Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Presenting an Intelligent Stock Price Prediction Model based on Deep Learning in Tehran Stock Exchange Market

Document Type : Research Paper

Authors
1 PhD student of Information Technology Management, Department of Information Technology Management, Faculty of Management and Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 Assistant professor,Department of Information Technology Management, Faculty of Management and Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 Assistant professor,Department Economics, Faculty of Management and Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Abstract
Forecasting stock prices and returns is one of the most complicated and controversial topics in financial markets. Stock market is constantly influenced by the state of the national economy, investors' perceptions, and political events. Furthermore, the price series is highly non-linear and unstable. Ongoing research and updates in economic and stock market theories have gradually revealed the components necessary for predicting stock price indices, making accurate predictions possible. This research aims to develop an intelligent stock price prediction model based on deep learning for the Tehran Stock Exchange market. This model incorporates dimensionality reduction techniques to manage the capital portfolio, thereby increasing returns and reducing investment risks. The data from 2020 to 2023 were sourced from the Kodal system and were coded and analyzed using the RISP method and the Python programming language. A combination of LSTM, PCA, GRP, and SVD algorithms was used for the proposed model. A comparison of dimensionality reduction methods with artificial intelligence techniques shows that the PCA dimensionality reduction method can enhance the performance of deep learning compared to other data dimensionality reduction methods.
Keywords
Subjects

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Volume 15, Issue 3 - Serial Number 3
Summer 2023
Pages 160-178

  • Receive Date 08 March 2023
  • Revise Date 19 May 2023
  • Accept Date 20 June 2023