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Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

Comparative Study of Deep Learning Models for Predicting Stock Prices

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DOI: http://dx.doi.org/10.15439/2022R02

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 103108 ()

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Abstract. The stock market is volatile,dynamic, and nonlinear. Hence, predicting the stock prices has been a challenging task for any researcher in time seriesforecasting. 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.


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