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Article
Publication date: 29 April 2014

Ghassan Hossari

The purpose of this paper is to undertake an analysis of two recent classification schemes in the literature for ratio-based modelling of corporate collapse; namely the…

433

Abstract

Purpose

The purpose of this paper is to undertake an analysis of two recent classification schemes in the literature for ratio-based modelling of corporate collapse; namely the dual-classification scheme (DCS) and the multi-classification scheme (MCS). Its contribution to the literature lies in investigating whether a trade-off exists between the structural efficiency and the practical adeptness of these two schemes.

Design/methodology/approach

The methodological approach for the DCS relies on a combination of multiple discriminant analysis and multi-level modelling, whereas that for the MCS utilizes multi-classification constrained-covariance regression analysis.

Findings

Based on a unified data set of 112 collapsed companies and 341 non-collapsed companies utilised across both the DCS and the MCS, the results indicate that whilst both classification schemes are comparable in their predictive accuracy with respect to signalling collapse, they exhibit a trade-off between their structural efficiency and their practical adeptness.

Originality/value

Whilst novel classification schemes such as the DCS and the MCS have been successful in addressing the inherent problem of identifying unclassifiable companies in the literature for ratio-based modelling of corporate collapse, thus far no attempt has been made to investigate the trade-off between their structural efficiency and their practical adeptness. Moreover, by utilising a unified data set, the robustness of this investigation is enhanced. Accordingly, this paper provides economic insight into more stable financial modelling.

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Article
Publication date: 27 July 2012

Ghassan Hossari

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…

281

Abstract

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.

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