# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 6 Robustness Test
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Evaluation Index** & **BA-SVR** & **ANN** & **RF** \\ \hline \multirow{4}{*}{SSE} & RMSE & 0.0980 ******* & 0.0729 & 0.0732 \\ \cline{2-5}  & MAE & 0.0782 ******* & 0.0562 & 0.0578 \\ \cline{2-5}  & MAPE & 0.1390 ******* & 0.0968 & 0.1007 \\ \hline \multirow{4}{*}{SSE A shares} & RMSE & 0.1137 ******* & 0.0725 & 0.0720 \\ \cline{2-5}  & MAE & 0.0903 ******* & 0.0581 & 0.0572 \\ \cline{2-5}  & MAPE & 0.1607 ******* & 0.1039 & 0.0992 \\ \hline \multirow{4}{*}{SSE B shares} & RMSE & 0.0832 ******* & 0.0631 & 0.0612 \\ \cline{2-5}  & MAE & 0.0642 ******* & 0.0503 & 0.0465 \\ \cline{2-5}  & MAPE & 0.1030 ******* & 0.0824 & 0.0750 \\ \hline \multirow{4}{*}{SSE 380} & RMSE & 0.1375 ******* & 0.0988 & 0.0874 \\ \cline{2-5}  & MAE & 0.1107 ******* & 0.0802 & 0.0681 \\ \cline{2-5}  & MAPE & 0.1727 ******* & 0.1243 & 0.1084 \\ \hline \multirow{4}{*}{SSE 180} & RMSE & 0.1270 ******* & 0.0899 & 0.0795 \\ \cline{2-5}  & MAE & 0.1017 ******* & 0.0712 & 0.0624 \\ \cline{2-5}  & MAPE & 0.1870 ******* & 0.1355 & 0.1168 \\ \hline \multirow{4}{*}{SSE 50} & RMSE & 0.1199 ******* & 0.0810 & 0.0761 \\ \cline{2-5}  & MAE & 0.0974 ******* & 0.0667 & 0.0596 \\ \cline{2-5}  & MAPE & 0.1900 ******* & 0.1354 & 0.1164 \\ \hline \multirow{4}{*}{CSI 300} & RMSE & 0.1089 ******* & 0.0852 & 0.0844 \\ \cline{2-5}  & MAE & 0.0896 ******* & 0.0698 & 0.0670 \\ \cline{2-5}  & MAPE & 0.1557 ******* & 0.1234 & 0.1158 \\ \hline \multirow{4}{*}{CSI 1000} & RMSE & 0.1199 ******* & 0.0825 & 0.0810 \\ \cline{2-5}  & MAE & 0.0909 ******* & 0.0645 & 0.0616 \\ \cline{2-5}  & MAPE & 0.1397 ******* & 0.1049 & 0.0966 \\ \hline \multirow{4}{*}{CSI 100} & RMSE & 0.1224 ******* & 0.0870 & 0.0795 \\ \cline{2-5}  & MAE & 0.0946 ******* & 0.0716 & 0.0630 \\ \cline{2-5}  & MAPE & 0.1702 ******* & 0.1272 & 0.1144 \\ \hline \multirow{4}{*}{CSI 500} & RMSE & 0.1618 ******* & 0.0948 & 0.0955 \\ \cline{2-5}  & MAE & 0.1280 ******* & 0.0740 & 0.0754 \\ \cline{2-5}  & MAPE & 0.2073 ******* & 0.1199 & 0.1221 \\ \hline \multirow{4}{*}{CSI 800} & RMSE & 0.1112 ******* & 0.1038 & 0.0875 \\ \cline{2-5}  & MAE & 0.0911 ******* & 0.0815 & 0.0702 \\ \cline{2-5}  & MAPE & 0.1541 ******* & 0.1352 & 0.1188 \\ \hline \multirow{4}{*}{SZCI} & RMSE & 0.1691 ******* & 0.1124 & 0.1188 \\ \cline{2-5}  & MAE & 0.1369 ******* & 0.0894 & 0.0960 \\ \cline{1-1} \cline{2-5}  & MAPE & 0.2150 ******* & 0.1421 & 0.1497 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The 60-day mid-term forecast effect of 18 stock indexes. Table 17 shows the long-term prediction effects of the three models on stock index prices. Among the 18 stock indexes, results of 11 stock indexes show that the long-term prediction effect of BA-SVR model is better than that of RF model, and the results of 2 stock indexes show that the long-term prediction effect of BA-SVR model is significantly worse than that of RF model. Only one stock index shows that the prediction performance of BA-SVR model is better than ANN model, and the results of 8 stock indexes show that the prediction performance of BA-SVR model is significantly worse than ANN model. Nevertheless, generally speaking, BA-SVR model still has good prediction effect in short-term, medium-term and long-term stock index prediction. At the same time, bat algorithm may be more widely used in parameter optimization of various prediction models in the future.