Essays on Corporate Default Prediction

Corporate bankruptcy prediction has received paramount interest in academic research, business practice and government regulation. The recent financial crisis, during which unexpected corporate insolvencies had caused severe damage to the aggregate economy, highlights the crucial importance of an accurate corporate default prediction. Consequently, accurate default probability prediction is extremely important. The purpose of this research is to offer a unique contribution to the extant literature. This dissertation consists of three essays.In the first essay (Chapter 1), we propose to apply a discrete transformation family of survival models to corporate default risk predictions. A class of Box-Cox transformations and logarithmic transformations are naturally adopted. The proposed transformation model family is shown to include the popular Shumway’s model and grouped relative risk model. We show that a transformation parameter different from those two models is needed for default prediction using the bankruptcy data. In addition, out-of-sample validation statistics show improved performance. The estimated default probability is further used to examine a popular asset pricing question whether the default risk has carried a premium. Due to some distinct features of bankruptcy application, the proposed class of discrete transformation survival models with time-varying covariates is different from the continuous survival models in the literature. Their links and differences are also discussed.Essay 2 (Chapter 2) introduces a robust variable selection technique, the least absolute shrinkage and selection operator (LASSO), to investigate formally the relative importance of various bankruptcy predictors commonly used in the existing literature. Over the 1980 to 2009 period, LASSO admits the majority of Campbell, Hilscher, and Szilagyi’s (2008) predictive variables into the bankruptcy forecast model. Interestingly, the total debt to total assets ratio and the current liabilities to total assets ratio constructed from only accounting data also contain significant incremental information about future default risk. LASSO-selected variables have superior out-of-sample predictive power and outperform (1) those advocated by Campbell, Hilscher, and Szilagyi (2008) and (2) the distance to default from Merton’s (1974) structural model. Furthermore, study on the international market reveals the uniform significance brought by the activity indicator, sales / total assets.Essay 3 (Chapter 3) devotes special care to an important aspect of the bankruptcy prediction modeling: data sample selection issue. To investigate the effect of the different data selection methods, three models are adopted: logistic regression model, Neural Networks (NNET) and Support Vector Machines (SVM). A Monte Carlo simulation study and an empirical analysis on an updated bankruptcy database are conducted to explore the effect of different data sample selection methods. By comparing the out-of-sample predictive performances, we conclude that if forecasting the probability of bankruptcy is of interest, complete data sampling technique provides more accurate results. However, if a binary bankruptcy decision or classification is desired, choice based sampling technique may still be suitable.