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2005-03-01Buch DOI: 10.18452/3875
Predicting Bankruptcy with Support Vector Machines
dc.contributor.authorHärdle, Wolfgang
dc.contributor.authorMoro, Rouslan A.
dc.contributor.authorSchäfer, Dorothea
dc.date.accessioned2017-06-15T22:51:40Z
dc.date.available2017-06-15T22:51:40Z
dc.date.created2005-08-31
dc.date.issued2005-03-01
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4527
dc.description.abstractThe purpose of this work is to introduce one of the most promising among recently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful information from financial data, although extensive data sets are required in order to fully utilize their classification power.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subject.ddc330 Wirtschaft
dc.titlePredicting Bankruptcy with Support Vector Machines
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10045061
dc.identifier.doihttp://dx.doi.org/10.18452/3875
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages25
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2005
local.edoc.container-issue9
local.edoc.container-year2005
local.edoc.container-erstkatid2195055-6

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