The going-concern opinion and the adverse credit rating: an analysis of their relationship

Purpose The purpose of this study is to examine the relationship between going-concern (GC) opinions issued by the Big 4 audit firms and adverse credit ratings from the two largest credit rating agencies (CRAs) – Standard & Poor’s (S&P) and Moody’s. This question is relevant because there have been suggestions that auditors and CRAs should become more similar to each other, and because the two largest CRAs have different ownership structures that could affect their ratings. Design/methodology/approach Univariate and multivariate analyses are performed using a sample of firms that filed for bankruptcy between January 1, 2002 and December 31, 2013 that also had an audit opinion signed during the 12 months prior to bankruptcy, along with a credit rating issued by either or both S&P and Moody’s. Both influence each other. The likelihood of an auditor issuing a GC opinion is related to the credit rating issued by both S&P and Moody’s in the month prior to the audit report signing. The results also show differences between the CRAs. S&P reacted in the month after an auditor issued a GC opinion by downgrading its ratings 68% of the time. However, Moody’s did not react as strongly as S&P, downgrading its ratings only 24% of the time. Findings Both audit reports and credit ratings influence each other. The likelihood of an auditor issuing a GC opinion is related to the credit rating issued by both S&P and Moody’s in the month prior to the audit report signing. The results also show differences between the CRAs. S&P reacted in the month after an auditor issued a GC opinion by downgrading its ratings 68% of the time. However, Moody’s did not react as strongly as S&P, downgrading its ratings only 24% of the time. Originality/value Auditors are more likely to issue GC opinions when there is a downgrade to the credit rating, and CRAs are more likely to downgrade their ratings when there is a GC opinion. The study highlights that CRAs with different ownership structures provide different credit rating outcomes.

Dynamic modelling of corporate credit ratings and defaults

In this article, we propose a longitudinal multivariate model for binary and ordinal outcomes to describe the dynamic relationship among firm defaults and credit ratings from various raters. The latent probability of default is modelled as a dynamic process which contains additive firm-specific effects, a latent systematic factor representing the business cycle and idiosyncratic observed and unobserved factors. The joint set-up also facilitates the estimation of a bias for each rater which captures changes in the rating standards of the rating agencies. Bayesian estimation techniques are employed to estimate the parameters of interest. Several models are compared based on their out-of-sample prediction ability and we find that the proposed model outperforms simpler specifications. The joint framework is illustrated on a sample of publicly traded US corporates which are rated by at least one of the credit rating agencies S&P, Moody's and Fitch during the period 1995–2014.

ESG as a Measure of Credit Ratings

The aim of this study was to examine the impact of environmental, social, and governance (ESG) measures on credit ratings given to non-financial institutions by the largest credit rating agencies according to economic sector divisions. The hypotheses were as follows: a strong negative impact on non-financial institutions’ credit rating changes will result from ESG risk changes, and the reaction of credit rating changes will vary in different sectors. Panel event models were used to verify these hypotheses. The study used data from the Thomson Reuters Database for the period 2010–2020. The analysis was based on the literature on credit rating determinants and on papers and reports on COVID-19, ESG factors, and their impact on credit rating changes. Linear decomposition was used for the analysis. To verify these hypotheses, long-term issuer credit ratings presented by Moody’s and Fitch for European companies listed on these stock exchanges have been used. In the analyses, financial and non-financial factors were also considered. The results suggested that, within the last year, the methodology presented by credit rating agencies has changed, and ESG factors are one of the basic measures that are used to verify credit rating changes, especially those related to the pandemic.

The Effect of Ownership Structure on Disclosure Quality and Credit Ratings in Family Firms: The Moderating Role of Auditor Choice

Despite the substantial degree of heterogeneity within family firms, little is known about how their heterogeneity affects firm behavior and the implication for the shareholder–debtholder agency problem. Our study contributes to the literature by examining whether family firms with a higher level of control-ownership divergence would disclose less information and whether Big 4 auditors play a moderating role in mitigating the negative impact of control-ownership divergence on disclosure quality resulting in improved credit ratings. Using data from the emerging economy of Taiwan, we provide support for our three hypotheses. Our contributions will interest family firm owners, researchers, auditors, and policymakers.

Credit Rating Model of Small Enterprises Based on Optimal Discriminant Ability and Its Empirical Study

As an important part of the national economy, small enterprises are now facing the problem of financing difficulties, so a scientific and reasonable credit rating method for small enterprises is very important. This paper proposes a credit rating model of small enterprises based on optimal discriminant ability; the credit score gap of small enterprises within the same credit rating is the smallest, and the credit score gap of small enterprises between different credit ratings is the largest, which is the dividing principle of credit rating of small enterprises based on the optimal discriminant ability. Based on this principle, a nonlinear optimization model for credit rating division of small enterprises is built, and the approximate solution of the model is solved by a recursive algorithm with strong reproducibility and clear structure. The small enterprise credit rating division not only satisfies the principle that the higher the credit grade, the lower the default loss rate, but also satisfies the principle that the credit group of small enterprises matches the credit grade, with credit data of 3111 small enterprises from a commercial bank for empirical analysis. The innovation of this study is the maximum ratio of the sum of the dispersions of credit scores between different credit ratings and the sum of the dispersions of credit scores within the same credit rating as the objective function, as well as the default loss rate of the next credit grade strictly larger than the default loss rate of the previous credit grade as the inequality constraint; a nonlinear credit rating optimal partition model is constructed. It ensures that the small enterprises with small credit score gap are of the same credit grade, while the small enterprises with large credit score gap are of different credit grades, overcoming the disadvantages of the existing research that only considers the small enterprises with large credit score gap and ignores the small enterprises with small credit score gap. The empirical results show that the credit rating of small enterprises in this study not only matches the reasonable default loss rate but also matches the credit status of small enterprises. The test and comparative analysis with the existing research based on customer number distribution, K-means clustering, and default pyramid division show that the credit rating model in this study is reasonable and the distribution of credit score interval is more stable.

Probabilistic Calibration and Genetic Algorithm-based Bank Credit Strategies for MSMEs and Enlightenment to Tobacco Enterprise Management

Objectives: To further strengthen the role of Micro, Small and Medium Enterprises (MSMEs) in maintaining the vitality of national economy, governments around the world introduced many special policies. They kept guiding the banking industry to increase the support for MSMEs and reduce their financing difficulties in banks. Basing on the analysis of the bank's credit strategy for small and medium-sized enterprises of similar size, this paper gives the management strategy for small and medium-sized enterprises in tobacco industry to obtain bank credit when they cannot expand their turnover. In this paper, we proposed a binary classification model-based probabilistic calibration algorithm to calculate the default probability of enterprises in the formation of risk measurement model, and found the optimal solution of credit strategy using an improved genetic algorithm. Firstly, we discovered the enterprise’s information and invoice data of 123 micro and medium-sized enterprises with existing credit ratings. We extracted several features from multiple perspectives, such as size, relationship in supply chain, profitability, performance ability, and level of development, and removed the correlations among the indicators using principal component analysis (PCA). Secondly, the retained principal components were used as covariates, and we determined the credit ratings of the firms and the probability of default using discrete variables such as the credit ratings of the firms and whether they defaulted. Finally, we substituted the probability of default into the credit risk model to calculate the loss expectation and profit expectation of the credit portfolio, and used the profit expectation of the credit portfolio as the objective function of the 0-1 programming equation to derive the credit strategy with the lowest risk exposure and the highest return basing on the genetic algorithm.