Performance evaluation of technical indicators for forecasting the moroccan stock index using deep learning

Abstract

Navigating the complex terrain of financial markets requires accurate forecasting tools, underscoring the need for effective forecasting methods to assist investors and policymakers alike. This paper explores deep learning techniques for forecasting the Moroccan all shares index (MASI), a prominent indicator of the Moroccan stock market. The study aims to evaluate the performance of technical indicators in enhancing the accuracy of MASI predictions. A comprehensive dataset of daily closing prices of the MASI index is collected and 26 technical indicators are computed from the historical price data. Deep learning models based on artificial neural networks (ANNs) are trained and optimized using the dataset. The performance of the models is evaluated using standard metrics such as mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Additionally, feature selection techniques are employed to identify the subset of technical indicators that contribute most significantly to the prediction accuracy. The findings provide insights into the effectiveness of deep learning models and the impact of technical indicators on MASI prediction accuracy. This research has important implications for investors, financial analysts, and policymakers, enhancing investment strategies and risk management approaches.