Bankruptcy Prediction by Generalized Additive Models

We compare several accounting-based models for bankruptcy prediction. The models are developed and tested on large data sets containing annual financial statements 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 depreciation 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 exhibits a desirable robustness to yearly fluctuations that is not present in the one-year model.