Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder

作者：Tianyi Cao, Xinrui Wan, Huanhuan Wang, Xin Yu, Libo Xu · 发表于：Journal of theoretical and applied electronic commerce research · 年份：2024 · DOI：10.3390/jtaer19030086 · 被引用次数：5 · 研究领域：Stock Market Forecasting Methods、Complex Systems and Time Series Analysis、Financial Markets and Investment Strategies

In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model’s exceptional ...