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A precise prediction of stock prices is crucial for making well-informed trading choices and maximizing investment returns. Additionally, accurate pre-dictions assist investors in minimizing risks and building a solid portfolio. However, the complex nature of stocks, characterized by nonlinearity and Volatility, presents significant challenges. This research focuses on predicting the opening prices of the Moroccan All Shares Index (MASI) over different time periods (7, 14, and 25 days) using four algorithms: Decision Tree, Linear Regression, LSTM (long short-term memory), and Facebook Prophet, evaluated using MSE MAE, and MAPE as performance metrics.

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A Comparative Analysis of Machine Learning Algorithms in Stock Prediction: A Case Study of Moroccan All Shares Index

  • 会议论文

  • 发表日期：

    2025年

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  • Mohamed Abijoue,
  • Mohammed Benkhalifa,
  • Hajar El Hannach

摘要

A precise prediction of stock prices is crucial for making well-informed trading choices and maximizing investment returns. Additionally, accurate pre-dictions assist investors in minimizing risks and building a solid portfolio. However, the complex nature of stocks, characterized by nonlinearity and Volatility, presents significant challenges. This research focuses on predicting the opening prices of the Moroccan All Shares Index (MASI) over different time periods (7, 14, and 25 days) using four algorithms: Decision Tree, Linear Regression, LSTM (long short-term memory), and Facebook Prophet, evaluated using MSE MAE, and MAPE as performance metrics.