Prediction of Corporate Financial Distress: An Application of the Composite Rule Induction System

The economic consequence of corporate failure is enormous, especially for the stakeholders of public-held companies. Prior to a corporate failure, the firm's financial status is frequently in distress. Consequently, finding a method to identify corporate financial distress as early as possible...

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Bibliographic Details
Published inInternational Journal of Digital Accounting Research Vol. 1; no. 1; p. 69
Main Authors Ko, Li-Jen, Blocher, Edward J, Lin, P Paul
Format Trade Publication Article
LanguageEnglish
Published Huelva International Journal of Digital Accounting Research 01.01.2001
Subjects
Accuracy
Algorithms
Artificial intelligence
Bank failures
Bankruptcy
Bias
Business failures
Current liabilities
Discriminant analysis
Financial ratios
Forecasting techniques
Net income
Neural networks
Normal distribution
Ratios
Regression analysis
Studies
Working capital
Taiwan
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Summary:The economic consequence of corporate failure is enormous, especially for the stakeholders of public-held companies. Prior to a corporate failure, the firm's financial status is frequently in distress. Consequently, finding a method to identify corporate financial distress as early as possible is clearly a matter of considerable interest to investors, creditors, auditors and other stakeholders. This paper uses a composite rule induction system (CRIS; Liang 1992) to derive rules for predicting corporate financial distress in Taiwan. In addition, this paper compares the prediction performance of cris, neural computing and the logit model. The empirical results indicate that both CRIS and neural computing outperform the logit model in predicting financial distress. Although both CRIS and neural computing perform rather well, CRIS has the advantage that the derived rules are easier to understand and interpret. [PUBLICATION ABSTRACT]
ISSN:1577-8517
2340-5058