Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
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Date
2018-05
Authors
Al-Abd Awajan, Ahmad Mohammad
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
The stock market indices are typically non-linear and non-stationary with high
heteroscedasticity data, which affect the accuracy and validity of the results of traditional
forecasting methods. Therefore, this study focuses on decomposition method to
solve the problem of non-linearity and non-stationarity in data with high heteroscedasticity
behavior to improve the accuracy of stock market forecasting. Recently, Empirical
mode decomposition (EMD) method has been introduced as an effective technique
for overcoming the non-linearity and non-stationarity in time series data. EMD
presents several characteristics that other decomposition methods do not have. Thus,
this thesis proposes five different techniques to forecast the stock markets by combining
EMD with traditional forecasting techniques or bootstrapping EMD with traditional
technique to solve the forecasting inaccuracy problem in financial time series
data and therefore to obtain improving forecasting results. The five new techniques,
namely, EMD with moving average, EMD with Holt-Winter (EMD-HW), EMD with
random walk (EMD-RW), EMD with exponential smoothing method (EMD-EXP),
and bootstrapping of EMD with HW (EMD-HW bagging) are proposed. These proposed
five techniques are compared with eight traditional forecasting methods, ten different
daily stock market indexes of over one thousand five hundred observations for
each set are used. Based on five error measures (i.e. RMSE, MAE, MAPE, MASE, and
TheilU), the results show that the five proposed techniques are more accurate than the
existing forecasting techniques on the stock market forecasting.
Description
Keywords
Decomposition method to solve the problem , improve the accuracy of stock market forecasting