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Pareto multi-objective non-linear regression modelling to aid CAPM analogous forecasting

dc.contributor.authorFieldsend, Jonathan E.
dc.contributor.authorSingh, Sameer
dc.date.accessioned2013-07-10T13:04:47Z
dc.date.issued2002-08-07
dc.description.abstractRecent studies confront the problem of multiple error terms through summation. However this implicitly assumes prior knowledge of the problem's error surface. This study constructs a population of Pareto optimal Neural Network regression models to describe a market generation process in relation to the forecasting of its risk and return.en_GB
dc.identifier.citation2002 International Joint Conference on Neural Networks (IJCNN '02), Honolulu, Hawaii, 12-17 May 2002, pp. 388 - 393en_GB
dc.identifier.doi10.1109/IJCNN.2002.1005503
dc.identifier.urihttp://hdl.handle.net/10871/11685
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_GB
dc.subjectNeural networksen_GB
dc.subjectoptimizationen_GB
dc.subjectPredictionen_GB
dc.subjectfinancial data processingen_GB
dc.subjectforecasting theoryen_GB
dc.subjectneural netsen_GB
dc.subjectoptimisationen_GB
dc.subjectrisk managementen_GB
dc.subjectstock marketsen_GB
dc.subjecttime seriesen_GB
dc.subjectCastingen_GB
dc.subjectComputer errorsen_GB
dc.subjectEconometricsen_GB
dc.subjectEconomic forecastingen_GB
dc.subjectEuclidean distanceen_GB
dc.subjectInput variablesen_GB
dc.subjectPredictive modelsen_GB
dc.subjectSmoothing methodsen_GB
dc.titlePareto multi-objective non-linear regression modelling to aid CAPM analogous forecastingen_GB
dc.typeConference paperen_GB
dc.date.available2013-07-10T13:04:47Z
dc.identifier.isbn0780372786
dc.identifier.issn1098-7576
dc.descriptionCopyright © 2002 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_GB


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