SELECTION AND PORTFOLIO OPTIMIZATION BY GENETIC ALGORITHMS USING THE MEAN SEMI-VARIANCE MARKOWITZ MODEL

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PAKMARAM ASGAR; BAHRI SALES JAMAL; VALIZADEH MOSTAFA

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Journal: FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT) Year:2017 | Volume:8 | Issue:31 Page(s): 19-42

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Title

SELECTION AND PORTFOLIO OPTIMIZATION BY GENETIC ALGORITHMS USING THE MEAN SEMI-VARIANCE MARKOWITZ MODEL

Author(s)

PAKMARAM ASGAR | BAHRI SALES JAMAL | VALIZADEH MOSTAFA | Issue Writer Certificate

Keywords

GENETIC ALGORITHMSQ2

PARTICLE SWARM ALGORITHMSQ2

CULTURAL ALGORITHMSQ1

Abstract

One of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other Words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society.In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this.In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk.Since the objective function of GENETIC ALGORITHMS was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of these algorithms in the selection of the optimal portfolio.

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APA: Copy

PAKMARAM, ASGAR, BAHRI SALES, JAMAL, & VALIZADEH, MOSTAFA. (2017). SELECTION AND PORTFOLIO OPTIMIZATION BY GENETIC ALGORITHMS USING THE MEAN SEMI-VARIANCE MARKOWITZ MODEL. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), 8(31 ), 19-42. SID. https://sid.ir/paper/197723/en

Vancouver: Copy

PAKMARAM ASGAR, BAHRI SALES JAMAL, VALIZADEH MOSTAFA. SELECTION AND PORTFOLIO OPTIMIZATION BY GENETIC ALGORITHMS USING THE MEAN SEMI-VARIANCE MARKOWITZ MODEL. FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)[Internet]. 2017;8(31 ):19-42. Available from: https://sid.ir/paper/197723/en

IEEE: Copy

ASGAR PAKMARAM, JAMAL BAHRI SALES, and MOSTAFA VALIZADEH, “SELECTION AND PORTFOLIO OPTIMIZATION BY GENETIC ALGORITHMS USING THE MEAN SEMI-VARIANCE MARKOWITZ MODEL,” FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT), vol. 8, no. 31 , pp. 19–42, 2017, [Online]. Available: https://sid.ir/paper/197723/en

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