发表机构
National Chengchi University（国立政治大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对深度学习股票排名方法问题，提出MAPLE框架，结合统一预测头、极端排名加权损失和多样性正则化器，在四个股票市场表现出色，参数和训练时间减少，在多种骨干架构上有比率提升，证明损失设计和容量分配的重要性。
AI 中文摘要
经典阿尔法挖掘通过组合多个低相关预测信号实现强劲的风险调整回报。然而，深度学习股票排名方法通常为每只股票生成单个阿尔法，依赖日益复杂的架构且收益递减，仅通过单独模型或隐式路由获得多样性，未明确控制阿尔法间相关性。我们引入MAPLE（多阿尔法位置感知列表式集成），一个与骨干无关的框架，在单次训练中恢复这种多样性原则。MAPLE结合统一的、容量缩放的预测头、极端排名加权列表式排名损失和明确惩罚阿尔法间成对相关性的多样性正则化器。在跨越美国、中国和日本的四个股票市场中，MAPLE在九个基线中实现了最佳平均夏普比率和卡尔玛比率，使用的参数少55倍，训练时间少2.5倍，并且在五种骨干架构上具有10%-23%和17%-43%的夏普比率和卡尔玛比率提升。行为分析进一步表明每个组件的作用原理：统一的头部在应用任何多样性损失之前就已经降低了阿尔法间的相关性，极端排名损失使多样性正则化在容量缩放维持规模平衡时提高而非侵蚀每个阿尔法的排名质量。这些结果表明，有原则的损失设计和容量分配而非架构复杂性驱动了多样且有效的多阿尔法生成。
英文摘要
Classical alpha mining achieves strong risk-adjusted returns by combining many low-correlated predictive signals, yet deep learning stock-ranking methods typically produce a single alpha per stock, rely on increasingly complex architectures with diminishing gains, and obtain diversity only through separate models or implicit routing, without explicitly controlling inter-alpha correlation. We introduce MAPLE (Multi-Alpha Position-aware Listwise Ensembling), a backbone-agnostic framework that recovers this diversity principle within a single training pass. MAPLE combines a unified, capacity-scaled prediction head with an extreme-rank weighted listwise ranking loss and a diversity regularizer that explicitly penalizes pairwise correlation across alphas. Across four equity markets spanning the US, China, and Japan, MAPLE achieves the best average Sharpe and Calmar ratios among nine baselines, using up to 55x fewer parameters and 2.5x less training time, and generalizes across five backbone architectures with Sharpe and Calmar Ratio gains of 10-23% and 17-43%, respectively. Behavioral analysis further shows why each component works: the unified head already reduces inter-alpha correlation before any diversity loss is applied, and the extreme-rank loss lets diversity regularization improve rather than erode per-alpha ranking quality as capacity scaling sustains this balance at scale. These results show that principled loss design and capacity allocation, rather than architectural complexity, drive diverse and effective multi-alpha generation.
