@techreport{Hardle2007default,
abstract = {In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Furthermore we showthat oversampling can be employed to gear the tradeoff between error types. Finally, we illustrate graphically how different variants of SSVM can be used jointly to support the decision task of loan officers.},
address = {Berlin},
author = {Wolfgang Karl H\"{a}rdle and Yuh-Jye Lee and Dorothea Sch\"{a}fer and Yi-Ren Yeh},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {G30; C14; G33; C45; 330; Insolvency Prognosis; SVMs; Statistical Learning Theory; Non-parametric Classfication; Kreditw\"{u}rdigkeit; Prognoseverfahren; Support Vector Machine; Theorie},
language = {eng},
number = {757},
publisher = {Deutsches Institut f\"{u}r Wirtschaftsforschung (DIW)},
title = {The default risk of firms examined with Smooth Support Vector Machines;},
type = {Discussion papers // German Institute for Economic Research},
url = {http://hdl.handle.net/10419/27281},
year = {2007}
}
