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Open AccessArticle

2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization

1
Department of Financial Engineering, Raja University of Qazvin, Qazvin 341451177, Iran
2
Department of Information Engineering, Electronics and Telecommunications, University of Rome Sapienza, Via Eudossiana 18, 00184 Rome, Italy
3
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
4
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Authors to whom correspondence should be addressed.
Algorithms 2017, 10(4), 130; https://doi.org/10.3390/a10040130
Received: 17 October 2017 / Revised: 17 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA). In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm. View Full-Text
Keywords: multi-objective optimization; portfolio selection; Evolutionary Algorithm; NSGA II; 2-phase NSGA II multi-objective optimization; portfolio selection; Evolutionary Algorithm; NSGA II; 2-phase NSGA II
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MDPI and ACS Style

Eftekharian, S.E.; Shojafar, M.; Shamshirband, S. 2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization. Algorithms 2017, 10, 130.

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