用动态图注意力模型优化选股，提升实际交易收益。
STN-TGAT: Top-K Portfolio Construction via Prior-Guided Graph Attention with Learnable Soft-Threshold Sparsification
- 融合时序与股票间关系的图注意力网络，自适应建模市场动态。
- 在标普500前50中选前5只股，年化收益率显著高于基线模型。
- 适合关注真实交易场景下选股策略的量化投资者。
本文针对现实投资环境中股票排序与组合构建问题，提出软阈值NMI先验图注意力网络（STN-TGAT），结合时序Transformer与图注意力网络，捕捉长期序列模式与动态股票关联。基于NMI的先验图结构配合可学习软阈值稀疏化机制，在抑制噪声相关性的同时保留有效连接，增强模型鲁棒性。组合构建过程考虑实际因素：从标普500前50只股票中选取前5只，明确权重分配并调整交易成本，评估贴近真实交易条件。实证结果表明，STN-TGAT在预测准确率与投资回报率上持续优于基准模型，验证了决策对齐训练与自适应关系建模协同带来的有效性。
原文摘要 · Abstract (English)
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.
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