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

Tzu-Ya Lai, Wen Jung Cheng, Jun-En Ding
1/15/2023
q-fin.STcs.LG

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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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Cite this paper

@article{lai2023sequential,
  title  = {Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract)},
  author = {Tzu-Ya Lai and Wen Jung Cheng and Jun-En Ding},
  year   = {2023},
  eprint = {2301.10153},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.10153v1}
}

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