Ensemble classification based on generalized additive models
Computational Statistics & Data Analysis
Using partial least squares and support vector machines for bankruptcy prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Comparative analysis of data mining methods for bankruptcy prediction
Decision Support Systems
Expert Systems with Applications: An International Journal
Forecasting corporate bankruptcy with an ensemble of classifiers
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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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. Copyright © 2006 John Wiley & Sons, Ltd.