# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 6 Conclusions  
### Research Conclusions  
This article proposes a CNN-BiLSTM-AT model optimized using the Hyperband algorithm, which predicts financial difficulties for listed companies based on their financial indicators over the past 3 years and 12 quarters. The empirical results demonstrated the model's strong predictive performance across various configurations, notably achieving the highest accuracy of 0.994 with a time step of 8 and a batch size of 32. The complementary nature of the model's structure enhances its ability to extract meaningful time series features. The CNN layers excel at capturing local patterns and short-term fluctuations, such as sudden changes in quarterly financial data, while the BiLSTM layers model long-term dependencies, helping the model grasp the trends and cycles of a company's financial evolution. Additionally, the attention mechanism further strengthens the model's ability to focus on critical financial indicators and key time points, allowing for more reasonable weight allocation.  
The use of the SMOTE algorithm plays a crucial role in mitigating the bias caused by class imbalance, which is often a challenge in financial distress prediction, as financially troubled companies are relatively rare. Traditional models are often dominated by the majority class, which leads to the underrepresentation of minority class features. By generating synthetic samples, SMOTE improved the representation of distressed companies, allowing the model to learn their characteristics better, which significantly enhanced the recognition rate of financially distressed firms and, in turn, improved the overall predictive performance. The introduction of the Hyperband optimization algorithm further contributed to the model's superior performance. Compared with random search and Bayesian optimization, Hyperband efficiently allocates resources to the most promising hyperparameter combinations, enabling the model to find better configurations in less time. Although Hyperband requires a slightly longer overall search time, its higher accuracy demonstrates its effectiveness in complex tasks with large parameter spaces. Feature ablation analysis combined with attention weights revealed that financial indicators such as the working capital ratio, return on assets, current ratio, and return on total assets consistently played significant roles in the model.  
This model has important application value in financial decision-making and investment practice. Analyzing historical financial indicators and training them can lead to accurately predict financial difficulties, providing strong support for investors, credit ana  
Figure 12: Probability distribution of financial risk prediction for Company B.  
lysts, and financial institutions. Key financial indicators such as return on assets, quick ratio, working capital ratio, current liabilities ratio, and net profit margin on total assets can help investors identify stable companies and avoid high-risk ones. Integrating this model into the early warning platform can enhance forward-looking and data-driven decision-making, reduce risks, and improve long-term returns, providing more accurate decision support for investors and financial institutions.