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

• Published in: International 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
• Language: English
• 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
• ISSN: 1577-8517

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]