An ensemble model for Net asset value prediction

In this paper, we propose a robust and novel ensemble model for Net asset value prediction of Mutual fund. The proposed model is constituted of two non-linear models: Radial basis function (RBF) and Functional link artificial neural network (FLANN). In order to improve the prediction performance of the hybrid model a boosting technique is used. The sum of the weighted outputs of the two models is compared with the target values to minimize the mean square error. The proposed model shows improved performance in terms of MAPE and RMSE values in comparison to each individual model.