This study investigates the ability of three versions of Altman's Z-Score model (Z, Z', and Z”) of distress prediction developed in the U.S. to predict the corporate distress in the emerging market of Sri Lanka. The results show that these models have a remarkable degree of accuracy in...

This study is motivated by the continuing popularity of the Altman Z-score as a measure of distress risk. Altman first introduced the ‘Z' score in 1968 and 50 years later it is still going strong as a means to predicting bankruptcy. During these 50 years, academicians have studied the...

A financial distress of company should be able anticipated smartly by its management to rerun the business without having any loss due to business failure. Thus, we need a model which could provide an early signal to company the probability of financial distress so that remedial efforts can be...

We estimate and test several default risk models using new and unique data on corporate defaults in the German stock market. While defaults were extremely rare events in the 1990s, they have been a characteristic feature of the German stock market since the early 2000s. We apply the structural...

This paper aims to focus on the comparison of the artificial neural network model and logistic regression model in the prediction of companies' bankruptcy in Tehran stock exchange (TSE) in 3, 2 and 1 year in advance. This study exercises an analytic-mathematical approach which has been utilized...

This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms' DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from quantitative...