NDLI: Predicting corporate bankruptcy using modular neural networks

Content Provider	IEEE Xplore Digital Library
Author	Nasir, M.L. John, R.I. Bennett, S.C. Russell, D.M.
Copyright Year	2000
Description	Author affiliation: Centre for Comput. Intelligence, De Montfort Univ., Leicester, UK (Nasir, M.L.)
Abstract	The paper reports on the use of modular neural networks for predicting corporate bankruptcy. We obtained our financial, as well as, political and economic data from The London Stock Exchange, JORDANS financial database of major British public and private companies, and the Bank of England. In the past, various statistical techniques, such as univariate and multivariate discriminant analysis have been used in the modelling of corporate bankruptcy prediction. We use domain expert knowledge to select, and organise data in the modular neural network architecture constructed for this study. There are three sub-networks representing the periods, 1994, 1995, and 1996. Each sub-network is made of five adjacent networks representing the Balance Sheet network, the Profit and Loss network, the Financial Summary network, the Key Financial Ratios network, and the Economic and Political factors network. These adjacent networks although coupled but not linked at the input level represent five facets of failure in predicting corporate bankruptcy. The training sets represent data for 2500 companies selected randomly from a population of 270000 sample. The trained neural network will access 435000 data records before making a prediction for the particular company. The results obtained shows that neural networks outperform statistical techniques in modelling corporate failure prediction.
Starting Page	86
Ending Page	91
File Size	376173
Page Count	6
File Format	PDF
ISBN	0780364295
DOI	10.1109/CIFER.2000.844606
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-03-28
Access Restriction	Subscribed
Rights Holder	Authors
Subject Keyword	Neural networks Economic forecasting Predictive models Computational intelligence Finance Stock markets Databases Pattern recognition Sections Convergence
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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