Strategy Saturation and Regime Failure in Financial ML: Evaluative Control and the Limits of Optimization

Machine learning has substantially expanded optimisation capacity in financial systems. It has not resolved alpha compression, strategy crowding, or regime-driven instability. This article argues that these phenomena share a common structural source: optimisation under fixed evaluative criteria. Financial systems optimise over strategies while holding constant the criteria that define what improvement means — Sharpe ratio, volatility, drawdown, liquidity — inducing evaluative compression, a progressive contraction in the set of strategies admissibly superior under a shared evaluative ordering. As optimisation becomes more efficient, this contraction accelerates. Machine learning amplifies the problem it is presumed to solve. The article introduces a distinction between first-order failure — performance degradation under a given objective — and second-order failure — inadequacy of the objective itself. Contemporary systems are architecturally capable of detecting and responding to the former. They have no category for the latter. Analysis of the 2007 quant equity crisis demonstrates that shared evaluative criteria generate structural coupling across independently developed strategies, producing correlated losses that recalibration alone cannot resolve. Similar dynamics are identified in the March 2020 liquidity dislocations and the 2022 UK LDI crisis. To address this structural limitation, the article proposes evaluative operation: a minimal, constrained architectural extension in which performance criteria become admissible objects of system-level transformation, triggered by persistent signals of evaluative misalignment. A six-stage procedural pipeline specifies how such transformation operates in practice. The article develops evaluative accountability as the corresponding governance requirement, introduces evaluative arbitrage as a primary governance risk, and proposes three empirically testable predictions. The central implication is that, as machine learning scales in finance, the binding constraint is no longer optimisation efficiency but control over the criteria under which optimisation proceeds.