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THE DETERMINANTS OF CREDIT RATING LEVELS: EVIDENCE FROM
BRAZILIAN NON-FINANCIAL COMPANIES
Fabiano Guasti Lima
School of Economics, Business Administration and Accounting
University of São Paulo, Brazil
E-mail: [email protected]
Camila Veneo C. Fonseca
Institute of Economics
University of Campinas, Brazil
E-mail: [email protected]
Rodrigo Lanna F. da Silveira
Institute of Economics
University of Campinas, Brazil
E-mail: [email protected]
ABSTRACT
The purpose of this study is to identify the determinants of the credit ratings of Brazilian non-
financial listed companies. Specifically, we seek to evaluate whether the rating levels reflect
company and market indices. An ordered logistic model is applied, using an unbalanced panel
of 45 Brazilian non-financial listed companies during the period 2010-2015. Results show
evidence that financial leverage, profitability, cost of capital, systemic risk and size variables
consistently affect credit rating levels. Further, specific estimates for investment- and
speculative-grade firms show similar results, highlighting the role of cost of capital variable.
Keywords: credit rating, accounting quality, rating properties, ordered logistic model.
RESUMO
O objetivo deste estudo é identificar os determinantes dos ratings de crédito de companhias
abertas não financeiras brasileiras. Busca-se avaliar se os níveis de classificação de risco de
crédito refletem índices econômico-financeiros das empresas. Um modelo Logit ordenado é
aplicado, usando um painel não balanceado de 45 empresas listadas não financeiras brasileiras
no período 2010-2015. Os resultados apontam que alavancagem financeira, rentabilidade, custo
de capital, risco sistêmico e tamanho influenciaram os níveis de rating de crédito das
companhias. Análises adicionais, separando firmas com grau especulativo e de investimento,
mostraram resultados similares, destacando o papel da variável custo de capital.
Palavras-chave: avaliação de crédito, qualidade dos dados contábeis, propriedades do rating,
modelo Logit ordenado.
Área 8 - Microeconomia, Métodos Quantitativos e Finanças
JEL G24 e G3
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INTRODUCTION
Efficient corporate decisions depend, among other factors, on an appropriate assessment of the
risks involved in companies’ operations. Such analysis, in general, requires an evaluation of the
existing risks, their magnitude, their interpretation and the consequent decision-making in
relation to the risk management procedures.
Among the different risks that a company is exposed to, credit risk has highlighted
importance. This arises from the possibility of a credit default. In this context, credit rating
agencies (CRAs) play an important role in financial markets, providing key information widely
used by stakeholder groups in investment decisions, corporate financing process, and market
regulation. Accessing information from balance sheets, senior management interviews, and
economic and industrial environment, it is assumed that CRAs provide an independent analysis
of debt securities’ risk, decreasing the asymmetric information between investors and issuers
of such securities. Nevertheless, the occurrence of massive corporate accounting scandals
during 2000’s and 2007-2008 global financial crisis has led questions about the quality of
information announced by CRAs. As a result, CRAs have faced increasing criticism and
regulatory pressure, which is demanding greater regulatory measures and higher levels of
accountability.
The understanding of what determines the rating of a company is a very useful work,
both to allow stakeholders to build risk management mechanisms supported in the credit risk
classification system and to learn what factors may influence the movement of this rating. In
addition, knowing the variables that influence the variation of credit rating can help companies
regarding their investment and financing decisions taken over time.
A number of recent empirical studies have analyzed issues related to rating information
quality. Most of those studies, however, have focused on U.S. and European markets (Doumpos
et al., 2015; Mizen and Tsoukas, 2012; Jorion et al., 2009; Duff and Einig, 2009; Pasiouras et
al., 2006; Huang et al., 2004). Thus, these issues have been relatively under-examined in
emerging markets, where firms, in general, face different institutional, economic, and business
conditions. The objective of this study is to identify the determinants of the credit ratings of
Brazilian non-financial listed companies between 2010 and 2015, the period after the
compulsory adoption of the IFRS (International Financial Reporting Standards). Specifically,
we seek to evaluate whether the rating levels reflect company and market indices.
PREVIOUS STUDIES
The activities of a company are subject to a number of risks, which are inherent to its area of
activity and/or business environment. The advancement of financial and commercial
globalization, observed in recent decades, has deepened the exposure of production units to
different risks, while giving new possibilities of return to investors. Therefore, as emphasized
by Damodaran (2008), it is evidenced the dual character of the risk, which combines threat and
opportunity. Given that framework, in an efficient business management, risks are expected to
be identified, measured and, then, managed according to their nature and intensity. If, on the
one hand, a company that neutralizes all its risks reduces its potential for profit, on the other
hand, an incorrect exposure can lead to significant losses.
By focusing the analysis on the financial risk, it is classified into five categories:
operational, liquidity, legal, market and credit risk (Jorion, 2007). The latter, as noted earlier,
is the possibility of default by a counterpart in a certain transaction, being such an event caused
by a combination of factors that are internal and external to the company. The risk in question
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can still involve the possibility of deterioration in the quality of the agent´s credit, which
increases the likelihood of default. We should also note that the analysis of credit risk involves
not only the risk of default, but also the risk of exposure and recovery. While the risk of
exposure is associated with the uncertainty concerning the amount due at the time of the default,
the risk of recovery involves the value retrieved by the creditor if the event in question happens.
We can therefore note that the default risk closely relates to the economic and financial
characteristics of the borrower, and the risks of exposure and recovery come from the credit
agreement (Brito et al., 2009; Brito and Assaf Neto, 2008).
Given the difficulty underlying the identification and measurement of the credit risk of
a particular company, the rating agencies appear as a relevant market agent, as it is their role
collect, filter and spread the information about the various companies. Their function involves,
thus, the development of a clear and uniform criterion of credit risk analysis, which serves as a
reference for different types of decisions, as well as information for regulators. In the context
of intense financial globalization, characterized by the emergence and dissemination of
products of high complexity in their structure, these agents and their instruments have gained
particular notoriety, becoming one of the main references of investors in relation to the risk
associated with the companies in which they plan to invest.
The instruments used in the mentioned risk classification are called credit ratings and
they consist in an assessment scale of a company capacity to honor its financial commitments
within the time established in the agreement, being a good indicator of a company´s risk of
default (Minardi, 2006; Soares et al. 2012). The proposed ratings differ among different rating
agencies, and, in general, letters express the ratings whose order describes the scale of risk. The
assessment, on the other hand, usually considers not only information about the accounting and
financial condition of the company, but also aspects that are specific to the business, such as
market share, competitive strategy, corporate governance standard and, particularly, industry
risk (Caouette et al., 1998). Thus, most of the determinant information of the default risk should
be contained in the company´s rating. The methodology associated with their development
process should therefore assume, in addition to the financial profile, a thorough economic
analysis that considers the economic situation of the company´s country of domicile,
geopolitical, legal and institutional aspects, among others. For such reasons, the importance of
ratings goes beyond the mitigation of information asymmetry between private agents, going
through regulatory issues imposed by official agents.
The study of the ratings assigned by the rating agencies, its main determinants, as well
as its alternation, became object of academic studies already in the 1960s. The focus so far has
been the American market, and the main objective was to identify the set of economic and
financial indicators used by the specialized agencies in determining the ratings of the
companies. Horrigan (1966), for example, was a pioneered in the proposition of a multiple
regression model whose purpose was to predict the rating of corporate securities using financial
indicators. Having predicted correctly, on average, 55% of the ratings from Moody's and S&P,
the main conclusion of the author was that the rating agencies tend to emphasize different
variables over time, being their behavior systematically related to the stability of the
macroeconomic environment.
Altman (1968), in turn, developed a multivariate analytical technique that incorporated
economic and financial indicators in the prediction of insolvency of industrial companies. The
discriminant analysis model proved to be accurate, having predicted correctly 94% of the cases
in the initial sample, with validation of this precision in subsequent samples. Later, Altman and
Katz (1976) applied the same method of analysis to predict the ratings of debt securities of
American energy utilities, correctly predicting more than 80% of the cases.
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In addition to the dissemination of studies on potential determinants of ratings, research
began to focus on the informational content of reclassifications. Katz (1974), using regression
analysis with data from American companies of the electrical energy sector, has found that there
is no anticipation to a rating reclassification, securities prices adjust to the new rating with a lag
of between six and ten weeks, pointing to the non-existence of a semi-strong efficiency in this
market. This conclusion was supported by Grier and Katz (1976), who have, by employing the
same method of analysis, pointed out that the ratings had informational content reflected in
market prices, what Pinches and Singleton (1978) claim to have positive relationship with
abnormal returns of the shares in reclassification episodes.
In addition to advances in the methods employed, with the progressive use of the
ordered Logit and Probit models, new research hypotheses have been proposed (Kaplan and
Urwitz, 1979; Ederington, 1985). Blume et al. (1998), for example, have analyzed the
hypothesis that the significant drop in ratings assigned to American corporations, between the
1970s and 1990s, could be explained by a worsening in the quality of their securities. Having
applied an ordered Probit model to a sample of companies with investment grade in the period
1978-1995, the authors have shown that the behavior of the regression intercepts over time
indicated a downward trend in the rating not explained by the financial and market indicators.
They have found, therefore, evidence that the pattern of stricter credit rating could partly explain
the observed bias of a fall, particularly during the 1990s.
Jorion et al. (2009) tested the premise of Blume et al. (1998) for the period 1985-2002.
By using the same analysis technique, the authors have obtained evidences that did not
corroborate the previous work. In addition to not seeing a decrease in the ratings among
speculative grade companies, the results showed an increase in the credit risk (and a consequent
decrease of the ratings) of the investment grade companies associated with a worsening in the
quality of their accounting information.
More recently, Mizen and Tisoukas (2012) have evaluated the capacity of different
ordered Probit models in predicting the ratings of companies using company-specific, financial,
and business risks information. Using data on American companies, which have issued
securities rated by the Fitch over the years 2000 to 2007, have been proposed alternative
specifications that take into consideration the initial and immediately prior credit rating of the
company. The authors concluded that both ratings had a substantial influence on the rating
prevision.
Recent research focuses in the European market. Doumpos et al. (2015), for example,
developed a multi-criteria classification approach, in order to test whether a structural model
would provide supplementary information and improve the capacity of models to predict the
credit risk rating. From a sample of European public companies in the period 2002-2012, the
results indicate that the "distance-to-default", obtained from the structural model, added
expressive information compared to traditional financial indexes. Duff and Einig (2009), in
turn, have developed an exploratory research based on interviews with key stakeholders in the
corporate bond rating process of United Kingdom public companies. Their main objective was
to clarify the determinants of the rating quality. The hypothesis was that the quality of the
classifications went beyond the competence and independence of agencies, including broader
intermediation and technical factors, since it assumes transparency in the decision-making of
the agencies, credibility and ability to communicate the meaning of the ratings and their
modifications to the different market participants. The authors point out two implications of the
theory developed: i) regulators should seek to supervise the diagnoses made by the agencies
rather than process them; ii) the rating agencies are of particular relevance to the market
participants and their problems are mostly related to the difficulty in attracting and retaining
good employees.
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Recent analyzes on this framework have been also developed for Brazilian companies.
Damasceno et al. (2008) built upon Blume et al. (1998) and Jorion et al. (2009) work in order
to verify a possible increase in the accuracy of the rating agencies in measuring credit risk
among Brazilian companies. Moreover, the authors have developed a predictive methodology
for ratings based on financial indicators. From the use of an ordered Probit model, the results
lead to the rejection of the hypothesis of higher accuracy by the ratings agencies over time. In
terms of predictive capacity of the model, it correctly predicted 64.1% of the ratings in the
sample. The statistically significant explanatory variables were the proxies for profitability,
capital structure and a dummy indicative of the fact that the company belonged or not to the
stock exchange of São Paulo (BOVESPA) index. Brito and Assaf Neto (2008) have also used
the Logit method for credit risk assessment from financial indicators. The sample was
composed of Brazilian public companies, classified as solvent or insolvent in the period
between 1994 and 2004. The degree of success in this case reached 88.3% of the ratings, and
the results indicated a capacity to predict the events of default with up to one year in advance.
Recently, Soares et al. (2012) have developed a similar research of those described, but
different in the sense that they have included an indicator of standard of corporate governance
among credit ratings explanatory variables. The sample comprised seventy-two Brazilian non-
financial companies, which explanatory variables relied on information from the financial
statements in 2009 and the ratings in 2010. The model was able to estimate correctly 59% of
the ratings. In addition, the results support the hypothesis that corporate governance, in addition
to the size of the assets and the interest coverage ratio, helps to explain business ratings.
However, in the case of governance, it was evidenced an opposite relation to the expected. That
is, companies with better standard of corporate governance usually have worse ratings.
Fernandino et al. (2014) have also evaluated the capacity of traditional financial indicators in
predict the long-term national ratings assigned to Brazilian companies by Fitch. From a sample
of fifty-six companies and with the use of a binomial Logit, the authors have concluded that the
size and the return on asset increased the probability of a company be classified in the rating
levels of low or very low risk of default - AA (bra) or AAA (bra), respectively. In terms of
predictability, the proposed model was able to predict correctly 81% of the sample data.
For Latin American public companies, Kanandani and Minardi (2013) have used
structural models in order to analyze their capacity to anticipate changes in credit ratings. They
have analyzed changes in the rating of listed companies in Brazil, Mexico, Chile and Argentina
in the period 2000-2012. They based methodology on Merton’s model (1974) and on the
simplification proposed by Bharath and Shumway (2004), called Naive KMV. The results,
besides showing similarities in the estimates of the two models, indicate ratings changes by the
agencies just three months in advance. According to the authors, the reason for the short notice
in predicting the changes might be the relative stability of the ratings related with the position
of credit agencies that such ratings should reflect only long-term structural components.
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RESEARCH METHOD
In order to analyze the determinants of the ratings of Brazilian companies, we considered the
level of credit risk as the dependent variable of the model of such companies, RATit, being i the
companies and t the quarter of the information. In addition, the model considered the
explanatory variables (also named independent variables, IV) based on economic and financial
indicators (Chart 2). The latter were obtained from the quarterly financial statements within the
period analyzed, available in the system Economática. The choice to use such indicators was
based on the studies shown in the section ‘literature review’ of this study.
Chart 2. Description of the independent variables adopted.
Type of risk Variable Formula Expected
relationshipa
Market
Risk
Systematic risk of the business (SR) 𝑆𝑅 = 𝜎𝑖2𝑅2 +
Debt/Equity ratio (DEB) 𝐷𝐸𝐵 =D
𝐸
+
Weighted average cost of capital
(WACC) b 𝑊𝐴𝐶𝐶 = 𝑘𝑑 (
𝐷
𝐷 + 𝐸) + 𝑘𝑒 (
𝐸
𝐷 + 𝐸)
+
Operational
Risk
Return on investment (ROI) 𝑅𝑂𝐼 =𝑁𝑂𝑃𝐴𝑇
𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
−
Degree of operating leverage (DOL) 𝐷𝑂𝐿 =∆ 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑃𝑟𝑜𝑓𝑖𝑡
∆ 𝑆𝑎𝑙𝑒𝑠
+
Liquidity
Risk Company size (SIZE) 𝑆𝐼𝑍𝐸 = ln (𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)
−
Credit
Risk
Degree of financial leverage (DFL) 𝐷𝐹𝐿 =∆ 𝑁𝑒𝑡 𝑃𝑟𝑜𝑓𝑖𝑡
∆ 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑃𝑟𝑜𝑓𝑖𝑡
+
Ability to pay debts (D/EBITDA) 𝐷/𝐸𝐵𝐼𝑇𝐷𝐴 =D
𝐸𝐵𝐼𝑇𝐷𝐴
+
Notes: a In the assessment of the expected relationship between the explanatory variables and the ratings (RATit),
we must take into account that the levels of credit risk vary between 0 and 7, and the higher the level, the worse
the rating. b kd represents the cost of debt and ke is the cost of equity.
The indicators used in the research, explained above, sought to identify the different
types of risk that a company is exposed. For market risk, we considered three variables, these
being associated with the degree of uncertainty about the behavior of the economy and the level
of interest rates. In this sense, the first variable was based on systematic risk (SR). It was
measured from the behavior of the annual volatility1 of the continuous daily returns of the share
(of greater trading volume) at the end of each quarter multiplied by the coefficient of
determination (R2) obtained in the regression of returns of the share against the returns of
IBOVESPA to obtain the beta coefficient of the share. The period adopted to calculate the
volatility and regression analysis is one year, being the date of reference the last business day
of each quarter, taking the daily prices from a year before.
The second variable was the debt level of the company (DEB), using the D/E ratio,
where D refers to the onerous liabilities of the company and E to the equity at market value
(and not at carrying value). Although the variable DEB has been classified as representative of
the market risk, it also captures credit risk, since it measures the level of leverage of the
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company. It is also valid to note that the high value of E at market value tends to reduce the
relative magnitude of this variable.
Finally, we considered the weighted average cost of capital, WACC2. For the calculation
of WACC, while the cost of debt was calculated by the ratio of financial expenses and onerous
debt (being obtained from the accounting statements of each company), the cost of equity was
obtained from the Institute Assaf, considering the indicator for the sector of activity of the
company2. For the calculation of the weights of each of the costs, we considered the market
value of the net equity and the onerous liabilities of the companies.
For operational risks, we considered return on investment (ROI), calculated by the ratio
of operating profit after taxes (NOPAT) and investment, the latter being obtained by the sum of
(accounting) NE and onerous liabilities. Additionally, we included the degree of operating
leverage (DOL), which takes into account the amount of fixed costs and expenses in the cost
structure of a company. Companies with high proportion of fixed costs and expenses and,
consequent high DOL, assume greater risks because of the greater variability of their operating
results in relation to a change in sales.
Liquidity risk is represented by the size of the company (SIZE), which is calculated
using the natural logarithm of the accounting Total Assets, on the understanding that larger
companies tend to have greater ability to honor credit commitments. As for credit risk, we took
into account the degree of financial leverage (DFL) of the company, resulting from the
participation of onerous resources in the capital structure of the company. In addition, we
included an indicator of the ability of the company to pay its total debts with cash generation
from business activity, which is given by the ratio between onerous liabilities and EBITDA
(D/EBITDA).
We also inserted intercept dummy variables for each year, in order to test the hypothesis
that CRAs are being stricter in their analyses, as performed by Blume et al. (1998) and Jorion
et al. (2009). As the period analyzed comprises the years from 2010 to 2015, we created five
dummy variables for the years 2011 up to 2015, when the constant captures the year 2010.
In order to assess the relationship between RATit and the independent variables (IV) set
out in Chart 2, equation (1) was estimated using an ordered Logit, using the method of
maximum likelihood (Greene, 2003). Such a model is justified by the use of an ordinal
qualitative dependent variable. The model is constructed from a latent regression for the RATit
variable, named 𝑅𝐴𝑇𝑖𝑡∗ , which is associated with RATit through the relation:
𝑅𝐴𝑇𝑖𝑡∗ = 𝛽 × 𝐼𝑉𝑖𝑡 + 𝜀𝑡 (1)
Where, IV represents all independent variables for i-th company in the period t and 𝜀𝑡 is the
error term with normal distribution with zero mean and variance 𝜎2. After knowing the
coefficients 𝛽, we have:
𝑅𝐴𝑇𝑖𝑡 = 𝛼 ↔ 𝜇𝛼−1 ≤ 𝑅𝐴𝑇𝑖𝑡∗ ≤ 𝜇𝛼 (2)
Where 𝜇𝛼−1 and 𝜇𝛼 are the cut-off points in each range of values with probabilities calculated
by:
𝑃𝑟𝑜𝑏(𝑅𝐴𝑇𝑖𝑡 = 𝛼|𝐼𝑉) = Φ (𝜇𝛼 − 𝛽𝐼𝑉
𝜎) − Φ (
𝜇𝛼−1 − 𝛽𝐼𝑉
𝜎)
(3)
Being, 𝛼 = 0, ..., 7, distributed at the intervals −∞ = 𝜇−1 ≤ 𝜇0 = 0 ≤ ⋯ ≤ 𝜇𝑛 = ∞; t = 2010,
..., 2015; Φ(. ) represents the Logit function.
Four models were implemented. In Model I, we considered only the independent variables,
shown in Chart 2, without the dummies for year, which were included in Model II. The other
two models had the same specifications of Model II, being distinguished only in the
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composition of the sample – in Model III (IV) we used data from companies with investment
(speculative) grade.
DATA
The sample of this study is based on Brazilian companies listed in BM&FBOVESPA which
had their credit rated by Moody's during the period 2010-2015 (after the adoption of the IFRS).
Financial companies and insurers were excluded from the sample as they present different
financial indicators in relation to non-financial companies because of their financial structure.
In order to expand the database, we considered the information from Standard & Poor's for
those companies not rated by Moody's.
For each company, we adopted the credit rating level available at the end of each quarter,
and we chose the long-term rating on a national scale as it assigns a lower weight to factors
related to sovereign risk (Damasceno et al., 2008). We also highlight that the rating grades,
categorized according to Chart 1, follow a scale from 0 to 7, and 0 indicates the highest and 7
the worst. It should be noted, however, that we did not observe the ratings 0, 1 and 7 in the
sample of the study, only 2 to 6.
Chart 1. Equivalence of CRA ratings and credit risk level considered in the study.
Moody’s Standard & Poor’s Credit risk level adopted Meaning
Aaa AAA 0
Investment grade (high
quality and low risk)
Aa1 AA+
Aa2 AA 1
Aa3 AA-
A1 A+
2 A2 A
A3 A-
Baa1 BBB+
3 Investment grade
(average quality) Baa2 BBB
Baa3 BBB-
Ba1 BB+
4
Speculative grade
(low quality)
Ba2 BB
Ba3 BB-
B1 B+
5 B2 B
B3 B-
Caa1 CCC+
6 Speculative grade
(high risk of default and
low interest)
Caa2 CCC
Caa3 CCC-
Ca CC
7 Ca C
C D Source: for the rating levels, Standard Poor’s and Moody’s.
From the rating information of the companies, we obtained a total sample of forty-five
Brazilian non-financial public companies. Table 1 presents the distribution of the sample,
considering the year and rating level assumed. As mentioned, we have not observed companies
with credit risk levels of 0, 1 and 7. In addition, around 36% of the companies were classified
as investment grade (levels 2 and 3) and 64% were speculative-grade (levels 4 to 6). Finally,
we can note a greater amount of information in recent years, especially in 2014 and 2015, since
the sample exceeded 40 companies.
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Table 1. Distribution of the sample according to year and the respective levels of credit risk.
Year
Credit risk levela Gradeb
2 3 4 5 6 Investment
grade
Speculative
grade Total
Panel A: Companies number
2010 0 9 13 2 0 9 15 24
2011 1 10 15 4 0 11 19 30
2012 2 10 18 6 0 12 24 36
2013 2 13 17 5 0 15 22 37
2014 2 14 19 6 1 16 26 42
2015 1 12 22 6 0 13 28 41
Total 8 68 104 29 1 76 134 210
Panel B: Percentage
2010 0,0 37,5 54,2 8,3 0,0 37,5 62,5 100
2011 3,3 33,3 50,0 13,3 0,0 36,7 63,3 100
2012 5,6 27,8 50,0 16,7 0,0 33,3 66,7 100
2013 5,4 35,1 45,9 13,5 0,0 40,5 59,5 100
2014 4,8 33,3 45,2 14,3 2,4 38,1 61,9 100
2015 4,9 34,1 46,3 14,6 2,4 31,7 68,3 100
Total 2,4 29,3 53,7 14,6 0,0 36,2 63,8 100 a Analysis based on the first quarter of each year. b While levels 2 and 3 represent investment grade ratings, levels
4, 5 and 6 correspond to the speculative grade.
Table 2 shows the descriptive statistics for companies’ indices. In addition, descriptive
statistics of the variables, according to the rating level of the companies, are presented in Table
3. In general, companies with better credit ratings present, on average, lower levels of
indebtedness (DEB), weighted average cost of capital (WACC) and systemic risk (SR), in
addition to greater return on investment (ROI) and size (Size), and such differences are
statistically significant.
Table 2. Descriptive statistics of the variables.
DEB ROI D/EBITDA DFL DOL WACCa Size SR
Mean (%) 144.64 5.07 7.74 0.74 2.23 5.84 16.64 10.31
Median (%) 79.84 3.68 5.39 1.59 1.88 4.57 16.59 8.22
Minimum (%) 5.76 -10.77 -416.37 -148.36 -24.35 1.06 13.69 0.00
Maximum (%) 1,696.52 48.71 394.51 32.34 59.79 37.15 20.65 37.61
Std. Deviation (%) 196.57 5.98 31.03 9.59 3.37 4.05 1.43 7.40
Kurtosis 22.88 11.99 92.97 156.38 135.16 853.45 0.14 0.43
Skewness 4.21 2.67 -1.64 -10.80 6.90 251.83 0.38 0.97
Observations (n) 744 744 744 744 744 744 744 744
Note: the rates for the WACC are quarterly.
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Table 3. Descriptive statistics of the variables, by rating level. N DEB ROI D/EBITDA DFL DOL WACCa Size SR
Mean
Level 2 32 25.88 11.30 5.51 1.63 1.10 5.33 18.11 3.01
Level 3 254 111.92 6.68 4.78 2.10 2.02 5.24 17.44 9.69
Level 4 358 119.75 4.31 8.72 1.22 2.45 5.58 16.15 10.54
Level 5 92 339.58 2.02 15.12 -6.33 2.51 8.40 15.96 13.24
Level 6 8 530.53 -2.17 -17.71 13.54 0.42 8.85 14.69 15.17
Investment Gradec 286 102.29 7.20 4.86 2.05 1.92 5.25 17.51 8.95
Speculative grade c 458 171.02 3.74 9.53 -0.08 2.42 6.21 16.08 11.18
Comparison (p-valor)b
Level 2 x Level 4 0.00 0.00 0.53 0.39 0.06 0.69 0.00 0.00
Level 2 x Level 5 0.00 0.00 0.39 0.08 0.09 0.00 0.00 0.00
Level 2 x Level 6 0.00 0.00 0.12 0.00 0.69 0.06 0.00 0.00
Level 3 x Level 4 0.46 0.00 0.04 0.00 0.00 0.24 0.00 0.17
Level 3 x Level 5 0.00 0.00 0.01 0.00 0.13 0.00 0.00 0.00
Level 3 x Level 6 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.05
Investment Grade x Speculative grade 0.00 0.00 0.05 0.00 0.05 0.00 0.00 0.00
Std. Deviation
Level 2 32 22.39 9.82 39.20 0.44 4.76 3.54 0.52 1.77
Level 3 254 147.16 7.47 17.84 2.08 2.09 3.54 1.47 7.67
Level 4 358 112.88 3.60 26.73 2.67 3.84 3.57 1.18 7.24
Level 5 92 365.66 3.71 58.86 25.20 3.72 5.62 0.88 6.23
Level 6 8 351.68 5.35 24.21 7.94 0.73 7.48 0.71 9.41
Investment Grade 286 141.48 7.88 21.21 1.97 2.54 3.54 1.42 7.56
Speculative grade 458 220.50 3.85 35.77 12.07 3.79 4.31 1.14 7.18
Comparison (p-valor)
Level 2 x Level 4 0.00 0.00 0.01 0.00 0.14 0.89 0.00 0.00
Level 2 x Level 5 0.00 0.00 0.00 0.00 0.12 0.00 0.00 0.00
Level 2 x Level 6 0.00 0.02 0.06 0.00 0.00 0.05 0.39 0.00
Level 3 x Level 4 0.00 0.00 0.00 0.00 0.11 0.89 0.00 0.32
Level 3 x Level 5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01
Level 3 x Level 6 0.02 0.13 0.39 0.00 0.00 0.05 0.00 0.60
Investment Grade x Speculative grade 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05
Notes: a The rates for the WACC are quarterly. b p-value for the hypothesis test in which, under H0, the difference between the statistics is equal to zero. c While levels 2 and 3
represent investment grade ratings, levels 4, 5 and 6 correspond to the speculative grade.
11
RESULTS
The estimated coefficients for the models are reported in Table 4. Results for the model without
time-effect dummy variables are reported in column I (Model A), and for the model with these
dummies in column II (Model B). The statistical significance and sign of the coefficients of
these binary variables indicate no support for the hypothesis of tightening of credit standards
during the period 2010-2015. In the sequence, we performed the Wald test in order to verify if
the coefficients of the dummies for year were jointly equal to zero. The null hypothesis was
rejected, indicating that Model B has a better specification than Model A.
In addition, results from Models A and B show a reduced variability in the estimated
coefficients, as well as in their signs. Furthermore, the same variables remained significant.
Particularly, we can see the influence of three proxy variables for market risk (SR, DEB and
WACC), one for operational risk (ROI) and one for liquidity risk (Size). In other words, we
found no evidence that the variables related to credit risk (DFL and D/EBITDA) have a
statistically significant impact on the credit rating of Brazilian companies, despite debt level
(DEB) also being associated with credit risk.
Focusing on the results from Model B, systematic risk (SR), debt level (DEB) and
weighted average cost of capital (WACC) presented a statistically significant and positive effect.
Thus, results suggest that companies with smaller credit risk levels tend to present higher
systemic risk and, consequently, greater exposure to interest rate risk. We also highlight the
relevance of the weighted average cost of capital (WACC). In addition to being statistically
significant at 1% level, the variable has the most expressive estimated coefficient of the model,
showing its important influence on the credit risk of Brazilian listed companies.
With respect to profitability, results indicate that the higher ROI the higher the credit
rating. According to Damasceno et al. (2008), operational efficiency is one of the basic
characteristics attributed to companies with low credit risk, justifying the result obtained. In
Brazil, Minardi et al. (2006), Damasceno et al. (2008) and Fernandino et al. (2014) found
similar conclusions, even though all three cases measured the operating performance using
return on assets (ROA).
Finally, we obtained evidence of the existence of a negative relationship between size
and credit rating level. In other words, results show that larger companies, since they have
greater ability to honor their credit commitments, are classified with higher ratings levels.
According to Blume et al. (1998), this evidence can be explained by the fact that larger
companies, in general, have a greater stability of product lines and a higher diversification of
revenue sources. Moreover, the size of the company within the liquidity risk analysis
corroborates the fact that larger companies tend to have better conditions to manage results, in
addition to specialized consultants and audits that are more proactive in their results. The
relationship evidenced is supported by previous studies. Mizen and Tsoukas (2012) have shown
a positive relationship between the variable "total sales" (size proxy) and the rating. Similarly,
Soares et al. (2012) and Fernandino et al. (2014) corroborate the fact that larger companies, in
general, present better rating levels.
The variables related to credit risk – ability to pay debts (D/EBITDA) and degree of
financial leverage (DFL), as well as the degree of operating leverage (DOL), were not
statistically significant. Kaplan and Urwitz (1979) and Minardi et al. (2006) have also
concluded that the ability to pay was not significant in their respective studies.
12
Table 4. Estimation results for Brazilian non-financial companies.
(I) Model A (II) Model B (III) Model Ca (IV) Modelo D
Coef. p-value Coef. p-value Coef. p-value Coef. p-value
SR 0.0874 0.0000 0.0950 0.0000 0.6045 0.0000 0.0700 0.0060
DEB 0.0030 0.0000 0.0029 0.0000 0.0199 0.1110 0.0036 0.0000
WACC 11.4628 0.0000 13.2871 0.0000 12.5900 0.0700 17.1102 0.0000
ROI -0.1638 0.0000 -0.1694 0.0000 -0.1897 0.0000 -0.3000 0.0000
DOL 0.0007 0.9790 0.0026 0.9150 0.4762 0.1020 -0.0409 0.3300
SIZE -1.0058 0.0000 -1.0300 0.0000 -2.6072 0.0000 -0.3274 0.0220
D/EBITDA 0.0016 0.6030 0.0021 0.4870 -0.0378 0.3050 -0.0004 0.9130
DFL -0.0039 0.7890 -0.0018 0.8900 0.1519 0.3920 0.0010 0.9200
Dummies
2011 -0.2295 0.4330 0.5990 0.5950 0.0624 0.9380
2012 -0.5999 0.0410 -0.0363 0.9730 0.2743 0.7120
2013 -0.2656 0.3450 1.0581 0.3070 0.8171 0.2650
2014 0.1998 0.4610 1.5042 0.1540 1.1631 0.1090
2015 -0.5388 0.0710 1.0239 0.2750 -0.2319 0.7680
C 43.9660 0.0000
n 744 744 286 458
R2 0.2935 0.3013 0.5867 0.3270 a As there are only two rating levels (2 and 3) for investment-grade companies, we applied a classic Logit model.
Estimated coefficients for the model that only considered investment (speculative) grade
companies are reported in column III - Model C (column IV - Model D). The analysis of market
risk proxies shows that, in general, they remained statistically significant for the two models –
with the exception of the variable debt (DEB) among the investment grade companies (Model
C). One possible explanation for such evidence is the lower participation of the cost of debt in
the capital structure of this group of companies (Table 3), which leads to less exposure to
interest rate changes. Consequently, the debt level does not impact the credit risk of companies
with investment grade. In addition, when considering the group of speculative grade companies,
we can see a greater sensitivity of the rating in relation to changes of the cost of capital.
The variables ROI and SIZE remained, for Models III and IV, statistically significant
and with the expected signs. When comparing the estimated parameters, we can note a higher
impact of the variable SIZE among companies with investment grade in relation to the other
ones, possibly justified by the fact that such companies are mostly large-sized ones, especially
when compared to speculative grade companies, which makes this control variable particularly
relevant in the first group. Finally, the variables D/EBITDA, DFL and DOL had again no
influence on credit risk level.
CONCLUSIONS
This work explored the determinants of the ratings among Brazilian non-financial listed
companies during the period 2010-2015. This analysis is especially relevant for investors,
consultants, financial institutions and regulators, which can refine their perceptions on the
13
understanding of the factors that impact companies’ credit risk in an emerging market. For
managers, this research is important both to enable the identification and analysis of the most
relevant indicators that influence credit rating levels and to reinforce the premise that economic,
financial and accounting data, of high quality, can mitigate the risks associated with the
investment and financing strategies of the company. For businesses, better ratings can mean
lower debt cost and higher available debt amount and debt deadline. For such reasons,
maintaining the rating level represents a factor of fundamental importance in the management
of companies.
To achieve the objective, we used econometric techniques based on panel data, with the
application of an ordered Logit model. The variables determining credit ratings, in turn, were
based on companies’ indices. In general, results agreed with previous studies. We found that:
i) larger companies and with higher returns on investment had higher chances of being classified
as of low risk of default, reaching ratings of better quality; ii) companies with more debt, with
higher costs of capital and associated with a greater systemic risk, as they have greater chances
of not respecting their obligations, had higher probability of default, and their ratings were
downgraded.
We highlight, among the results obtained, in terms of statistical significance and
magnitude of the coefficient, the variable cost of capital (WACC). It has exercised the greatest
influence on the credit ratings of all the variables considered, regardless of the sample of
companies used (investment grade, speculative grade or both together). In general, a company
has some control over the cost of capital with its investment, dividend and capital structural
policy; however, on the other hand, there is a risk tied to the behavior of interest rates. This
evidence points to the importance of the use of tools to manage this type of risk (using, for
example, interest rate derivatives), which, ultimately, would guarantee a lower oscillation of
the cost of capital of the company, leading to a lower credit risk.
Finally, this research contributes to the debate about the determinants of credit risk
rating by including economic and financial indicators of companies in the analysis considering
market value information. The results show the statistical significance of the variables DEB and
WACC, calculated from market value information. In addition, the sample period begins after
the mandatory adoption of IFRS, which allows us to work with higher quality data regarding
accounting information. Future work can move forward on this subject, using a sample with
financial institutions. In addition, investigations may be conducted in order to understand which
factors explain the variations of the credit risk ratings.
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NOTES
1 Annual volatility was calculated using the standard deviation of daily returns, being such a measure
multiplied by the square root of 252 days.
2 The methodology used to calculate the cost of equity is referenced in Assaf Neto (2014), being made
by benchmark and taking into account the country risk and the inflation differential between Brazil and
United States.
