Comparing Machine Learning Methods - SVR, XGBoost, LSTM, CNN-LSTM, and MLP - in Forecasting the Moroccan Stock Market

Forecasting and modeling time series data is a crucial aspect of financial research for academics and business practitioners. The volatility of stock market returns impacts different economic and financial sectors worldwide. 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, stock price 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 aim, we propose a comparative study between the results obtained from applying the following machine learning methods: Support Vector Regression (SVR), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Convolutional-LSTM (CNN-LSTM), and Multilayer Perceptron (MLP) models. The results show that the LSTM and SVR models perform better than the other models and achieve high forecasting accuracy for daily prices.