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This study uses generalized additive models to identify and analyze nonlinear relationships between accounting-based and market-based independent variables and how these affect bankruptcy predictions. Specifically, it examines the independent variables that Altman (J Financ 23:589–609, 1968; Predicting financial distress of companies. Revisiting the Z-score and ZETA® models. Working paper, 2000) and Campbell et al. (J Financ 63:2899–2939, 2008) used and analyzes what specific form these nonlinear relationships take. Drawing on comprehensive data on listed U.S. companies, we show empirically that the bankruptcy prediction is influenced by statistically and economically relevant nonlinear relationships. Our results indicate that taking into account these nonlinear relationships improves significantly several statistical validity measures. We also use a validity measure that is based on the profitability of the bankruptcy prediction models in the context of credit scoring. The findings demonstrate that taking into account nonlinear relationships can substantially increase the discriminatory power of bankruptcy prediction models.

The structural and linguistic characteristics of companies’ annual reports (e.g., their length, complexity, and linguistic tone) and the qualitative information they contain (e.g., on the risks a company potentially faces) provide useful insights that can help increase the accuracy of predicting bankruptcy. In this study we use a sample of German companies that we compiled through propensity score matching to examine what type of textual information allows us to discriminate accurately between companies that are likely to go bankrupt and companies that, although financially distressed, are likely to remain solvent. Our findings provide empirical evidence that both the structural and linguistic characteristics of annual reports and the qualitative information they contain help discriminate between companies that are effectively bankrupt and companies that are solvent but financially distressed. Furthermore, this study provides empirical evidence that the “management obfuscation hypothesis” is valid because the tone of annual reports produced by bankrupt companies is quantifiably less negative than that of reports produced by companies that, although financially distressed, are likely to remain solvent.

The cure of a defaulted company has important implications for the estimation of the loss given default. In this study, we estimate the probability of a defaulted company being cured using data on a large international sample of defaulted companies. More specifically, we examine whether historic accounting information on a defaulted company and loan-related information are associated with that company's probability of being cured. The main finding of our analysis is that both accounting-based and loan-related independent variables increase the validity of cure prediction models.

This study uses a generalized additive model to analyze nonlinear relationships and their effect on the bankruptcy prediction in terms of a company’s probability of default. Specifically, this study examines which of the performance indicators that are contained in a company’s annual financial statements affect nonlinearly that company’s probability of bankruptcy. Furthermore, this study examines the specific form that such nonlinear relationships take and interprets their economic relevance to predicting bankruptcy. On the basis of comprehensive data on German companies, this study shows empirically that there are statistically and economically relevant nonlinear relationships that influence the bankruptcy prediction. These nonlinear relationships are observed both below and above specific thresholds with respect to a company’s adjusted equity ratio, asset structure ratio based on tangible assets, adjusted return on assets, sales, and age. Our findings show that, to increase the accuracy of bankruptcy forecasts, it is necessary to take into account nonlinear relationships in models of bankruptcy prediction.

Zusammenfassung Zielsetzung Unternehmen des Gesundheitssektors unterscheiden sich in einer Vielzahl an leistungswirtschaftlichen sowie betriebswirtschaftlichen Merkmalen von anderen Wirtschaftsunternehmen. Der vorliegende Beitrag untersucht den Zusammenhang zwischen bilanziellen Strukturmerkmalen und dem Zahlungsausfall von Unternehmen des Gesundheitssektors. Methodik Die Analyse basiert auf Jahresabschlussdaten von deutschen Gesundheitsunternehmen der Jahre 2005 bis 2009 sowie zugehöriger Zahlungsausfalldaten. Unter Verwendung des Propensity Score Matching werden insgesamt 61 Zahlungsausfälle (18 Krankenhäuser sowie 43 sonstige Gesundheitsunternehmen) mit Hilfe der logistischen Regressionsanalyse statistisch ausgewertet. Ergebnisse Ein hoher Eigenkapitalanteil sowie ein großes Umlaufvermögen reduziert die Wahrscheinlichkeit des Zahlungsausfalls eines Unternehmens des Gesundheitssektors. Im Gegensatz zu anderen Wirtschaftsunternehmen hat die Rentabilität eines Unternehmens des Gesundheitssektors keinen empirisch nachweisbaren Einfluss auf die Wahrscheinlichkeit des Zahlungsausfalls. Schlussfolgerung Eine solide Finanzausstattung sowie eine zum großen Anteil auch kurzfristig liquidierbare Vermögensstruktur schützen die Unternehmen des Gesundheitssektors vor einem Zahlungsausfall. Die Rentabilität hat für die Unternehmensfortführung keine empirisch nachweisbare Aussagekraft. Dies resultiert möglicherweise aus der Regulierung des Gesundheitssektors in Bezug auf die medizinischen Leistungen sowie die damit einhergehenden Erlöse.

This study uses data on consumer credit provided by a German retail and trading company along with generalized additive models to analyze nonlinear relationships and their effect on predicting the probability of default in the context of consumer credit scoring. In particular, this study examines which aspects of the contract and which characteristics of the debtor are nonlinearly related to the probability that specific debtors will default on the loans they are seeking to obtain. The findings of our analysis provide clear empirical evidence that certain contract and debtor characteristics have nonlinear and nonmonotonic effects on the probability that a specific debtor will default on their consumer credit. The results of our analysis and the forms that these nonlinear relationships take are interpreted with regard to their economic relevance. Specifically, the study shows that the nonlinear relationships identified are economically relevant because they take into account the costs of a range of misclassification errors. On the basis of this evidence, the study recommends that analysts include nonlinear relationships in the models they design to predict consumer default, although including such relationships increases the complexity of the models that are applied for this purpose.