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Multiclass Corporate Failure Prediction by Adaboost.M1

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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 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.

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Fig. 1

Notes

  1. 1.

    In this paper, only the information from the previous year was used, but this is a part of a larger research. That is the reason for the requirement of complete data available for the five previous years

  2. 2.

    The R program is a set of packages for data manipulation, calculus, and graphics (R Development Core Team 2004). Among other characteristics, it has a well-developed and effective-programming language (R language). The R program has much in common with the well known S-Plus program, but unlike the latter, R is a distribution-free program available on the web at http://cran.r-project.org/.

References

  1. Alfaro, E., Gámez, M., & García, N. (2006). adabag: implements adaboost.M1 and bagging. R package version 1.0. http://www.R-project.org

  2. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.

  3. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithm: bagging, boosting and variants. Machine Learning, 36, 105–142.

  4. Beaver, W. H. (1966). Financial ratios as predictors of failure. Empirical research in accounting: selected studies. Journal of Accounting Research, 4(Supplement), 71–111.

  5. Breiman, L. (1998). Arcing classifiers. Annals of Statistics, 26(3), 801–849.

  6. Breiman, L., Friedman, J. H., Olshen, R., & Stone, C. J. (1984). Classification and regression trees. Belmont: Wadsworth International Group.

  7. Dietterich, T. G. (2000). Ensemble methods in machine learning. In J. Kittler, & F. Roli (Eds.). Multiple Classifier Systems, vol. 1857 of Lecture Notes in Computer Science (pp. 1–15). New York: Springer.

  8. Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the 13th International Conference on Machine Learning (pp. 148–156). Bari, Italy.

  9. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.

  10. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics, 38(2), 337–374.

  11. Frydman, H., Altman, E., & Kao, D. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance, 40(1), 269–291.

  12. Kuncheva, L. I. (2004). Combining pattern classifiers. Methods and algorithms. New Jersey: Wiley.

  13. Ohlson, J. A. (1980) Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 5–12.

  14. R Development Core Team (2004). R: A language and environment for statistical computing. Viena: R Foundation for Statistical Computing. http://www.R-project.org

  15. Ripley, B. D. (2004). Rpart: Recursive Partitioning. R package version 3.1-20. http://www.R-project.org

  16. Valentini, G., & Masulli, F. (2002). Ensembles of learning machines. In M. Marinaro, & R. Tagliaferri (Eds). Proceedings of the 13th Italian Workshop on Neural Nets, vol. 2486 of Lecture Notes in Computer Science (pp. 3–19) Berlin Heidelberg New York: Springer.

  17. Wilson, R. L., & Sharda, R. (1994). Bankruptcy prediction using neural network. Decision Support Systems, 11(5), 545–557.

  18. Zmijewski, M. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–86.

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Author information

Correspondence to Esteban Alfaro Cortés.

Appendix

Appendix

Table A1 Predictor Variables

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Alfaro Cortés, E., Gámez Martínez, M. & García Rubio, N. Multiclass Corporate Failure Prediction by Adaboost.M1. Int Adv Econ Res 13, 301–312 (2007). https://doi.org/10.1007/s11294-007-9090-2

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Keywords

  • Corporate failure prediction
  • Ensemble classifiers
  • Adaboost.M1

JEL

  • C10
  • G30
  • M00