# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 6 Conclusions
### Research Limitations and Prospects  
Although the model performs well in predicting financial distress, limitations remain. The model's performance is highly dependent on the dataset used, and the current dataset only covers Chinese A-share listed companies. The research results may not directly apply to other markets or nonlisted companies, which limits their universality and applicability to some extent. In addition, although the model makes predictions based on historical financial data, it may not fully capture sudden market shocks, regulatory changes, or other external factors such as political events or global economic changes. The model did not consider textual data (such as news sentiment or social media indicators), which can provide valuable supplementary perspectives for predicting financial distress.  
Another important limitation is that the model lacks sufficient transparency. Although the model can extract key financial indicators and the impact of these indicators has been identified, the internal decision-making process is still not straightforward enough due to the black box nature of deep learning methods. This lack of transparency is crucial for financial decision-making, as understanding the decision-making logic behind model predictions is key to improving decision trust and acceptability.  
Future research can explore the adaptability of this model in different market environments, especially for nonlisted companies, to evaluate its robustness in different financial contexts. At the same time, combining external data sources such as macroeconomic indicators, market sentiment, and textual data may improve the accuracy of the model's predictions of financial distress. In addition, it is crucial for future improvements to address the limitations of the model in handling complex, noisy, or unstructured data and enhancing its ability to respond to sudden market changes. Improving the transparency and interpretability of deep learning models will be a key research direction to promote their widespread acceptance in the financial field.  
Conceptualization, P.B.; methodology, P.B.; software, Y.S.; validation, Y.S. and M.C.; formal analysis, P.B., Y.S. and M.C.; investigation, P.B.; resources, Y.S.; data curator, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, P.B.; visualization, P.B. and Y.S.; supervision, P.B.; project administration, P.B. All authors have read and agreed to the published version of the manuscript.  
This research project was financially supported by Mahasarakham University and the Guangdong Philosophy and Social Science Foundation Project (GD24 XGL031).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
We would like to thank the referees for their comments and suggestions on the manuscript. The authors are grateful to the reviewers for their valuable and constructive comments. Observational data in China were provided by Shenzhen GTA Education Tech Ltd. (CSMAR database accessed on 1 May 2025) at [[https://data.csmar.com/](https://data.csmar.com/)]([https://data.csmar.com/](https://data.csmar.com/)).  
The authors declare no potential conflicts of interest.