In this paper, a novel nonlinear Radial Basis Function Neural Network (RBF-NN) ensemble model based on ν-Support Vector Machine (SVM) regression is presented for financial time series forecasting. In the process of ensemble modeling, the first stage the initial data set is divided into different training sets by used Bagging and Boosting technology. In the second stage, these training sets are input to the different individual RBF-NN models, and then various single RBF-NN predictors are produced based on diversity principle. In the third stage, the Partial Least Square (PLS) technology is used to choosing the appropriate number of neural network ensemble members. In the final stage, ν-Support Vector Machine (SVM) regression is used for ensemble of the RBF-NN to prediction purpose. For testing purposes, this paper compare the new ensemble model's performance with some existing neural network ensemble approaches in terms of two financial time series: S & P 500 and Nikkei 225. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed nonlinear ensemble technique provides a promising alternative to financial time series prediction.