发表机构
University College London; University of Oxford（伦敦大学学院; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对现实投资场景下股票排名与投资组合构建问题，提出STN-TGAT，集成时间Transformer与图注意力网络，结合基于NMI的先验图及软阈值稀疏化机制，纳入实际投资考量，实证显示其在预测和盈利方面优于基准模型，为投资组合构建提供有效框架。
AI 中文摘要
本文通过联合建模时间动态和横截面依赖性，解决了现实投资环境下的股票排名和投资组合构建问题。我们提出了软阈值NMI先验Transformer图注意力网络（STN-TGAT），它将时间Transformer与图注意力网络集成，以捕获长期序列模式和动态股票间关系。基于NMI的先验图结合软阈值稀疏化机制，通过减轻噪声相关性同时保留信息连接来增强结构稳健性。投资组合形成过程纳入实际考量，包括在标准普尔500指数前50大成分股中进行前5筛选、明确权重分配和交易成本调整。实证结果表明，STN-TGAT在预测准确性和投资组合回报衡量的投资盈利能力方面始终优于基准模型。这些发现表明，将决策对齐训练与自适应关系建模相结合为数据驱动的投资组合构建提供了一个连贯且实际有效的框架。
英文摘要
This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and cross-sectional dependencies. We propose the Soft-Threshold NMI-prior Transformer Graph Attention Network (STN-TGAT), which integrates a temporal Transformer with a Graph Attention Network to capture long-horizon sequential patterns and dynamic inter-stock relationships. An NMI-based prior graph combined with a soft-threshold sparsification mechanism enhances structural robustness by mitigating noisy correlations while preserving informative connections. The portfolio formation process incorporates practical considerations, including Top-5 selection within the Top-50 $S\&P$ 500 constituents, explicit weight allocation, and transaction cost adjustment, thereby aligning the evaluation with real-world trading conditions. Empirical results on real-world data demonstrate that STN-TGAT consistently outperforms benchmark models from predictive accuracy and investment profitability measured by portfolio returns. These findings suggest that combining decision-aligned training with adaptive relational modeling provides a coherent and practically effective framework for data-driven portfolio construction.
