Buying and selling strategy in the Iranian stock market using machine learning models, with feature selection using the Cuckoo Search algorithm

Document Type : Original Article

Authors

Faculty of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran

Abstract

In Iran, the stock market is facing different conditions compared to the rest of the world. One of the most important challenges in this market is the lack of transparency in market information and information on trading companies. Also, the lack of appropriate and complete historical data for use in forecasting algorithms is another important challenge. In stock price forecasting, due to the dynamic interactions of the stock market and price changes in short periods of time, the use of artificial intelligence is used as a powerful tool in price forecasting and decisions related to buying and selling stocks. In this paper, a method based on machine learning, including five steps (including data labeling, feature extraction, feature selection, classification, and signal presentation), is presented. For this purpose, various technical characteristics have been extracted from the price data, and the data has been labeled using the threshold labeling method. Then, various machine learning models are trained on this data and provide buy and sell signals at the output. To improve the performance of the machine learning model, feature selection has been done using the Cuckoo Search algorithm. In order to evaluate the proposed method, several years of Iranian stock market data and various indices have been used. The results of the evaluation show the effectiveness of the proposed method.

Keywords

Main Subjects

References

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Soft Computing Journal
Volume 12, Issue 2 - Serial Number 24
February 2024
Pages 130-145

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History

  • Receive Date: 24 April 2023
  • Revise Date: 06 July 2023
  • Accept Date: 26 July 2023

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