Summary of Predicting Liquidity Coverage Ratio with Gated Recurrent Units: a Deep Learning Model For Risk Management, by Zhen Xu et al.
Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management
by Zhen Xu, Jingming Pan, Siyuan Han, Hongju Ouyang, Yuan Chen, Mohan Jiang
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 GRU-based LCR prediction model utilizes deep learning technology to accurately forecast liquidity coverage ratios (LCRs) in financial institutions. By leveraging complex patterns learned from historical data, the model outperforms traditional methods in terms of mean absolute error (MAE). This innovative approach not only provides a reliable tool for financial institutions to manage their liquidity risk but also supports regulators in formulating more scientific policies, ultimately enhancing financial system stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new model is developed to predict how much money banks have available for lending and borrowing. The model uses a type of artificial intelligence called deep learning to learn from past data and make accurate predictions about the future. This helps banks manage their risk better and makes it easier for regulators to create rules that keep the financial system stable. |
Keywords
» Artificial intelligence » Deep learning » Mae
