# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 3 Dataset
### Results and Discussion  
The comparative analysis of training loss and value loss for all six Asian indices shown in Figure 9a-f is achieved by the Bi-Directional Gated Recurrent Unit (BGRU) and evaluated during the training phase. The training phase for all indices is executed with 100 epochs in BGRU, and it is observed that Figure 9a,c,f show almost the same loss at the 100 th epoch. The decrease in loss during training is depicted for the BSE, HSI, TWII, JKSE, JPXCY, and KS11 exchange, respectively. The study employs data gathered between 1 April 2020 and 31 March 2024 to build the BGRU model. Upon further scrutiny of Figure 9b, it becomes apparent that the loss value tends to stabilize after the 80 th iteration (epoch) when all deep learning models have an epoch set to 100 to complete the training as indicated in Table 2. In addition, Figure 9a,f demonstrate that the BGRU model achieves the fastest stability in reducing the loss and value for HSI and KS11 exchange, while the BGRU model achieves the slowest stability in the case of JKSE. From Figure 10a-f, the predictions made by the BGRU model for all six Asian exchange markets stock markets on 31 March 2024 are shown. To summarize, the assessment of the six deep neural network models demonstrates the ability to accurately replicate the observed patterns of index value fluctuations in the six Asian stock exchanges. On the other hand, the expected yield curve shows a significant difference compared to the original value curve. Compared to the different exchange markets, the BGRU model produces the TWII index curve, which exhibits the most significant deviation from the original value curve in Figure 10c. Conversely, the BGRU model produces the BSE prediction curve that resembles the original value curve in Figure 10a. Figure 10c displays a variety of possibilities depicted on the Taiwan stock market chart. The prediction curve of TWII has the highest level of deviation from the original value on this graph compared to the curves of the other exchange markets. The BGRU model accurately creates the prediction curve for the KS11 as shown in Figure 10f. In addition, BGRU achieves the highest accuracy in predicting the curve for JKSE, while the BSE exchange remains an excellent performer for BGRU. These data show that none of the six Asian exchange markets under study have the highest level of prediction accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Conv. RNN** & **Conv. LSTM** & **Conv. GRU** & **Conv. BRNN** & **Conv. BLSTM** & **Conv. BGRU** \\ \hline Conv. layer filters & 128 & 128 & 128 & 128 & 128 & 128 \\ Conv. layer kernel size & 3 & 3 & 3 & 3 & 3 & 3 \\ Stride & 2 & 2 & 2 & 2 & 2 & 2 \\ Pooling & 2 & 2 & 2 & 2 & 2 & 2 \\ Learning Rate & 1e-5 & 1e-5 & 1e-5 & 1e-5 & 1e-5 & 1e-5 \\ Conv. Activation function & RELU & RELU & RELU & RELU & RELU \\ LSTM layer hidden unit & 100 & 100 & 100 & 100 & 100 & 100 \\ LSTM Activation Function & Linear & Linear & Linear & Linear & Linear & Linear \\ LSTM Optimizer & Adam & Adam & Adam & Adam & Adam & Adam \\ Loss function & MSE, MAE & MSE, MAE & MSE, MAE & MSE, MAE & MSE, MAE \\ Epochs & 100 & 100 & 100 & 100 & 100 & 100 \\ Dropout layer & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ Batch Size & 40 & 40 & 40 & 40 & 40 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters for the ongoing research on the six-dimensional deep learning model. Further, the authors employ all six deep learning models to evaluate comparative loss and value on the BSE exchange market throughout the training phase. Figure 11 illustrates the comparative loss and value decrease accomplished in these models. The relative decreases in training loss are shown in Figure 11a-f for the Conv. RNN, Conv. LSTM, Conv. GRU, Conv. BRNN, Conv. BLSTM, and Conv. BGRU models that are used. The BSE exchange is created using data collected from 1 April 2020 to 31 March 2024, as stated in the paper. Table 2 demonstrates that all deep learning models have an epoch value of 100.