Affiliation:
1. 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
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.
Reference35 articles.
1. Volatility transmissions across international oil market, commodity futures and stock markets: Empirical evidence from China;Ahmed;Energy Economics,2021
2. Azzam, Henry T. (2015). The Emerging Middle East Financial Markets, AuthorHouse.
3. Determinants of the variation in the liquidity behavior of the casablanca stock exchange: A global econometric analysis on time series;Baali;Finance & Finance Internationale,2023
4. Evaluating multiple classifiers for stock price direction prediction;Ballings;Expert Systems with Applications,2015
5. Bao, Wei, Yue, Jun, and Rao, Yulei (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE, 12.