# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 5 Empirical Research and Result Analysis
### Comparison and Analysis of Short-Term Forecast Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & \(\mathbf{C}\) & \(\mathbf{\epsilon}\) & \(\mathbf{\sigma}\) \\ \hline SSE & 95.9770 & 0.0907 & 2.6613 \\ \hline SSE A shares & 79.9952 & 0.8021 & 2.5208 \\ \hline SSE B shares & 64.8835 & \(-\)0.3853 & 1.6273 \\ \hline SSE 380 & 94.2018 & 0.0601 & 1.1234 \\ \hline SSE 180 & 92.7542 & 0.7241 & 1.5854 \\ \hline SSE 50 & 65.0537 & 2.2530 & 1.6814 \\ \hline CSI 300 & 86.8630 & \(-\)0.2276 & 1.3855 \\ \hline CSI 1000 & 73.3592 & 0.5046 & 2.4751 \\ \hline CSI 100 & 73.0068 & \(-\)0.0131 & 1.4336 \\ \hline CSI 500 & 96.5140 & 2.3154 & 4.1270 \\ \hline CSI 800 & 72.1978 & 1.6316 & 1.5218 \\ \hline SZCI & 81.6892 & 0.9107 & 2.8827 \\ \hline SZSE B shares & 67.3532 & \(-\)0.2837 & 1.7597 \\ \hline SZSE & 93.4442 & \(-\)0.1453 & 1.8848 \\ \hline SZSE A shares & 64.7818 & 0.9474 & 1.7707 \\ \hline SME & 82.8261 & 0.4680 & 2.0065 \\ \hline SME 300 & 83.4304 & 0.0740 & 1.9187 \\ \hline SME CI & 97.6447 & 0.7531 & 1.3207 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SVR parameters obtained by short-term bat algorithm optimization. However, in the overall view of the 18 stock indices, the SVR model after the bat algorithm search outperforms the other two control models with unoptimized free parameters in terms of short-term forecasting performance. Comparing the forecasting performance of the two control models, we can also see that the polynomial kernel function outperforms the sigmoid kernel function for 16 of the 18 indices. The opposite is true for the SZSE B and SSE B indices, where the RMSE and MAE of the sigmoid kernel function SVR model are smaller than those of the BA-SVR model and the polynomial kernel function SVR model. \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 & 0.0336 & 0.0851 & 0.9655 \\ \cline{2-5}  & MAE & 0.0279 & 0.0766 & 0.9583 \\ \cline{2-5}  & MAPE & 0.0329 & 0.0914 & 1.1311 \\ \hline \multirow{4}{*}{SSE A shares} & RMSE & 0.0332 & 0.0860 & 0.9883 \\ \cline{2-5}  & MAE & 0.0275 & 0.0775 & 0.9813 \\ \cline{2-5}  & MAPE & 0.0324 & 0.0921 & 1.1551 \\ \hline \multirow{4}{*}{SSE B shares} & RMSE & 0.0341 & 0.0843 & 0.0178 \\ \cline{2-5}  & MAE & 0.0246 & 0.0836 & 0.0162 \\ \cline{2-5}  & MAPE & 0.1091 & 0.3753 & 0.0714 \\ \hline \multirow{4}{*}{SSE 380} & RMSE & 0.0319 & 0.0334 & 0.7144 \\ \cline{2-5}  & MAE & 0.0252 & 0.0282 & 0.7104 \\ \cline{2-5}  & MAPE & 0.0306 & 0.0345 & 0.8578 \\ \hline \multirow{4}{*}{SSE 180} & RMSE & 0.0747 & 0.2544 & 2.6924 \\ \cline{2-5}  & MAE & 0.0691 & 0.2519 & 2.6898 \\ \cline{2-5}  & MAPE & 0.0712 & 0.2618 & 2.7865 \\ \hline \multirow{4}{*}{SSE 50} & RMSE & 0.1673 & 0.2725 & 2.9874 \\ \cline{2-5}  & MAE & 0.1631 & 0.2694 & 2.9853 \\ \cline{2-5}  & MAPE & 0.1673 & 0.2787 & 3.0778 \\ \hline \multirow{4}{*}{CSI 300} & RMSE & 0.0768 & 0.2518 & 3.1914 \\ \cline{2-5}  & MAE & 0.0713 & 0.2478 & 3.1871 \\ \cline{2-5}  & MAPE & 0.0726 & 0.2555 & 3.2739 \\ \hline \multirow{4}{*}{CSI 1000} & RMSE & 0.0242 & 0.0752 & 0.5674 \\ \cline{2-5}  & MAE & 0.0201 & 0.0739 & 0.5662 \\ \cline{2-5}  & MAPE & 0.0424 & 0.1533 & 1.1819 \\ \hline \multirow{4}{*}{CSI 100} & RMSE & 0.1157 & 0.3242 & 2.9216 \\ \cline{2-5}  & MAE & 0.1089 & 0.3204 & 2.9181 \\ \cline{2-5}  & MAPE & 0.1114 & 0.3298 & 2.9944 \\ \hline \multirow{4}{*}{CSI 500} & RMSE & 0.0392 & 0.0288 & 0.4527 \\ \cline{2-5}  & MAE & 0.0329 & 0.0220 & 0.4318 \\ \cline{1-1} \cline{2-5}  & MAPE & 0.0417 & 0.0277 & 0.5424 \\ \hline \end{tabular}
\end{table} 
Table 6: The 20-day short-term forecast effect of 18 stock indexes. Table 7 depicts the average results of the forecasting results under the three models for the 18 stock indices. From this table, it can be concluded that all three forecasting performance evaluation metrics, RMSE, MAE and MAPE, reflect that the SVR model after the bat algorithm optimization search exhibits higher forecasting performance improvement in the short term when analyzed in comparison with the two control models.