Financial risk mitigation in China’s listed corporations: a deep learning-based model

Financial risk prediction is a crucial indicator for maintaining the stability and growth of Chinese listed firms, as accurate forecasting can help reduce the impact of financial distress. This paper proposes a novel deep learning–based approach using the TabNet architecture, optimized with the Multiple-Strategy Siberian Tiger Optimization (MSSTO) algorithm, to predict financial risk in Chinese companies. The proposed model aims to enhance prediction accuracy and computational efficiency by optimizing hyperparameters through MSSTO, which simulates the natural hunting behavior of Siberian tigers to achieve effective global exploration and local exploitation of the search space. The scalable attention mechanism of TabNet is integrated into the framework to improve feature selection and model interpretability, enabling deeper insights into key financial risk factors. Model performance is evaluated using multiple metrics, including R-squared, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). The experimental results demonstrate strong predictive capability, with an R-squared value of 0.9766, indicating that the model explains 97.66% of the variance in the data. Overall, the findings highlight the effectiveness of the proposed approach in supporting accurate financial risk prediction and informed decision-making for financial analysts, investors, and policymakers.