A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
Elsevier LtdFecha de edición
2015-11Cita
10.1016/j.asoc.2015.06.053
Applied Soft Computing 36 (2015): 125 – 142
ISSN
1568-4946DOI
10.1016/j.asoc.2015.06.053Versión del editor
http://dx.doi.org/10.1016/j.asoc.2015.06.053Materias
Combinatorial optimization; Genetic algorithms; Portfolio selection; Transaction costs; InformáticaNota
This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, Vol 36 (2015) DOI 10.1016/j.asoc.2015.06.053Derechos
© 2015 Elsevier B.V. All rights reservedEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Resumen
A memetic approach that combines a genetic algorithm (GA) and quadratic programming is used to address the problem of optimal portfolio selection with cardinality constraints and piecewise linear transaction costs. The framework used is an extension of the standard Markowitz mean–variance model that incorporates realistic constraints, such as upper and lower bounds for investment in individual assets and/or groups of assets, and minimum trading restrictions. The inclusion of constraints that limit the number of assets in the final portfolio and piecewise linear transaction costs transforms the selection of optimal portfolios into a mixed-integer quadratic problem, which cannot be solved by standard optimization techniques. We propose to use a genetic algorithm in which the candidate portfolios are encoded using a set representation to handle the combinatorial aspect of the optimization problem. Besides specifying which assets are included in the portfolio, this representation includes attributes that encode the trading operation (sell/hold/buy) performed when the portfolio is rebalanced. The results of this hybrid method are benchmarked against a range of investment strategies (passive management, the equally weighted portfolio, the minimum variance portfolio, optimal portfolios without cardinality constraints, ignoring transaction costs or obtained with L1 regularization) using publicly available data. The transaction costs and the cardinality constraints provide regularization mechanisms that generally improve the out-of-sample performance of the selected portfolios.
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Google Scholar:Ruiz-Torrubiano, Rubén
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Suárez González, Alberto
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