Cross-sectional stock prediction is closer to a ranking problem than to ordinary return-magnitude regression, since portfolio decisions depend on the relative ordering of assets within each trading date. Existing temporal, graph-based, and market-conditioned attention models have improved stock representation learning, yet the final prediction head is often treated as a minor implementation detail. This paper argues that, under information-coefficient-oriented evaluation, score formation is a critical bottleneck: an over-flexible head can fit unstable return magnitude, whereas an overly linear head may underuse cross-feature interactions. We therefore develop RankGLU, a residual bottleneck gated linear unit for cross-sectional stock ranking. RankGLU keeps a direct linear scoring path and adds a bounded multiplicative branch, thereby preserving a stable ordering route while allowing controlled nonlinear interactions. The method is evaluated on CSI300 and CSI800 under a unified protocol with cross-sectional score normalization and an IC-augmented objective. Multi-seed experiments show that, on CSI300, RankGLU achieves the strongest mean IC among the internally controlled variants, improving from 0.0654+/-0.0052 for the original backbone and 0.0697+/-0.0030 for the ranking-aware backbone to 0.0727+/-0.0037, a gain that is consistent across all five seeds. Its best-seed result also exceeds the corresponding baselines. Ablation results further indicate that removing the GLU prediction head causes the clearest degradation among the tested component changes. Additional relation-path calibrations can produce high single-seed peaks, but their multi-seed behavior is less stable. The evidence suggests that ranking-aware stock models benefit most reliably from bounded residual score formation rather than from indiscriminate architectural expansion.

翻译：截面股票预测更接近于一个排序问题，而非普通的收益率幅度回归，因为投资组合决策取决于每个交易日内资产的相对排序。现有的基于时序、图结构和市场条件注意力模型改进了股票表征学习，然而最终预测头常被视为一个次要的实现细节。本文认为，在面向信息系数的评估下，得分形成是一个关键瓶颈：过度灵活的预测头可能拟合不稳定的收益幅度，而过于线性的预测头可能无法充分利用跨特征交互。因此，我们开发了RankGLU，一种用于截面股票排序的残差瓶颈门控线性单元。RankGLU保留了一条直接的线性得分路径，并增加了一个有界乘法分支，从而在保持稳定排序路径的同时，允许受控的非线性交互。该方法在统一协议下，基于截面得分归一化和IC增强目标，在CSI300和CSI800上进行了评估。多种子实验表明，在CSI300上，RankGLU在内部控制变体中实现了最强的平均IC，从原始骨干网络的0.0654+/-0.0052和排序感知骨干网络的0.0697+/-0.0030提升至0.0727+/-0.0037，该增益在所有五个种子上一致。其最佳种子结果也超过了相应的基线。消融实验进一步表明，在测试的组件变更中，移除GLU预测头造成了最明显的性能下降。额外的关系路径校准可以产生高的单种子峰值，但其多种子行为较不稳定。证据表明，排序感知股票模型最可靠地受益于有界残差得分形成，而非无差别的架构扩展。