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Despite the appealing nature of the stock market, forecasting stock prices remains a complex endeavor owing to its fast-paced and fluctuating volatility. Effectively forecasting the fluctuation of stock prices has the potential to mitigate the risk associated with stock investments and enhance the overall investment yield. In this research, we combine the advantages of XGBoost for feature selection with the autoregressive integrated moving average (ARIMAX) time series model for forecasting to improve the accuracy of predicting next-day stock prices. A dual important features selection approach is proposed to extract key features for the ARIMAX model from a pool of 87 technical indicators. To demonstrate the effectiveness of this method, we compared it with four other methods \u2013 long-short term memory, genetics algorithms with long-short term memory, XGBoost, and Meta Prophet \u2013 in predicting the next day\u2019s closing price of the Vietnam stock index from January 2013 to April 2023. The results indicate that the performance of our method is better than others and suitable for traders to make stock investment decisions.<\/jats:p>","DOI":"10.1515\/jisys-2024-0101","type":"journal-article","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T05:27:47Z","timestamp":1740806867000},"source":"Crossref","is-referenced-by-count":3,"title":["Stock price prediction based on dual important indicators using ARIMAX: A case study in Vietnam"],"prefix":"10.1515","volume":"34","author":[{"given":"Pai-Chou","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Information Management, Southern Taiwan University of Science and Technology , Tainan , 710301, Taiwan , Republic of China"}]},{"given":"Tram Thi Hoai","family":"Vo","sequence":"additional","affiliation":[{"name":"College of Business, Southern Taiwan University of Science and Technology , Tainan , 710301, Taiwan , Republic of China"}]}],"member":"374","published-online":{"date-parts":[[2025,3,1]]},"reference":[{"key":"2025122009032190890_j_jisys-2024-0101_ref_001","unstructured":"Fama EF. 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