Abstract

Emerging markets, particularly in Africa, present unique challenges for portfolio optimization due to increased volatility and limited historical data. This study explores the potential of deep learning models, specifically Long Short-Term Memory (LSTM), Deep Multilayer Perceptron (DMLP), and Convolutional Neural Networks (CNN), to predict the movements of three key African stock indices: MASI (Morocco), BRVM Composite (West Africa) and TUNINDEX (Tunisia). By comparing the performance of these architectures, this research aims to identify the models most suitable for prediction in a the context of limited data and high volatility. The results of this study will provide crucial insights for investors, portfolio managers, and researchers, thereby contributing to the development of more robust and effective portfolio optimization strategies for African markets, often overlooked by traditional methods.