Predicting Stock Index Using an Integrated Model of NLICA, SVR and PSO

Abstract

Predicting stock index is a major activity of financial firms and private investors. However, stock index prediction is regarded as a challenging task of the prediction problem since the stock market is a complex, evolutionary, and nonlinear dynamic system. In this study, a stock index prediction model by integrating nonlinear independent component analysis (NLICA), support vector regression (SVR) and particle swarm optimization (PSO) is proposed. In the proposed model, first, the NLICA is used as preprocessing to extract features from observed stock index data. The features which can be used to represent underlying/hidden information of the original data are then served as the inputs of SVR to build the stock index prediction model. Finally, PSO is applied to optimize the parameters of the SVR prediction model since the parameters of SVR must be carefully selected in establishing an effective and efficient SVR model. Experimental results on Shanghai Stock Exchange composite (SSEC) closing cash index show that the proposed stock index prediction method is effective and efficient compared to the four comparison models.