Dept. of Math. University of Oslo Statistical Research Report No. 1 ISSN 0806–3842 January 2005 Bankruptcy Prediction by Generalized Additive Models Daniel Berg ∗† Department of Mathematics, University of Oslo, Norway. Abstract We compare several accounting based models for bankruptcy prediction. The models are developed and tested on large data sets containing annual financial state- ments for Norwegian limited liability firms. Out-of-sample and out-of-time validation shows that generalized additive models significantly outperform popular models like linear discriminant analysis, generalized linear models and neural networks at all levels of risk. Further, important issues like default horizon and performance de- preciation are examined. We clearly see a performance depreciation as the default horizon is increased and as time goes by. Finally a multi-year model, developed on all available data from three consecutive years, is compared with a one-year model, developed on data from the most recent year only. The multi-year model exhibit a desirable robustness to yearly fluctuations that is not present in the one-year model. J.E.L. Subject Classification: C13, C14, C44, C51, C52, G33. Keywords: Bankruptcy Prediction, Generalized Additive Models, Default Horizon, Performance Depreciation, Multi-year model. ∗ Address for correspondence: Department of Mathematics, University of Oslo, P.O. Box 1053 Blindern, NO-0316 Oslo, Norway. † E-mail: daniel@nr.no. 1