# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 3 Dataset
### Results and Discussion
GRU; (**d**) training and value loss for BSE index with Conv. BRNN; (**e**) training and value loss for BSE index with Conv. BLSTM; (**f**) training and value loss for BSE index with Conv. BGRU. terms of the Root Mean Squared Error (RMSE), the Convolutional GRU algorithm has a value of 0.1582, the Convolutional RNN algorithm has a value of 0.1450, the Convolutional BLSTM algorithm has a value of 0.1494, the Convolutional BGRU algorithm has a value of 0.1205, and the Convolutional RNN algorithm has a value of 0.1545. The Convolutional BGRU and the Convolutional LSTM combinations are two models with the lowest total Root Mean Squared Error (RMSE) estimates. When simultaneously forecasting financial time-series data with many distinct components, the preliminary findings indicate that these two models perform much better than their competitors. Figure 12: Actual and prediction values for BSE index with Conv. RNN, Conv. LSTM, Conv. BRNN, Conv. GRU, Conv. BLSTM and Conv. BGRU models. (**a**) Actual and prediction values for BSE index with Conv. RNN model; (**b**) actual and prediction values for BSE index with Conv. LSTM model; (**c**) actual and prediction values for BSE index with Conv. BRNN model; (**d**) actual and prediction values for BSE index with Conv. RNN; (**e**) actual and prediction values for BSE index with Conv. BLSTM; (**f**) actual and prediction values for BSE index with Conv. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Root Mean Squared Error 1**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Conv. BLSTM 2**} & \multicolumn{2}{c}{**Conv. BGRU 3**} & \multicolumn{2}{c}{**Conv. RNN 4**} \\ \cline{2-7}
**Index** & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\ \hline BSE & 0.0132 & 0.0143 & 0.0227 & 0.0239 & 0.0147 & 0.0158 \\ HSI & 0.0263 & 0.0276 & 0.0251 & 0.0267 & 0.0179 & 0.0195 \\ TWII & 0.0197 & 0.0219 & 0.0213 & 0.0226 & 0.0189 & 0.0203 \\ JKSE & 0.0222 & 0.0236 & 0.0215 & 0.0233 & 0.0312 & 0.0336 \\ JPXGY & 0.0145 & 0.0154 & 0.0257 & 0.0269 & 0.0273 & 0.0294 \\ KS11 & 0.0362 & 0.0376 & 0.0332 & 0.0348 & 0.0249 & 0.0264 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of error for the prediction of BSE, HSI, TWII, JKSE, JPXGY, and KS11 indexes. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Root Mean Squared Error 1**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Conv. LSTM 2**} & \multicolumn{2}{c}{**Conv. GRU 3**} & \multicolumn{2}{c}{**Conv. RNN 4**} \\ \cline{2-7}
**Index** & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\ \hline BSE & 0.0132 & 0.0143 & 0.0227 & 0.0239 & 0.0147 & 0.0158 \\ HSI & 0.0263 & 0.0276 & 0.0251 & 0.0267 & 0.0179 & 0.0195 \\ TWII & 0.0197 & 0.0219 & 0.0213 & 0.0226 & 0.0189 & 0.0203 \\ JKSE & 0.0222 & 0.0236 & 0.0215 & 0.0233 & 0.0312 & 0.0336 \\ JPXGY & 0.0145 & 0.0154 & 0.0257 & 0.0269 & 0.0273 & 0.0294 \\ KS11 & 0.0362 & 0.0376 & 0.0332 & 0.0348 & 0.0249 & 0.0264 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of error for prediction of BSE, HSI, TWII, JKSE, JPXGY, and KS11 indexes. Further, Table 5 presents the comparative analysis of outcomes of the suggested hybrid model, where layers of each of the six models are included and tested on the BSE stock exchange and other recent frameworks on RMSE, MAPE, and MAE evaluation parameters. The proposed hybrid model achieves an RMSE of 0.0122 while tested on the BSE index, and that is the least in comparison with other indices due to the large corpus available in BSE. Further, the hybrid model achieves a MAPE of 1.31% and an MAE of 0.0082 on the BSE index's test data. Based on the study conducted by other researchers, it has been consistently shown that hybrid models tend to outperform individual models across various types of datasets.