The purpose of this study is to examine three major issues. First, the authors compare the performance of economic information, technical indicators, historical information, and investor sentiment measures in financial predictions using backpropagation neural networks (BPNN). Granger causality tests are applied to each category of information to select the relevant variables that statistically and significantly affect stock market shifts. Second, the authors investigate the effect of combining all of these four categories of information variables selected by Granger causality test on the prediction accuracy. Third, the effectiveness of different numerical techniques on the accuracy of BPNN is explored. The authors include conjugate gradient algorithms (Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart), quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), and the Levenberg-Marquardt (LM) algorithm which is commonly used in the literature. Fourth, the authors compare the performance of the BPNN and support vector machine (SVM) in terms of stock market trend prediction. Their comparative study is applied to S&P500 data to predict its future moves. The out-of-sample forecasting results show that (i) historical values and sentiment measures allow obtaining higher accuracy than economic information and technical indicators, (ii) combining the four categories of information does not help improving the accuracy of the BPNN and SVM, (iii) the LM algorithm is outperformed by Polak-Ribière, Powell-Beale, and Fletcher-Reeves algorithms, and (iv) the BPNN outperforms the SVM except when using sentiment measures as predictive information.