[Paper Review] Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models

Wenying Sun, Zhen Xu+4 more|arXiv (Cornell University)|Dec 4, 2024

Insurance and Financial Risk ManagementEconomics, Econometrics and Finance3 citations

TL;DR

This paper proposes a Time Series Transformer model to predict bank stability indices by capturing complex temporal dependencies in multidimensional financial data. The model outperforms LSTM, GRU, CNN, TCN, and RNN-Transformer in both MSE and MAE, demonstrating superior predictive accuracy for financial risk management.

ABSTRACT

This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to complex market changes because they rely on single-dimensional macroeconomic data. This paper proposes a prediction framework based on the Time Series Transformer, which uses the self-attention mechanism of the model to capture the complex temporal dependencies and nonlinear relationships in financial data. Through experiments, we compare the model with LSTM, GRU, CNN, TCN and RNN-Transformer models. The experimental results show that the Time Series Transformer model outperforms other models in both mean square error (MSE) and mean absolute error (MAE) evaluation indicators, showing strong prediction ability. This shows that the Time Series Transformer model can better handle multidimensional time series data in bank stability prediction, providing new technical approaches and solutions for financial risk management.

Motivation & Objective

• To develop a robust framework for predicting bank stability indices using advanced deep learning techniques.
• To address the limitations of traditional models that rely on single-dimensional macroeconomic indicators and fail under complex market dynamics.
• To evaluate the performance of the Time Series Transformer against established models like LSTM, GRU, and CNN in predicting financial stability.
• To provide a scalable, data-driven solution for real-time financial risk assessment in banking institutions.

Proposed method

• The study employs a Time Series Transformer architecture that leverages self-attention mechanisms to model long-range and nonlinear temporal dependencies in multidimensional financial time series.
• The model is trained on historical bank-level and macroeconomic data to predict the bank stability index, a composite indicator of financial health and risk resistance.
• The framework incorporates multi-head attention and position-wise feed-forward networks to enhance representation learning in sequential data.
• Model performance is evaluated using standard regression metrics: mean squared error (MSE) and mean absolute error (MAE).
• Comparative experiments are conducted against LSTM, GRU, CNN, TCN, and RNN-Transformer models under identical training and testing conditions.
• Hyperparameter tuning and cross-validation are applied to ensure model generalization and robustness.

Experimental results

Research questions

• RQ1 Can the Time Series Transformer model effectively capture complex temporal dependencies in multidimensional financial data for bank stability prediction?
• RQ2 How does the Time Series Transformer compare to established models like LSTM, GRU, and CNN in predicting the bank stability index?
• RQ3 To what extent does the self-attention mechanism improve prediction accuracy compared to recurrent and convolutional architectures?
• RQ4 Does the model maintain superior performance across varying market conditions and data complexity?

Key findings

• The Time Series Transformer model achieves the lowest mean squared error (MSE) among all compared models in predicting the bank stability index.
• The model also records the lowest mean absolute error (MAE), indicating higher precision in stability index forecasting.
• The superior performance is attributed to the model's ability to capture long-range and nonlinear dependencies through its self-attention mechanism.
• The results demonstrate that the Time Series Transformer significantly outperforms LSTM, GRU, CNN, TCN, and RNN-Transformer in both MSE and MAE metrics.
• The framework provides a scalable and accurate technical solution for financial institutions seeking advanced risk prediction tools.
• The study confirms that multidimensional time series data, when processed with attention-based models, yield more reliable stability assessments than univariate or shallow models.