Summary of Dgdnn: Decoupled Graph Diffusion Neural Network For Stock Movement Prediction, by Zinuo You et al.
DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction
by Zinuo You, Zijian Shi, Hongbo Bo, John Cartlidge, Li Zhang, Yan Ge
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 paper proposes a novel graph learning approach to forecast future stock trends by capturing intrinsic interdependencies between stocks that rapidly evolve. The method constructs dynamic stock graphs using entropy-driven edge generation from a signal processing perspective, and learns task-optimal dependencies between stocks via a generalized graph diffusion process on constructed stock graphs. Additionally, the approach captures distinctive hierarchical intra-stock features through a decoupled representation learning scheme. Experimental results demonstrate substantial improvements over state-of-the-art baselines on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make it easier to predict what will happen with stock prices in the future. It does this by looking at how different stocks are connected and changing over time. The approach uses special math techniques to build a picture of these connections, which helps it make better predictions than other methods. This is important because understanding how stocks are connected can help people make smart investment decisions. |
Keywords
* Artificial intelligence * Diffusion * Representation learning * Signal processing
