Show simple item record

2006-11-16Buch DOI: 10.18452/4007
Estimation of Default Probabilities with Support Vector Machines
dc.contributor.authorChen, Shiyi
dc.contributor.authorHärdle, Wolfgang Karl
dc.contributor.authorMoro, Rouslan
dc.date.accessioned2017-06-15T23:18:14Z
dc.date.available2017-06-15T23:18:14Z
dc.date.created2006-11-22
dc.date.issued2006-11-16
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4659
dc.description.abstractPredicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support VectorMachine (SVM) to estimate default probabilities of German firms. Our analysis is based on the Creditreform database. The results reveal that the most important eight predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Basedon the performance measures, the SVM tool can predict a firms default risk and identify the insolvent firm more accurately than the benchmark logit model. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of the SVM parameters. Based on the nonparametric Nadaraya-Watson estimator, the expected returns predicted by the SVM for regression have a significant positive linear relationship with the risk scores obtained for classification. This evidence is stronger than empirical results for the CAPM based on a linear regression and confirms that higher risks need to be compensated by higher potential returns.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectCAPMeng
dc.subjectBankruptcyeng
dc.subjectSupport Vector Machineeng
dc.subjectDefault Probabilities Predictioneng
dc.subjectExpected Profitabilityeng
dc.subject.ddc330 Wirtschaft
dc.titleEstimation of Default Probabilities with Support Vector Machines
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10071186
dc.identifier.doihttp://dx.doi.org/10.18452/4007
dc.subject.dnb17 Wirtschaft
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages43
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2006
local.edoc.container-issue77
local.edoc.container-year2006
local.edoc.container-erstkatid2195055-6

Show simple item record