Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition

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-stati...

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Main Author:	Awajan, Ahmad Mohammad Al-Abd
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA1-939 Mathematics
Online Access:	http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf
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id	my-usm-ep.43955
record_format	uketd_dc
spelling	my-usm-ep.439552019-04-12T05:24:50Z Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition 2018-05 Awajan, Ahmad Mohammad Al-Abd QA1-939 Mathematics 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. 2018-05 Thesis http://eprints.usm.my/43955/ http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1-939 Mathematics
spellingShingle	QA1-939 Mathematics Awajan, Ahmad Mohammad Al-Abd Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
description	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.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Awajan, Ahmad Mohammad Al-Abd
author_facet	Awajan, Ahmad Mohammad Al-Abd
author_sort	Awajan, Ahmad Mohammad Al-Abd
title	Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
title_short	Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
title_full	Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
title_fullStr	Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
title_full_unstemmed	Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
title_sort	forecasting performance of nonlinear and nonstationary stock market data using empirical mode decomposition
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Matematik
publishDate	2018
url	http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf
_version_	1747821310137860096

Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition

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