Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market

Hassan Oukhouya · Khalid El Himdi

49 citations0/0 files storedInternational Online Conference on Mathematics and Applications (IOCMA)

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

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.

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2017A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring 2020A CNN-LSTM-Based Model to Forecast Stock Prices 2020Deep learning for financial applications : A survey 2019Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data 2021A graph-based CNN-LSTM stock price prediction algorithm with leading indicators 2023Machine learning approaches in stock market prediction: A systematic literature review 2022Forecasting stock market for an efficient portfolio by combining XGBoost and Hilbert–Huang transform 2022Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning

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