# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 5 Results
### Analysis of the Prediction Effect of Hyperband-CNN-BiLSTM-AT Model
#### 5.1.2 Comparative Analysis of CNN-BiLSTM-AT Model Structure

Table 7: CNN-BiLSTM-AT model structure table. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Time Step**} & \multicolumn{4}{c}{**Batch Size = 32**} & \multicolumn{4}{c}{**Batch Size = 64**} \\ \cline{2-9}  & **m1** & **m2** & **m3** & **m4** & **m1** & **m2** & **m3** & **m4** \\ \hline \(w=\) 4 & 0.948 & 0.947 & 0.917 & 0.923 & 0.956 & 0.946 & 0.917 & 0.908 \\ \(w=\) 5 & 0.963 & 0.958 & 0.946 & 0.928 & 0.955 & 0.961 & 0.946 & 0.935 \\ \(w=\) 6 & 0.984 & 0.987 & 0.953 & 0.967 during model training. Instead, each model was independently run 15 times with different random initializations. Figure 5 shows the accuracy distribution of the four models (m1, m2, m3, m4) in 15 experiments. The accuracy of m1 and m4 is relatively stable, while m2 and m3 exhibit significant fluctuations. The average performance across these runs was used to conduct the Friedman test [23], a non-parametric statistical test suitable for comparing multiple models across multiple datasets. From Table 9, the resulting Friedman statistic was 34.3061 with a \(p\)-value of 0.000, indicating statistically significant differences among the models. To further determine which models had significant differences, we used Bonferroni-Dunn post hoc testing [24]. From Table 10, the results show that model m1 performed significantly better than m2, m3, and m4, with Bonferroni corrected \(p\)-values of 0.0349, 0.0000, and 0.0000, respectively. However, the differences between m2, m3, and m4 were insignificant (\(p>0.05\)). These findings confirm that the performance improvement of m1 is statistically significant, rather than due to random variations. (2) Results analysis of the best CNN-BiLSTM-AT models  
After conducting experiments on different CNN-BiLSTM-AT model structures, we found that the m1 model (1 CNN-1 BiLSTM-AT) achieved the highest validation accuracy (0.994) at a time step of 8, batch size of 32, and 180 epochs of training. The loss value and accuracy trend chart in Figure 6 show that the model exhibits good convergence and stability during the training and validation process. This indicates that the m1 model has strong generalization ability and excellent performance under the current optimal configuration of the Hyperband algorithm, making it an ideal model structure. Figure 5: The accuracy distribution of the four models. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **m1** & **m2** & **m3** & **m4** \\ \hline Mean Rank & 1.0000 & 2.3000 & 3.3333 & 3.3667 \\ \hline Friedman statistic & & & 34.3061 & \\ \(p\)-value & & & 0.000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Friedman test results of model performance comparison. According to the performance evaluation table for the best CNN-BiLSTM-AT model (Table 11), the overall accuracy was 99.4%, indicating strong performance. Among all samples predicted as the non-ST class, 99.5% were correctly identified as non-ST, and 99.8% of all predicted non-ST samples were accurately classified. For the ST class, 99.1% of predicted ST samples were correctly identified, while 96.6% of actual ST samples were accurately predicted as ST. In addition, compared with the model without SMOTE, the model's accuracy was only 97.9%, and all evaluation indicators were lower than those of the SMOTE model, indicating the effectiveness of the SMOTE method. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Comparison** & **Z-Score** & **Bonferroni-Corrected \(p\)-Value** \\ \hline m1 and m2 & 2.7577 & 0.0349 \\ m1 and m3 & 4.9497 & 0.0000 \\ m1 and m4 & 5.0205 & 0.0000 \\ m2 and m3 & 2.1920 & 0.1703 \\ m2 and m4 & 2.2627 & 0.1419 \\ m3 and m4 & 0.0707 & 1.0000 \\ \hline \hline \end{tabular} Notes: m1 = ICNN-1 BiLSTM-AT; m2 = ICNN-2 BiLSTM-AT; m3 = 2 CNN-1 BiLSTM-AT; m4 = 2 CNN-2 BiLSTM-AT.  
\end{table} 
Table 10: Post hoc comparison using Bonferroni correction. Figure 6: Trend chart of accuracy and loss for the best CNN-BiLSTM-AT models.