Summary of Design and Optimization Of Big Data and Machine Learning-based Risk Monitoring System in Financial Markets, by Liyang Wang et al.
Design and Optimization of Big Data and Machine Learning-Based Risk Monitoring System in Financial Markets
by Liyang Wang, Yu Cheng, Xingxin Gu, Zhizhong Wu
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Risk Management (q-fin.RM)
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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 presents a novel risk monitoring system for financial institutions, leveraging big data and machine learning techniques to tackle the complexity of modern markets. The system is designed as a four-layer architecture that combines large-scale financial data with advanced algorithms, including LSTM networks, Random Forest, and Gradient Boosting Trees. Apache Flink’s real-time processing capabilities ensure accurate and timely risk monitoring. Experimental results show significant improvements in efficiency and accuracy, particularly in identifying market crash risks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new system for tracking financial risks using big data and machine learning. This means the computer can look at lots of information to predict when the market might go down. The system uses four different parts to make it work: long-term memory networks, random forests, gradient boosting trees, and a special way of processing data called Apache Flink. The results show that this new approach is much better than what was used before. |
Keywords
» Artificial intelligence » Boosting » Lstm » Machine learning » Random forest » Tracking
