Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks

Authors:
Esteban Alfaro;Noelia García;Matías Gámez;David Elizondo
Affiliations:
Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Plaza de la Universidad, 1. 02071 Albacete, Spain;Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Plaza de la Universidad, 1. 02071 Albacete, Spain;Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Plaza de la Universidad, 1. 02071 Albacete, Spain;School of Computing, De Montfort University, The Gateway, Leicester LE1 9BH, U.K.
Venue:
Decision Support Systems
Year:
2008

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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.