Stock price prediction based on dual important indicators using ARIMAX: A case study in Vietnam
Vietnam’s stock market is characterized as a frontier market and focuses on emerging market status by 2025. Tisco Advisory’s report showed that Vietnam’s stock market is expecting to draw 4 billion in foreign capital in 2024. Despite the appealing nature of the stock market, forecasting stock prices...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-03-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2024-0101 |
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| Summary: | Vietnam’s stock market is characterized as a frontier market and focuses on emerging market status by 2025. Tisco Advisory’s report showed that Vietnam’s stock market is expecting to draw 4 billion in foreign capital in 2024. 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 – long-short term memory, genetics algorithms with long-short term memory, XGBoost, and Meta Prophet – in predicting the next day’s 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. |
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| ISSN: | 2191-026X |