Forecasting stock prices in the capital market with an artificial intelligence approach
Subject Areas :
Asgar Noorbakhsh
1
,
Mostafa Shaygani
2
1 - Assistant Professor, Department of Financial Management, Faculty of Management and Accounting, Farabi Campus, University of Tehran, Qom, Iran
2 - Master of Financial Management, Faculty of Management and Accounting, Farabi Colleges of Tehran University, Qom, Iran
Keywords: Artificial Neural Network, Short-Term persistent Memory, Convolutional Neural Network, Price Prediction,
Abstract :
The main objective of this research is to predict stock prices using two types of neural networks in the Tehran Stock Exchange. Python language in the VS Code editor has been used to conduct this research. The statistical population of this research is the Tehran Stock Exchange. The sample size of this research includes data from three symbols of the Tehran Stock Exchange, namely Iran Khodro, Alborz Darou, and Iran Zinc Mines Development. In this research, eight price features are used within the daily timeframe from 2001 to 2021, including the highest price, lowest price, closing price, opening price, transaction value, transaction volume, the difference in closing price between two consecutive days, and daily return. Three metrics of mean absolute error, root mean square error, and coefficient of determination have been used to evaluate the models' performance. The results indicate that the convolutional neural network model has the ability to predict with good accuracy. Recurrent neural networks are among the best types of networks for price prediction, but the results show that the convolutional neural network has performed better than the short-term memory neural network. The results suggest that deep learning models, when selecting features (independent variables) that can express the highest level of significance in interpreting the causes of price fluctuations during market booms and recessions, have the ability and capability to predict prices with acceptable accuracy.
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