Hybrid AI Approaches for Stock Market Prediction: Evidence from The Moroccan Stock Exchange - Boulaksili , Abdelhamid; Jeddou , Elmehdi; Tamiri , Mohamed Anis; Hmid , Amine; Yousra , El Hajel; Mawi , Taha; Nabil , Soufiane (International Journal of Accounting and Economics Studies, 2025)
The prediction of stock market dynamics remains a central challenge in financial economics due to the complexity and volatility of financial time series. Traditional econometric approaches, while useful, struggle to capture nonlinear patterns and long-term dependencies inherent in stock market behavior. Recent advances in artificial intelligence (AI), particularly in deep learning and ensemble learning, offer promising alternatives for improving predictive accuracy and robustness. This study revisits the Moroccan stock market, building upon prior research that tested neural network architectures such as MLP, RNN, CNN, and LSTM. Using daily data from the MASI index and seven sectoral indices from 2017 to 2024, we propose a hybrid methodology combining the Temporal Fusion Transformer (TFT) with gradient boosting models (XGBoost and LightGBM) in a stacking ensemble. The results demonstrate that hybrid models outperform standalone deep learning architectures, offering more reliable forecasts and improved economic backtesting performance. Our findings highlight the potential of probabilistic AI models to enhance financial decision-making and risk management in emerging markets.