Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)
This work addresses stock market prediction for investors, but it is incremental as it builds on existing graph and attention methods.
The study tackled stock trend prediction by combining sequential graph structures with attention mechanisms, achieving state-of-the-art performance on Taiwan Stock datasets across multiple industries.
The stock market is characterized by a complex relationship between companies and the market. This study combines a sequential graph structure with attention mechanisms to learn global and local information within temporal time. Specifically, our proposed "GAT-AGNN" module compares model performance across multiple industries as well as within single industries. The results show that the proposed framework outperforms the state-of-the-art methods in predicting stock trends across multiple industries on Taiwan Stock datasets.