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Algorithms 2017, 10(3), 100; https://doi.org/10.3390/a10030100

# Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

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School of economic and management, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China
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College of Information Science & Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China
*
Author to whom correspondence should be addressed.
Received: 7 August 2017 / Revised: 21 August 2017 / Accepted: 23 August 2017 / Published: 25 August 2017

# Abstract

The portfolio optimization problem is the central problem of modern economics and decision theory; there is the Mean-Variance Model and Stochastic Dominance Model for solving this problem. In this paper, based on the second order stochastic dominance constraints, we propose the improved biogeography-based optimization algorithm to optimize the portfolio, which we called $ε$ BBO. In order to test the computing power of $ε$ BBO, we carry out two numerical experiments in several kinds of constraints. In experiment 1, comparing the Stochastic Approximation (SA) method with the Level Function (LF) algorithm and Genetic Algorithm (GA), we get a similar optimal solution by $ε$ BBO in $[ 0 , 0 . 6 ]$ and $[ 0 , 1 ]$ constraints with the return of 1.174% and 1.178%. In $[ - 1 , 2 ]$ constraint, we get the optimal return of 1.3043% by $ε$ BBO, while the return of SA and LF is 1.23% and 1.26%. In experiment 2, we get the optimal return of 0.1325% and 0.3197% by $ε$ BBO in $[ 0 , 0 . 1 ]$ and $[ - 0 . 05 , 0 . 15 ]$ constraints. As a comparison, the return of FTSE100 Index portfolio is 0.0937%. The results prove that $ε$ BBO algorithm has great potential in the field of financial decision-making, it also shows that $ε$ BBO algorithm has a better performance in optimization problem. View Full-Text
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MDPI and ACS Style

Ye, T.; Yang, Z.; Feng, S. Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints. Algorithms 2017, 10, 100.

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

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