A boosting approach for corporate failure prediction

Authors:
Esteban Alfaro Cortés;Matías Gámez Martínez;Noelia García Rubio
Affiliations:
Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Albacete, Spain 02071;Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Albacete, Spain 02071;Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Albacete, Spain 02071
Venue:
Applied Intelligence
Year:
2007

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Abstract

Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure/success of a corporation. Despite the importance of this problem, until now only classical machine learning tools have been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we introduce novel discerning measures to rank independent variables in a generic classification task. On the other hand, we apply boosting techniques to improve the accuracy of a classification tree. We apply this methodology to a set of European firms, considering the usual predicting variables such as financial ratios, as well as including novel variables rarely used before in corporate failure prediction, such as firm size, activity and legal structure. We show that our approach decreases the generalization error about thirty percent with respect to the error produced with a classification tree. In addition, the most important ratios deal with profitability and indebtedness, as is usual in failure prediction studies.