CART-based selection of bankruptcy predictors for the logit model

Expert Systems with Applications

Volumn 39, Issue 11, 2012, Pages 10153-10159

  Brezigar Masten, Arjana ^a,b   Masten, Igor ^c  

^a UNIVERSITY OF PRIMORSKA   (Slovenia)

^b Institute of Macroeconomic Analysis and Development   (Slovenia)

^c UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Bankruptcy prediction; CART; Model selection

Indexed keywords

BANKRUPTCY PREDICTION; CART; CLASSIFICATION TREE METHOD; CLASSIFICATION TREES; CORPORATE FINANCIAL FAILURE; DEFAULT PROBABILITIES; FINANCIAL RATIOS; LOGIT MODELS; MODEL SELECTION; NON-PARAMETRIC REGRESSION; NONLINEAR EFFECT; PREDICTOR VARIABLES; SELECTION METHODS; SELECTION PROCEDURES; SINGLE INDEX MODELS;

FORESTRY; STANDARDS;

MATHEMATICAL MODELS;

BANKRUPTCIES; FINANCE; MATHEMATICAL MODELS; PARAMETERS; PERFORMANCE; REGRESSION ANALYSIS; STANDARDS;

EID: 84859428734     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.02.125     Document Type: Article

References (17)

1. E. Altman Financial Ratios. Discriminant analysis and the prediction of corporate bankruptcy Journal of Finance 23 4 1968 589 609
2. W. Beaver Financial ratios as predictors of failure Journal of Accounting Research 4 Supplement 1966 71 111
3. L. Breiman, H.L. Friedmann, R.A. Olshen, and C.J. Stone Classification and regression trees 1984 Wadsworth International Group
4. M-Y. Chen Predicting corporate financial distress based on integration of decision tree classification and logistic regression Expert Systems with Applications 38 2011 11261 11272
5. S. Cho, H. Hong, and B-C. Ha A hybrid approach based on the combination of variables selection using decision trees and case-based reasoning using the mahalanobis distance: For bankruptcy prediction Expert Systems with Applications 37 2010 3482 3488
6. H. Frydman, E. Altman, and D. Kao Introducing recursive partitioning for financial classification: The case of financial distress Journal of Finance XL 1 1985 269 291
7. H. Li, J. Sun, and J. Wu Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods Expert Systems with Applications 37 2010 5895 5904
8. M.L. Marais, J.M. Patell, and M.A. Wolfson The experimental design of classification models: An application of recursive partitioning and bootstrapping to commercial bank loan classifications Journal of Accounting Research 22 supplement 1984 87 118
9. D. Martin Early warning of bank failure: A logit regression approach Journal of Banking & Finance 1 3 1977 249 276
10. J.H. Min, and C. Jeong A Binary classification method for bankruptcy prediction Expert Systems with Applications 36 2009 5256 5263
11. J. Ohlson Financial ratios and the probabilistic prediction of bankruptcy Journal of Accounting Research 18 1 1980 109 131
12. M. Paliwal, and U.A. Kumar Neural networks and statistical techniques: A review of applications Expert Systems with Applications 36 2009 2 17
13. Pantalone, C. C., & Platt, M. B. (1987). Predicting commercial bank failure since deregulation, New England economic review, Jul/Aug, 37-47.
14. B.D. Ripley Pattern recognition and neural networks 1996 Cambridge University Press Cambridge
15. A.M. Santomero, and J.D. Vinso Estimating the probability of failure for commercial banks and the banking system Journal of Banking & Finance 1 2 1977 185 205
16. Shirata, C.Y. (1998), Financial ratios as predictors of bankruptcy in Japan: An empirical research. In Proceedings of the second Asian-Pacific interdisciplinary research in accounting conference, pp. 437-445.
17. M. Zmijewski Methodological issues related to the estimation of financial distress prediction models Journal of Accounting Research 22 Supplement 1984 59 82