Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)

2023/01/15 by Lai, Tzu-Ya, Cheng, Wen Jung, Ding, Jun-En
#FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Statistical Finance (q-fin.ST)

paper · doi:10.48550/arxiv.2301.10153

Abstract

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.

Related

• EP-GAT: Energy-based Parallel Graph Attention Neural Network for Stock Trend Classification
• Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction
• NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction
• Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning
• HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction