Machine Learning for Stock Price Prediction on the Casablanca Stock Exchange: A Comparative Study of ANN and LSTM Approaches

Abstract

Capital markets play a fundamental role in the economy by facilitating the flow of funds between investors with capital surpluses and those with financing needs. However, these markets' inherent complexity and high volatility—amplified by economic crises and geopolitical events—make decision-making particularly challenging. In this context, artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has become increasingly relevant for modeling complex financial time series such as stock prices. Among various learning approaches, Long Short-Term Memory (LSTM) networks stand out for their ability to capture long-term dependencies in sequential data.

This study compares the predictive performance of LSTM and Artificial Neural Networks (ANN) models, on ten stocks comprising the MADEX index of the Casablanca Stock Exchange, across three forecasting horizons (10, 20, and 30 days). Results demonstrate that the LSTM model consistently outperforms the ANN model in terms of accuracy and trend detection. For instance, over a 30-day horizon, the LSTM correctly predicted 8 out of 10 stocks, compared to only 4 for the ANN. This work is part of a broader research effort aimed at identifying the most effective model for stock price forecasting. Building on the results of this and previous studies, particularly those involving LSTM models optimized using genetic algorithms, future research will explore other models such as Gated Recurrent Units (GRU) and Support Vector Machines (SVM) to further enhance prediction accuracy and robustness.