UAE Stock Markets Prediction: Machine Learning Application

Randa A. Abdelkarim, Yousif Abdelbagi Abdalla () and Ibrahim Abaker Hashem ()
Additional contact information
Randa A. Abdelkarim: University of Khartoum
Yousif Abdelbagi Abdalla: University of Sharjah
Ibrahim Abaker Hashem: University of Sharjah

Chapter Chapter 10 in Business Analytics and Decision Making in Practice, 2024, pp 109-118 from Springer

Abstract: Abstract The Effective prediction of stock returns holds immense significance in guiding investment strategies, bolstering risk management protocols, and shaping financial policies. This paper endeavors to explore the relationship and predictive prowess encompassing the stock markets of the Gulf Cooperation Council (GCC) nations (Bahrain, Kuwait, Oman, Qatar, and Saudi Arabia), China, as well as oil and gold prices, concerning their impact on the United Arab Emirates (UAE) stock markets. Employing a robust multiple regression technique, this study analyses weekly data spanning from June 2010 to June 2023. The utilization of the multiple regression methodology not only facilitates the evaluation of GCC countries and China stock markets but also casts a spotlight on their respective contributions to the variance observed in the performance of the UAE stock market. Through a meticulous analysis, this study determines interesting patterns. When dissecting the stock markets in Dubai and Abu Dhabi separately, the outcomes distinctly indicate a distinct dependence on Qatar, Oman, Bahrain, and Kuwait in both markets. Notably, Dubai exhibits a discernible dependence on the Chinese and Saudi Arabian markets, surpassing that of Abu Dhabi. In the backdrop of this investigation, it emerges that the variables encompassing oil and gold do not wield statistically significant impact. A few studies are solely concerned with the UAE markets, but to our best knowledge, none of them explores the relationship between all the previously listed factors and the UAE markets. This study collectively unveils a comprehensive understanding of the intricate dynamics governing stock market interactions, presenting invaluable insights for investors, risk managers, and policy formulators.

Keywords: GCC stock market; China; Multiple regression; Machine learning (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-61589-4_10

Ordering information: This item can be ordered from
http://www.springer.com/9783031615894

DOI: 10.1007/978-3-031-61589-4_10

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().