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

Tzu-Ya Lai; Wen Jung Cheng; Jun-En Ding

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arXiv · 2301.10153

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

Tzu-Ya Lai·Wen Jung Cheng·Jun-En Ding

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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.

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Tzu-Ya Lai, Wen Jung Cheng, Jun-En Ding. 2023-01-15. Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract). https://arxiv.org/abs/2301.10153

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