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In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. An optimization model is introduced which generates optimal weights for proposed model; the model is solved using genetic algorithms. The results confirm about the accuracy of the prediction performance of recurrent neural network. As expected, an outstanding prediction performance has been obtained from …