Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy

du Jardin, Philippe (2010): Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy. Published in: Neurocomputing , Vol. 73, No. 10-12 (2010): pp. 2047-2060.

Abstract

We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction.

• Item Type: MPRA Paper
• Language: English
• Keywords: Financial failure; Variable selection; Neural network
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; G - Financial Economics > G3 - Corporate Finance and Governance > G33 - Bankruptcy ; Liquidation

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