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TitleCorporate bankruptcy prediction using data mining techniques
Author(s)Santos, Manuel Filipe
Cortez, Paulo
Pereira, José
Quintela, Hélder
KeywordsData mining
Knowledge discovery from databases
Decision support
Corporate bankruptcy
Artificial neural networks
Decision trees
Issue date2006
PublisherWIT Press
JournalWIT Transactions on Information and Communication Technologies
CitationZANASI, A. ; BREBBIA, C.A. ; EBECKEN, N.F.F., ed. – “Data Mining VII : data, text and web mining and their business applications”. [Southampton] : WIT Press, 2006. ISBN 1-84564-178-7. p. 349-357.
Abstract(s)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.
TypeConference paper
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
DSI - Engenharia da Programação e dos Sistemas Informáticos

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