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Provide a stock price forecasting model using deep learning algorithms and its use in the pricing of Islamic bank stocks
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Mehran Saeidi aghdam   1, Ahmad Sadeghi2 , Alireza Bahiraie3 , Seyed yousef Haji asghari4 |
1- Islamic Azad University Qazvin , mehransaedi@gmail.com
2- Assistant Professor of Shahid Beheshti University
3- Assistant Professor of Semnan University
4- Assistant Professor f Mianeh, Islamic Azad University |
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Abstract: (1434 Views) |
Predicting stock prices is complicated; various components, such as the general state of the economy, political events, and investor expectations, affect the stock market. The stock market is in fact a chaotic nonlinear system that depends on various political, economic and psychological factors. To overcome the limitations of traditional analysis techniques in predicting nonlinear patterns, experts over the last two decades have used intelligent techniques, especially networking. They have used artificial neural networks and genetic algorithms to improve stock price forecasting. This study, considering the increasing development of forecasting methods in financial markets and also, since stock price is one of the most important factors influencing investment decisions and its forecasting can play an important role in this field, In this research, an attempt has been made to provide a model based on which the movement of the stock price can be predicted with high accuracy. Accordingly, a hybrid model for predicting stock price movement using artificial neural network is presented. For the statistical sample, the top companies of the stock exchange in the second quarter of 1399 have been selected. Then for each purpose, 32 variables were calculated. These variables are the input of the model and are optimized using the artificial neural network algorithm. The results show that the model performs much better in predicting the movement of stock prices and has a higher accuracy compared to traditional methods. |
Article number: 5 |
| Keywords: Prediction, Stock Price, Nervous Network, Deep Learning |
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Full-Text [PDF 980 kb]
(1044 Downloads)
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Article type: Research |
Subject:
Special
Received: 2020/08/2 | Accepted: 2021/01/28 | Published: 2023/03/1
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