Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models

Paper: arXiv 2412.03606

Authors: Wenying Sun, Zhen Xu, Wenqing Zhang, Kunyuan Ma, You Wu, Mengfang Sun

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.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 2.5/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper presents a standard application of vanilla Transformer equations to financial time series without substantive mathematical innovation. Methodologically, it suffers from a fundamental mismatch by attempting to evaluate bank stability prediction using the ‘Bank Marketing Dataset’ (a standard UCI classification dataset for phone campaign targeting), rendering the empirical findings invalid for risk management contexts.

Research Flowchart

  flowchart TD
    A[Goal: Predict Bank Stability Index & Assess Risk] --> B[Data Input: Multidimensional Financial & Macroeconomic Time Series]
    B --> C[Framework: Time Series Transformer with Self-Attention]
    C --> D[Model Training & Comparative Evaluation vs. LSTM, GRU, CNN, TCN, RNN-Transformer]
    D --> E[Outcome: Transformer Outperforms Baselines in MSE & MAE]
    E --> F[Impact: Improved Capture of Complex Temporal Dependencies for Financial Risk Management]