Abstract
Predicting stock prices accurately is a key goal of investors in the stock market. Unfortunately, stock prices are constantly changing and affected by many factors, making the process of predicting them a challenging task. This paper describes a method to build models for predicting stock prices using long short-term memory network (LSTM). The LSTM-based model, which we call dynamic LSTM, is initially built and continuously retrained using newly augmented data to predict future stock prices. We evaluate the proposed method using data sets of four stocks. The results show that the proposed method outperforms others in predicting stock prices based on different performance metrics.
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Nguyen, D.H.D., Tran, L.P., Nguyen, V. (2019). Predicting Stock Prices Using Dynamic LSTM Models. In: Florez, H., Leon, M., Diaz-Nafria, J., Belli, S. (eds) Applied Informatics. ICAI 2019. Communications in Computer and Information Science, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-32475-9_15
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DOI: https://doi.org/10.1007/978-3-030-32475-9_15
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