Graphical data representation is an important tool for model selection in bankruptcy analysis since the problem is highly non-linear and its numerical representation is much less transparent. In classical rating models a convenient representation of ratings in a closed form is possible reducing...

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...

We build a dynamic model to link two empirical patterns:\ the negative failure probability-return relation (Campbell, Hilscher, and Szilagyi, 2008) and the positive distress risk premium-return relation (Friewald, Wagner, and Zechner, 2014). We show analytically and quantitatively that (i)...

Intuition suggests that firms with higher cash holdings should be 'safer' and have lower credit spreads. Yet empirically, the correlation between cash and spreads is robustly positive. This puzzling finding can be explained by the precautionary motive for saving cash, which in our model causes...

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...

Probability of default prediction is one of the important tasks of rating agencies as well as of banks and other financial companies to measure the default risk of their counterparties. Knowing predictors that significantly contribute to default prediction provides a better insight into...

Although the cost of financial distress is a central issue in capital structure and credit risk studies, reliable estimates of its size are difficult to come by. This paper proposes a novel method of extracting the cost of default from the change in the market value of a firm's assets upon...

Using a local adaptive Forward Intensities Approach (FIA) we investigate multiperiod corporate defaults and other delisting schemes. The proposed approach is fully datadriven and is based on local adaptive estimation and the selection of optimal estimation windows. Time-dependent model...

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...