A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

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A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis

Author Info

Juliana Yim () (RMIT University)
Heather Mitchell (RMIT University)

Abstract

This paper looks at the ability of a relatively new technique, hybrid ANN's, to predict corporate distress in Brazil. These models are compared with traditional statistical techniques and conventional ANN models. The results suggest that hybrid neural networks outperform all other models in predicting firms in financial distress one year prior to the event. This suggests that for researchers, policymakers and others interested in early warning systems, hybrid networks may be a useful tool for predicting firm failure.

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Publisher Info
Article provided by Economics Department, Universidade Federal de Minas Gerais (Brazil) in its journal Nova Economia.

Volume (Year): 15 (2005)
Issue (Month): 1 (January-April)
Pages: 73-93
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Handle: RePEc:nov:artigo:v:15:y:2005:i:1:p:73-93

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Find related papers by JEL classification:
G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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