Machine Learning-Based Forecasting of Sukuk Index Movements: Evidence from Turkey and Malaysia

Islamic finance has rapidly expanded into a major component of global financial systems, positioning Sukuk as a core instrument for Sharia-compliant investment and capital raising. Despite growing academic attention to Sukuk pricing and valuation, to the best of our knowledge, no prior study has systematically classified the directional movements of Sukuk index prices using machine learning techniques. This study addresses that gap by developing predictive classification models for the directional (upward or downward) movements of Sukuk indices and applying them to both Turkey and Malaysia. It represents one of the first systematic attempts to forecast Sukuk index direction and the first application of machine learning-based directional forecasting to the Turkish Sukuk market. Using a diverse set of financial and macroeconomic indicators, we employ advanced machine learning algorithms including extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and support vector machines (SVMs), and further enhance prediction accuracy through ensemble methods such as majority voting, weighted averaging, and soft voting. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The findings indicate that SVM consistently delivers the strongest standalone performance across both markets, while ensemble methods generate substantial improvements in Malaysia. Overall, predictive performance is higher in Malaysia, which may be associated with its more stable and liquid Sukuk market environment compared to Turkey.