Assessing the Predictive Power of Transformers, ARIMA, and LSTM in Forecasting Stock Prices of Moroccan Credit Companies

Karima Lahboub () and Mimoun Benali
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Karima Lahboub: Laboratory of Research and Studies in Management, Entrepreneurship and Finance (LAREMEF), Nation School of Commerce and Management of Fez, Sidi Mohamed Ben Abdellah University, Fes 30050, Morocco
Mimoun Benali: Laboratory of Research and Studies in Management, Entrepreneurship and Finance (LAREMEF), Nation School of Commerce and Management of Fez, Sidi Mohamed Ben Abdellah University, Fes 30050, Morocco

JRFM, 2024, vol. 17, issue 7, 1-16

Abstract: In this paper, we present a data-driven approach to forecasting stock prices in the Moroccan Stock Exchange. Our study tests three predictive models: ARIMA, LSTM, and transformers, applied to the historical stock price data of three prominent credit companies (EQD, LES, and SLF) listed on the Casablanca Stock Exchange. We carefully selected and optimized hyperparameters for each model to achieve optimal performance. Our results showed that the LSTM model achieved high accuracy, with R-squared values exceeding 0.99 for EQD and LES and surpassing 0.95 for SLF. These findings highlighted the effectiveness of LSTM in stock price forecasting. Our study offers practical insights for traders and investors in the Moroccan Stock Exchange, demonstrating how predictive modeling can aid in making informed decisions. This research contributes to advancing stock market forecasting in Morocco, providing valuable tools for navigating the Casablanca Stock Exchange.

Keywords: stock market; Moroccan Stock Exchange; machine learning; LSTM; transformer (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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