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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/52599
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dc.contributor.authorGhandar, A.-
dc.contributor.authorMichalewicz, Z.-
dc.contributor.authorSchmidt, M.-
dc.contributor.authorTo, T.-
dc.contributor.authorZurbrugg, R.-
dc.date.issued2009-
dc.identifier.citationIEEE Transactions on Evolutionary Computation, 2009; 13(1):71-86-
dc.identifier.issn1089-778X-
dc.identifier.issn1941-0026-
dc.identifier.urihttp://hdl.handle.net/2440/52599-
dc.descriptionCopyright © 2008 IEEE-
dc.description.abstractThis paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results of applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.-
dc.description.statementofresponsibilityAdam Ghandar, Zbigniew Michalewicz, Martin Schmidt, Thuy-Duong Tô, and Ralf Zurbrugg-
dc.language.isoen-
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc-
dc.source.urihttp://dx.doi.org/10.1109/tevc.2008.915992-
dc.subjectevolutionary computation-
dc.subjectfuzzy systems-
dc.subjectportfoliomanagement-
dc.subjectstock market-
dc.subjecttrading systems-
dc.titleComputational intelligence for evolving trading rules-
dc.typeJournal article-
dc.identifier.doi10.1109/TEVC.2008.915992-
pubs.publication-statusPublished-
dc.identifier.orcidZurbrugg, R. [0000-0002-8652-0028]-
Appears in Collections:Aurora harvest 5
Computer Science publications

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