Determining Credit Ratings Research Articles

Credit ratings and financialized political capitalism in a Chinese development zone

China’s state driven urbanization and the revenues earned through conversion of land from rural (legally non-commodified) to urban (legally commodified) status have been studied intensively. What makes this new urban land valuable is less discussed in the context of financializaton. Through a study of bond financing in one of China’s “national level new areas”, Tianfu New Area, on the outskirts of Chengdu, south-western China, this paper argues that financialization is not only created through conversion of land, but also through the capitalization of perceived political favour. The link between state and market is frequently conceptualized as consisting of non-transparent vested semi-corrupt networks between the state and well-connected state owned or private enterprises. We argue that an additional link exists between state and market through financialized political capitalism. This is a system where certain narratives and interpretations of policy suggest an implicit guarantee of bailouts of those debtors who are perceived to be politically important. These narratives and perceptions of political ranking become factors in determining credit ratings and issuing bonds both on the Chinese and the global market and thus the creation of capital based on real estate in China.

Identification of factors for assessing credit rating of non-financial companies

Goal: an improvement of the quality and accuracy of credit rating estimation, to add non-financial companies’ characteristics to the assessment, and to compare the importance of factors for different industries and countries. Methodology: the use of exploratory factor analysis and ordered logistic regression models. The study is based on publicly available data on non-financial companies from different industries and countries for the period from 2007 to 2021. Findings: a group of financial variables is the most significant in credit risk assessment for companies from developed countries, for emerging markets, macroeconomic variables mostly determine credit ratings instead. The nonfinancial variables have diverse effects, but as a group increase the accuracy of the credit rating assessment. Originality and contribution of the authors: there is limited research on the topic of credit rating assessment of non-financial companies with the inclusion of non-financial factors. In addition, the presented research contributes to the search for new relevant indicators that can be introduced instead of the qualitative variables used by international rating agencies and requiring expert assessment.

Financial determinants of corporate credit ratings: An Indian evidence

AbstractCredit rating is a vital tool in the hands of investors and other users. Standard mathematical formulas do not exist for determining credit ratings. Instead, rating agencies exercise their judgment and use their experience to determine what information is to be important in providing a rating. Thus, knowledge and understanding about factors influencing credit ratings is very crucial for companies being rated and other users. Most of the prior studies in related context have used developed countries' data, and the effect in emerging markets, especially in Indian context, is still unclear due to lack of such studies. So, this study attempts to identify the long‐term and short‐term financial determinants of credit ratings issued to Indian companies and examines their impact on credit ratings on the basis of panel data and cross‐sectional approaches. Ordered probit analysis is used with credit ratings as the dependent variable and six financial factors as the independent variables. Results indicate that size, profitability and leverage have a significant relationship with corporate credit ratings in both panel data and cross‐sectional approaches. Also, size has the highest impact on credit ratings followed by leverage and profitability in both the approaches. The major contribution of present study is that two different mathematical models are framed which have a reasonably well correct prediction percentage. These models can be used by the investors' academicians, practitioners and other users for predicting the rating categories of various companies and to get a fair idea about their creditworthiness and safety.

DETERMINANTS OF SOVEREIGN CREDIT RATINGS: AN APPLICATION OF THE NAÏVE BAYES CLASSIFIER

This is an analysis of South Africa’s (SA) sovereign credit rating (SCR) using Naïve Bayes, a Machine learning (ML) technique. Quarterly data from 1999 to 2018 of macroeconomic variables and categorical SCRs were analyzed and classified to predict and compare variables used in assigning SCRs. A sovereign credit rating (SCR) is a measurement of a sovereign government’s ability to meet its financial debt obligations. The differences by Credit Rating Agencies (CRA) on rating grades on similar firms and sovereigns have raised questions on which elements truly determine credit ratings. Sovereign ratings were split into two (2) categories that is less stable and more stable. Through data cross-validation for supervised learning, the study compared variables used in assessing sovereign rating by the major rating agencies namely Fitch, Moody’s and Standard and Poor’s. Cross-validation splits the dataset into train set and test set. The research applied cross-validation to reduce the effects of overfitting on the Naïve Bayes Classification model. Naïve Bayes Classification is a Machine-learning algorithm that utilizes the Bayes theorem in classification of objects by following a probabilistic approach. All variables in the data were split in the ratio of 80:20 for the train set and test set respectively. Naïve Bayes managed to classify the given variables using the two SCR categories that is more stable and less stable. Variables classified under more stable indicates that ratings are high or favorable and those for less stable show unfavorable or low ratings. The findings show that CRAs use different macroeconomic variables to assess and assign sovereign ratings. Household debt to disposable income, exchange rates and inflation were the most important variables for estimating and classifying ratings.

Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach

It is commonly observed that high grade loans with better ratings are often associated with low recoveries if they default (i.e. with relatively high loss-given-default (LGD)). To address the mismatch problem, this paper proposes a credit risk approach by minimizing LGD for higher rated loans as a risk-rating matching standard in the sense that the decreasing LGD from creditors’ perspective is associated with higher credit rating for the borrower. This standard forces customers’ credit rating of each grade to be optimally determined in correspondence to its LGD, which means the LGD of high grade loans tends to be low. The approach is then tested using three credit datasets from China, i.e. credit data from 2044 farmers, 2157 small private businesses and 3111 SMEs. The empirical results show that the proposed approach indeed guides the way to solve the mismatch phenomenon between credit ratings and LGDs in the existing credit rating literature. By optimally determining credit ratings, the findings derived from this paper help provide a valuable reference for bankers, and bond investors to manage their credit risk.

The impact of credit ratings on corporate behavior: Evidence from Moody's adjustments

Moody's adjusts a firm's reported leverage across several dimensions to determine credit ratings. I find that changes to this adjustment methodology affect firm capital structure and investment decisions. In particular, in 2006, Moody's made several changes to its adjustment methodologies, which are arguably exogenous to changes in firm fundamentals. I show these changes significantly affect adjustments for firms in this year. I then show that these changes to adjustments in 2006 affect capital structure and investment decisions in 2007, especially for those firms with greatest exposure to the methodology changes. These results show that rating agencies have the power to affect corporate decisions.

Effect of capitalizing operating leases on credit ratings: Evidence from Japan

The purpose of this study is to examine the effects of constructively capitalizing operating leases on credit ratings in Japan. In particular, this study investigates whether and how a credit rating agency considers operating lease information when determining credit ratings. First, this study shows that constructively capitalized operating leases are associated with credit ratings. Second, this study finds that the associations between operating leases versus finance leases and credit ratings are not substantially different. However, when operating lease disclosures are less reliable, this study finds that operating leases are not associated with credit ratings and that the risk relevance of operating leases is substantially different from that of finance leases. This study reports that the reliability of accounting information has significant effects on the risk relevance of operating leases. These results indicate that a credit rating agency considers operating lease information in determining credit ratings to the extent that this information is reliable. This study contributes to the literature on the usefulness of operating lease disclosures and to the discussions on the global convergence of accounting standards.

Financial Factors Determining CAREs Ratings

Rating agencies evaluate a number of qualitative and quantitative factors while assigning rating to a particular company. Standard mathematical formulas do not exist for determining credit ratings. Instead, credit rating agencies use their experience and judgement in assigning ratings. What factors rating agencies consider significant in providing ratings to the companies is an important question. The present study aims to contribute to the above mentioned area by identifying the financial determinants of credit ratings assigned to Indian companies by CARE, one of the top rating agencies of India. Ordered probit analysis is used on unbalanced panel data with credit rating as the dependent categorical variable and six financial factors viz. size, liquidity, profitability, interest coverage, leverage, and growth as the independent variables. Results from ordered probit analysis indicate that the likelihood of credit ratings to be on higher side is more with increase of size, liquidity, profitability, interest coverage, growth and with a decrease in leverage. Further, size, profitability, and leverage are found to be statistically significant factors at the 1% level, liquidity and growth at the 5% level and interest coverage at the 10% level.

Penentuan Penilaian Kredit Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization

Bad credit is one of the credit risk faced by the financial and banking industry. Bad credit can be avoided by means of an accurate credit analysis of the debtor. The accuracy of credit ratings is crucial to the profitability of financial institutions. Improved accuracy of credit ratings can be done by doing the selection of attributes, because the selection of attributes reduce the dimensionality of the data so that operation of the data mining algorithms can be run more effectively and more cepat.Banyak research has been conducted to determine credit ratings. One of the methods most widely used method of Naive Bayes. In this study will be used method Naive Bayes and will do the selection of attributes by using particle swarm optimization to determine credit ratings. After testing the results obtained are Naive Bayes produce accuracy value of 72.40% and AUC value of 0.765. Then be optimized by using particle swarm optimization results show values higher accuracy is equal to 75.90% and AUC value of 0.773. So as to achieve the increased accuracy of 3.5%, and increased the AUC of 0.008. By looking at the accuracy and AUC values, the Naive Bayes algorithm based on particle swarm optimization into the classification category enough.

On the Nonlinearity of the Financial Ratios-Credit Ratings Relationship

In this paper, we study the specificity of financial ratios in determining credit ratings. Specifically, we examine the nonlinearity of the financial ratios-credit ratings relationship. Among financial ratios, the interest coverage and debt coverage ratios have the most pronounced effect on credit ratings. To determine the form of the nonlinearity, the interest and debt coverage ratios are divided to four sub-variables with different weights associated to each increment. We find that different coefficients are associated to different increments of the interest coverage and debt coverage ratios. An interest coverage ratio loses all significance when it is less than zero and when it exceeds 20. Similarly, a debt coverage ratio loses all significance when it less than negative one and when it exceeds one. Our results confirm the nonlinearity of the financial ratios-credit rating relationship.

Statistical properties and stability of ratings in a subset of US firms

Standard explanatory variables that determine credit ratings do not achieve significant effects in a sample of 100 US non-financial firms in an ordered probit panel estimation. Sample size and selection as well as the distribution of explanatory variables across rating classes may be the cause this problem. Furthermore, we find evidence to suggest that variable coefficients vary over rating classes when analysed with an unordered loogit model. The sample reproduces well-established macroeconomic effects of credit ratings found by Blume et al. (1998) and highlights the influence of the rating agencies’ through-the-cycle approach on rating transitions.

The Real and Financial Effects of Credit Ratings: Evidence from Moody's Adjustments

Moody’s adjusts a firm’s reported leverage across several dimensions to determine credit ratings. I find that changes to these adjustments affect capital structure and investment decisions, especially for firms near the investment grade boundary and with bond covenants containing ratings triggers. Further, in 2006, Moody’s made several changes to its adjustment methodologies. I find that changes to adjustments made in 2006 affect capital structure and investment decisions in 2007. Firms most affected by these methodology changes in 2006 also alter their financing and investment behavior as predicted. These results show that rating agencies have the power to affect firm financial and investment policy.