Abstract
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.
Highlights
Stock index forecast is a non-linear dynamic system
The parameters (C, σ) of support vector regression (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
Summary
Stock index forecast is a non-linear dynamic system. There are many factors affecting the stock index, which goes with the complex fluctuation [1]. It had become a popular and interesting research issue to calculate the stock index to avoid the investment risk [2]. To quantitatively forecast the stock index price, traditional time-series models were introduced, such as autoregressive moving average model, which still failed in non-linear and non-stationary prediction [4]. The research methods for stock index prediction vary from time series to artificial intelligence
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