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Comparative Study of Deep Learning Models for Predicting Stock Prices

Duong Van-Thang, Pham Thi-Thu-Hang, Nguyen Duc-Tuan-Anh, Nguyen Van-Hau, Le Van-Quoc Anh

Annals of Computer Science and Information Systems

2022

Vol. 33

103--108

The stock market is volatile, dynamic, and nonlinear. Hence, predicting the stock prices has been a challenging task for any researcher in time series forecasting. Accurately predicting stock prices has been a hot topic for both financial and technical researchers. In this paper, we deploy six deep learning models (i.e., MLP, CNN, RNN, LSTM, GRU, and AE) to predict the closing price, one day ahead, of 20 different companies (i.e. 5 groups of 4) in the S&P 500 markets over the 7-years range (Jan 2015 - August 2022). The experimental results do not provide interesting insights, but also help us to deepen our understanding of how to use deep learning models in financial markets.