When Alpha Breaks: Two-Level Uncertainty for Safe Deployment of Cross-Sectional Stock Rankers

Cross-sectional equity ranking models are typically deployed as if point predictions were sufficient: the model outputs scores, and the portfolio follows the induced ordering. Under non-stationarity, however, historically profitable rankers can become systematically unreliable during regime shifts, creating substantial deployment risk. This paper studies the deployment problem in a realistic setting where a LightGBM-based equity ranker achieves strong overall performance but experiences a regime break during a 2024 AI-thematic market rotation. We frame deployment as two distinct decisions: identifying which individual predictions require caution and determining whether the strategy should trade at all. We adapt Direct Epistemic Uncertainty Prediction (DEUP) to cross-sectional ranking by modeling rank displacement and constructing a per-stock epistemic uncertainty signal using a strictly point-in-time baseline. We document a structural coupling between epistemic uncertainty and signal strength in ranking systems, showing that conventional inverse-uncertainty position sizing can degrade portfolio performance by systematically reducing exposure to the strongest signals. To address this, we propose a two-level deployment architecture consisting of (i) a strategy-level regime-trust gate that decides when the model should trade, and (ii) a position-level epistemic tail-risk cap that limits exposure only for the most uncertain predictions. Empirical results demonstrate that this architecture improves risk-adjusted performance under regime stress while remaining operationally simple. The findings suggest that uncertainty in financial ranking systems provides the greatest economic value as a discrete deployment control—through abstention and tail-risk constraints—rather than as a continuous sizing denominator.