Summary of Dynamic Graph Representation with Contrastive Learning For Financial Market Prediction: Integrating Temporal Evolution and Static Relations, by Yunhua Pei et al.
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations
by Yunhua Pei, Jin Zheng, John Cartlidge
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); 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 Dynamic Graph Representation with Contrastive Learning (DGRCL) framework addresses the limitation of traditional methods that ignore the interplay between dynamic temporal changes and static relational structures between stocks in predicting stock trends. The framework combines two key components: the Embedding Enhancement module, which dynamically captures the temporal evolution of stock data, and the Contrastive Constrained Training module, which enforces static constraints based on stock relations. By integrating these modules, DGRCL improves the accuracy of stock trend prediction and provides a robust framework for capturing complex dynamics in dynamic graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DGRCL is a new way to predict what will happen with stocks by looking at how they relate to each other over time. Most methods only look at one or the other, but this approach combines both ideas. It uses two special parts: one that looks at how stock prices change over time and another that considers how different stocks are related to each other. This helps make predictions more accurate. |
Keywords
» Artificial intelligence » Embedding
