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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
http://eprints.usm.my/43955/
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spelling my.usm.eprints.43955 http://eprints.usm.my/43955/ Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition 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 NonPeerReviewed application/pdf en http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf Awajan, Ahmad Mohammad Al-Abd (2018) Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
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
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
publishDate 2018
url http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf
http://eprints.usm.my/43955/
_version_ 1643710877209198592
score 13.160551

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