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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...
Persistent link: https://www.econbiz.de/10009125559
Persistent link: https://www.econbiz.de/10009231331
to one group and the rest to another group. Estimation of the probability of default (PD) values can be calculated from …
Persistent link: https://www.econbiz.de/10003973650
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...
Persistent link: https://www.econbiz.de/10010281539
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 underlying statistical models and represent the results of...
Persistent link: https://www.econbiz.de/10003633940
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/10003402291
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/10012966238
decision task of loan officers. -- Insolvency Prognosis ; SVMs ; Statistical Learning Theory ; Non-parametric Classification …
Persistent link: https://www.econbiz.de/10003636001
Predicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform...
Persistent link: https://www.econbiz.de/10009526609
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 underlying statistical models and represent the results of...
Persistent link: https://www.econbiz.de/10012989283