A novel hybrid approach, integrating stepwise regression analysis (SRA), adaptive neuro-fuzzy inference system (ANFIS) and capital asset pricing model (CAPM), is addressed in this paper for stock portfolio optimization. The SRA is applied to select some of the features from technical indicators that these selected important features improve the performance of the prediction model. In order to create a more accurate forecasting model, ANFIS is applied to forecast future trend values of the Bombay stock exchange (BSE) indices like BSE SENSEX and BSE BANKEX using technical indicators. Stock portfolio optimization aims to determine which of the stocks are to be added to a portfolio based on the investor's needs and changing economic and market conditions. The proposed hybrid optimization technique offers significant improvements in managing investments in a stock portfolio under volatile and uncertainty stock market without the need for human intervention, with diversification procedure, and thus provides acceptable returns with minimal risks. Furthermore, the proposed hybrid SRA-ANFIS-CAPM portfolio model achieves satisfactory performance among the various portfolio models in the presence of fluctuation in a stock market environment.

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