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Estimation of Default Probabilities with Support Vector Machines

Shiyi, Chen; Härdle, Wolfgang K.; Moro, Rouslan Arthur - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2006

Predicting 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 Vector Machine (SVM) to...

Persistent link: https://www.econbiz.de/10005861245

The Default Risk of Firms Examined with Smooth Support Vector Machines

Härdle, Wolfgang; Lee, Yuh-Jye; Schäfer, Dorothea; … - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2007

In the era of Basel II a powerful tool for bankruptcy prognosis isvital for banks. The tool must be precise but also easily adaptable tothe bank's objections regarding the relation of false acceptances (TypeI error) and false rejections (Type II error). We explore the suitabil-ity of Smooth...

Persistent link: https://www.econbiz.de/10005860752

Estimating probabilities of default with support vector machines

Härdle, Wolfgang (contributor); Moro, Rouslan (contributor) - 2007

This paper proposes a rating methodology that is based on a non-linear classification method, the support vector … machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to … tested. -- Bankruptcy ; Company rating ; Default probability ; Support vector machines …

Persistent link: https://www.econbiz.de/10003633940

Graphical data representation in bankruptcy analysis

Härdle, Wolfgang (contributor); … - 2006

highly non-linear and its numerical representation is much less transparent. In classical rating models a convenient … rating ; default probability ; support vector machines ; colour coding …

Persistent link: https://www.econbiz.de/10003324316

Learning machines supporting bankruptcy prediction

Härdle, Wolfgang; Moro, Rouslan; Hoffmann, Linda - 2010

In 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...

Persistent link: https://www.econbiz.de/10003973650

Forecasting corporate distress in the Asian and Pacific region

Moro, Russ; Härdle, Wolfgang; Aliakbari, Saeideh; … - 2011

This study analyses credit default risk for firms in the Asian and Pacific region by applying two methodologies: a Support Vector Machine (SVM) and a logistic regression (Logit). Among different financial ratios suggested as predictors of default, leverage ratios and the company size display a...