AI systems represent and process market information.

Figures & tables

Target Empirical MomentPrimary Structural Role
Annualized VolatilityExogenous fundamental risk ( σϵ\sigma_{\epsilon} )
Unconditional Price ImpactBaseline temporary impact ( λ0\lambda_{0} )
Conditional Tail Price Impact (Top 5% Vol)Liquidity fragility parameter ( αλ\alpha_{\lambda} )
Extreme Tail Price Impact (Top 1% Vol)Liquidity capacity saturation ( βλ\beta_{\lambda} )
Return Autocorrelation (Lag 1)Baseline inventory premium ( ψ0\psi_{0} )
Conditional Tail Return Autocorrelation (Top 5%)Inventory fragility parameter ( αψ\alpha_{\psi} )
Table 1: SMM Target Moments and Identification Strategy
ScenarioνW\nu_{W}Interpretation
No convergence0Negative control
Slow convergence0.001Gradual industry standardization
Baseline convergence0.01Moderate adoption of common AI infrastructure
Fast convergence0.1Rapid industry consolidation
Table 2
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