| Popis: |
This study proposes a stock price forecasting model based on Kernel Principal Component Analysis (KPCA) and Light Gradient Boosting Machine (LightGBM) to examine the connection between the impact factors and the CSI 300 index. As a case, this study focuses on the CSI 300 Index. Data gathered from January 2005 to July 2022, including 16 clear indicators such as CPI, IPI, and Dow Jones Industrial Index, were simplified into five dimensions by the KPCA. Subsequently, the LightGBM algorithm was applied through 5-fold cross-validation to adjust the parameters and achieve optimal model performance. The results demonstrate that the combined KPCA-LightGBM model has strong generalizability and stability, with an RMSE and R2 of 0.001 and 0.837, respectively. Furthermore, reducing factors to five dimensions by KPCA can also provide improved decision-making metrics for investors. |
