Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem

► We model a multi-objective portfolio optimization problem. ► Particle Swarm Optimization (PSO) algorithm has been used to solve the problem. ► The PSO model is tested on various risk investment portfolios. ► The results show that the PSO model are more effective than GAs and VBA solvers. One of th...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications Vol. 38; no. 8; pp. 10161 - 10169
Main Authors: Zhu, Hanhong, Wang, Yi, Wang, Kesheng, Chen, Yun
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2011
Subjects:
Computational efficiency
Constraints
Expert system
Expert systems
Financing
Investment
Optimal portfolio
Optimization
Particle Swarm Optimization (PSO)
Portfolio management (PM)
Sharp Ratio (SR)
Solvers
Swarm Intelligence (SI)
Portfolio management (PM)
Optimal portfolio
Expert system
Swarm Intelligence (SI)
Particle Swarm Optimization (PSO)
Sharp Ratio (SR)
ISSN:0957-4174, 1873-6793
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:► We model a multi-objective portfolio optimization problem. ► Particle Swarm Optimization (PSO) algorithm has been used to solve the problem. ► The PSO model is tested on various risk investment portfolios. ► The results show that the PSO model are more effective than GAs and VBA solvers. One of the most studied problems in the financial investment expert system is the intractability of portfolios. The non-linear constrained portfolio optimization problem with multi-objective functions cannot be efficiently solved using traditionally approaches. This paper presents a meta-heuristic approach to portfolio optimization problem using Particle Swarm Optimization (PSO) technique. The model is tested on various restricted and unrestricted risky investment portfolios and a comparative study with Genetic Algorithms is implemented. The PSO model demonstrates high computational efficiency in constructing optimal risky portfolios. Preliminary results show that the approach is very promising and achieves results comparable or superior with the state of the art solvers.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2011.02.075