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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/10005207929
A popular view of limited liability in financial contracting is that it is the result of societal preferences against excessive penalties. The view of most financial economists is instead that limited liability emerged as an optimal institution when, in the absence of a clear limit on economic...
Persistent link: https://www.econbiz.de/10005784849
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/10005677958
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/10008568137
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/10009021755