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dc.contributor.authorOng, CSen_US
dc.contributor.authorHuang, HJen_US
dc.contributor.authorTzeng, GHen_US
dc.date.accessioned2014-12-08T15:18:12Z-
dc.date.available2014-12-08T15:18:12Z-
dc.date.issued2005-10-15en_US
dc.identifier.issn0096-3003en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.amc.2004.10.080en_US
dc.identifier.urihttp://hdl.handle.net/11536/13164-
dc.description.abstractAs we know, the performance of the mean-variance approach depends on the accurate forecast of the return rate. However, the conventional method (e.g. arithmetic mean or regression-based method) usually cannot obtain a satisfied solution especially under the small sample situation. In this paper, the proposed method which incorporates the grey and possibilistic regression models formulates the novel portfolio selection model. In order to solve the multi-objective quadric programming problem, multi-objective evolution algorithms (MOEA) is employed. A numerical example is also illustrated to show the procedures of the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide the more flexible and accurate results. (c) 2004 Elsevier Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectmean-variance approachen_US
dc.subjectportfolio selectionen_US
dc.subjectGrey modelen_US
dc.subjectpossibilistic regression modelen_US
dc.subjectmulti-objective evolution algorithms (MOEA)en_US
dc.titleA novel hybrid model for portfolio selectionen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.amc.2004.10.080en_US
dc.identifier.journalAPPLIED MATHEMATICS AND COMPUTATIONen_US
dc.citation.volume169en_US
dc.citation.issue2en_US
dc.citation.spage1195en_US
dc.citation.epage1210en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000232811600037-
dc.citation.woscount19-
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