Summary of Stock Type Prediction Model Based on Hierarchical Graph Neural Network, by Jianhua Yao et al.
Stock Type Prediction Model Based on Hierarchical Graph Neural Network
by Jianhua Yao, Yuxin Dong, Jiajing Wang, Bingxing Wang, Hongye Zheng, Honglin Qin
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Hierarchical Graph Neural Network (HGNN) model effectively predicts stock types by capturing multi-level information and relational structures in the stock market. The HGNN integrates stock relationship data and hierarchical attributes, using a graph convolution operation and temporal attention aggregator to model the macro market state. This comprehensive approach addresses challenges in utilizing stock relationship data and modeling hierarchical attributes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to analyze stock data by using a special kind of computer program called Hierarchical Graph Neural Network (HGNN). The HGNN helps make predictions about what kinds of stocks will do well or poorly based on how they are related to each other. It’s like looking at a map to see where the different parts of the market are connected and how that affects the whole. |
Keywords
» Artificial intelligence » Attention » Graph neural network
