Corporate Bankruptcy Prediction Using Data Mining Techniques
Price
Free (open access)
Volume
37
Pages
9
Published
2006
Size
466 kb
Paper DOI
10.2495/DATA060351
Copyright
WIT Press
Author(s)
M. F. Santos, P. Cortez, J. Pereira & H. Quintela
Abstract
The interest in the prediction of corporate bankruptcy is increasing due to the implications associated with this phenomenon (e.g. economic, and social) for investors, creditors, competitors, government, although this is a classical problem in the financial literature. Two kinds of models are generally adopted for bankruptcy prediction: (i) accounting ratios based models and (ii) market based models. In the former, classical statistical techniques such as discriminant analysis or logistic regression models have been used, while in the latter the Moody’s KMV model was adopted. This paper follows the first approach (i), and it is based on the analysis of the evolution of several financial indicators during a three-year period. A framework was developed, encompassing a total of 16 models. These differ in the data mining algorithm (e.g. Artificial Neural Networks or Decision Trees), the data used (all three years or just the last one) and the input attributes adopted (e.g. all accounting ratios or just the most significant ones). The experiments were conducted using the new Business Intelligence Development Studio of the Microsoft SQL Server. Very good results were achieved, with performances between 86% and 99% for all 16 models. Keywords: data mining, knowledge discovery from databases, decision support, corporate bankruptcy, artificial neural networks, decision trees.
Keywords
data mining, knowledge discovery from databases, decision support,corporate bankruptcy, artificial neural networks, decision trees.