Abstract
The stock market is an attractive channel for many investment funds. The stock indices of several largest capitalized companies are the key indicators of the status of the economy. The VN 30 Index in Vietnam is calculated from the top 30 enterprises with the largest capitalization and liquidity. Forecasting for these market indices is always a stubborn challenge but rewarding. With recent research, many univariate models including LSTM, and GRU are proposed to achieve great performance in extracting temporal trends. However, the stock indices are affected by many factors that univariate is not able to represent all the information. Therefore, this paper studies multivariate time series with different approaches from statistical models, and machine learning regression to deep learning models to predict the VN 30 Index with the support from multivariate time series of the largest and most influenced stocks in the current Vietnam stock market.
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Nguyen, N.T., Vo, H.Q., Nguyen, N.M., Nguyen, T.D. (2023). Apply Multivariate Time Series Approaches for Forecasting Vietnam Index 30. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_8
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