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Using Quantum-Behaved Particle Swarm Optimization for Portfolio Selection Problem

2010
Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Saeed Farzi, Alireza Rayati Shavazi, Abbas Rezaei Pandari, IAJIT
Dibuat : 2012-06-23, dengan 1 file

Keyword : Swarm Algorithm, Portfolio Selection, Genetic Algorithms, Risk, Return.
Subjek : Using Quantum-Behaved Particle Swarm Optimization for Portfolio Selection Problem
Url : http://www.ccis2k.org/iajit/PDF/vol.10,no.2/7-2761.pdf
Sumber pengambilan dokumen : Internet

One of the popular methods for optimizing combinational problems such as portfolio selection problem is swarm-


based methods. In this paper, we have proposed an approach based on Quantum-Behaved Particle Swarm Optimization


(QPSO) for the portfolio selection problem. The particle swarm optimization (PSO) is a well-known population-based swarm


intelligence algorithm. QPSO is also proposed by combining the classical PSO philosophy and quantum mechanics to improve


performance of PSO. Generally, investors, in portfolio selection, simultaneously consider such contradictory objectives as the


rate of return, risk and liquidity. We employed Quantum-Behaved Particle Swarm Optimization (QPSO) model to select the


best portfolio in 50 supreme Tehran Stock Exchange companies in order to optimize the objectives of the rate of return,


systematic and non-systematic risks, return skewness, liquidity and sharp ratio. Finally, the obtained results were compared


with Markowitz`s classic and Genetic Algorithms (GA) models indicated that although return of the portfolio of QPSO model


was less that that in Markowitz’s classic model, the QPSO had basically some advantages in decreasing risk in the sense that


it completely covers the rate of return and leads to better results and proposes more versatility portfolios in compared with the


other models. Therefore, we could conclude that as far as selection of the best portfolio is concerned, QPSO model can lead to


better results and may help the investors to make the best portfolio selection

Deskripsi Alternatif :

One of the popular methods for optimizing combinational problems such as portfolio selection problem is swarm-


based methods. In this paper, we have proposed an approach based on Quantum-Behaved Particle Swarm Optimization


(QPSO) for the portfolio selection problem. The particle swarm optimization (PSO) is a well-known population-based swarm


intelligence algorithm. QPSO is also proposed by combining the classical PSO philosophy and quantum mechanics to improve


performance of PSO. Generally, investors, in portfolio selection, simultaneously consider such contradictory objectives as the


rate of return, risk and liquidity. We employed Quantum-Behaved Particle Swarm Optimization (QPSO) model to select the


best portfolio in 50 supreme Tehran Stock Exchange companies in order to optimize the objectives of the rate of return,


systematic and non-systematic risks, return skewness, liquidity and sharp ratio. Finally, the obtained results were compared


with Markowitz`s classic and Genetic Algorithms (GA) models indicated that although return of the portfolio of QPSO model


was less that that in Markowitz’s classic model, the QPSO had basically some advantages in decreasing risk in the sense that


it completely covers the rate of return and leads to better results and proposes more versatility portfolios in compared with the


other models. Therefore, we could conclude that as far as selection of the best portfolio is concerned, QPSO model can lead to


better results and may help the investors to make the best portfolio selection

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