This paper studies the effects of systematic distress and sectoral distress in the context of default/bankruptcy prediction using a large sample of U.S. public company default data from 1991 to 2009. I construct measures to proxy for economy-wide systematic distress and sectoral distress based...

This paper studies the relation between macroeconomic fluctuations and corporate defaults while conditioning on industry affiliation and an extensive set of firm-specific factors. Using a logit approach on a panel data set for all incorporated Swedish businesses over 1990-2002, we find strong...

We use a large panel dataset that includes nearly 31,000 Greek private firms to investigate which variables impact on the prediction of corporate financial distress. Based on a multi-period logit model that accounts for industry effects, we identify six firm-specific variables that best describe...

In this paper I investigate the relation between accounting based variables and bankruptcy risk, using information available for non-listed firms. I use a large sample of firms and find that many of the variables suggested earlier in the literature seem to provide little explanatory power....

The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy....

The acceleration of the firm failure rate in France between 2008 and 2010 was preceded by a surge in the number of firms' creations in 2003-2004.Therefore, identifying the impact of the 2008 crisis requires to distinguish, among the failures occurring over the period, those resulting from the...

We demonstrate improvements in predictive power when introducing spline functions to take account of highly non-linear relationships between firm failure and earnings, leverage, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive non-linearities yields...

We demonstrate improvements in predictive power when introducing spline functions to take account of highly non-linear relationships between firm failure and earnings, leverage, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive non-linearities yields...

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...