A Hybrid Model for Financial distress Prediction

Kuldeep Kumar, Clarence Tan

Research output: Contribution to journalArticleResearchpeer-review

Abstract

in view of the failure of many high profile firms, bankruptcy
prediction has become a topic of high interest. In this paper we
have briefly reviewed the various techniques for the financial
distress prediction. These techniques consist of statistical'tech¬
niques like logit and probit models, multiple discriminant
analysis, multivariate cusum methods etc. and Artificial Neu¬
ral Network techniques. We have used some of these techniques .
to over 1000 us companies which consisted of a mixture of
surviving and failed firms. We have developed a hybrid model
for predicting financial distress.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalJournal of Combinatorics, Information and System Sciences
Volume27
Issue number1-4
Publication statusPublished - 2002

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title = "A Hybrid Model for Financial distress Prediction",
abstract = "in view of the failure of many high profile firms, bankruptcyprediction has become a topic of high interest. In this paper wehave briefly reviewed the various techniques for the financialdistress prediction. These techniques consist of statistical'tech¬niques like logit and probit models, multiple discriminantanalysis, multivariate cusum methods etc. and Artificial Neu¬ral Network techniques. We have used some of these techniques .to over 1000 us companies which consisted of a mixture ofsurviving and failed firms. We have developed a hybrid modelfor predicting financial distress.",
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A Hybrid Model for Financial distress Prediction. / Kumar, Kuldeep; Tan, Clarence.

In: Journal of Combinatorics, Information and System Sciences, Vol. 27, No. 1-4, 2002, p. 1-14.

Research output: Contribution to journalArticleResearchpeer-review

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AB - in view of the failure of many high profile firms, bankruptcyprediction has become a topic of high interest. In this paper wehave briefly reviewed the various techniques for the financialdistress prediction. These techniques consist of statistical'tech¬niques like logit and probit models, multiple discriminantanalysis, multivariate cusum methods etc. and Artificial Neu¬ral Network techniques. We have used some of these techniques .to over 1000 us companies which consisted of a mixture ofsurviving and failed firms. We have developed a hybrid modelfor predicting financial distress.

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