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Computational intelligence for evolving trading rules

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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.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.identifier.citationIEEE Transactions on Evolutionary Computation, 2009; 13(1):71-86
dc.identifier.doi10.1109/TEVC.2008.915992
dc.identifier.issn1089-778X
dc.identifier.issn1941-0026
dc.identifier.orcidZurbrugg, R. [0000-0002-8652-0028]
dc.identifier.urihttp://hdl.handle.net/2440/52599
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.source.urihttps://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
pubs.publication-statusPublished

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