Examining the Efficiency Models, Conditional Value at Risk and Mean Absolute Deviation and Particle Swarm Optimization Algorithm under CVAR and MAD risk criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange

Subject Areas : Financial engineering

dariuosh adinehvand ¹ , Ebrahim ali Razini Rahmani ² , Mahmod khoddam ³ , Fereydon Ohadi ⁴ , alhamsadat hashemizadeh ⁵

1 - Department of accounting, Karaj Branch, Islamic Azad University, Karaj, Iran.
2 - Department of Management, Karaj Branch, Islamic Azad University, Karaj, Iran.
3 - Department of industrial Management, Karaj Branch, Islamic Azad University, Karaj, Iran.
4 - Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
5 - Department of Mathematics, Karaj Branch, ,Islamic Azad University, Karaj, Iran

Received: 2021-11-17 Accepted : 2021-12-29 Published : 2022-09-23

Keywords: particle swarm optimization, Optimization, Conditional Value at Risk and Mean Absolute Deviation,

Abstract :

Choosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days. Then and their portfolio risk is calculated using the models of absolute deviation risk and conditional value at risk, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the following, the deviation - absolute risk model and conditional value at risk model of metaheuristic method using MATLAB (R2019) software are compared. The results show that the PSO model of metaheuristic method compared to the classical method in solving portfolio optimization problem showed more return in PSO-MAD criteria and therefore it is a better method to solve such portfolio optimization problems.