NDLI: Neural and Wavelet Network Models for Financial Distress Classification

Content Provider	Springer Nature Link
Author	Becerra, Victor M. Galvão, Roberto K. H. Abou Seada, Magda
Copyright Year	2005
Abstract	This work analyzes the use of linear discriminant models, multi-layer perceptron neural networks and wavelet networks for corporate financial distress prediction. Although simple and easy to interpret, linear models require statistical assumptions that may be unrealistic. Neural networks are able to discriminate patterns that are not linearly separable, but the large number of parameters involved in a neural model often causes generalization problems. Wavelet networks are classification models that implement nonlinear discriminant surfaces as the superposition of dilated and translated versions of a single “mother wavelet” function. In this paper, an algorithm is proposed to select dilation and translation parameters that yield a wavelet network classifier with good parsimony characteristics. The models are compared in a case study involving failed and continuing British firms in the period 1997–2000. Problems associated with over-parameterized neural networks are illustrated and the Optimal Brain Damage pruning technique is employed to obtain a parsimonious neural model. The results, supported by a re-sampling study, show that both neural and wavelet networks may be a valid alternative to classical linear discriminant models.
Starting Page	35
Ending Page	55
Page Count	21
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	11
Issue Number	1
e-ISSN	1573756X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	financial distress neural networks wavelets finance classification Data Structures, Cryptology and Information Theory Information Storage and Retrieval Computing Methodologies Artificial Intelligence (incl. Robotics) Statistics Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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