# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 5 Empirical Research and Result Analysis
### Comparison and Analysis of Long-Term Forecast Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Evaluation Index** & **BA-SVR** & **Poly-SVR** & **Sigm-SVR** \\ \hline \multirow{4}{*}{SSE} & RMSE & 0.1453 & 0.1147 & 0.6738 \\ \cline{2-5}  & MAE & 0.1033 & 0.0830 & 0.4761 \\ \cline{2-5}  & MAPE & 0.1756 & 0.1425 & 0.6693 \\ \hline \multirow{4}{*}{SSE A shares} & RMSE & 0.1503 & 0.1172 & 0.6827 \\ \cline{2-5}  & MAE & 0.1076 & 0.0850 & 0.4824 \\ \cline{2-5}  & MAPE & 0.1811 & 0.1443 & 0.6749 \\ \hline \multirow{4}{*}{SSE B shares} & RMSE & 0.4561 & 0.3342 & 0.0633 \\ \cline{2-5}  & MAE & 0.3375 & 0.3141 & 0.0540 \\ \cline{2-5}  & MAPE & 9.4998 & 11.0853 & 1.6000 \\ \hline \multirow{4}{*}{SSE 380} & RMSE & 0.1699 & 0.1532 & 0.9220 \\ \cline{2-5}  & MAE & 0.1063 & 0.1186 & 0.6649 \\ \cline{2-5}  & MAPE & 0.1366 & 0.1585 & 0.8050 \\ \hline \multirow{4}{*}{SSE 180} & RMSE & 0.4130 & 0.4709 & 0.1376 \\ \cline{2-5}  & MAE & 0.3036 & 0.3469 & 0.1084 \\ \cline{2-5}  & MAPE & 0.2796 & 0.3291 & 0.1074 \\ \hline \multirow{4}{*}{SSE 50} & RMSE & 0.4156 & 0.5091 & 2.8872 \\ \cline{2-5}  & MAE & 0.3083 & 0.3709 & 2.1719 \\ \cline{2-5}  & MAPE & 0.2800 & 0.3410 & 1.9834 \\ \hline \multirow{4}{*}{CSI 300} & RMSE & 0.5526 & 0.8857 & 3.3729 \\ \cline{2-5}  & MAE & 0.4104 & 0.6293 & 2.4598 \\ \cline{2-5}  & MAPE & 0.3395 & 0.5208 & 2.0195 \\ \hline \multirow{4}{*}{CSI 1000} & RMSE & 0.0626 & 0.0836 & 0.5834 \\ \cline{2-5}  & MAE & 0.0446 & 0.0700 & 0.5532 \\ \cline{2-5}  & MAPE & 0.1206 & 0.1503 & 1.3440 \\ \hline \multirow{4}{*}{CSI 100} & RMSE & 0.4276 & 0.6335 & 0.1540 \\ \cline{2-5}  & MAE & 0.3052 & 0.4407 & 0.1213 \\ \cline{2-5}  & MAPE & 0.2674 & 0.3912 & 0.1145 \\ \hline \multirow{4}{*}{CSI 500} & RMSE & 0.1702 & 0.1345 & 0.8757 \\ \cline{2-5}  & MAE & 0.1209 & 0.0981 & 0.6133 \\ \cline{2-5}  & MAPE & 0.1598 & 0.1384 & 0.7756 \\ \hline \multirow{4}{*}{CSI 800} & RMSE & 0.5121 & 0.6544 & 3.5002 \\ \cline{2-5}  & MAE & 0.3873 & 0.4791 & 2.5436 \\ \cline{2-5}  & MAPE & 0.3390 & 0.4245 & 2.1716 \\ \hline \multirow{4}{*}{SZCI} & RMSE & 0.6069 & 1.0894 & 5.5251 \\ \cline{2-5}  & MAE & 0.4734 & 0.7986 & 4.1649 \\ \cline{1-1} \cline{2-5}  & MAPE & 0.3603 & 0.5951 & 3.1142 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The 250-day long-term forecast effect of 18 stock indexes. According to the results in Table 9, it can be seen that for the 18 stock indices, the long-term forecasting performance of the Sigmoid kernel function SVR model is the worst among the three models, implying that the RMSE, MAE and MAPE values are larger compared to the other two models in the same group. Experimental results for 13 of the 18 stock indices show that the BA-SVR model has higher forecasting performance than the polynomial kernel function SVR model. The values of RMSE, MAE and MAPE for the polynomial kernel function SVR model for the SSE Composite Index, SSE A shares and CSI 500 data are smaller than those of the BA-SVR model in the same group, indicating that the polynomial kernel function SVR model has better forecasting performance in these groups. The RMSE and MAE of the polynomial kernel function SVR model under the SSE B-share data are smaller than BA-SVR, but the MAPE is larger than BA-SVR. The RMSE of the polynomial kernel function SVR model under the SSE 380 data is smaller than BA-SVR, but the MAE and MAPE indicators are greater than the BA-SVR model. While the BA-SVR model is not the best performing model in terms of forecasting in individual groups, the difference between the predicted RMSE, MAE and MAPE values of the BA-SVR model and the better polynomial kernel function SVR model is not significant in these groups. This difference is much smaller than the difference between these two models \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **BA-SVR** & **Poly-SVR** & **Sigm-SVR** \\ \hline Average long-Term RMSE & 0.3336 & 0.4249 & 1.8695 \\ \hline Average long-Term MAE & 0.2499 & 0.3115 & 1.3848 \\ \hline Average long-Term MAPE & 0.7571 & 0.9097 & 1.3982 \\ \hline \hline \end{tabular}
\end{table}