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Financial Times Series such as stock price and exchange rates are, often,non-linear and non-stationary. Use of decomposition models has been found toimprove the accuracy of predictive models. The paper proposes a hybrid approachintegrating the advantages of both decomposition model (namely, Maximal OverlapDiscrete Wavelet Transform (MODWT)) and machine learning models (ANN and SVR)to predict the National Stock Exchange Fifty Index. In first phase, the data isdecomposed into a smaller number of subseries using MODWT. In next phase, eachsubseries is predicted using machine learning models (i.e., ANN and SVR). Thepredicted subseries are aggregated to obtain the final forecasts. In finalstage, the effectiveness of the proposed approach is evaluated using errormeasures and statistical test. The proposed methods (MODWT-ANN and MODWT-SVR)are compared with ANN and SVR models and, it was observed that the return oninvestment obtained based on trading rules using predicted values of MODWT-SVRmodel was higher than that of Buy-and-hold strategy.

Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index