  Financial Times Series such as stock price and exchange rates are, often,
non-linear and non-stationary. Use of decomposition models has been found to
improve the accuracy of predictive models. The paper proposes a hybrid approach
integrating the advantages of both decomposition model (namely, Maximal Overlap
Discrete Wavelet Transform (MODWT)) and machine learning models (ANN and SVR)
to predict the National Stock Exchange Fifty Index. In first phase, the data is
decomposed into a smaller number of subseries using MODWT. In next phase, each
subseries is predicted using machine learning models (i.e., ANN and SVR). The
predicted subseries are aggregated to obtain the final forecasts. In final
stage, the effectiveness of the proposed approach is evaluated using error
measures 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 on
investment obtained based on trading rules using predicted values of MODWT-SVR
model was higher than that of Buy-and-hold strategy.
