Learning to Rank with Weak Return Signals: Shortcut-Orthogonal Gradient Allocation for Cross-Sectional Stock Ranking

Cross-sectional stock ranking predicts relative future returns, so learning to rank fits the task. Weighted ranking objectives emphasize comparisons based on return differences, rank positions, or ranking importance. These methods decide which comparisons matter; whether the placement of the weights can also encourage a competing ordering when the directional signal is weak has received little attention. We show that weight allocation itself can induce an objective-level shortcut preference and that, in stock ranking, return magnitude is such a competing ordering. If weighting creates this preference, it should be visible before the model learns anything. At equal scores, the loss response toward a candidate shortcut depends only on training labels and comparison weights. We propose Shortcut-Orthogonal Gradient Allocation (SOGA), a diagnose-then-correct framework that measures the excess response over uniform weighting and minimally corrects risky allocations while retaining the original comparison emphasis as far as the constraints allow. Empirically, every tested base allocation flagged by the diagnostic degrades ranking relative to uniform weighting. Across the four primary equity panels, SOGA consistently improves flagged ranking objectives, with a corrected objective surpassing every tested uncorrected published or reference objective. The correction also transfers across backbones and outperforms uniform shrinkage in matched comparisons.