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Article

Deep Learning for Stock Market Prediction

1
Faculty of Mechanical Engineering, Tarbiat Modares University, Tehran 14115-143, Iran
2
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 1439956153, Iran
3
Department of Economics, Payame Noor University, West Tehran Branch, Tehran 1455643183, Iran
4
Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
5
Department of Informatics, J. Selye University, 94501 Komarno, Slovakia
6
Institute of IR4.0, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
7
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Authors to whom correspondence should be addressed.
Received: 23 June 2020 / Revised: 27 July 2020 / Accepted: 28 July 2020 / Published: 30 July 2020
(This article belongs to the Special Issue Information Transfer in Multilayer/Deep Architectures)
The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost. View Full-Text
Keywords: stock market prediction; machine learning; regression analysis; tree-based methods; deep learning; long short-term memory; LSTM; business intelligence; finance; stock market; financial forecast; information economics; economics; information science stock market prediction; machine learning; regression analysis; tree-based methods; deep learning; long short-term memory; LSTM; business intelligence; finance; stock market; financial forecast; information economics; economics; information science
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MDPI and ACS Style

Nabipour, M.; Nayyeri, P.; Jabani, H.; Mosavi, A.; Salwana, E.; S., S. Deep Learning for Stock Market Prediction. Entropy 2020, 22, 840. https://0-doi-org.brum.beds.ac.uk/10.3390/e22080840

AMA Style

Nabipour M, Nayyeri P, Jabani H, Mosavi A, Salwana E, S. S. Deep Learning for Stock Market Prediction. Entropy. 2020; 22(8):840. https://0-doi-org.brum.beds.ac.uk/10.3390/e22080840

Chicago/Turabian Style

Nabipour, M., P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and Shahab S.. 2020. "Deep Learning for Stock Market Prediction" Entropy 22, no. 8: 840. https://0-doi-org.brum.beds.ac.uk/10.3390/e22080840

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