Data preprocessing and data parsimony in corporate failure forecast models: Evidence from Australian materials industry

Accounting and Finance

Volumn 46, Issue 2, 2006, Pages 327-345

  Wu, Weiping ^a   Lee, Vincent Cheng Siong ^a   Tan, Ting Yean ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Classification; Corporate failure; Machine learning and feature selection; Prediction; Statistical models

Indexed keywords

EID: 33744968656     PISSN: 08105391     EISSN: 1467629X     Source Type: Journal    
DOI: 10.1111/j.1467-629X.2006.00170.x     Document Type: Article

References (25)

1. Altman, E., 1968, Financial ratios - Discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance 23, 589-609.
2. Altman, E., R. Haldeman, and P. Narayanan, 1977, Zeta analysis: a new model to identify bankruptcy risk of corporations, Journal of Banking & Finance 1, 29-54.
3. Beaver, W. H., 1966, Financial ratios as predictors of failure, Journal of Accounting Research 4(Supplement), 71-111.
4. Catlett, J., 1991, On changing continuous attributes into ordered discrete attributes, European Working Session on Learning 6-8, 164-178.
5. Fawcett, T., 2004, ROC graphs: notes and practical considerations for researchers. [Online; cited 30 August 2004], Available: http://www.hpl.hp.com/personal/Tom_Fawcett/papers/ROC101.pdf
6. Fayyad, U. M., and K. B. Irani, 1993, Multi-interval discretization of continuous-valued attributes, in: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (Morgan Kaufmann), 1022-1027.
7. Hall, M. A., 1998, Correlation-based feature selection for machine learning. PhD thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand.
8. Harrell, F. E., and K. L. Lee, 1985, A comparison of the discrimination of discriminant analysis and logistic regression under multivariate normality, in: P. K. Sen, ed. Biostatistics: Statistics in Biomedical, Public Health, and Environmental Sciences (North Holland, Amsterdam).
9. Hopwood, W., J. McKeown, and J. Mutchler, 1988, The sensitivity of financial distress prediction models to departures from normality, Contemporary Accounting Research 5, 284-198.
10. Hosmer, D. W., and S. Lemeshow, 2000, Applied Logistic Regression, 2nd edn. (John Wiley & Sons, New York).
11. John, G. H., R. Kohavi, and K. Pfleger, 1994, Irrelevant features and subset selection problem, in: Proceedings for the Eleventh International Conference on Machine Learning.
12. Karels, G. V., and A. J. Prakash, 1987, Multivariate normality and forecasting of business bankruptcy, Journal of Business Finance & Accounting 14, 573-593.
13. Kerber, R., 1992, ChiMerge: discretization of numeric attributes, in: Proceedings of the Ninth International Conference on Artificial Intelligence, AAAI-92 (The MIT Press), 123-128.
14. Kohavi, R., and G. H. John, 1997, Wrappers for feature subset selection, Artificial Intelligence 97, 273-324.
15. Koller, D., and M. Toward Sahami, 1996, Optimal feature selection, in: Proceedings for the Thirteenth International Conference on Machine Learning.
16. le Cessie, S., and J. C. van Houwelingen, 1992, Ridge estimators in logistic regression, Applied Statistics 41, 191-201.
17. Negnevitsky, M., 2005, Artificial Intelligence - A Guide to Intelligent Systems, 2nd edn. (Addison, Australia).
18. Ohlson, J., 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research 18, 109-131.
19. Penman, S. H., 1996, The articulation of price-earnings ratios and market-to-book ratios and the evaluation of growth, Journal of Accounting Research 34, 235-259.
20. Platt, H. D., and M. B. Platt, 1990, Development of a class of stable predictive variables: the case of bankruptcy prediction, Journal of Business Finance and Accounting 17, 31-51.
21. Piramuthu, S., H. Ragavan, and M. J. Shaw, 1998, Using feature construction to improve performance of neural networks, Management Science 44, 416-430.
22. Press, S. J., and S. Wilson, 1978, Choosing between logistic regression and discriminant analysis, Journal of American Statistical Association 73, 699-705.
23. Siegel, S., 1956, Nonparametric Statistics for the Behavioral Sciences (Mcgran-hill Book Company, New York), 116-166.
24. Sriram, R. S., R. Srikanth, and R. George, 1998, A comparative analysis of artificial neural network learning algorithms: a bank financial risk model, in: Applications of Fuzzy Sets and the Theory of Evidence to Accounting II (JAI Press, Stamford, CT), 265-276.
25. West, P. M., P. L. Brockett, and L. L. Golden, 1997, A comparative analysis of neural networks and statistical methods for predicting consumer choice, Marketing Science 16, 370-391.