A HYBRID EMD-MA FOR FORECASTING STOCK MARKET INDEX

Abstract

Nowadays, stock market data forecasting has drawn a high attention in the field of non-stationary and nonlinear time series data with a high heteroscedasticity, since improving the forecasting accuracy is a hot topic for the researchers.  Therefore, in this article the authors are proposing a new methodology via combining Empirical Mode decomposition and Moving Average model as a modified method to improve forecasting accuracy in content of stock market data.  The strength of this proposed methodology lies in its ability to forecast non-linear and non-stationary financial data without a need to use any transformation method.  Moreover, this method provides a better model with sufficient forecasting accuracy.  The daily stock market data of fourteen countries is applied to show the forecasting performance of the proposed method.  Based on the five forecast accuracy measures, the results indicate that proposed forecasting method performance is superior to four selected forecasting techniques.