NDLI: Predicting company failure-a comparison between neural networks and established statistical techniques by applying the McNemar test

Content Provider	IEEE Xplore Digital Library
Author	Van Eyden, R.J. De Wit, P.W.C. Arron, J.C.
Copyright Year	1995
Description	Author affiliation: Pretoria Univ., South Africa (Van Eyden, R.J.; De Wit, P.W.C.; Arron, J.C.)
Abstract	The behaviour of a given system may be forecast using two general methodologies. The first depends upon knowledge of the laws that govern a particular phenomenon. When this knowledge is expressed in terms of a precise set of equations, which, in principle can be solved, then, providing that the initial conditions are specified, the future behaviour of the system may be predicted. However, in cases of systems belonging to behavioural science and economics, for example, the rules governing the behaviour of the system are not readily available. A second, less powerful method, involves the discovery of empirical regularities in observations of the system. As emphasised by Refenes, Azema-Barac, Chen and Karoussos (1993), such regularities are often masked by noise, whilst phenomena that seem random, without apparent periodicities, remain recurrent in a generic sense. As with any technology that is readily available, those companies that are using neural networks successfully are probably remaining silent so as to maintain their competitive advantage. As for the less than silent ones, it is doubtful whether they have discovered the advantages that neural networks may offer. This research, using a backpropagation neural network methodology, proposes to establish whether using neural networks to predict company failure is more successful than using established methodologies.
Starting Page	91
Ending Page	96
File Size	562044
Page Count	6
File Format	PDF
ISBN	0780321456
DOI	10.1109/CIFER.1995.495257
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-04-09
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Failure analysis Testing Databases Data analysis Performance analysis Companies Financial management Africa Equations
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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