# 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  
A series of experiments was designed to compare the effects of different model structures, batch counts, and time steps. The same dataset was used for training and evaluation: m1 represents 1 CNN layer + 1 BiLSTM layer + 1 AT layer, m1 represents 1 CNN layer + 2 BiLSTM layers + 1 AT layer, m3 represents 2 CNN layers + 1 BiLSTM layer + 1 AT layer, and m4 represents 2 CNN layers + 2 BiLSTM layers + 1 AT layer, as shown in Table 7. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Hyperparameter** & **Search Scope** & **Step** & **Optimal Value** \\ \hline Conv\_filters & [16; 128] & 16 & 64 \\ CNN\_Dropout & [0.1, 0.5] & 0.1 & 0.5 \\ Bilstm\_units1 & [32; 128] & 32 & 64 \\ Bilstm\_Dropout1 & [0.1, 0.5] & 0.1 & 0.2 \\ Bilstm\_units2 & [32; 128] & 32 & 128 \\ Bilstm\_Dropout2 & [0.1, 0.5] & 0.1 & 0.1 \\ Dense\_units1 & [32; 256] & 32 & 128 \\ Dense\_Dropout1 & [0.1, 0.5] & 0.1 & 0.1 \\ Dense\_units2 & [32; 256] & 32 & 128 \\ Dense\_Dropout2 & [0.1, 0.5] & 0.1 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hyperparameter optimization results. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Time (Seconds)** & **Test Accuracy** \\ \hline Hyperband & 2186.32 & 0.9876 \\ Random search & 628.25 & 0.9798 \\ Bayesian optimization & 591.74 & 0.9764 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of hyperparameter optimization methods. (1) Analysis of Results from Different Model Structures  
Table 8 shows the accuracy of four models (m1 to m4) under different time steps (w) and batch sizes (batch_2) settings. Overall, as the time step increased, the model's accuracy generally showed a trend of first increasing and then decreasing, especially reaching a high level of accuracy in the range of w = 6 to w = 10. When the time step was w = 8 and the batch size was 32, the accuracy of the m1 model was the highest at 99.4%. From the perspective of model structure, the m1 and m2 models generally exhibited high accuracy in all time steps and batch size settings, especially in larger time steps (w = 6 to w = 10), where their performance was remarkably stable and excellent, demonstrating good learning and generalization abilities, whereas m3 and m4 exhibited inevitable fluctuations at specific time steps, with slightly lower overall accuracy, which may indicate that the model structures of m3 and m4 had relatively weak processing power in financial indicator series data. From the perspective of batch size, there was no significant difference in model performance between the two batch size settings in most cases. However, in certain cases (m4 model from w = 5 to w = 12), the accuracy of models with a batch size of 32 was generally slightly higher. From the perspective of time steps, models with smaller time steps (w = 4 to w = 5) exhibited relatively lower accuracy. The models showed high accuracy with larger time steps (w = 6 to w = 10). However, their performance sharply declined at w = 12, indicating that excessively long time steps caused dataset bias and made it difficult for the models to learn effectively. To ensure the reliability of the significance test results and verify that the observed differences in model accuracy are not due to random chance, we did not fix the random seed \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & **m1:** & **m2:** & **m3:** & **m4:** \\  & **1 CNN-1 BiLSTM-AT** & **1 CNN-2 BiLSTM-AT** & **2 CNN-1 BiLSTM-AT** & **2 CNN-2 BiLSTM-AT** \\ \hline Conv1D & 3 \(\times\) 64 (ReLU) & 3 \(\times\) 64 (ReLU) & 3 \(\times\) 64 (ReLU) \\ MaxPool & 2 \(\times\) 2 & 2 \(\times\) 2 & 2 \(\times\) 2 & 2 \(\times\) 2 \\ Conv1D & - & - & 3 \(\times\) 128 (ReLU) & 3 \(\times\) 128 (ReLU) \\ MaxPool & - & - & 2 \(\times\) 2 & 2 \(\times\) 2 \\ BiLSTM & 64 (Tanh) & 64 (Tanh) & 64 (Tanh) & 64 (Tanh) \\ BiLSTM & - & 128 (Tanh) & - & 128 (Tanh) \\ Attention & ✓ & ✓ & ✓ & ✓ \\ Dense & 128 (ReLU) & 128 (ReLU) & 128 (ReLU) & 128 (ReLU) \\ \hline \hline \end{tabular}
\end{table}