Multiobjective optimization using differential evolution for real-world portfolio optimization

Portfolio optimization is an important aspect of decision-support in investment management. Realistic portfolio optimization, in contrast to simplistic mean-variance optimization, is a challenging problem, because it requires to determine a set of optimal solutions with respect to multiple objective...

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Bibliographic Details
Published in:Computational management science Vol. 8; no. 1-2; pp. 157 - 179
Main Authors: Krink, Thiemo, Paterlini, Sandra
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.04.2011
Springer
Springer Nature B.V
Subjects:
Asset allocation
Business and Management
Evolution
Evolutionary algorithms
Financing
Genetic algorithms
Heuristic
Investment advisors
Linear programming
Management
Management science
Mathematical models
Operations Research/Decision Theory
Optimization
Original Paper
Pareto optimum
Portfolio management
Quadratic programming
Searching
Simulated annealing
Studies
Portfolio optimization
Multiobjective
Real-world constraints
Differential evolution
Value-at-Risk
ISSN:1619-697X, 1619-6988
Online Access:Get full text
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Summary:Portfolio optimization is an important aspect of decision-support in investment management. Realistic portfolio optimization, in contrast to simplistic mean-variance optimization, is a challenging problem, because it requires to determine a set of optimal solutions with respect to multiple objectives, where the objective functions are often multimodal and non-smooth. Moreover, the objectives are subject to various constraints of which many are typically non-linear and discontinuous. Conventional optimization methods, such as quadratic programming, cannot cope with these realistic problem properties. A valuable alternative are stochastic search heuristics, such as simulated annealing or evolutionary algorithms. We propose a new multiobjective evolutionary algorithm for portfolio optimization, which we call DEMPO—Differential Evolution for Multiobjective Portfolio Optimization. In our experimentation, we compare DEMPO with quadratic programming and another well-known evolutionary algorithm for multiobjective optimization called NSGA-II. The main advantage of DEMPO is its ability to tackle a portfolio optimization task without simplifications, while obtaining very satisfying results in reasonable runtime.
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ISSN:1619-697X
1619-6988
DOI:10.1007/s10287-009-0107-6