mlsanityData to 7 Oct 2026

Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management

arXiv 2410.19211 ↗First posted 24 Oct 2024cs.LGcited by 6

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Abstract (arXiv)

With the global economic integration and the high interconnection of financial markets, financial institutions are facing unprecedented challenges, especially liquidity risk. This paper proposes a liquidity coverage ratio (LCR) prediction model based on the gated recurrent unit (GRU) network to help financial institutions manage their liquidity risk more effectively. By utilizing the GRU network in deep learning technology, the model can automatically learn complex patterns from historical data and accurately predict LCR for a period of time in the future. The experimental results show that compared with traditional methods, the GRU model proposed in this study shows significant advantages in mean absolute error (MAE), proving its higher accuracy and robustness. This not only provides financial institutions with a more reliable liquidity risk management tool but also provides support for regulators to formulate more scientific and reasonable policies, which helps to improve the stability of the entire financial system.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 24 Oct 2024.

Builds on

6 of 17 references.

References · 6

  1. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  2. 2024Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networkscited by 19
  3. 2024A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Imagescited by 11
  4. 2024Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessmentcited by 9
  5. 2024Contrastive Learning for Knowledge-Based Question Generation in Large Language Modelscited by 8
  6. 2024Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processingcited by 7

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Citing papers per year: 2024: 6.

6 papers.

  1. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generationcited by 13
  2. 2024Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learningcited by 13
  3. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  4. 2024Enhancing Few-Shot Learning with Integrated Data and GAN Model Approachescited by 9
  5. 2024An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reductioncited by 9
  6. 2024A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generationcited by 7