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

The objective of this paper is to investigate a model to derive a measure of bank stability index using the Time Series-based Transformer. The bank stability index is an essential indicator designed to gauge the health status and risk resistance of financial institutions. Traditional prediction methods can't adapt to complex market changes because they depend on single-dimensional macroeconomic data. This paper proposes a prediction framework built upon Time Series Transformer, which leverages the model's self-attention mechanism to seize the complicated temporal dependencies and nonlinear correlations present in the financial data. We compared our model with LSTM, GRU, CNN, TCN, and RNN-Transformer models. Experimentation reveals the model to have superior prediction capabilities as shown by an MSE and MAE evaluation across other models. Therefore, applying the Time Series Transformer model permits better management of multidimensional time series data in bank stability predictions, presenting a distinct set of technological references and technical solutions in the area of financial risk management.