Bankruptcy Distributions and Modelling for Swedish Companies Using Logistic Regression

University essay from KTH/Matematisk statistik

Abstract: This thesis discusses the concept of bankruptcy, or default, for Swedish companies. The actual distribution over time is considered both on aggregate level and within different industries. Several models are constructed to best possible describe the default frequency. Mainly logistic regression models are designed for this purpose, but various other models are considered. Some of these are constructed for comparison and for the ambition to produce the most accurate model possible. A large data set of nearly 30 million quarterly observations is used in the analysis. Taking into account micro and macro economic data. The derived models cover different time periods, considering different variables and display varying levels of accuracy. The most exact model is a logistic regression model considering both micro and macro data. It is tested both in sample and out of sample and perform very well in both areas. This model is estimated on first a subset of the data set to be able to compare with a real scenario. Then an equivalent model is constructed from the whole data set to best possibly describe future scenarios. Here Vector Auto-Regressive (VAR) models, and empirical models constructed by OLS regression estimating the firm values, are used in combination with the logistic regression model to predict the future. All three models are used to describe the most likely scenarios, as well as the worst case scenarios. From the worst case scenarios risk measures, such as the empirical value at risk, can be derived. From all this analysis the most significant results are compiled. Namely, that the Logistic regression model performs remarkably well both in-sample and out-of-sample, if macro variables are taken into account. Further, the future results are harder to interpret. Yet, the analysis has arguments for prediction accuracy and interesting results of a continued low default frequency within the next year.

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