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

arXiv: https://arxiv.org/abs/2301.10153 · HTML: https://mlsanity.com/paper/2301.10153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks](https://mlsanity.com/paper/2106.10159/) (2021)

## Abstract (arXiv)

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.