Intraday Multi-timeframe Prediction of the Thailand Stock Market Index Futures
คำสำคัญ:
Intraday Multi-timeframe Prediction, Thailand Stock Market, Index Futures, Machine Learning, XGBoost, LSTMบทคัดย่อ
This quantitative research study investigates the intraday multi-timeframe prediction of SET50 Index Futures prices in the Thailand stock market, employing advanced machine learning techniques within the context of management science. The research objectives are to enhance predictive accuracy and improve strategic decision-making for futures trading by integrating technical indicators across multiple timeframes. The study focuses on the SET50 Index Futures, initiated by the Thailand Futures Exchange (TFEX) in 2006, recognizing these contracts as critical tools for price discovery and risk management. The research scope encompasses the application and comparison of two machine learning models: eXtreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM). These models are utilized to analyze technical indicators derived from multiple intraday timeframes of the SET50 Index Futures data. The study employs a comprehensive set of technical indicators and conducts extensive experiments to evaluate the models' effectiveness in various timeframe configurations. The research results reveal that XGBoost consistently outperforms LSTM, particularly in multi-timeframe configurations. This finding underscores the importance of multi-timeframe analysis in effective risk management and strategic planning for futures trading. The superior performance of XGBoost in processing complex, multi-dimensional data offers valuable insights for managers and researchers in optimizing futures trading strategies and improving operational efficiency in the context of the Thailand stock market.
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