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Multiclass Corporate Failure Prediction by Adaboost.M1

Author

Listed:
  • Esteban Alfaro Cortés
  • Matías Gámez Martínez
  • Noelia García Rubio

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 or success of a corporation. Despite the complexity of the matter, a two-class problem has usually been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we apply the Adaboost.M1 algorithm to improve the accuracy of a classification tree in a multiclass corporate failure prediction problem using a set of European firms. On the other, we introduce novel discerning measures to rank independent variables in a generic classification task. Copyright International Atlantic Economic Society 2007

Suggested Citation

  • Esteban Alfaro Cortés & Matías Gámez Martínez & Noelia García Rubio, 2007. "Multiclass Corporate Failure Prediction by Adaboost.M1," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(3), pages 301-312, August.
    • Handle: RePEc:kap:iaecre:v:13:y:2007:i:3:p:301-312:10.1007/s11294-007-9090-2
    DOI: 10.1007/s11294-007-9090-2
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    References listed on IDEAS

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    More about this item

    Keywords

    Corporate failure prediction; Ensemble classifiers; Adaboost.M1; C10; G30; M00;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General

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