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Predicting stock market index prices using Facebook Prophet and XGBoost: evidence from the Saudi stock market

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Listed:
  • KHLEDJ meryem

    (University of Tamanrasset (Algeria))

  • MANSOURI hadjmoussa

    (University of Tamanrasset (Algeria))

  • Borgi Hela

    (Princess Nourah bint Abdulrahman University (Saudi Arabia))

Abstract

This study investigates the effectiveness of two machine learning models—Facebook Prophet and XGBoost—in predicting the Saudi Stock Market Index (TASI). The study relied on a daily series of prices during the post-COVID-19 recovery period. The results reveal that there is a difference in prediction accuracy, as the XGBoost model outperformed the Facebook Prophet model in various accuracy criteria (MSE, RMSE, and MAPE). The study concludes that integrating the strengths of both models can enhance stock index forecasting accuracy, providing valuable insights for financial analysts and policymakers.

Suggested Citation

Handle: RePEc:bjm:ijep00:v:8:y:2025:i:02:id:389
DOI: 10.54241/2065-008-002-017
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