Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks

Financial market dynamics forecasting has long been a focus of economic research. A stochastic time effective function neural network (STNN) with principal component analysis (PCA) developed for financial time series prediction is presented in the present work. In the training modeling, we first use the approach of PCA to extract the principal components from the input data, then integrate the STNN model to perform the financial price series prediction. By taking the proposed model compared with the traditional backpropagation neural network (BPNN), PCA-BPNN and STNN, the empirical analysis shows that the forecasting results of the proposed neural network display a better performance in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, HS300, S&P500 and DJIA in the established model, and the corresponding statistical comparisons of the above market indices are also exhibited.