Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market
Year: 2023
Type: conference-paper
Abstract: Forecasting and modeling time series is a crucial aspect of economic research for academics and business practitioners. The ability to predict the direction of stock prices is vital for creating an investment plan or determining the optimal time to make a trade. However, market movements can be complex to predict, non-linear, and chaotic, making it difficult to forecast their evolution. In this paper, we investigate modeling and forecasting the daily prices of the new Morocco Stock Index 20 (MSI 20). To this end, we propose a comparative study between the results obtained from the application of the various Machine Learning (ML) methods: Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM) models. The results show that using the Grid Search (GS) optimization algorithm, the SVR and MLP models outperform the other models and achieve high accuracy in forecasting daily prices.
Authors: Hassan Oukhouya, Khalid El Himdi
Institution: Mohammed V University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4382582948