Integrating independent component analysis-based denoising scheme with neural network for stock price prediction

The forecasting of stock price is one of the most challenging tasks in investment/financial decision-making since stock prices/indices are inherently noisy and non-stationary. In this paper, an integrated independent component analysis (ICA)-based denoising scheme with neural network is proposed for stock price prediction. The proposed approach first uses ICA on the forecasting variables to generate the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables. The reconstructed forecasting variables will contain less noise information and are served as the input variables of the neural network model to build the forecasting model. The TAIEX closing index and Nikkei 225 opening index are used as illustrative examples to evaluate the performance of the proposed model. Experimental results show that the proposed model outperforms the integrated wavelet denoising technique with BPN model, the BPN model with non-filtered forecasting variables, and a random walk model.