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

Hussein A. Abdou Orcid Logo, Ahmed A. Elamer Orcid Logo, Mohammad Abedin, Bassam A. Ibrahim

Energy Economics, Volume: 132

Swansea University Author: Mohammad Abedin

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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...

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Published in: Energy Economics
ISSN: 0140-9883
Published: Elsevier BV 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65694
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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.
Keywords: Oil prices; Global stock markets; Saudi stock market; Machine learning; Neural networks
College: Faculty of Humanities and Social Sciences
Funders: The authors received no financial support for the research, authorship, and/or publication of this article.