Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index predict...Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.展开更多
文摘Stock index forecast is regarded as a challenging task of financial time-series prediction. In this paper, the non-linear support vector regression (SVR) method was optimized for the application in stock index prediction. The parameters (C, σ) of SVR models were selected by three different methods of grid search (GRID), particle swarm optimization (PSO) and genetic algorithm (GA).The optimized parameters were used to predict the opening price of the test samples. The predictive results shown that the SVR model with GRID (GRID-SVR), the SVR model with PSO (PSO-SVR) and the SVR model with GA (GA-SVR) were capable to fully demonstrate the time-dependent trend of stock index and had the significant prediction accuracy. The minimum root mean square error (RMSE) of the GA-SVR model was 15.630, the minimum mean absolute percentage error (MAPE) equaled to 0.39% and the correspondent optimal parameters (C, σ) were identified as (45.422, 0.012). The appreciated modeling results provided theoretical and technical reference for investors to make a better trading strategy.
