Summary of Mtrgl:effective Temporal Correlation Discerning Through Multi-modal Temporal Relational Graph Learning, by Junwei Su et al.
MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning
by Junwei Su, Shan Wu, Jinhui Li
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Economics (econ.GN); Trading and Market Microstructure (q-fin.TR)
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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 This study investigates the intersection of deep learning and financial market applications, specifically focusing on pair trading. The authors introduce a novel framework called Multi-modal Temporal Relation Graph Learning (MTRGL) that combines time series data and discrete features into a temporal graph. MTRGL uses a memory-based temporal graph neural network to reframes temporal correlation identification as a temporal graph link prediction task, which has shown empirical success. The study’s experiments on real-world datasets confirm the superior performance of MTRGL in refining automated pair trading strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how deep learning can help with financial trading. It focuses on something called “pair trading,” where you compare two things to see if they’re related. To do this, you need a special kind of computer program that can understand patterns and relationships over time. The researchers create a new type of program called MTRGL (Multi-modal Temporal Relation Graph Learning) that combines different types of data into one picture. They use this program to predict how well two things are related, which helps with trading decisions. The study shows that their new program works better than other programs in making good trading decisions. |
Keywords
* Artificial intelligence * Deep learning * Graph neural network * Multi modal * Time series
