Summary of Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns, by Haoren Zhu et al.
Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns
by Haoren Zhu, Pengfei Zhao, Wilfred Siu Hung NG, Dik Lun Lee
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); 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 A novel approach is proposed to model the complex dependency structures among financial assets, which are crucial for creating diversified portfolios and mitigating risk in volatile markets. The Asset Dependency Matrix (ADM) is treated as an image sequence, enabling the application of deep learning-based video prediction methods to capture spatiotemporal dependencies among assets. However, unlike images, assets do not have a natural order, posing challenges to organizing relational assets for ADM forecasting. To tackle this issue, the Asset Dependency Neural Network (ADNN) is proposed, employing Convolutional Long Short-Term Memory (ConvLSTM) networks and static/dynamic transformation functions to optimize ADM representations. Extensive experiments demonstrate that ADNN consistently outperforms baselines in ADM prediction and downstream application tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Financial assets have complex relationships that are important for investors to create portfolios and reduce risk. A new way to understand these dependencies is proposed, using images as an analogy. The approach treats the relationships between financial assets like a video sequence, allowing it to use powerful deep learning techniques to capture patterns over time. However, unlike videos where frames naturally follow each other, asset relationships don’t have a clear order. To fix this issue, a special type of neural network is proposed that can handle these complex relationships. The results show that this approach performs better than previous methods in predicting the relationships between financial assets. |
Keywords
* Artificial intelligence * Deep learning * Neural network * Spatiotemporal
