The Algorithmic Consensus Hypothesis: Systemic Risk in the Age of Uniform AI Driven Markets

Abstract

This paper introduces and develops the Algorithmic Consensus Hypothesis (ACH), which argues that the proliferation of machine learning-driven investment strategies among institutional investors has produced measurable convergence in factor exposures, signal consumption, and portfolio positioning, generating a new and underappreciated class of systemic risk. Unlike traditional contagion channels rooted in balance sheet linkages or funding structures, algorithmic consensus operates through epistemic homogeneity: when competing agents train on overlapping alternative data, adopt similar neural architectures, and optimize against shared benchmark signals, their strategies cohere silently and without coordination. Drawing on evidence from documented quant crowding episodes, cross-sectional return co-movement anomalies, and the theoretical literature on information cascades and reflexivity, the paper argues that market diversity, long treated as an emergent property of competitive heterogeneity, is now an engineered outcome that markets may be systematically eroding. A conceptual empirical framework is proposed, utilizing hedge fund 13-F filings, factor model residuals, and shock-response correlation analysis to test convergence directly. The paper concludes that regulatory attention to model diversity, not merely to capital and leverage, is increasingly urgent, and that alpha decay in quantitative strategies may be the market's early warning signal that consensus has already arrived.

Keywords

Convergence Science - Social Science

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