Constrained portfolio selection model at considering risk-adjusted measure by using the Genetic Network Programming
محورهای موضوعی : Design of Experiment
iman bavarsad salehpoor
1
(Industrial Engineering Department, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran)
کلید واژه: Portfolio optimization, GNP, Iran Stock Exchange, efficient Boundary Points,
چکیده مقاله :
This article present a new Decision making method of Stock portfolio optimization issues in different risk Sizes by Using Evolutionary computing mode which is mentioned genetic network programming (GNP).Then; its Ability compared to the Ability of the mean–variance model in efficient Boundary Points of Optimal constraints. Based on mean–variance Method by Markowitz we collected Three Risk Levels; mean absolute deviation (MAD), semi variance (SV) and variance with skewness (VWS). It is showed that these stock portfolio optimization issues with four risk sizes able to solve genetic network programming. The Sustainability of this proposed model is verified by 50 Iranian factories mentioned on the Stock Exchange. Finally, genetic network programming (GNP) compared with genetic algorithm (GA) both with and without cardinality constraint. Results demonstrated that GNP has a more efficient frontier than GA.
This article present a new Decision making method of Stock portfolio optimization issues in different risk Sizes by Using Evolutionary computing mode which is mentioned genetic network programming (GNP).Then; its Ability compared to the Ability of the mean–variance model in efficient Boundary Points of Optimal constraints. Based on mean–variance Method by Markowitz we collected Three Risk Levels; mean absolute deviation (MAD), semi variance (SV) and variance with skewness (VWS). It is showed that these stock portfolio optimization issues with four risk sizes able to solve genetic network programming. The Sustainability of this proposed model is verified by 50 Iranian factories mentioned on the Stock Exchange. Finally, genetic network programming (GNP) compared with genetic algorithm (GA) both with and without cardinality constraint. Results demonstrated that GNP has a more efficient frontier than GA.
