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

Authors

  • Tzu-Ya Lai National Taipei University Master of Arts in Economics
  • Wen Jung Cheng University of Connecticut Master in Financial Technology
  • Jun-En Ding National Yang Ming Chiao Tung University Institute of Hospital and Health Care Administration

DOI:

https://doi.org/10.1609/aaai.v37i13.26982

Keywords:

Graph Attention Network, Graph Neural Networks, Stock Prediction, Time Series

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.
AAAI-23 Proceedings Cover

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Published

2023-09-06

How to Cite

Lai, T.-Y., Cheng, W. J., & Ding, J.-E. (2023). Sequential Graph Attention Learning for Predicting Dynamic Stock Trends (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16244-16245. https://doi.org/10.1609/aaai.v37i13.26982

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program