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Predicting Dividend Payouts in Moroccan Firms Using AI With Emphasis on Transparency
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Author(s): Mohamed-Amine El Khayati (Cadi Ayyad University, Morocco)and Charaf Saidi (Cadi Ayyad University, Morocco)
Copyright: 2026
Pages: 52
Source title:
Transparency in AI-Assisted Management Decisions
Source Author(s)/Editor(s): Abdelfattah Jamal (Cadi Ayyad University, Morocco), Karima Aissaoui (Mohammed First University, Morocco), Lhoussaine Alla (Sidi Mohamed Ben Abdellah University, Morocco), Bouchra Alj (Hassan II University, Morocco)and Badr Bentalha (Sidi Mohammed Ben Abdellah University, Morocco)
DOI: 10.4018/979-8-3373-1737-3.ch006
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Abstract
This chapter explores the prediction of dividend payout ratios among Moroccan listed companies by applying Random Forest Regression combined with Explainable Artificial Intelligence (XAI) techniques. Focusing on firm-specific financial indicators and macroeconomic variables over the period 2013–2023, the study aims to identify key drivers of dividend policy in an emerging market context. The analysis demonstrates that profitability, firm size, and historical payout behavior are the most influential predictors, while macroeconomic indicators such as inflation, GDP, and interest rates show limited impact. SHAP values are employed to enhance transparency, allowing for robust interpretation of model outputs. This chapter contributes to financial modeling, corporate governance, and machine learning literature by offering an interpretable AI-based framework for dividend forecasting tailored to the Moroccan market. The findings provide actionable insights for investors, managers, and policymakers seeking to understand or optimize dividend strategies in emerging economies.
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