RankGLU: Residual Gated Score Formation for Cross-Sectional Stock Prediction
2026-06-08 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors explain that predicting stock rankings is more about ordering stocks correctly than predicting exact returns. They argue that the way the model creates scores to rank stocks is very important. To improve this, they introduce RankGLU, a new method that balances simple linear scoring with controlled nonlinear features to keep rankings stable and useful. Tests on Chinese stock indexes show RankGLU performs better and more consistently than previous methods. They also find that removing their new scoring method hurts performance the most, highlighting its importance.
cross-sectional stock predictionranking probleminformation coefficientgated linear unit (GLU)residual networksscore formationportfolio optimizationCSI300cross-feature interactionsablation study
Authors
Huixiang Xiao, Jian Xu, Feiyu Qu, Zixuan Xie, Xiangyu Li
Abstract
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.