Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization
In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints...
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doaj-c4990d243af7442db46e3efdd61c8cd02020-11-24T23:55:28ZengMDPI AGAlgorithms1999-48932018-05-011157210.3390/a11050072a11050072Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio OptimizationYixuan Ren0Tao Ye1Mengxing Huang2Siling Feng3College of Information Science & Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, ChinaSchool of Economic and Management, Hainan University, No. 58 Renmin Avenue, Haikou 570228, ChinaCollege of Information Science & Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, ChinaCollege of Information Science & Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, ChinaIn the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf’s social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization.http://www.mdpi.com/1999-4893/11/5/72portfolio optimizationgray wolf optimizationsecond-order stochastic dominanceriskconstraint |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yixuan Ren Tao Ye Mengxing Huang Siling Feng |
spellingShingle |
Yixuan Ren Tao Ye Mengxing Huang Siling Feng Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization Algorithms portfolio optimization gray wolf optimization second-order stochastic dominance risk constraint |
author_facet |
Yixuan Ren Tao Ye Mengxing Huang Siling Feng |
author_sort |
Yixuan Ren |
title |
Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization |
title_short |
Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization |
title_full |
Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization |
title_fullStr |
Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization |
title_full_unstemmed |
Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization |
title_sort |
gray wolf optimization algorithm for multi-constraints second-order stochastic dominance portfolio optimization |
publisher |
MDPI AG |
series |
Algorithms |
issn |
1999-4893 |
publishDate |
2018-05-01 |
description |
In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf’s social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization. |
topic |
portfolio optimization gray wolf optimization second-order stochastic dominance risk constraint |
url |
http://www.mdpi.com/1999-4893/11/5/72 |
work_keys_str_mv |
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