Predicting Corporate Profitability in Morocco: Comparing Classical Regression and Machine Learning

Abstract

To the best of our knowledge, this study provides the first systematic comparison between classical regression and advanced machine learning models for predicting the profitability of Moroccan firms listed on the Casablanca Stock Exchange. While prior research has largely focused on developed markets, profitability prediction in emerging economies such as Morocco remains underexplored, despite the market’s structural particularities (sectoral concentration, reliance on bank financing, and limited disclosure practices). This article provides the first systematic comparative analysis between regression and machine learning approaches applied to Moroccan listed companies, highlighting the advantages and limitations of each method in capturing complex and non-linear financial dynamics. Using a dataset covering ten years of financial statements, we evaluate multiple models, including OLS, Ridge regression, Random Forest, Gradient Boosting, Support Vector Regression, KNN, and XGBoost. Results show that machine learning models consistently outperform regression in predictive accuracy, while regression retains value in interpretability. Findings contribute to academic research by extending profitability forecasting studies to an under-explored emerging market, and to practice by offering investors, policymakers, and managers tools that improve risk assessment, capital allocation, and decision-making under conditions of uncertainty. These implications are particularly relevant for emerging economies, where informational asymmetries and structural heterogeneity complicate financial forecasting.