A boosting approach for corporate failure prediction |
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Authors: | Esteban Alfaro Cortés Matías Gámez Martínez Noelia García Rubio |
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Affiliation: | (1) Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Plaza de la Universidad, 1., 02071 Albacete, Spain |
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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.
E. A. Cortés · M. G. Martínez · N. G. Rubio. The authors teach Statistics at the Faculty of Economic and Business Sciences in the University of Castilla-La Mancha. Esteban
Alfaro completed his degree in Business in 1999 and got his Ph.D. in Economics in 2005, both in the University of Castilla-La
Mancha. His thesis dealt with the application of ensemble classifiers to corporate failure prediction. Matías Gámez got his
degree in Mathematics at the University of Granada in 1991 and finished a Master in Applied Statistics a year after. He completed
his Ph.D. in Economics at the University of Castilla-La Mancha in 1998 on the application of geo-statistical techniques to
the estimation of housing prices. Noelia García got her degree in Economics at the University of Madrid (UAM) in 1996 and
completed her Ph.D. in Economics in 2004 on the construction of an intelligent and automated system for property valuation
through the combination of neural nets and a geographic information system (GIS). Current research deals with spatial statistics
and the combination of classifiers (decision trees and neural nets) for solving heated topics in the Economics. |
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Keywords: | Ensemble classifiers Boosting Corporate failure prediction |
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