UAE Stock Markets Prediction: Machine Learning Application

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.