SELEÇÃO DE ATRIBUTOS NA PREVISÃO DE INSOLVÊNCIA: APLICAÇÃO E AVALIAÇÃO USANDO DADOS BRASILEIROS RECENTES

Bankruptcy prediction may have great utility to financial and nonfinancial institutions with regard to take in advance the best possible decisions regarding loans or investments. In specific literature, many bankruptcy prediction models have made use of data mining. The main objective of this work i...

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Published inRevista de administração Mackenzie Vol. 15; no. 1; p. 125
Main Authors Horta, Rui Américo Mathiasi, Alves, Francisco José Dos Santos, De Carvalho, Frederico Antônio Azevedo
Format Journal Article
LanguagePortuguese
Published São Paulo Mackenzie Presbyterian University 01.02.2014
Subjects
Bankruptcy
Data mining
Feature selection
Insolvency
Selection
Studies
Brazil
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Summary:Bankruptcy prediction may have great utility to financial and nonfinancial institutions with regard to take in advance the best possible decisions regarding loans or investments. In specific literature, many bankruptcy prediction models have made use of data mining. The main objective of this work is to compare two approaches for evaluating subsets of attributes: filter and wrapper. Despite being based on data mining techniques and widely used in the step of feature selection in bankruptcy prediction models, these techniques are rarely used to treat data from financial statements of Brazilian companies. Therefore, the empirical basis of this study consists of a sample of Brazilian industrial and commercial enterprises, collecting data for the period 2004 to 2011. The results indicated that, in this sample, the filter approach was more efficient, providing better classification results both for logistic regression (91.80%) and for neural networks (93.98%).
ISSN:1518-6776
1678-6971
DOI:10.1590/S1678-69712014000100006