Examining the Efficiency Models, Markowitz Mean–Semi Varianc, VaR and multi-objective Genetic Algorithm (NSGAII) under MSV and CVAR risk criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange
Quarterly Journal of Securities Exchange

Quarterly Journal of Securities Exchange

Examining the Efficiency Models, Markowitz Mean–Semi Varianc, VaR and multi-objective Genetic Algorithm (NSGAII) under MSV and CVAR risk criterion in Selection Optimal Portfolio Shares Listed Firms on Stock Exchange

Document Type : Original Article

Authors
dariuosh adinehvand 1
Ebrahim Ali Razini 2
Mahmod khoddam 3
Fereydoun Ohadi 4
Elham hashemizadeh 5
1 Ph.D. Student, Department of Accounting, Karaj Branch, Islamic Azad University, Karaj, Iran
2 Assistant Professor, Department of Management, Islamic Azad University, Karaj, Iran
3 Assistant Professor, Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj, Iran
4 Assistant Professor, Department of Industrial Engineering, Technical-Engineering, Islamic Azad University, Karaj, Iran
5 Assistant Professor, Department of Mathematical, Karaj Branch, Islamic Azad University, Karaj, Iran
10.22034/jse.2022.11826.1876
Abstract
Choosing the optimal stock portfolio is an important issue in the economy. 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 mean–semi variance 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 mean–semi variance risk model and conditional value at risk model of metaheuristic method using MATLAB (R2019) software are compared. The results showed that the metaheuristic method of genetic algorithm compared to the classical method of mean–semi variance and conditional risk value of deviation in solving the problem of portfolio optimization had higher returns and lower risk under the risk criterion of mean–semi variance and therefore a better method for solve optimization problems.
Keywords
Conditional Value at Risk
genetic algorithm
mean–semi variance
optimization
stock portfolio
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Quarterly Journal of Securities Exchange
Volume 16, Issue 61
Spring 2023
Pages 159-180