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dc.contributor.authorGraham, David
dc.contributor.authorCraven, Matthew
dc.date.accessioned2020-01-13T18:22:10Z
dc.date.issued2020-02-13
dc.identifier.issn0160-5682
dc.identifier.issn1476-9360
dc.identifier.urihttp://hdl.handle.net/10026.1/15298
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.

dc.format.extent1-17
dc.languageen
dc.language.isoen
dc.publisherTaylor & Francis
dc.subjectportfolio selection
dc.subjectcardinality constraint
dc.subjectdeterministic methods
dc.subjectevolutionary algorithm
dc.subjectoperational research
dc.subjectquadratic programming
dc.titleAn Exact Algorithm for Small-Cardinality Constrained Portfolio Optimisation
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000513982300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue6
plymouth.volume72
plymouth.publication-statusPublished online
plymouth.journalJournal of the Operational Research Society
dc.identifier.doi10.1080/01605682.2020.1718019
plymouth.organisational-group/Plymouth
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/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences/UoA 10 - Former and non-independent
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
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2020-01-12
dc.rights.embargodate2021-2-12
dc.identifier.eissn1476-9360
dc.rights.embargoperiodNot known
rioxxterms.versionAccepted Manuscript
rioxxterms.versionofrecord10.1080/01605682.2020.1718019
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2020-02-13
rioxxterms.typeJournal Article/Review


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