A Hybrid Approach EMD-MA for Short-Term Forecasting of Daily Stock Market Time Series Data

Ahmad M Awajan (), Mohd Tahir Ismail and Al Wadi S
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Ahmad M Awajan: University Science Malaysia, 11800 Gelugor, Penang, Malaysia
Mohd Tahir Ismail: University Science Malaysia, 11800 Gelugor, Penang, Malaysia
Al Wadi S: University of Jordan, Queen Rania str., Amman, Jordan

Journal of Internet Banking and Commerce, 2017, vol. 22, issue 01, 01-10

Abstract: Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Moving Average Model (EMD-MA) is used to improve forecasting performances in financial time series. The strength of this EMD-MA lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-MA has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 10 countries is applied to show the forecasting performance of the proposed EMD-MA. Based on the five forecast accuracy measures, the results indicate that EMD-MA forecasting performance is superior to traditional Moving Average forecasting model.

Keywords: Forecast Time Series; Empirical Mode Decomposition; Moving Average; Intrinsic Mode Function; Forecast Accuracy Measures (search for similar items in EconPapers)
JEL-codes: A11 (search for similar items in EconPapers)
Date: 2017
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