Fang Lexin, Zhao Tianlong, Yu Junlei, Guo Qiang, Li Xuemei, Zhang Caiming
School of Software, Shandong University, Jinan 250101, China.
School of Computing and Artificial Intellgence, Shandong University of Finance and Economics, Jinan 250014, China.
Neural Netw. 2025 Oct;190:107729. doi: 10.1016/j.neunet.2025.107729. Epub 2025 Jun 18.
Stock data analysis has become one of the most challenging tasks in time series data analysis due to its dynamism, complexity, and nonlinearity. Recently, relational graphs have become popular for describing certain important relationships in data, particularly by mapping indirect and direct relationships between stocks into non-Euclidean spaces. Existing graph-based methods mainly capture simple pairwise and static relationships between stocks, so they cannot effectively identify higher-order relationships and characterize the dynamic trends of stock relationships. This limitation restricts the performance of stock return prediction models. A variety of stock data types reveal complex relationships among stocks, such as stock prices, industry links, and wiki relationships. This paper proposes a novel Trend-Driven Hypergraph Convolutional Network (TD-HCN) that integrates these data types in order to predict stock rankings through a cooperative learning method of local dynamic and global static relationships across temporal dimensions. To be concrete, we employ a Prior-constrained Relational Learning (PCRL) model that leverages explicit prior knowledge to guide the discovery of latent high-order relationships among stocks. In order to comprehensively capture and utilize dynamic trends in relationships among stocks, a Disentanglement Representation Learning (DRL) mechanism is developed to enhance the key trend features through the disentanglement operation and dual attention module. Extensive experiments on NASDAQ and NYSE datasets show that TD-HCN consistently outperforms the state-of-the-art methods by a considerable margin in terms of returns. It is also effective and robust in learning the dynamic relationships among stocks and capturing key changes in trends within those relationships.
由于股票数据的动态性、复杂性和非线性，股票数据分析已成为时间序列数据分析中最具挑战性的任务之一。最近，关系图在描述数据中的某些重要关系方面变得很流行，特别是通过将股票之间的间接和直接关系映射到非欧几里得空间。现有的基于图的方法主要捕捉股票之间简单的成对和静态关系，因此它们无法有效地识别高阶关系并刻画股票关系的动态趋势。这种局限性限制了股票回报预测模型的性能。各种股票数据类型揭示了股票之间的复杂关系，如股票价格、行业联系和维基关系。本文提出了一种新颖的趋势驱动超图卷积网络（TD-HCN），它整合了这些数据类型，以便通过跨时间维度的局部动态和全局静态关系的协同学习方法来预测股票排名。具体而言，我们采用了一种先验约束关系学习（PCRL）模型，该模型利用明确的先验知识来指导发现股票之间潜在的高阶关系。为了全面捕捉和利用股票关系中的动态趋势，我们开发了一种解缠表示学习（DRL）机制，通过解缠操作和双重注意力模块来增强关键趋势特征。在纳斯达克和纽交所数据集上进行的大量实验表明，TD-HCN在回报方面始终大幅优于现有最先进的方法。它在学习股票之间的动态关系以及捕捉这些关系中趋势的关键变化方面也有效且稳健。
