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

Tzu-Ya Lai, Wen Jung Cheng and Jun-En Ding

Papers from arXiv.org

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.

Date: 2023-01
New Economics Papers: this item is included in nep-big
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