Financial distress and bankruptcy prediction using accounting, market and macroeconomic variables

This thesis investigates the information content of different types of variables in the field of financial distress/default prediction. Specifically, the thesis tests empirically, for the first time, the utility of combining accounting data, market-based variables and macroeconomic indicators to explain corporate credit risk. Models for listed companies in the United Kingdom are developed for the prediction of financial distress and corporate failure. The models used a combination of accounting data, stock market information, proxies for changes in the macroeconomic environment, and industry controls. Furthermore, novel finance-based and technical definitions of firm distress and failure are introduced as outcome variables. The thesis produced binary and polytomous models with enhanced predictive accuracy, practical value, and macro dependent dynamics that have relevance for stress testing. The results unambiguously show the advantages, in terms of predictive accuracy and timeliness, of combining these types of variables. Unlike previous research works that employed discrete choice, non-linear regression methodologies, this thesis provided new evidence on the effects of the different types of variables on the probability of falling into each of the individual outcomes (e.g., financial distress, corporate failure). The analysis of graphic representations of changes in predicted probabilities, a primer in the field of risk modelling, offered new insights with regard to the behaviour of the vectors of predicted probabilities following a given change in the magnitude of a specific covariate. Additionally, and in line with the main area of study, the thesis provides historical evidence on the types of variables and the information sharing mechanisms employed by American and British investors and financial institutions to assess the riskiness of individuals, businesses and fixed-income instruments before the emergence of modern institutions such as the credit rating agencies and prior to the development of complex statistical models, filling thus a crucial gap in the credit risk literature.