The Iranian stock market operates under unique conditions in comparison to other global stock markets. Transparency of market information and trading company data is a significant challenge. Additionally, a lack of complete historical data hinders the development of forecasting algorithms. Given the dynamic nature of stock market interactions and rapid changes in pricing, artificial intelligence serves as a powerful tool for stock price prediction and decision-making related to stock buying and selling. Machine learning algorithms are commonly used for stock price prediction. This article extracts various technical features from price data and labels the data using the threshold labeling method. Several machine learning models are trained on this data to provide buy and sell signals. To enhance model performance, the cuckoo search algorithm is employed for feature selection. The model is then evaluated using established evaluation criteria and the confusion matrix.
Saberi, E., Radmand, E., Pirgazi, J., & Kermani, A. (2023). Buying and selling strategy in the Iranian stock market using machine learning models along with feature selection using the Cuckoo Search algorithm. Soft Computing Journal, (), -. doi: 10.22052/scj.2023.252793.1144
MLA
Erfan Saberi; Elnaz Radmand; Jamshid Pirgazi; Ali Kermani. "Buying and selling strategy in the Iranian stock market using machine learning models along with feature selection using the Cuckoo Search algorithm". Soft Computing Journal, , , 2023, -. doi: 10.22052/scj.2023.252793.1144
HARVARD
Saberi, E., Radmand, E., Pirgazi, J., Kermani, A. (2023). 'Buying and selling strategy in the Iranian stock market using machine learning models along with feature selection using the Cuckoo Search algorithm', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2023.252793.1144
VANCOUVER
Saberi, E., Radmand, E., Pirgazi, J., Kermani, A. Buying and selling strategy in the Iranian stock market using machine learning models along with feature selection using the Cuckoo Search algorithm. Soft Computing Journal, 2023; (): -. doi: 10.22052/scj.2023.252793.1144
)