Integration of nonlinear independent component analysis and support vector regression for stock price forecasting

Abstract

Forecasting stock prices is a major activity of financial firms and private investors. In developing a stock price forecasting model, the first step is usually feature extraction. Nonlinear independent component analysis (NLICA), a novel feature extraction technique that assumes the observed mixtures are non-linear combinations of latent source signals, is used to find independent sources when observed data are mixtures of unknown sources, and prior knowledge of the mixing mechanisms is not available. In this paper, a stock price forecasting model which first uses NLICA as preprocessing to extract features from forecasting variables is developed. Then the features, called independent components (ICs), serve as the inputs of support vector regression (SVR) to build the forecasting model. The advantage of the proposed methodology is that the information hidden in the original data can be discovered by feature extraction. Therefore, NLICA can provide more valuable information for financial forecasting. Two datasets of major Asian stock markets—China and Japan, Shanghai Stock Exchange Composite (SSEC) and Nikkei 225 stock indexes, are used as illustrative examples. For comparison, the integration of traditional principal component analysis (PCA) with SVR (called PCA–SVR), linear ICA with SVR (called LICA–SVR) and single SVR approaches were applied to evaluate the prediction accuracy of the proposed approach. Empirical results show that the proposed method (NLICA–SVR) not only improves the prediction accuracy of the SVR approach but also outperforms the PCA–SVR, LICA–SVR and single SVR methods.