Detection of financial distress via multivariate statistical analysis

S Ganesalingam, Kuldeep Kumar

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)

Abstract

Describes the ability of modern computer-driven multivariate statistical analysis to deal with
complex data and the development of statistical models for predicting financial distress.
Applies multivariate techniques to 1986-1991 financial ratio data for Australian failed (29)
and nonfailed (42) companies; and explains the techniques used (principal components
analysis, factor analysis, discriminant analysis and cluster analysis) and the different types of
information they can provide to help identify the distress levels of companies. Predicts that
multivariate methods will change the way researchers think about problems and design their
research. An unusually clear exposition of the application of multivariate methods.
Original languageEnglish
Pages (from-to)45-55
Number of pages11
JournalManagerial Finance
Volume27
Issue number4
DOIs
Publication statusPublished - 2001

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Financial distress
Statistical analysis
Cluster analysis
Design research
Discriminant analysis
Distress
Statistical model
Principal component analysis
Financial ratios
Factor analysis

Cite this

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Detection of financial distress via multivariate statistical analysis. / Ganesalingam, S; Kumar, Kuldeep.

In: Managerial Finance, Vol. 27, No. 4, 2001, p. 45-55.

Research output: Contribution to journalArticleResearchpeer-review

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