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This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II) for various stock market forecasts. The proposed technique simultaneously optimizes two mutually conflicting objectives: the structure (the number of centers in the hidden layer) and the output mean square error (MSE) of the model. The best compromised non-dominated solution-based model was determined from the optimal Pareto front using fuzzy set theory. The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500) and Dow Jones Industrial Average (DJIA) stock data. The results of the simulation of the new model demonstrate a prediction performance superior to that of the conventional radial basis function (RBF)-based forecasting model in terms of the mean average percentage error (MAPE), directional accuracy (DA), Thelis’ U and average relative variance (ARV) values

On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices