A representation-learning-based approach to predict stock price trend via dynamic spatiotemporal feature embedding

Stock price trend prediction is a fascinating but difficult research topic. Recently, GNN-based models have been continuously proposed, which are believed to be more effective since they consider the information about stocks themselves and the information between stocks. However, the graph data are often static, unstructured and not all-inclusive, which cannot dynamically reflect all relationships between stocks. Therefore, we propose a novel model PriceExploration-Network (PE-Net), effectively utilizing both temporal and cross-sectional information contained in price to predict the price trend. PE-Net only requires the price data effectively saving the trouble of fetching alternative data and is able to capture the dynamic implicit relations between stocks by combining clustering techniques and GAT architecture. The effectiveness of PE-Net is examined on real-world S&P 500 constituents and the results demonstrate that PE-Net can outperform state-of-the-art models w.r.t. both accuracy and AUC.