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

Battle Count: 69

Directly addresses systemic risks arising from AI-driven trading agents, which is highly relevant to quantitative trading firms deploying ML-based strategies. The paper provides structural insights into how shared feature-extraction pipelines and foundation models can create synchronized trading behavior, hidden leverage accumulation, and cascading deleveraging events. The representation-readout architecture mirrors actual ML trading systems. The critical threshold concept and volatility paradox mechanism have direct implications for risk management, position sizing, and strategy diversification in quantitative trading. The matched forecast-distance experiment design is particularly relevant for firms evaluating whether their models are truly diverse or merely appear so.

Implementation requires: (1) multi-agent simulation with N agents each having a two-layer neural network, (2) SMM calibration with global search (Sobol sequences) and local optimization, (3) 2x2x2 factorial design with Monte Carlo replications, (4) matrix-valued OU process for representation drift, (5) matched forecast-distance experiment construction, (6) segmented regression for threshold estimation, (7) multiple negative control experiments. The mathematical framework is sophisticated with function-space distances, Lipschitz bounds, and integrated vs. state-conditional disagreement measures. However, the core simulation loop is tractable given the reduced-form pricing and linear readout structure.

The paper is a draft version with detailed mathematical formulations and experimental design specifications. However, no code repository is provided, no specific empirical dataset is identified for calibration, and key parameters (e.g., number of agents N, state dimension K_s, feature dimension K) are not fully specified. The SMM calibration procedure is described but the exact empirical moments used for calibration are not provided. Monte Carlo simulation parameters (number of replications M) are not specified.