The impact of oil and global markets on Saudi stock market predictability: A machine learning approach

Abdou, Hussein A.; Elamer, Ahmed A.; Abedin, Mohammad; Ibrahim, Bassam A.

doi:10.1016/j.eneco.2024.107416

Journal article 191 views 47 downloads

The impact of oil and global markets on Saudi stock market predictability: A machine learning approach

Hussein A. Abdou

, Ahmed A. Elamer

, Mohammad Abedin, Bassam A. Ibrahim

Energy Economics, Volume: 132

Swansea University Author: Mohammad Abedin

PDF | Version of Record

© 2024 The Authors. This is an open access article under the CC BY license.
Download (5.21MB)

Check full text

DOI (Published version): 10.1016/j.eneco.2024.107416

Abstract

This study investigates the predictability power of oil prices and six international stock markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock market using five Machine Learning (ML) techniques and the Generalized Method of Moments (GMM). Our analysis reveals that prio...

Full description

Published in:	Energy Economics
ISSN:	0140-9883
Published:	Elsevier BV 2024
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa65694
Tags:	Add Tag No Tags, Be the first to tag this record!

first_indexed	2024-02-25T14:23:40Z
last_indexed	2024-02-25T14:23:40Z
id	cronfa65694
recordtype	SURis
fullrecord	<?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>65694</id><entry>2024-02-25</entry><title>The impact of oil and global markets on Saudi stock market predictability: A machine learning approach</title><swanseaauthors><author><sid>4ed8c020eae0c9bec4f5d9495d86d415</sid><firstname>Mohammad</firstname><surname>Abedin</surname><name>Mohammad Abedin</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-02-25</date><deptcode>BAF</deptcode><abstract>This study investigates the predictability power of oil prices and six international stock markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock market using five Machine Learning (ML) techniques and the Generalized Method of Moments (GMM). Our analysis reveals that prior to the 2006 collapse, oil exerted the least influence on the Saudi market, while the UK and Japan were the most influential stock markets. However, after the collapse, oil became the most influential factor, highlighting the strong dependence of Saudi Arabia's economic structure on oil production. This finding is particularly noteworthy given Saudi Arabia's efforts to reduce its reliance on oil through Vision 2030. We further demonstrate that China's influence on the Saudi market increased significantly after the 2006 collapse, surpassing that of the UK. This is attributable to the substantial trade between China, Japan, and Saudi Arabia, as well as the rise in Saudi foreign direct investment in China, and the decline in such investment in the UK post-collapse. Our results carry important implications for stock market investors and policymakers alike. We suggest that policymakers in Saudi Arabia should continue to diversify their economy away from oil and strengthen economic ties with emerging markets, particularly China, to reduce their vulnerability to oil price fluctuations and ensure sustainable economic growth.</abstract><type>Journal Article</type><journal>Energy Economics</journal><volume>132</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0140-9883</issnPrint><issnElectronic/><keywords>Oil prices; Global stock markets; Saudi stock market; Machine learning; Neural networks</keywords><publishedDay>1</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-04-01</publishedDate><doi>10.1016/j.eneco.2024.107416</doi><url/><notes/><college>COLLEGE NANME</college><department>Accounting and Finance</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BAF</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>The authors received no financial support for the research, authorship, and/or publication of this article.</funders><projectreference/><lastEdited>2024-03-26T10:14:06.9988434</lastEdited><Created>2024-02-25T14:22:50.0477611</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Accounting and Finance</level></path><authors><author><firstname>Hussein A.</firstname><surname>Abdou</surname><orcid>0000-0001-5580-1276</orcid><order>1</order></author><author><firstname>Ahmed A.</firstname><surname>Elamer</surname><orcid>0000-0002-9241-9081</orcid><order>2</order></author><author><firstname>Mohammad</firstname><surname>Abedin</surname><order>3</order></author><author><firstname>Bassam A.</firstname><surname>Ibrahim</surname><order>4</order></author></authors><documents><document><filename>65694__29846__627e2eed841348cf896904938d00c6e2.pdf</filename><originalFilename>65694_VOR.pdf</originalFilename><uploaded>2024-03-26T10:10:48.2835261</uploaded><type>Output</type><contentLength>5465783</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2024 The Authors. This is an open access article under the CC BY license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling	v2 65694 2024-02-25 The impact of oil and global markets on Saudi stock market predictability: A machine learning approach 4ed8c020eae0c9bec4f5d9495d86d415 Mohammad Abedin Mohammad Abedin true false 2024-02-25 BAF This study investigates the predictability power of oil prices and six international stock markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock market using five Machine Learning (ML) techniques and the Generalized Method of Moments (GMM). Our analysis reveals that prior to the 2006 collapse, oil exerted the least influence on the Saudi market, while the UK and Japan were the most influential stock markets. However, after the collapse, oil became the most influential factor, highlighting the strong dependence of Saudi Arabia's economic structure on oil production. This finding is particularly noteworthy given Saudi Arabia's efforts to reduce its reliance on oil through Vision 2030. We further demonstrate that China's influence on the Saudi market increased significantly after the 2006 collapse, surpassing that of the UK. This is attributable to the substantial trade between China, Japan, and Saudi Arabia, as well as the rise in Saudi foreign direct investment in China, and the decline in such investment in the UK post-collapse. Our results carry important implications for stock market investors and policymakers alike. We suggest that policymakers in Saudi Arabia should continue to diversify their economy away from oil and strengthen economic ties with emerging markets, particularly China, to reduce their vulnerability to oil price fluctuations and ensure sustainable economic growth. Journal Article Energy Economics 132 Elsevier BV 0140-9883 Oil prices; Global stock markets; Saudi stock market; Machine learning; Neural networks 1 4 2024 2024-04-01 10.1016/j.eneco.2024.107416 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University Another institution paid the OA fee The authors received no financial support for the research, authorship, and/or publication of this article. 2024-03-26T10:14:06.9988434 2024-02-25T14:22:50.0477611 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Hussein A. Abdou 0000-0001-5580-1276 1 Ahmed A. Elamer 0000-0002-9241-9081 2 Mohammad Abedin 3 Bassam A. Ibrahim 4 65694__29846__627e2eed841348cf896904938d00c6e2.pdf 65694_VOR.pdf 2024-03-26T10:10:48.2835261 Output 5465783 application/pdf Version of Record true © 2024 The Authors. This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title	The impact of oil and global markets on Saudi stock market predictability: A machine learning approach
spellingShingle	The impact of oil and global markets on Saudi stock market predictability: A machine learning approach Mohammad Abedin
title_short	The impact of oil and global markets on Saudi stock market predictability: A machine learning approach
title_full	The impact of oil and global markets on Saudi stock market predictability: A machine learning approach
title_fullStr	The impact of oil and global markets on Saudi stock market predictability: A machine learning approach
title_full_unstemmed	The impact of oil and global markets on Saudi stock market predictability: A machine learning approach
title_sort	The impact of oil and global markets on Saudi stock market predictability: A machine learning approach
author_id_str_mv	4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv	4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author	Mohammad Abedin
author2	Hussein A. Abdou Ahmed A. Elamer Mohammad Abedin Bassam A. Ibrahim
format	Journal article
container_title	Energy Economics
container_volume	132
publishDate	2024
institution	Swansea University
issn	0140-9883
doi_str_mv	10.1016/j.eneco.2024.107416
publisher	Elsevier BV
college_str	Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id	facultyofhumanitiesandsocialsciences
hierarchy_top_title	Faculty of Humanities and Social Sciences
hierarchy_parent_id	facultyofhumanitiesandsocialsciences
hierarchy_parent_title	Faculty of Humanities and Social Sciences
department_str	School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str	1
active_str	0
description	This study investigates the predictability power of oil prices and six international stock markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock market using five Machine Learning (ML) techniques and the Generalized Method of Moments (GMM). Our analysis reveals that prior to the 2006 collapse, oil exerted the least influence on the Saudi market, while the UK and Japan were the most influential stock markets. However, after the collapse, oil became the most influential factor, highlighting the strong dependence of Saudi Arabia's economic structure on oil production. This finding is particularly noteworthy given Saudi Arabia's efforts to reduce its reliance on oil through Vision 2030. We further demonstrate that China's influence on the Saudi market increased significantly after the 2006 collapse, surpassing that of the UK. This is attributable to the substantial trade between China, Japan, and Saudi Arabia, as well as the rise in Saudi foreign direct investment in China, and the decline in such investment in the UK post-collapse. Our results carry important implications for stock market investors and policymakers alike. We suggest that policymakers in Saudi Arabia should continue to diversify their economy away from oil and strengthen economic ties with emerging markets, particularly China, to reduce their vulnerability to oil price fluctuations and ensure sustainable economic growth.
published_date	2024-04-01T10:14:04Z
_version_	1794583344467410944
score	11.017797

The impact of oil and global markets on Saudi stock market predictability: A machine learning approach

Similar Items