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...

Full description

Bibliographic Details
Main Author: Awajan, Ahmad Mohammad Al-Abd
Format: Thesis
Language:English
Published: 2018
Subjects:
Online Access:http://eprints.usm.my/43955/
http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf
_version_ 1848879937966047232
author Awajan, Ahmad Mohammad Al-Abd
author_facet Awajan, Ahmad Mohammad Al-Abd
author_sort Awajan, Ahmad Mohammad Al-Abd
building USM Institutional Repository
collection Online Access
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.
first_indexed 2025-11-15T17:55:10Z
format Thesis
id usm-43955
institution Universiti Sains Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T17:55:10Z
publishDate 2018
recordtype eprints
repository_type Digital Repository
spelling usm-439552019-04-12T05:24:50Z 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.
spellingShingle QA1-939 Mathematics
Awajan, Ahmad Mohammad Al-Abd
Forecasting Performance Of Nonlinear And Nonstationary Stock Market Data Using Empirical Mode Decomposition
title 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_short 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
topic QA1-939 Mathematics
url http://eprints.usm.my/43955/
http://eprints.usm.my/43955/1/AHMAD%20MOHAMMAD%20AL-%20ABD%20AWAJAN.pdf