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<p>Predicting stock market trends is crucial for investors and policymakers, particularly in emerging markets like the Middle East and North Africa (MENA) region, where research on stock market prediction remains relatively scarce. This study provides a comparative analysis of several advanced forecasting models, including Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and HistGradientBoosting (HGB), to evaluate their effectiveness in predicting stock market movements in the region. We utilized the Skforecast library to implement the ARIMAX model and applied the Grid Search algorithm for hyperparameter tuning of SVR and the XGBoost family of models. Model performance was assessed using a comprehensive dataset and a suite of metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Mean Square Error (NMSE), R-squared (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\text {R}^2\)</EquationSource> </InlineEquation>), Theil’s U-statistic, Directional Symmetry (DS), and Weighted Directional Symmetry (WDS). Additionally, we analyzed the interpretability of the XGBoost model using Shapley Additive exPlanations (SHAP) and evaluated overall model performance with a Taylor diagram. Our findings indicate that both ARIMAX and SVR outperform the other models in terms of predictive accuracy and reliability. However, SVR emerges as the best model, achieving the lowest MAE and RMSE, the highest <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\text {R}^2\)</EquationSource> </InlineEquation>, and gives the most favorable results for the NMSE, DS, WDS, and Theil’s U statistic. This study highlights the superior performance of SVR for forecasting stock market trends in the MENA region, providing valuable insights for financial analysts and investors.</p>

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Forecasting stock markets in the MENA region: ARIMAX and ensemble machine learning models with SHAP interpretability

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • Hassan Oukhouya,
  • Fatima Ezzahra Lfaze,
  • Raby Guerbaz,
  • Khalid Belkhoutout,
  • Aziz Lmakri,
  • Mohamed Fihri,
  • Lalitha Krishnasamy,
  • Abdellatif El Afia

摘要

Predicting stock market trends is crucial for investors and policymakers, particularly in emerging markets like the Middle East and North Africa (MENA) region, where research on stock market prediction remains relatively scarce. This study provides a comparative analysis of several advanced forecasting models, including Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and HistGradientBoosting (HGB), to evaluate their effectiveness in predicting stock market movements in the region. We utilized the Skforecast library to implement the ARIMAX model and applied the Grid Search algorithm for hyperparameter tuning of SVR and the XGBoost family of models. Model performance was assessed using a comprehensive dataset and a suite of metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Mean Square Error (NMSE), R-squared ( \(\text {R}^2\) ), Theil’s U-statistic, Directional Symmetry (DS), and Weighted Directional Symmetry (WDS). Additionally, we analyzed the interpretability of the XGBoost model using Shapley Additive exPlanations (SHAP) and evaluated overall model performance with a Taylor diagram. Our findings indicate that both ARIMAX and SVR outperform the other models in terms of predictive accuracy and reliability. However, SVR emerges as the best model, achieving the lowest MAE and RMSE, the highest \(\text {R}^2\) , and gives the most favorable results for the NMSE, DS, WDS, and Theil’s U statistic. This study highlights the superior performance of SVR for forecasting stock market trends in the MENA region, providing valuable insights for financial analysts and investors.