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Article
Publication date: 7 June 2013

Shyam B. Bhandari and Rajesh Iyer

Business failures during the economic recession of 2008‐2010 years were unusually high in the USA. The purpose of this paper is to build a new model to predict business…

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Abstract

Purpose

Business failures during the economic recession of 2008‐2010 years were unusually high in the USA. The purpose of this paper is to build a new model to predict business failure, using mostly cash flow statement based measures as predictor variables and discriminant analysis technique.

Design/methodology/approach

The authors' data matrix consisted of 100 firms and seven predictor variables. A total of 50 “failed” firms were matched with 50 non‐failed firms according to Standard Industrial Classification (SIC) code and size. Financial statement data for the year prior to failed year were pulled from COMPUSTAT database. Seven predictor variables were selected, namely Operating cash flow divided by current liabilities, Cash flow coverage of interest, Operating cash flow margin, Operating cash flow return on total assets, Earning quality, Quick ratio and Three‐year sales growth. The SPSS‐19 software was used to perform discriminant analysis (DA).

Findings

The DA model classified 83.3 percent of original grouped cases correctly. The cross‐validated approach (jackknife or leave‐one‐out method) correctly classified 79.5 percent of cases. The chi‐square test of Wilks' lambda was significant at 0.000 level which means the model as a whole performed very well in predicting business failure.

Originality/value

This study is unique in many respects. First, the sample companies are not industry specific. They come from more than 20 different two‐digit SIC codes, which means the authors' model is very generic in nature. Second, the seven predictor variables (financial ratios) they selected are logically justified; these are not an outcome of step‐wise procedure. Third, most of the predictor variables use operating cash flow information from the cash flow statement. Fourth, all the failed firms in the authors' test sample are from the most recent, 2008‐2010, period.

Details

Managerial Finance, vol. 39 no. 7
Type: Research Article
ISSN: 0307-4358

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Article
Publication date: 1 January 1990

Mohamed E. Ibrahim, Saad A. Metawae and Ibrahim M. Aly

In recent years, a sizeable amount of research in finance and accounting has been devoted to the issue of bond rating and bond rating changes. A major thrust of these…

Abstract

In recent years, a sizeable amount of research in finance and accounting has been devoted to the issue of bond rating and bond rating changes. A major thrust of these research efforts was to develop and test some prediction‐based models using mainly financial ratios and their trends. This paper tests the ability of statistical decomposition analysis of financial statements to predict bond rating changes. The results show that the decomposition analysis almost does not beat the a priori probability model and is no better than multiple discriminant analysis using simple financial ratios. One important piece of information for participants in debt markets is the assessment of the relative risk associated with a particular bond issue, commonly known as bond ratings. These ratings, however, are not usually fixed for the life of the issues. From time to time, the rating agencies review their ratings of the outstanding bond issues and make changes to these ratings (either upward or downward) when needed. Over the years, researchers have attempted to develop and test some prediction based models in order to predict bond ratings or bond rating changes. These prediction models have employed some variables that are assumed to reflect the rating agency decision‐making activities. Although the rating process is complicated and based mainly on judgmental considerations, Hawkins, Brown and Campbell (1983, p. 95) reported that the academic research strongly suggests that a reliable estimate of a potential bond rating or rating change can be determined by a few key financial ratios. Information theory decomposition measures have received in recent years considerable attention as a potential tool for predicting corporate events, namely corporate bankruptcy (e.g., Lev 1970; Moyer 1977; Walker, Stowe and Moriarity 1979; Booth 1983). The underlying proposition in these studies is that corporate failure, as an event, is expected to be preceded by significant changes in the company's assets and liabilities structure. Although the event of bond rating changes is different from the bankruptcy event in terms of consequences, one can still propose that a bond rating change, as a corporate event, is also expected to be preceded by some significant changes in the company's assets and liabilities structure. Therefore, the decomposition analysis may have a predictive ability in the case of bond rating changes. The purpose of this paper is to empirically test and compare the classification and predictive accuracy of the decomposition analysis with the performance of a multiple discriminant model that uses financial ratios and their trends in the context of bond rating changes.

Details

Managerial Finance, vol. 16 no. 1
Type: Research Article
ISSN: 0307-4358

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