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2010-06-08Buch DOI: 10.18452/4260
Learning Machines Supporting Bankruptcy Prediction
dc.contributor.authorHärdle, Wolfgang Karl
dc.contributor.authorMoro, Rouslan
dc.contributor.authorHoffmann, Linda
dc.date.accessioned2017-06-16T00:09:41Z
dc.date.available2017-06-16T00:09:41Z
dc.date.created2010-06-09
dc.date.issued2010-06-08
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4912
dc.description.abstractIn many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification rule called a score or an SVM. Companies with scores above zero belong to one group and the rest to another group. Estimation of the probability of default (PD) values can be calculated from the scores provided by an SVM. The transformation used in this paper is a combination of weighting ranks and of smoothing the results using the PAV algorithm. The conversion is then monotone. This discussion paper is based on the Creditreform database from 1997 to 2002. The indicator variables were converted to financial ratios; it transpired out that eight of the 25 were useful for the training of the SVM. The results showed that those ratios belong to activity, profitability, liquidity and leverage. Finally, we conclude that SVMs are capable of extracting the necessary information from financial balance sheets and then to predict the future solvency or insolvent of a company. Banks in particular will benefit from these results by allowing them to be more aware of their risk when lending money.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectSupport Vector Machineeng
dc.subjectBankruptcyeng
dc.subjectDefault Probabilities Predictioneng
dc.subjectProfitabilityeng
dc.subject.ddc330 Wirtschaft
dc.titleLearning Machines Supporting Bankruptcy Prediction
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-100112184
dc.identifier.doihttp://dx.doi.org/10.18452/4260
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages28
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2010
local.edoc.container-issue32
local.edoc.container-year2010
local.edoc.container-erstkatid2195055-6

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