Version 1
: Received: 15 March 2020 / Approved: 16 March 2020 / Online: 16 March 2020 (01:45:16 CET)
How to cite:
Nabipour, M.; Nayyeri, P.; Jabani, H.; Shamshirband, S.; Mosavi, A. Deep Learning for Stock Market Prediction. Preprints2020, 2020030256. https://doi.org/10.20944/preprints202003.0256.v1
Nabipour, M.; Nayyeri, P.; Jabani, H.; Shamshirband, S.; Mosavi, A. Deep Learning for Stock Market Prediction. Preprints 2020, 2020030256. https://doi.org/10.20944/preprints202003.0256.v1
Nabipour, M.; Nayyeri, P.; Jabani, H.; Shamshirband, S.; Mosavi, A. Deep Learning for Stock Market Prediction. Preprints2020, 2020030256. https://doi.org/10.20944/preprints202003.0256.v1
APA Style
Nabipour, M., Nayyeri, P., Jabani, H., Shamshirband, S., & Mosavi, A. (2020). Deep Learning for Stock Market Prediction. Preprints. https://doi.org/10.20944/preprints202003.0256.v1
Chicago/Turabian Style
Nabipour, M., Shahab Shamshirband and Amir Mosavi. 2020 "Deep Learning for Stock Market Prediction" Preprints. https://doi.org/10.20944/preprints202003.0256.v1
Abstract
Prediction of stock groups values has always been attractive and challenging for shareholders. 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 are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms 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 network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree_based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
