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Estimating The Credit Quality Of Chemical Companies | OMICS International
ISSN: 2167-0234
Journal of Business & Financial Affairs
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Estimating The Credit Quality Of Chemical Companies

Jan Henrik Wosnitza*

Research Assistant, Institute of Business Administration, University of Muenster, Germany

*Corresponding Author:
Jan Henrik Wosnitza
Research Assistant, Institute of Business Administration
University of Muenster, Leonardo-Campus 1, 48149 Muenster, Germany
E-mail: [email protected]

Received February 18, 2014; Accepted April 25, 2014; Published April 28, 2014

Citation: Wosnitza JH (2014) Estimating the Credit Quality of Chemical Companies. J Bus Fin Aff 3:118 doi: 10.4172/2167-0234.1000118

Copyright: © 2012 Wosnitza JH. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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In a recently published paper, Wosnitza and Leker [1] suggested a logistic regression model for estimating the rank order of international companies according to their credit quality. The aim of the article at hand is to compare the classification performances between the outcomes of the proposed model and ratings from Standard & Poor’s (S&P’s) for chemical companies. The data sample contains S&P’s ratings and financial ratios of eleven international companies from 2010. Despite small deviations, both approaches come to very similar results. Therefore, this paper validates Wosnitza’s and Leker’s logistic regression model and suggests to apply the equation for assessing the credit quality of chemical companies.

According to the Basel Committee on Banking Supervision [2], the logistic regression has developed into a standard method for estimating large companies’ probabilities of default (PDs) from financial ratios. In order to understand how the logistic regression estimates corporate PDs, let us consider a company k whose relevant default information is summarized in the vector equation . The logistic regression assigns a credit score between zero and one to company k according to the following relationship (Trustorff et al. [3]):

equation

Where and are predefined parameter values. Wosnitza and Leker [1] recently proposed a logistic regression model which allows the user to rank international corporates according to their credit qualities. They trained their logistic regression model on 618 financial reports of 312 international companies of which 156 had defaulted. Financial data from different industries except the financial industry was collected in order to obtain sufficient data. Their equation for calculating credit scores is based on the equity ratioequation and the net debt

equation

equation

equation

The higher the credit score, the lower is the credit quality of the respective company, and vice versa. Owing to the small value of its coefficient, the reader might get the idea to neglect the NDR. However, the incorporation of this financial ratio led to better classification results on test data than models without this ratio.

The purpose of the research note at hand is to compare the classification performances between the results of equation (2) and credit ratings from Standard and Poor’s (S&P’s) for chemical companies. To this end, the ERs and the NDRs were computed for eleven chemical companies from their 2010 financial statements which were downloaded from Data-stream [4]. The credit quality of this sample was also evaluated by S&P’s. From each available rating class, one chemical company was randomly selected and included in the data set. The random character of the sample helps to avoid a selection bias, i.e., a systematic error in choosing the individuals to take part in the study. Within this sample, A+ denotes the best and B the worst credit quality. S&P’s credit ratings were obtained from S&P’s Global Credit Portal [5]. The credit score and S&P’s rating are summarized in Table 1 for each company.

Company’s name Country of headquarter Credit score S&P’s rating
Monsanto Co. United States 0.25 A+
Sigma-Aldrich Corp. United States 0.22 A
Solvay S.A. Belgium 0.31 A-
AkzoNobel N.V. Netherlands 0.32 BBB+
K+S AG Germany 0.33 BBB
Methanex Corp. Canada 0.35 BBB-
NewMarket Corp. United States 0.34 BB+
Ashland Inc. United States 0.38 BB
EnergySolutionsInc. United States 0.41 BB-
Huntsman Corp. United States 0.53 B+
OMNOVA Solutions Inc. United States 0.55 B

Table 1: Comparison of two credit scores: The credit scores according to equation (1) and S&P’s credit ratings are listed for eleven chemical companies.

In order to evaluate the classification performance of equation (1), we compare the rank order produced by the credit scores with the ranking according to S&P’s ratings. The two rank orders differ only for four companies. Monsanto Co. and Sigma-Aldrich Corp. as well as NewMarket Corp. and Methanex Corp. changed places, respectively. However, the distances between these two pairs are small.

The close match between the two rankings confirms equation (1). This equation seems to be able to rank large chemical companies according to their credit quality.

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