Härdle, Wolfgang Karl Lee, Yuh-Jye Schäfer, Dorothea Yeh, Yi-Ren
Year of Publication:
SFB 649 discussion paper 2008,005
In the era of Basel II a powerful tool for bankruptcy prognosis is vital for banks. The tool must be precise but also easily adaptable to the bank's objections regarding the relation of false acceptances (Type I error) and false rejections (Type II error). We explore the suitability of Smooth Support Vector Machines (SSVM), and investigate how important factors such as selection of appropriate accounting ratios (predictors), length of training period and structure of the training sample influence the precision of prediction. Furthermore we show that oversampling can be employed to gear the tradeoff between error types. Finally, we illustrate graphically how different variants of SSVM can be used jointly to support the decision task of loan officers.
Insolvency Prognosis SVMs Statistical Learning Theory Non-parametric Classification