Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks ☆ Esteban Alfaro a, ⁎ , Noelia García a , Matías Gámez a , David Elizondo b a Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Plaza de la Universidad, 1. 02071 Albacete, Spain b School of Computing, De Montfort University, The Gateway, Leicester LE1 9BH, U.K. Received 21 February 2007; received in revised form 22 November 2007; accepted 3 December 2007 Available online 8 December 2007 Abstract The goal of this study is to show an alternative method to corporate failure prediction. In the last decades Artificial Neural Networks have been widely used for this task. These models have the advantage of being able to detect non-linear relationships and show a good performance in presence of noisy information, as it usually happens, in corporate failure prediction problems. AdaBoost is a novel ensemble learning algorithm that constructs its base classifiers in sequence using different versions of the training data set. In this paper, we compare the prediction accuracy of both techniques on a set of European firms, considering the usual predicting variables such as financial ratios, as well as qualitative variables, such as firm size, activity and legal structure. We show that our approach decreases the generalization error by about thirty percent with respect to the error produced with a neural network. © 2007 Elsevier B.V. All rights reserved. Keywords: Corporate Failure Prediction; Neural Network; AdaBoost 1. Introduction Predicting corporate failure is a hot topic in manage- ment science due to its importance for making correct business decisions. The accuracy of the forecasting model is clearly of crucial importance in failure prediction because many economic agents not only enterprises but financial institutions, auditors, consul- tants, policy makers or clients are affected by the bankrupt of a firm. In classification terms, the type I error is especially important, i.e. when a firm which will fail in the future is classified as healthy. Owing to this fact many researchers have focused their effort on finding the most efficient classifier. In the last decades artificial neural networks have received special attention and several studies have dealt with failure forecasting using this technique. Here we present some of them only as examples. Wilson and Sharda [55] used a sample of 129 firms, 65 of which went bankrupt between 1975 and 1982 and 64 non bankrupt firms matched on Available online at www.sciencedirect.com Decision Support Systems 45 (2008) 110 – 122 www.elsevier.com/locate/dss ☆ Work partially supported by the Spanish Government under grant TIN2006-07262 and by the Castilla-La Mancha University under grants TC20070075 and TC20070095. ⁎ Corresponding author. Tel.: +34 967599200; fax: +34 967599220. E-mail addresses: Esteban.Alfaro@uclm.es (E. Alfaro), Noelia.Garcia@uclm.es (N. García), Matias.Gamez@uclm.es (M. Gámez), elizondo@dmu.ac.uk (D. Elizondo). 0167-9236/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.dss.2007.12.002