# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 5 Empirical Research and Result Analysis
### Comparison and Analysis of Mid-Term Forecast Results  
As described above, the data of the last 60 days of the 18 stock indices in the total sample time selected in this paper are used as the prediction set data in the medium-term forecasting, and the other sample data are used as the training group. Tables 8-10 show the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Evaluation Index** & **BA-SVR** & **Poly-SVR** & **Sigm-SVR** \\ \hline \multirow{3}{*}{CSI 800} & RMSE & 0.0369 & 0.1978 & 3.0010 \\ \cline{2-5}  & MAE & 0.0304 & 0.1941 & 2.9954 \\ \cline{2-5}  & MAPE & 0.0311 & 0.2018 & 3.1007 \\ \hline \multirow{3}{*}{SZCI} & RMSE & 0.0573 & 0.1159 & 2.6407 \\ \cline{2-5}  & MAE & 0.0500 & 0.1133 & 2.6377 \\ \cline{2-5}  & MAPE & 0.0511 & 0.1175 & 2.7238 \\ \hline \multirow{3}{*}{SZSE B shares} & RMSE & 0.1565 & 0.1845 & 0.1226 \\ \cline{2-5}  & MAE & 0.1313 & 0.1485 & 0.1109 \\ \cline{2-5}  & MAPE & 0.1288 & 0.1469 & 0.1098 \\ \hline \multirow{3}{*}{SZSE} & RMSE & 0.0345 & 0.1206 & 2.5561 \\ \cline{2-5}  & MAE & 0.0275 & 0.1182 & 2.5533 \\ \cline{2-5}  & MAPE & 0.0291 & 0.1258 & 2.7060 \\ \hline \multirow{3}{*}{SZSE A shares} & RMSE & 0.0337 & 0.1199 & 2.5568 \\ \cline{2-5}  & MAE & 0.0270 & 0.1175 & 2.5540 \\ \cline{2-5}  & MAPE & 0.0286 & 0.1251 & 2.7076 \\ \hline \multirow{3}{*}{SME} & RMSE & 0.0353 & 0.1170 & 2.3135 \\ \cline{2-5}  & MAE & 0.0288 & 0.1133 & 2.3106 \\ \cline{2-5}  & MAPE & 0.0304 & 0.1212 & 2.4594 \\ \hline \multirow{3}{*}{SME 300} & RMSE & 0.0200 & 0.1021 & 1.8328 \\ \cline{2-5}  & MAE & 0.0166 & 0.0986 & 1.8309 \\ \cline{2-5}  & MAPE & 0.0186 & 0.1110 & 2.0489 \\ \hline \multirow{3}{*}{SME CI} & RMSE & 0.0453 & 0.0743 & 1.5805 \\ \cline{2-5}  & MAE & 0.0344 & 0.0669 & 1.5758 \\ \cline{1-1} \cline{2-5}  & MAPE & 0.0391 & 0.0762 & 1.7799 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(Cont.\) \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **BA-SVR** & **Poly-SVR** & **Sigm-SVR** \\ \hline Average short-term RMSE & 0.0583 & 0.1404 & 1.7834 \\ \hline Average short-term MAE & 0.0509 & 0.1345 & 1.7785 \\ \hline Average short-term MAPE & 0.0594 & 0.1625 & 1.9282 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of short-term forecast results. three SVR free parameters obtained from the mid-term bat algorithm search, the evaluation of the performance of the 18 stock indices for mid-term forecasting, and the combined evaluation table of the three models, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & \(\mathbf{C}\) & \(\mathbf{\epsilon}\) & \(\mathbf{\sigma}\) \\ \hline SSE & 92.9584 & 0.4514 & 2.7690 \\ \hline SSE A shares & 62.9068 & 0.6314 & 2.6689 \\ \hline SSE B shares & 88.2247 & \(-\)0.5090 & 3.8687 \\ \hline SSE 380 & 71.7724 & 0.2404 & 1.1615 \\ \hline SSE 180 & 82.8880 & 1.1762 & 2.3351 \\ \hline SSE 50 & 84.1108 & \(-\)0.8570 & 1.6173 \\ \hline CSI 300 & 96.9191 & 1.5592 & 1.1395 \\ \hline CSI 1000 & 34.4416 & \(-\)1.3293 & 2.5553 \\ \hline CSI 100 & 63.9939 & 0.1499 & 1.4121 \\ \hline CSI 500 & 62.9402 & 1.0332 & 4.2374 \\ \hline CSI 800 & 75.4408 & \(-\)0.1761 & 1.3945 \\ \hline SZCI & 94.4847 & 1.0223 & 2.9350 \\ \hline SZSE B shares & 97.3081 & 0.4358 & 1.8542 \\ \hline SZSE & 96.7984 & 0.8435 & 1.7298 \\ \hline SZSE A shares & 97.0854 & \(-\)0.9634 & 1.8159 \\ \hline SME & 57.0795 & 1.1820 & 1.8202 \\ \hline SME 300 & 64.5638 & \(-\)0.7880 & 1.8680 \\ \hline SME CI & 94.6832 & 0.2173 & 1.4299 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: SVR parameters obtained by mid-term bat algorithm optimization.