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A surrogate similarity measure for the mean-variance frontier optimization problem under bound and cardinality constraints

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A surrogate similarity measure for the mean-variance frontier optimization problem under bound and cardinality constraints

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Guijarro, F.; Tsinaslanidis, PE. (2021). A surrogate similarity measure for the mean-variance frontier optimization problem under bound and cardinality constraints. Journal of the Operational Research Society. 72(3):564-579. https://doi.org/10.1080/01605682.2019.1657367

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/164819

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Title: A surrogate similarity measure for the mean-variance frontier optimization problem under bound and cardinality constraints
Author: Guijarro, Francisco Tsinaslanidis, Prodromos E.
UPV Unit: Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials
Issued date:
Abstract:
[EN] This article deals with the mean-variance optimisation frontier problem when realistic constraints are considered. Our proposed methodology hybridises a heuristic algorithm with an exact solution approach. A genetic ...[+]
Subjects: Portfolio optimisation , Cardinality constraint , Bound constraint , Genetic algorithm
Copyrigths: Reserva de todos los derechos
Source:
Journal of the Operational Research Society. (issn: 0160-5682 )
DOI: 10.1080/01605682.2019.1657367
Publisher:
Nature Publishing Group - Macmillan Publishers
Publisher version: https://doi.org/10.1080/01605682.2019.1657367
Type: Artículo

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