Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, and Backtesting XGBoost Models

HASSAN OUKHOUYA; HAMZA KADIRI; KHALID  EL HIMDI; RABY GUERBAZ

doi:10.19139/soic-2310-5070-1822

HASSAN OUKHOUYA Laboratory LMSA, Department of Mathematics, Faculty of Sciences, Mohammed V University of Rabat
HAMZA KADIRI Laboratory MAEGE, Department of SMAEG, FSJES Ain Sebaa, Hassan II University of Casablanca, Morocco https://orcid.org/0009-0000-2278-4190
KHALID EL HIMDI Laboratory LMSA, Department of Mathematics, Faculty of Sciences, Mohammed V University of Rabat, Morocco
RABY GUERBAZ Laboratory MAEGE, Department of SMAEG, FSJES Ain Sebaa, Hassan II University of Casablanca, Morocco

DOI: https://doi.org/10.19139/soic-2310-5070-1822

Keywords: Time series, Modeling, Forecasting, Stock market, LSTM, XGBoost, Hybrid model, Backtesting, Grid Search

Abstract

Forecasting time series is crucial for financial research and decision-making in business. The nonlinearity of stock market prices profoundly impacts global economic and financial sectors. This study focuses on modeling and forecasting the daily prices of key stock indices - MASI, CAC 40, DAX, FTSE 250, NASDAQ, and HKEX, representing the Moroccan, French, German, British, US, and Hong Kong markets, respectively. We compare the performance of machine learning models, including Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost), and the hybrid LSTM-XGBoost, and utilize the skforecast library for backtesting. Results show that the hybrid LSTM-XGBoost model, optimized using Grid Search (GS), outperforms other models, achieving high accuracy in forecasting daily prices. This contribution offers financial analysts and investors valuable insights, facilitating informed decision-making through precise forecasts of international stock prices.

Author Biographies

HAMZA KADIRI, Laboratory MAEGE, Department of SMAEG, FSJES Ain Sebaa, Hassan II University of Casablanca, Morocco

Master's candidate specializing in Actuarial Science and Finance at the University of Hassan II of Casablanca. My expertise lies in statistical analysis, Big Data, Python, Machine Learning, Deep Learning, and Time Series Forecasting. My committed to advancing research and making significant contributions in these fields.

KHALID EL HIMDI, Laboratory LMSA, Department of Mathematics, Faculty of Sciences, Mohammed V University of Rabat, Morocco

RABY GUERBAZ, Laboratory MAEGE, Department of SMAEG, FSJES Ain Sebaa, Hassan II University of Casablanca, Morocco

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