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toraby pour, T., siadat, S. (2023). A way to predict the stock price of the Tehran Stock Exchange in relation to knowledge. Electronic and Cyber Defense, 10(4), 91-100.
tuba toraby pour; safieh siadat. "A way to predict the stock price of the Tehran Stock Exchange in relation to knowledge". Electronic and Cyber Defense, 10, 4, 2023, 91-100.
toraby pour, T., siadat, S. (2023). 'A way to predict the stock price of the Tehran Stock Exchange in relation to knowledge', Electronic and Cyber Defense, 10(4), pp. 91-100.
toraby pour, T., siadat, S. A way to predict the stock price of the Tehran Stock Exchange in relation to knowledge. Electronic and Cyber Defense, 2023; 10(4): 91-100.

A way to predict the stock price of the Tehran Stock Exchange in relation to knowledge

پدافند الکترونیکی و سایبری
Article 10, Volume 10, Issue 4 - Serial Number 40, January 2023, Pages 91-100    PDF (1.28 M)
Document Type: Original Article
Authors
tuba toraby pour1; safieh siadat* 2
1Master's degree, Payam Noor University, Tehran, Iran
2Assistant Professor, Payam Noor University, Tehran, Iran
Receive Date: 05 May 2022,  Revise Date: 29 May 2022,  Accept Date: 09 August 2022 
Abstract
In recent years, due to the profitability of the stock market in Iran, small and large investments were attracted to this market, but unfortunately, due to their lack of knowledge of the stock market and price forecasting, a large number of Iranians suffered great losses. In this study, we decided to use our previous research, which used a two-layer LSTM neural network, to strengthen its work and use a combination of convolution and lstm neural networks to predict stock prices on the Web Nation data set from the stock market. Use Tehran and its three databases, including ASP, car and construction. Finally, in order to evaluate the proposed method and the other two methods, three error functions, mean square error function (MSE), mean absolute error function (MAE) and root mean square function (RMSE) were evaluated. The results showed that it works much better in large datasets with high stock data and leads to fewer errors.
Keywords
Convolution neural network; stock price; LSTM neural network; deep learning
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