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- Title
Forecasting International Stock Market Trends: XGBoost, LSTM, LSTM-XGBoost, And Backtesting XGBoost Models.
- Authors
Oukhouya, Hassan; Kadiri, Hamza; El Himdi, Khalid; Guerbaz, Raby
- Abstract
Forecasting time series is crucial for financial research and decision-making in business. The non-linearity of stock market prices has a profound impact on 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.
- Subjects
HONG Kong (China); NASDAQ Stock Market; STOCK Exchange of Hong Kong Ltd.; MACHINE learning; STOCK price indexes; STOCK price forecasting; ECONOMIC trends; INTERNATIONAL markets; FORECASTING
- Publication
Statistics, Optimization & Information Computing, 2024, Vol 12, Issue 1, p200
- ISSN
2311-004X
- Publication type
Article
- DOI
10.19139/soic-2310-5070-1822