Oil price movements predictions in Kingdom of Saudi Arabia using financial and macro-economic variables

By combining financial and macroeconomic factors with machine learning techniques, this research paper proposes a novel method for forecasting oil price movements in the Kingdom of Saudi Arabia (KSA). Traditional methods generally struggle to capture the complex dynamics and nonlinear linkages in the oil market, which makes accurate oil price forecasting vital for decision-making in numerous sectors. In this paper, we offer a machine learning framework that leverages financial elements like stock market indices, currency rates, and interest rates, as well as macroeconomic data like GDP growth, inflation rates, and energy consumption, as predictors of oil price movements. These factors were chosen because of their significance and importance to the ways in which the oil market in KSA functions. We use several different machine learning techniques to construct the prediction models, some of which are regression-based (such as linear regression or support vector regression) while others are ensemble models (such as random forests or gradient boosting). The models are tested and refined using historical data spanning a sizable period of time and covering a wide range of market circumstances and pricing movements. Evaluation of the prediction models is carried out using conventional metrics like mean-squared error, mean absolute error, and R-squared [Formula: see text], and their robustness is evaluated using sensitivity analysis and cross-validation methods. Incorporating financial and macroeconomic variables vastly enhances the accuracy of oil price predictions compared to models based purely on historical price data, as shown by the preliminary findings, which underline the superiority of our approach. The machine learning models exhibit nonlinear pattern capture and responsiveness to market fluctuations. Insights into the relative significance of various variables and their effect on oil price movements in KSA are also provided by the sensitivity analysis. By demonstrating the usefulness of machine learning methods for oil price forecasting, particularly in the context of Saudi Arabia, this research adds to the body of knowledge already available. Our findings have important policy and market implications for oil price forecasting, benefiting policymakers, energy market participants, and investors. To improve the models’ ability to forecast the future, researchers may want to consider including more variables in their analyses, such as geopolitical developments and technology advances.