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Optimising errors in signaling corporate collapse using MCCCRA

Hossari, Ghassan 2012, Optimising errors in signaling corporate collapse using MCCCRA, International journal of accounting and information management, vol. 20, no. 3, pp. 300-316.

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Title Optimising errors in signaling corporate collapse using MCCCRA
Author(s) Hossari, Ghassan
Journal name International journal of accounting and information management
Volume number 20
Issue number 3
Start page 300
End page 316
Total pages 17
Publisher Emerald Group Publishing
Place of publication Bingley, England
Publication date 2012
ISSN 1834-7649
1758-9037
Keyword(s) accounting
business failures
corporate collapse
financial ratios
modelling
multi-classification constrained-covariance regres
multiple discriminant analysis
United States of America
Summary Purpose – The purpose of this paper is to put forward an innovative approach for reducing the variation between Type I and Type II errors in the context of ratio-based modeling of corporate collapse, without compromising the accuracy of the predictive model. Its contribution to the literature lies in resolving the problematic trade-off between predictive accuracy and variations between the two types of errors.

Design/methodology/approach – The methodological approach in this paper – called MCCCRA – utilizes a novel multi-classification matrix based on a combination of correlation and regression analysis, with the former being subject to optimisation criteria. In order to ascertain its accuracy in signaling collapse, MCCCRA is empirically tested against multiple discriminant analysis (MDA).

Findings – Based on a data sample of 899 US publicly listed companies, the empirical results indicate that in addition to a high level of accuracy in signaling collapse, MCCCRA generates lower variability between Type I and Type II errors when compared to MDA.

Originality/value – Although correlation and regression analysis are long-standing statistical tools, the optimisation constraints that are applied to the correlations are unique. Moreover, the multi-classification matrix is a first in signaling collapse. By providing economic insight into more stable financial modeling, these innovations make an original contribution to the literature.
Language eng
Field of Research 150103 Financial Accounting
150201 Finance
Socio Economic Objective 970115 Expanding Knowledge in Commerce, Management, Tourism and Services
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, Emerald Group Publishing
Persistent URL http://hdl.handle.net/10536/DRO/DU:30046226

Document type: Journal Article
Collections: Faculty of Business and Law
Deakin Graduate School of Business
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