Abstract
The direction of the stock market is always complex, stochastic, and highly volatile. In addition to traditional forecasting models such as linear regression and Automatic Regression Integrated Moving Average (ARIMA) models, analysts are now trying to apply modern deep learning models to predict trends direction of the stock market to achieve more accurate forecasting. In this conducting research, we have investigated and applied the state-of-the-art deep learning sequential model, namely the Stacked Long Short-Term Memory Model (Stacked LSTM) to the prediction of stock prices the next day. The experimental result on three benchmark datasets: stocks of Apple Inc. (AAPL), stocks of An Phat Bioplastic JSC (AAA), and stocks of Bank of Foreign Trade of Vietnam (VCB) has shown the effectiveness of the predictive model. Furthermore, we discovered that the suitable quantity of hidden layers is two, and when we continue to increase the quantity of hidden layers to three or four, the Stacked LSTM model does not improve the predictive power, even though it has a more complex model structure.
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This work was funded by Hanoi University of Mining and Geology under grant number 65/QD-MDC.
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Tuan, N.T., Nguyen, T.H., Duong, T.T.H. (2022). Stock Price Prediction in Vietnam Using Stacked LSTM. In: Nguyen, NT., Dao, NN., Pham, QD., Le, H.A. (eds) Intelligence of Things: Technologies and Applications . ICIT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 148. Springer, Cham. https://doi.org/10.1007/978-3-031-15063-0_23
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