A Six Sigma approach to predicting corporate defaults

The progression of a corporation from a status of financial stability into the status of financial distress usually happens over relatively large periods of time, raising the opportunity of identifying these ‘falling’ corporations ahead of time. Consequently, a critical risk management objective is to provide investment portfolio managers with an early notice of deteriorating financial status for a corporation. We consider a model built using equity inferred Probability of Default (PD) metrics. We follow a Design for Six Sigma Define, Measure, Analyze, Design, Verify (DMADV) approach to enhance the predictability power of the PD by constructing a two-dimensional risk space for estimating likelihood of default. We use such techniques as classification and regression tree (CART) analysis and logistic regression, and build a control plan using censored data analysis. We test our model on two actual portfolios. The potential savings revealed by these tests are significant, re-assuring us of the performance of our methodology.