Earticle

초록

영어

This paper investigates the performance of ARIMAX-based stock price forecasting models on highmarketcapitalization technology stocks: Apple Inc. (AAPL), Microsoft Corp. (MSFT), and Alphabet Inc. (GOOGL), listed on NYSE and NASDAQ. These stocks were selected due to their substantial impact on both the technology sector and the broader market. Utilizing historical data, we developed three distinct versions of ARIMAX models. Model performance was assessed using key metrics such as Root Mean Square Error (RMSE) and daily direction forecast accuracy. Our results show that the ARIMAX model with a minimal feature set, referred to as Mode 1, generally produced the lowest RMSE values for these specific stocks, indicating superior predictive accuracy. However, while some versions of the ARIMAX model demonstrated promise in predicting the daily direction of stock prices, their performance varied substantially across the evaluated stocks. This inconsistency suggests that further research is needed to identify a universally optimal ARIMAX model for predicting stock price direction. It should be noted that these models were not validated on stocks from other industries or those with different market capitalizations, limiting their generalizability. Additionally, as the models are based on historical data, caution is advised when applying them to predict future stock movements.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Auto Regressive Integrated Moving Average (ARIMA)
B. ARIMAX
III. METHODOLOGY
A. Data Collection
B. Data Preparation
C. Modeling and Evaluation
IV. EXPERIMENT & RESULT
V. CONCLUSION
REFERENCES

저자

• Chatchitsanu Pothisakha [ Department of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]
• Naruapon Suwanwijit [ Department of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]
• Khachon Mongkonchoo [ Department of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]
• Maleerat Maliyaem [ Department of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok, Thailand ]

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