Next Article in Journal
The NIRS Brain AnalyzIR Toolbox
Previous Article in Journal
Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population
Article Menu

Export Article

Open AccessArticle
Algorithms 2018, 11(5), 72; https://doi.org/10.3390/a11050072

Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

1
College of Information Science & Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China
2
School of Economic and Management, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China
3
State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China
*
Author to whom correspondence should be addressed.
Received: 11 April 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 15 May 2018
(This article belongs to the Special Issue Algorithms in Computational Finance)
View Full-Text   |   Download PDF [417 KB, uploaded 15 May 2018]   |  

Abstract

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. View Full-Text
Keywords: portfolio optimization; gray wolf optimization; second-order stochastic dominance; risk; constraint portfolio optimization; gray wolf optimization; second-order stochastic dominance; risk; constraint
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ren, Y.; Ye, T.; Huang, M.; Feng, S. Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization. Algorithms 2018, 11, 72.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top