# 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
\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 & 0.0582 & 0.0652 & 0.7365 \\ \cline{2-5}  & MAE & 0.0500 & 0.0523 & 0.6584 \\ \cline{2-5}  & MAPE & 0.0609 & 0.0641 & 0.7932 \\ \hline \multirow{4}{*}{SSE A shares} & RMSE & 0.0569 & 0.0662 & 0.7508 \\ \cline{2-5}  & MAE & 0.0487 & 0.0532 & 0.6720 \\ \cline{2-5}  & MAPE & 0.0591 & 0.0649 & 0.8076 \\ \hline \multirow{4}{*}{SSE B shares} & RMSE & 0.0674 & 0.0623 & 0.0241 \\ \cline{2-5}  & MAE & 0.0459 & 0.0552 & 0.0192 \\ \cline{2-5}  & MAPE & 0.1889 & 0.2304 & 0.0761 \\ \hline \multirow{4}{*}{SSE 380} & RMSE & 0.0349 & 0.0408 & 0.6151 \\ \cline{2-5}  & MAE & 0.0281 & 0.0331 & 0.5767 \\ \cline{1-1} \cline{2-5}  & MAPE & 0.0340 & 0.0402 & 0.6925 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The 60-day mid-term forecast effect of 18 stock indexes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Evaluation Index** & **BA-SVR** & **Poly-SVR** & **Sigm-SVR** \\ \hline \multirow{4}{*}{SSE 180} & RMSE & 0.2831 & 0.2652 & 2.0813 \\ \cline{2-5}  & MAE & 0.2353 & 0.2400 & 2.0152 \\ \cline{2-5}  & MAPE & 0.2300 & 0.2387 & 2.0172 \\ \hline \multirow{4}{*}{SSE 50} & RMSE & 0.2707 & 0.2946 & 0.1265 \\ \cline{2-5}  & MAE & 0.2159 & 0.2739 & 0.1216 \\ \cline{2-5}  & MAPE & 0.2106 & 0.2737 & 0.1214 \\ \hline \multirow{4}{*}{CSI 300} & RMSE & 0.2729 & 0.3306 & 2.7017 \\ \cline{2-5}  & MAE & 0.2467 & 0.3028 & 2.6301 \\ \cline{2-5}  & MAPE & 0.2350 & 0.2901 & 2.5368 \\ \hline \multirow{4}{*}{CSI 1000} & RMSE & 0.0359 & 0.0896 & 0.5897 \\ \cline{2-5}  & MAE & 0.0289 & 0.0869 & 0.5886 \\ \cline{2-5}  & MAPE & 0.0587 & 0.1725 & 1.1816 \\ \hline \multirow{4}{*}{CSI 100} & RMSE & 0.3717 & 0.4399 & 3.4173 \\ \cline{2-5}  & MAE & 0.3231 & 0.4071 & 3.3055 \\ \cline{2-5}  & MAPE & 0.2947 & 0.3762 & 3.0735 \\ \hline \multirow{4}{*}{CSI 500} & RMSE & 0.0425 & 0.0395 & 0.4184 \\ \cline{2-5}  & MAE & 0.0338 & 0.0308 & 0.3812 \\ \cline{2-5}  & MAPE & 0.0419 & 0.0379 & 0.4719 \\ \hline \multirow{4}{*}{CSI 800} & RMSE & 0.1846 & 0.2198 & 2.2432 \\ \cline{2-5}  & MAE & 0.1716 & 0.1997 & 2.1894 \\ \cline{2-5}  & MAPE & 0.1746 & 0.2039 & 2.2435 \\ \hline \multirow{4}{*}{SZCI} & RMSE & 0.2090 & 0.1528 & 1.9303 \\ \cline{2-5}  & MAE & 0.2032 & 0.1419 & 1.9018 \\ \cline{2-5}  & MAPE & 0.2146 & 0.1500 & 2.0113 \\ \hline \multirow{4}{*}{SZSE B shares} & RMSE & 0.1040 & 0.1457 & 0.0887 \\ \cline{2-5}  & MAE & 0.0710 & 0.1284 & 0.0668 \\ \cline{2-5}  & MAPE & 0.0801 & 0.1704 & 0.0762 \\ \hline \multirow{4}{*}{SZSE} & RMSE & 0.1793 & 0.1635 & 2.2240 \\ \cline{2-5}  & MAE & 0.1725 & 0.1561 & 2.2072 \\ \cline{2-5}  & MAPE & 0.1824 & 0.1661 & 2.3401 \\ \hline \multirow{4}{*}{SZSE A shares} & RMSE & 0.1788 & 0.1635 & 2.2268 \\ \cline{2-5}  & MAE & 0.1721 & 0.1561 & 2.2101 \\ \cline{2-5}  & MAPE & 0.1820 & 0.1661 & 2.3436 \\ \hline \multirow{4}{*}{SME} & RMSE & 0.1177 & 0.1546 & 1.8209 \\ \cline{2-5}  & MAE & 0.1062 & 0.1470 & 1.8049 \\ \cline{1-1} \cline{2-5}  & MAPE & 0.1139 & 0.1590 & 1.9451 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._The optimal penalty factor ranges from 34.4416 to 97.3081 after the bat algorithm has been used to find the optimum for the medium-term training set data of 18 stock indices. The absolute value of the insensitive loss factor ranges from 0.1761 to 1.5592. The radial basis kernel function bandwidth parameter ranges from 1.4121 to 4.2374. The optimal free parameters obtained from the 18 stock index interim training group data and the bat algorithm after the optimization search of the interim data were brought into the SVR model, respectively.