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Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics

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Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics

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García García, F.; Guijarro, F.; Oliver-Muncharaz, J. (2018). Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics. Neural Computing and Applications. 30(8):2625-2641. https://doi.org/10.1007/s00521-017-2882-2

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/121737

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Metadatos del ítem

Título: Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics
Autor: García García, Fernando Guijarro, Francisco Oliver-Muncharaz, Javier
Entidad UPV: Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials
Fecha difusión:
Fecha de fin de embargo: 2019-10-01
Resumen:
[EN] The aim of this study was to compare the performance of the well-known genetic algorithms and tabu search heuristics with the financial problem of the partial tracking of a stock market index. Although the weights of ...[+]
Palabras clave: Index tracking , Heuristics , S&P 500 , Artificial index
Derechos de uso: Reserva de todos los derechos
Fuente:
Neural Computing and Applications. (issn: 0941-0643 )
DOI: 10.1007/s00521-017-2882-2
Editorial:
Springer-Verlag
Versión del editor: http://doi.org/10.1007/s00521-017-2882-2
Tipo: Artículo

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