Extracting Predictors of Corporate Bankruptcy: Empirical Study on Data Mining Methods

Authors:
Cindy Yoshiko Shirata;Takao Terano
Affiliations:
-;-
Venue:
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Year:
2000

Citing 1
Cited 1

Induction of Decision Trees

Machine Learning

Context-Based Text Mining for Insights in Long Documents

PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

We presents some empirical results of a study regarding financial ratios as predictors of Japanese corporate bankruptcy based on a large sample of bankrupt and non-bankrupt firms' financial data. In this study, variable as predictors of bankruptcy had been selected by three Al-based data mining techniques and two conventional statistical methods, Logit analysis and Stepwise. After the selection of a set of variables for every method, discriminant power of each set was compared to verify the most suitable data mining technique to select financial variables. Finally, the study concludes that a set of variables selected by Logit analysis (with logit model) indicated the best discriminant power, more than 87% accuracy.