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...

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...

In this study we tried to compare two models in order to identify optimal neural networks models in predicting bankruptcy. Multi-layered perceptron (MLP) because of easy training and high efficiency and also integrated multi-layered perceptron (most used neural network in predicting bankruptcy)...

I employ a variety of machine learning techniques to predict corporate bankruptcies. I compare machine learning techniques' predictions with the ones of reduced-form regressions and structural models. To assess the performances of different models, I compute a range of scores both in-sample and...

In this paper, we estimate coefficients of bankruptcy forecasting models, such as logistic and neural network models, by maximizing their discriminatory power as measured by the Area Under Receiver Operating Characteristics (AUROC) curve. A method is introduced and compared with traditional...

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...

The prediction of financial distress has emerged as a significant concern over a prolonged period spanning more than half a century. This subject has garnered considerable attention owing to the precise outcomes derived from its predictive models. The main objective of this study is to predict...

This paper offers a novel framework, combining firm operational risk, IPO pricing risk, and market risk, to model IPO failure risk. By analyzing nearly a thousand variables, we observe that prior IPO failure risk models have suffered from a major missing-variable problem. Evidence reveals...

Creditors are increasingly transferring debt cash flow rights to other market participants while retaining control rights. We use the market for credit default swaps (CDSs) as a laboratory to show that such debt decoupling causes large adverse effects on firms whose shareholders have high...

Bharath and Shumway (2008) provide evidence that shows that it is the functional form of Merton’s (1974) distance to default (DD) model that makes it useful and important for predicting defaults. In this paper, we investigate whether the default predictability of the Merton DD model would be...