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A Comparative Study of Machine Learning Models for Forecasting the Stock Exchange of Thailand (SET) Index Based on Technical Analysis

ผศ.ดร.นิกร กรรณิกากลาง | Stock Forecasting Machine Learning Indicator Analysis

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A Comparative Study of Machine Learning Models for Forecasting the Stock Exchange of Thailand (SET) Index Based on Technical Analysis

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Collection	RMUTK Research Repository (RMUTK IR) บทความประชุมวิชาการ — Proceedings
ID RMUTK Digital	RMUTK000116
Title	A Comparative Study of Machine Learning Models for Forecasting the Stock Exchange of Thailand (SET) Index Based on Technical Analysis
Alternative title	A Comparative Study of Machine Learning Models for Forecasting the Stock Exchange of Thailand (SET) Index Based on Technical Analysis
Authors	ผศ.ดร.นิกร กรรณิกากลาง ผู้แต่งหลัก
Faculty	คณะบริหารธุรกิจ
Volume / Issue / Pages	pp.506-511
Conference	2025 9th International Conference on Information Technology (InCIT)
Conference Date	2025-11-12
Conference Location	Phuket, Thailand
Year	2568
Level	ระดับนานาชาติ
DOI	10.1109/InCIT66780.2025.11276123
ISBN / ISSN	978-1-6654-7749-9
Funding Source	Nakhon Phanom University
Abstract	Accurate stock forecasting in the Thai capital market remains challenging due to high volatility and pronounced non -linear dynamics in financial time-series. This study conducts a comparative evaluation of machine learning (ML) models for one-day-ahead prediction of daily closing prices of stocks listed on the Stock Exchange of Thailand (SET). Two widely used technical indicators-Simple Moving Average (SMA) and Exponential Moving Average (EMA)-are employed as input features to capture trend and momentum. Five years of daily data (2019–2023) for three large-cap stocks (PTT. BK, BBL.BK, and CP ALL.BK) are used to assess model robustness under multiple market conditions. We examine three train-test splits (70:30, 80:20, 90: 10) to evaluate stability across data regimes. Experiments show that Linear Regression consistently outperforms competing approaches, achieving the highest coefficient of determination (R2) of 0.9758, and producing one-day-ahead forecasts closely aligned with actual closes for PTT.BK, BBL.BK, and CPALL.BK. The findings indicate that a systematic pipeline grounded in carefully selected technical indicators can provide an effective framework for stock-trend analysis and support investment decision-making in the volatile Thai equity market.
Abstract (EN)	Accurate stock forecasting in the Thai capital market remains challenging due to high volatility and pronounced non -linear dynamics in financial time-series. This study conducts a comparative evaluation of machine learning (ML) models for one-day-ahead prediction of daily closing prices of stocks listed on the Stock Exchange of Thailand (SET). Two widely used technical indicators-Simple Moving Average (SMA) and Exponential Moving Average (EMA)-are employed as input features to capture trend and momentum. Five years of daily data (2019–2023) for three large-cap stocks (PTT. BK, BBL.BK, and CP ALL.BK) are used to assess model robustness under multiple market conditions. We examine three train-test splits (70:30, 80:20, 90: 10) to evaluate stability across data regimes. Experiments show that Linear Regression consistently outperforms competing approaches, achieving the highest coefficient of determination (R2) of 0.9758, and producing one-day-ahead forecasts closely aligned with actual closes for PTT.BK, BBL.BK, and CPALL.BK. The findings indicate that a systematic pipeline grounded in carefully selected technical indicators can provide an effective framework for stock-trend analysis and support investment decision-making in the volatile Thai equity market.
Keywords (TH)	Stock Forecasting Machine Learning Indicator Analysis Technical Indicators SET Index
Keywords (EN)	Stock Forecasting Machine Learning Indicator Analysis Technical Indicators SET Index
Access Level	Open Access (เปิดสาธารณะ)

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