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dc.contributor.authorGraham, Den
dc.contributor.authorCraven, Men
dc.date.accessioned2020-01-13T18:22:10Z
dc.identifier.issn0160-5682en
dc.identifier.urihttp://hdl.handle.net/10026.1/15298
dc.descriptionAccepted but not published, so I would like an indefinite embargo please.en
dc.description.abstract

Real-world portfolio optimisation problems are often NP-hard, their efficient frontiers (EFs) in practice being calculated by randomised algorithms. In this work, a deterministic method of decomposition of EFs into a short sequence of sub-EFs is presented. These sub-EFs may be calculated by a quadratic programming algorithm, the collection of such sub-EFs then being subjected to a sifting process to produce the full EF. Full EFs of portfolio optimisation problems with small cardinality constraints are computed to a high resolution, providing a fast and practical alternative to randomised algorithms. The method may also be used with other practical classes of portfolio problems, complete with differing measures of risk. Finally, it is shown that the identified sub-EFs correspond closely to local optima of the objective function of a case study evolutionary algorithm.

en
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.subjectportfolio selectionen
dc.subjectcardinality constrainten
dc.subjectdeterministic methodsen
dc.subjectevolutionary algorithmen
dc.subjectoperational researchen
dc.subjectquadratic programmingen
dc.titleAn Exact Algorithm for Small-Cardinality Constrained Portfolio Optimisationen
dc.typeJournal Article
plymouth.journalJournal of the Operational Research Societyen
dc.identifier.doi10.1080/01605682.2020.1718019en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/00 Groups by role
plymouth.organisational-group/Plymouth/00 Groups by role/Academics
plymouth.organisational-group/Plymouth/00 Groups by role/Professional Services staff
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/Marine Institute
dcterms.dateAccepted2020-01-12en
dc.rights.embargodate2022-01-01en
dc.rights.embargoperiodNot knownen
rioxxterms.versionAMen
rioxxterms.versionofrecord10.1080/01605682.2020.1718019en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeJournal Article/Reviewen


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