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Abstract

Credit rating agencies (CRAs) play an essential role in efficiently allocating capital in the financial system. However, several researchers have highlighted shortcomings of CRAs, leading to drastic consequences such as the 2008 financial crisis. The paper focuses on the competition among CRAs as one of the critical drivers of issues plaguing the credit rating industry. The paper uses quantitative and regression techniques to check the impact of competition on a firm’s credit rating. The paper utilizes firm-level financial and credit rating data from India. The paper uses dual ratings to check whether competition leads to rating inflation and shopping practices in the credit rating industry. The paper finds that CRA inflates a firm’s credit rating due to competition from other CRAs. Rating shopping is also evident in the credit rating industry, driven by competition between CRAs to gain new clients. The paper’s findings also indicate that increased competition for large-size firms business leads to CRAs showing leniency when rating such firms. The results raise the need for regulators to actively monitor and control the competition among CRAs to ensure the accuracy of credit ratings.

Keywords

credit rating competition rating inflation rating shopping

Introduction

Competition among firms operating in an industry is natural in the modern-day economy, where government intervention or control is minimal in businesses. Competition among firms has several benefits, such as higher quality products or services at lower prices for end customers and improved business efficiency (EU Commission, 2021; FTC, 2021). However, in the absence of relevant checks in critical industries such as financial systems, unwanted effects of competition can have a contagion effect on the economy. Adverse consequences of the financial industry competition, including the credit rating industry, were seen in the 2008 financial crisis in the US, which led to a worldwide economic crisis (Chu & Rysman, 2019; Griffin et al., 2013; Kim & Park, 2016). The paper focuses on the competition among Credit Rating Agencies (CRAs), which play a vital role in estimating risk in today’s financial system.

Credit rating by CRAs is mandatory for a corporate to borrow money through capital markets and even from a bank in several countries (Duff & Einig, 2015). Credit rating determines the borrowing rate of corporate (Tang, 2009) and lenders’ capital requirements in certain countries (Reserve Bank of India, 2015). Thus, credit rating plays an essential role in the fair pricing of risk in the financial system. However, large-scale corporate default and financial crises have raised questions about CRAs’ credit rating accuracy and a possible upward bias in credit rating (Bonsall et al., 2015; Sangiorgi & Spatt, 2017; Zhuo et al., 2017).

CRAs’ actions have come under the scrutiny of investors and regulators several times in the past. The first significant event was the Enron bankruptcy in 2001, followed by the Worldcom bankruptcy in 2002, in which CRAs’ role was criticized (Bedendo et al., 2018; Coskun, 2008). Following the Enron and Worldcom debacle, the Sarbanes–Oxley Act, 2002, and the Credit Rating Agency Reform Act, 2006 was passed in the US to increase credit rating quality by CRAs by increasing competition, transparency and accountability in the credit rating industry (Coskun, 2008). However, despite the steps taken, CRAs’ role in the 2008 financial crisis again came under scrutiny. Authorities concluded that CRAs inflated ratings were one of the driving forces of the financial crisis.

Consequently, the U.S. government filed civil lawsuits against CRAs for defrauding investors, which CRAs settled by paying heavy fines (The U.S. DoJ, 2015, 2017). Similarly, the regulator in India—Securities Exchange Board of India—had initiated actions against domestic CRAs for erroneous ratings, which the CRAs settled by paying fines (Business Today, 2018; CNBC TV18, 2020; Livemint, 2020). In each instance, the commonality was a sudden default or severe downgrade of a high-rated entity or instrument.

The main reason for such issues in the rating industry is the issuer-paid revenue model followed by CRAs and their competition to gain market share and increase revenues. The Issuer-pays model is an approach where the issuer of debt pays for evaluating its securities. Several researchers have highlighted that CRAs face a conflict of interest between providing informative ratings to investors and satisfying issuers’ rating preferences (Camanho et al., 2022; Cavallaro & Trotta, 2019; Mathis et al., 2009; Partnoy, 2006).

Researchers have highlighted how competition between CRAs leads to such issues in the credit rating industry. Competition could result in CRAs assigning inflated ratings to a firm (Beatty et al., 2019; Bolton et al., 2012) as well as the tendency of firms to do rating shopping, that is, move from one CRA to another to get a higher rating (Goldstein & Yang, 2019; Huang & Shen, 2019). The paper analyzes the prevalence of such rating issues due to competition in the Indian credit rating industry.

The paper employs empirical techniques to test the impact of competition on credit rating. The paper focuses on testing the presence of rating inflation and rating shopping due to competition in the credit rating industry. Several researchers have analyzed the impact of competition on credit rating, but mainly through an indirect approach using theoretical models with assumptions (Bolton et al., 2012; Camanho et al., 2022; Chu & Rysman, 2019; Lee & Schantl, 2019) or using market share data of CRAs as a proxy of competition (Bae et al., 2019; Becker et al., 2011; Flynn & Ghent, 2018; Vu et al., 2022). Existing literature on competition in the credit rating industry has primarily focused on the US (Becker et al., 2011; Flynn & Ghent, 2018; Griffin et al., 2013) and other markets such as Canada, Korea, and China (Bae et al., 2019; Chu & Rysman, 2019; Vu et al., 2022).

As far as the author knows, this paper is the first to investigate the impact of competition among CRAs in India’s domestic credit rating market. Moreover, it employs a distinct approach compared to existing works of literature. The paper uses dual rating, that is, multiple CRAs rating a firm, as a direct measure for competition to analyze its incremental impact on the firm’s credit rating.

The paper makes several contributions to the literature. The paper finds additional evidence for rating inflation in the credit rating industry due to competition. Unlike existing studies that use theoretical models or market share as a proxy of competition, the paper uses a direct measure of competition in the form of dual ratings and finds that CRA inflates a firm’s rating when facing direct competition from other CRAs. In addition, the paper provides evidence that points toward competition leading to CRAs being lenient while rating larger firms. The paper also provides evidence showing that competition leads to the initial rating assigned to a firm by a CRA being higher than the firm’s existing rating from another CRA, resulting in rating shopping in the credit rating industry.

The paper findings raise concern regarding the impact of competition among CRAs on their role as mitigants of risk in the financial system. It augments the existing literature supporting the unintended consequences of competition in the credit rating industry. The paper’s findings are helpful for regulators across countries to understand how competition between CRAs could manifest itself in the credit rating industry. Regulators need to regulate the competition in the industry from posing a systemic risk to the financial system over time. The paper’s findings suggest that investors should be more cautious in utilizing credit ratings, especially when multiple CRAs are involved or when issuers change CRA.

Literature Review

CRAs’ role in risk mitigation in the financial system has become increasingly important, given investors’ higher reliance on credit rating for decision-making (Boot et al., 2006). The different rating levels assigned by CRAs to instruments or issuers correspond to different risk levels (Moody’s Investors Service, 2021; S&P Global, 2021). Therefore, as per the risk-return tradeoff (Modigliani & Pogue, 1974), investors have a higher expectation of return from instruments rated at a higher risk level, with other conditions remaining the same. Thus, the capital cost of firms rated at a higher rating level (or lower risk level) will be lower and vice versa (Cantor & Packer, 1995). Consequently, credit rating changes affect a firm’s future cost of capital (Tang, 2009).

Several researchers have explored the impact of credit ratings on firms’ capital structure and found that credit rating changes can materially impact a firm’s investment and financing decisions. Kisgen (2006) concluded that firms issue less debt relative to equity when nearing a rating change compared to other firms. Kisgen (2009) found that firms reduce debt and are less likely to issue debt following a downgrade in credit rating. Similarly, Aktan et al. (2019) reported that firms issue less net debt than equity post a broad credit rating level change. The above highlights firms’ concerns regarding their credit ratings and firms motivation to improve them.

The primary source of revenue for CRAs is the fee receipt for the rating service. The prevalent model in rating markets is the “Issuer-pays” model, wherein the issuer/firm, whose debt/bond is rated by the CRA, pays for the rating service. The issuer-pays model leads to a conflict of interest for the CRAs. CRAs are looking to increase revenue by providing rating services to new debt issuers and gaining more business from existing issuers. The impact of this conflict of interest on CRAs rating becomes more severe due to other CRAs competing for the same rating business. The conflict of interest and competition could lead to rating inflation, that is, CRAs assigning higher ratings to firms, which is not commensurate with issuers’ creditworthiness and rating shopping, that is, firms moving from one CRA to another in order to get better ratings.

Competition among companies is natural in any industry, but unchecked competition in the credit rating industry leads to rating shopping by issuers and inflation of rating by CRAs (Gannon, 2012; He et al., 2012; Jiang & Packer, 2019; Xia & Strobl, 2012). The adverse impact of competition among CRAs is visible even in the domestic rating market. Park and Lee (2018) analyzed the Korean domestic rating market and found that CRAs and firms’ actions can lower credit rating quality in a competitive market. Singh and Chavan (2020) highlighted that credit ratings sometimes lag the asset quality of borrowers in India, raising concern about the rating quality of Indian CRAs. This paper looks into India’s domestic credit rating market and analyzes the impact of competition among CRAs on firms’ domestic ratings.

Globally, the three major credit rating agencies are—S&P Global Ratings, Moody’s Investor Service, and Fitch Ratings. These three CRAs account for around 95% of global rating markets (European CEO, 2020). The global credit rating market can be divided into two major parts—international and national or domestic ratings. Although the three CRAs dominate the global rating market, several other CRAs operate in different countries’ national debt markets. A nation-specific regulator generally governs the operations of these CRAs in a national debt market.

There are currently seven CRAs in India accredited by the regulator—Securities and Exchange Board of India (SEBI)—for assigning credit ratings to domestic capital market issuance by firms (SEBI, 2021). These seven rating agencies have also been accredited to assign credit ratings to bank loans, which banks utilize for risk-weighting their disbursed loan for capital adequacy purposes (Reserve Bank of India, 2015). However, the top four agencies—CRISIL, ICRA, CARE, and India Ratings—accounted for over 88% of the domestic credit rating market in FY20 and are the oldest CRAs in India. Therefore, the paper focuses on these four agencies to understand the impact of competition on a firm’s credit rating. Regulators in India has been actively trying to improve the credit rating quality given by CRAs. The regulator has imposed penalties on CRAs for violation of regulations or erroneous credit ratings in the past. CRAs had reached a settlement with SEBI for alleged breach of regulations (Business Standard, 2017; Business Today, 2018). SEBI has also fined several CRAs for lapses in assigning appropriate credit ratings (CNBC TV18, 2020; Livemint, 2020).

Regulators have brought several changes to address rating inflation by CRAs and rating shopping by issuers. However, the regulations have primarily been brought in response to severe lapses by CRAs. In 2002, the Sarbanes–Oxley Act was passed by regulators in the US, which ultimately led to the “Credit Agency Reform Act of 2006” augmenting the disclosure requirement and accountability of CRAs. Dodd-Frank Act, enacted in 2010 in the aftermath of the 2008 financial crisis, dealt with the conflict of interest in credit rating business, the liability of CRAs, and improving rating quality (Picciau, 2018). SEBI introduced CRA regulations in India through SEBI (Credit Rating Agencies) Regulations, 1999 (SEBI, 2022). SEBI has brought several amendments from time to time in response to failures of CRAs in India. In 2010, SEBI increased the disclosures, audit, and compliance requirements for CRAs (Prakash et al., 2017). Similarly, amendments have been brought in by SEBI in 2012, SEBI in 2016, SEBI in 2018 to increase transparency in the working of CRAs and ensure timely action by CRAs.

However, despite the regulator’s continuous focus on improving credit ratings’ reliability, the issues discussed above continue in the credit rating industry. The paper focuses on analyzing these issues of rating inflation and rating shopping among CRAs due to competition in the credit rating industry. Most researchers have analyzed the impact of competition on rating agencies indirectly. Several researchers have studied the competition among CRAs using a theoretical model with assumptions regarding the credit industry. Bolton et al. (2012) used a model to analyze the impact of competition on credit rating and found that competition reduces efficiency, increases rating shopping, and could result in rating inflation. Camanho et al. (2022) used a theoretical model to analyze competition among CRAs and found that competition exacerbates the problem of rating inflation in the credit rating industry. Lee and Schantl (2019) used a model to analyze how the dynamics between competition among CRAs and their gatekeeper’s role impact rating inflation in the industry.

Some researchers have used market share data as a proxy for competition to understand its impact on credit ratings. Vu et al. (2022) used market share data to investigate the impact on sovereign ratings due to competition between CRAs and concluded that competition lowers the quality of the ratings. Flynn and Ghent (2018) reported that incumbent CRAs inflate ratings as the market share of new entrants increases. Becker et al. (2011) used an increase in Fitch’s market share to measure increased competition and found that the rating quality of CRAs declined as the market share of Fitch increased. However, Bae et al. (2015) contradicted the above finding and concluded controlling for unobservable industry effects; there is no linkage between competition measured by Fitch market share and rating inflation. Beatty et al. (2019) found that Moody’s and Fitch, following the recalibration of their municipal debt rating scale in 2010, increased the credit rating of municipal bonds without a change in credit quality, resulting in increased market share. However, this paper tries to directly analyze whether the rating actions of CRAs are impacted due to competition by using dual ratings, that is, multiple CRAs rating a firm as a measure of competition among CRAs.

Hypothesis Development and Methodology

The paper’s main objective is to test whether competition leads to rating inflation and rating shopping in the credit rating industry. The paper adopts multiple approaches to achieve this objective. The paper analyzes the rating inflation tendency of one of the top four domestic CRA (designated as CRA “A”) due to competition from the other three of the top four domestic CRAs (designated as CRA “1,”“2,” and “3”). The tendency of issuers to indulge in rating shopping due to competition is investigated by analyzing the initial ratings given by other CRAs (CRA “1,”“2,” and “3”) to firms already rated by CRA “A” and vice versa. The paper uses t-tests and regression techniques to empirically analyze the impact of competition among CRAs on firms’ credit ratings.

The first approach compares key financial metrics of firms rated by only CRA “A” with those rated by more than one CRA through hypothesis 1. In cases where dual ratings are present, if firms’ key metrics are significantly worse than those rated only by CRA “A” for the same rating category, the presence of rating inflation could be inferred.

Hypothesis 1: There will be no difference in key credit metrics of firms rated by multiple CRAs compared to those rated by a single CRA for the same rating category.

As per hypothesis 1, in case of no rating inflation by CRA “A,” the mean of key credit metrics of firms where more than one CRA (CRA “A” and one or more CRAs) are rating the firm should be equal to those firms in which only CRA “A” is rating the firms for the same rating category. Hypothesis 1 is tested using a two-sample t-test to check whether the mean credit metrics of the two groups are equal.

The second approach involves using regression to determine whether a CRA provides an inflated rating in the presence of competition. Here, it is directly tested, through hypothesis 2, that a CRA will provide a higher rating, controlling for firm-specific factors, to a firm if other CRAs also rate the same firm.

Hypothesis 2: A CRA rating for a firm is not influenced by another CRA rating of the firm.

Therefore, in case of hypothesis 2 is rejected, it will mean that CRA “A” will tend to give a higher rating to a firm if another CRA is also rating the firm. Hypothesis 2 is tested using Ordinary Least Squares (OLS) regression and Ordered Probit regression, employed in several previous credit ratings related papers (Cubas-Díaz & Martínez Sedano, 2018; Gupta et al., 2017; Park & Lee, 2018). The firm’s credit rating is converted into numerical values to run the regression, explained later in the Results and Discussion section. In case of rejection of hypothesis 2, the dummy variable coefficient related to dual ratings in the regression should be of appropriate sign and significant.

The third approach involves testing whether rating shopping is prevalent in the industry using hypothesis 3.

Hypothesis 3: There are equal chances of a firm rated by a CRA getting an initial rating that is higher or lower, or equal to its existing rating from a new CRA.

In the case of rating shopping due to competition in the credit rating industry, the rating assigned by a new CRA to a firm will invariably be higher than the firm’s current rating. Therefore, hypothesis 3 will be rejected. For hypothesis 3, statistics of the difference between a firm rating from CRA “A” and the initial rating assigned by other CRAs is analyzed to check the presence of rating shopping. In addition, to test hypothesis 3, the paper also uses Ordinary Least Squares (OLS) regression and Ordered Probit regression. In case CRA “A” assigns a higher initial rating to a firm rated by other CRAs, the dummy variable coefficient related to dual rating should be positively correlated to the dependent variable (dependent variable is calculated by the difference between current rating by other CRAs and initial credit rating given by CRA `A’). The dependent variable should also be positive.

Data and Summary Statistics

The paper utilizes data from the Centre for Monitoring Indian Economy (CMIE)—ProwessIQ Database for credit rating and firms’ financial information. Several Indian studies have used the above database (Jaiswall & Bhattacharyya, 2016; Jaiswall & Raman, 2021; Jawed et al., 2019). The paper analyzes the competition among CRAs by focusing on India’s domestic credit rating industry. The credit rating of firms used in the paper represents the Long term rating assigned to the firm’s long-term bank loans or capital market debt. The paper utilizes the rating action of CRAs from FY08-FY20 for analysis as RBI regulation on bank loans rating came into existence from FY08. The sample firms include all firms for which credit rating is available in the database except financial firms (National Industrial Classification Code 64000-66999). The reason for excluding financial firms is that such firms’ credit ratings are driven by significantly different factors than non-financial firms. If a firm does not witness a credit rating action in a period from a CRA, the previous period credit rating given by CRA is taken as the firm’s outstanding rating from the CRA for analysis.

The rating-wise and year-wise sample distribution taken in the study is shown in Tables 1 and 2. The sample contains 14,561 firm years of observations. These observations correspond to the rating given to firms by CRA “A”, as the paper primarily focuses on the impact of competition on ratings assigned by CRA “A”. Sample observations are equally divided between investment grade (above BB+ rating) and non-investment grade (below BBB- rating) observations. The observations are distributed normally with a peak at the boundary of the investment-grade rating of BBB-. Firm-year observations in which dual ratings are present, that is, one or more agencies are rating along with CRA “A,” are around 22% of total observations.

Table 1.

Rating Category Wise Sample Distribution.

Rating category	AAA	AA+	AA	AA-	A+	A
No. of observations	208	111	491	502	730	640
Percentage of total observations	1.4	0.8	3.4	3.5	5.0	4.4
Dual ratings percentage	70.7	43.2	36.0	29.3	21.1	29.1
Rating category	A−	BBB+	BBB	BBB−	BB+	BB
No. of observations	872	1,044	1,379	1,765	1,428	1,292
Percentage of total observations	6.0	7.2	9.5	12.1	9.8	8.9
Dual ratings percentage	25.2	21.5	21.1	21.4	19.6	18.7
Rating category	BB−	B+	B	B−	C	D
No. of observations	861	979	604	243	102	1,310
Percentage of total observations	5.9	6.7	4.2	1.7	0.7	9.0
Dual ratings percentage	18.2	18.0	23.7	24.7	26.5	18.4

Table 2.

Year Wise Sample Distribution.

Year	Frequency	Percentage	Dual ratingspercentage
2008	2	0.0	50.0
2009	11	0.1	63.6
2010	11	0.1	72.7
2011	647	4.4	15.3
2012	1,499	10.3	17.3
2013	1,755	12.1	19.8
2014	1,864	12.8	21.9
2015	1,857	12.8	24.5
2016	1,757	12.1	26.5
2017	1,547	10.6	24.1
2018	1,574	10.8	25.9
2019	1,578	10.8	25.3
2020	459	3.2	14.6
Total	14,561	100.0	22.6

Table 2 shows rating distribution across different years. The number of observations is lower in years immediately after implementing the regulation of credit rating for bank loans in 2008. However, from 2012 the number of observations is equally distributed at around 10% each year. The percentage of firm ratings in which dual agencies are present is more than 20% in almost all years.

Results and Discussion

Comparison of Key Financial Metrics of Firm Rated by Multiple CRAs

To test hypothesis 1, we examine key credit metrics of leverage (Debt/EBITDA), interest coverage (EBITDA/Interest Expenses), and financial metrics of profitability (EBITDA/Sales) and sales to analyze the metrics of firms with or without dual ratings. We use a two-sample t-test to compare the mean credit metrics of the two groups. Several studies have highlighted that these financial metrics have a significant relationship with credit rating (Blume et al., 1998; Gray et al., 2006; Gupta et al., 2017). These financial metrics are winsorized at their 1% and 99% percentiles to eliminate outliers. Several other papers have done the winsorization of data (Baker & Wurgler, 2006; Bonsall et al., 2018; Chen et al., 2019; Chiang et al., 2019).

Table 3 shows the key financial metrics of firms. The firms have been divided into two groups: (1) Group 1—where only CRA “A” is the rating firm and (2) Group 2—dual ratings, that is, one or more CRA is the rating firm along with CRA “A”. The mean and median leverage of firms, measured as total debt/EBITDA, in Group 2 is higher than Group 1 firms. This higher leverage of group 2 firms is visible in both investment grade and non-investment grade rating categories. Using a two-sample t-test, we find that the mean debt/EBITDA of Group 1 is significantly lower than the mean of Group 2 across rating categories at a 1% level. The mean interest coverage ratio of Group 1 is significantly (at a 1% level) higher than the mean interest coverage ratio of Group 2 in the investment-grade rating category but not in the non-investment grade rating category. The mean profitability of Group 2 is significantly higher than that in Group 1 (at a 1% level) in both investment and non-investment grade ratings. The mean sales of firms in Group 2 are significantly higher than firms in Group 1 across rating categories. Rating Category “C” and “D” have not been included in the analysis. This is because the firms with these ratings have a very high risk of default or are expected to be in default (SEBI, 2011)and timely servicing of debt rather than financial metrics is the primary driver of firm rating at this level. In addition, CRAs define the investment-grade category ratings as having moderate to the highest level of safety. In contrast, non-investment-grade category ratings are defined as moderate to very high risk of default for timely servicing of obligations. Therefore, the accuracy of rating in the investment-grade category assumes more significance from a risk perspective.

Table 3.

Key Financial Metrics of Firm With or Without Dual CRAs Across Rating Categories.

Key Metrics of Firms
	Group-1: Firms rated by only CRA “A”			Group-2 : Firms rated by CRA “A” where at least one more CRA rating is outstanding
	N	Mean	Median	N	Mean	Median
All ratings
Leverage	9,174	2.77	2.94	2,946	4.34***	3.25***
Interest coverage	8,918	7.19	2.45	2,899	5.44***	2.53
Profitability	8,719	0.16	0.1	2,862	0.18***	0.11***
Sales	8,719	6,576	1,749	2,862	27,658.15***	4,067.5***
Investment grade ratings
Leverage	5,064	2.39	2.06	1,838	3.79***	2.79***
Interest Coverage	4,957	10.96	3.82	1,823	7.03***	3.16***
Profitability	4,870	0.2	0.12	1,807	0.22**	0.13**
Sales	4,870	9,927.84	3,091.5	1,807	40,078.2***	8,600.5***
Non-investment grade ratings
Leverage	4,110	3.25	4.07	1,108	5.25***	3.94***
Interest coverage	3,961	2.48	1.72	1,076	2.74	1.93***
Profitability	3,849	0.11	0.08	1,055	0.12	0.09**
Sales	3,849	2,335.13	893.6	1,055	6,384.58***	1,279***
AAA Category
Leverage	57	1.39	0.21	145	1.01	1.52***
Interest coverage	57	58.1	28.91	144	24.77***	8.57**
Profitability	57	0.29	0.28	143	0.33	0.2*
Sales	57	120,000	97,600.2	143	200,000***	210,000**
AA Category
Leverage	706	1.73	0.97	356	3.73***	2.48***
Interest coverage	703	21.93	9.82	356	8.34***	3.93***
Profitability	691	0.25	0.16	355	0.28	0.18*
Sales	691	21,962.36	11,717.9	355	71,207.84***	28,982.4***
A Category
Leverage	1,537	2.14	1.67	540	2.98*	2.49***
Interest coverage	1,521	13.49	5.02	540	6.05***	3.5***
Profitability	1,507	0.22	0.14	537	0.23	0.14
Sales	1,507	9,300.54	4,183.6	537	20,924.05***	9,397.5***
BBB Category
Leverage	2,764	2.71	2.72	797	4.86***	3.13***
Interest coverage	2,676	5.64	2.85	783	3.85***	2.46***
Profitability	2,615	0.17	0.11	772	0.16	0.11
Sales	2,615	4,745.22	2,122.3	772	10,336.69***	3,863.05***
BB Category
Leverage	2,270	3.91	3.73	575	4.43	3.56
Interest coverage	2,195	3.07	1.99	560	3.48	2.26***
Profitability	2,110	0.11	0.08	546	0.12	0.1***
Sales	2,110	2,407.05	1,019.65	546	6,586.07***	1,426.1***
B Category
Leverage	1,007	6.04	5.02	311	5.99	4.36***
Interest coverage	965	2.14	1.58	295	2.36	1.77***
Profitability	952	0.12	0.08	289	0.1	0.07
Sales	952	1,229.41	589.3	289	1,778.65***	918.4***

, **, and *** indicate significance at the .1, .05, and .01 levels, respectively.

The above analysis indicates that hypothesis 1 is rejected, that is, the Financial metrics of firms in Group 1 and Group 2 should be equal. We see that two key credit metrics of firms—leverage and interest coverage—in Group 2 are significantly worse off than Group 1—especially across the investment-grade categories. Thus, CRA “A” assigns a higher rating to a firm with a weaker credit profile when faced with competition in the form of dual ratings. The above analysis confirms rating inflation by CRAs in India due to competition in the credit rating industry. In addition, the size of firms in Group 2, as measured by sales, is significantly larger than those in Group 1, indicating that larger firms are more likely to get inflated ratings due to competition among CRAs. This could be partly explained based on large firms being better positioned to pay fees for credit rating services to multiple CRAs. In addition, large firms have a higher level of debt and thus are likely to pay higher rating fees, as fees are linked to the debt (ICRA Limited, 2021; India Ratings, 2021), and thus are more sought after by multiple CRAs. He et al. (2012) drew a similar conclusion that large issuers received inflated ratings than small issuers in the US mortgage-backed securities market.

Effect of Competition on the Credit Rating of a Firm by a CRA

As per hypothesis 2, it is tested whether CRA “A” will tend to give a higher rating if one or more CRA are also rating the firm. The following equation is used to test the hypothesis 2:

$\begin{matrix} C R_{it} = β_{°} + β_{1} CR A^{j} D_{it} + β_{2} Siz e_{it} + β_{3} Leverag e_{it} \\ + β_{4} Profitabilit y_{it} + β_{5} Interest Coverag e_{it} + ξ_{it} \end{matrix}$ (1)

Where, $C R_{it}$ is the credit rating of firm $i$ at time $t$ at CRA “A”

$CR A^{j} D_{it}$ is a dummy variable for dual rating which takes a value “1” if CRA j gives a rating to the firm $i$ at time $t$ , which has also been rated by CRA “A” and “0” otherwise

$Siz e_{it}$ , the natural logarithm of total assets of firm i at period t;

$Leverag e_{it},$ total debt divided by total debt and total equity of firm i at period t

$Profitabilit y_{it}$ , EBITDA divided by total assets of firm i at period t

$Interest Coverag e_{it}$ , EBITDA divided by total finance expenses of firm i at period t

Control variables of Size, Leverage, Profitability, and Interest Coverage account for firm-specific characteristics driving the firm’s rating, as used in several other studies (Gupta et al., 2017; Jiang et al., 2012; Kisgen, 2006). As explained earlier, rating levels “C” and “D” have been excluded from our analysis. The paper employs Ordinary Least Squares (OLS) regression and Ordered Probit regression to test hypothesis 2. The dependent variable $C R_{it}$ is generated by converting the credit rating of firm into numerical values as per Table 4. In case, CRA “A” inflates the rating due to competition from other CRAs, then the numerical value of rating of a firm by CRA “A,” which is being rated by another CRA, will be lower compared to the rating of a firm where CRA “A” is the only CRA rating the firm, controlling for firm-specific factors. This means that for CRA “A” to give an inflated rating due to competition, the coefficient of the dummy variable $CR A^{j} D_{it}$ , should be negative and significant.

Table 4.

Table for Rating Scale conversion to Numerical Scale.

Rating Level	Numerical value
AAA	1
AA+	2
AA	3
AA−	4
A+	5
A	6
A−	7
BBB+	8
BBB	9
BBB−	10
BB+	11
BB	12
BB−	13
B+	14
B	15
B−	16
C	17
D	18

The OLS and Ordered probit regression results using equation (1) are presented in Tables 5 to 7. Table 5 s1hows whether the ratings given by CRA “A” are inflated when CRA “1” is also rating the same firm. The results show separately β1, the dummy variable coefficient, for investment and non-investment grade ratings.

Table 5.

Impact on Credit Rating of a firm by CRA “A” due to the Presence of CRA “1”.

Dependent variable: Rating code, $C R_{it}$
	OLS regression			Probit regression
	All ratings	Investment grade ratings	Non-investmentgrade ratings	All ratings	Investmentgrade ratings	Non-investment grade ratings
main
CRA¹D_it	0.284*** (0.078)	−0.317*** (0.076)	0.368*** (0.066)	0.080*** (0.028)	−0.155*** (0.037)	0.261*** (0.048)
Coefficient economic size				0.21	−0.377	0.438
Sales	−1.140*** (0.016)	−0.703*** (0.016)	−0.272*** (0.018)	−0.412*** (0.007)	−0.331*** (0.008)	−0.195*** (0.013)
Leverage	0.409*** (0.033)	0.238*** (0.029)	0.163*** (0.039)	0.143*** (0.012)	0.104*** (0.013)	0.112*** (0.028)
Profitability	−2.336*** (0.275)	−1.137*** (0.256)	−0.868*** (0.269)	−0.800*** (0.099)	−0.511*** (0.121)	−0.670*** (0.193)
Interest coverage	−0.002*** (0.000)	−0.001*** (0.000)	−0.002** (0.001)	−0.001*** (0.000)	−0.001*** (0.000)	−0.002** (0.001)
N	10,414	6,584	3,830	10,414	6,584	3,830
adj. R²	0.360	0.263	0.073
F	1,170.222	471.237	61.357

, **, and *** indicate significance at the .1, .05, and .01 levels, respectively .

Table 6.

Impact on Credit Rating of a Firm by CRA “A” due to the Presence of CRA “2.”

Dependent variable: Rating code, $C R_{it}$
	OLS Regression			Probit regression
	All ratings	Investment grade ratings	Non-investmentgrade ratings	All ratings	Investment grade ratings	Non-investment grade ratings
CRA²D_it	−0.229** (0.097)	−0.216** (0.087)	−0.037 (0.101)	−0.093*** (0.035)	−0.094** (0.041)	−0.059 (0.075)
Coefficient economic size				−0.243	−0.230	−0.099
Sales	−1.123*** (0.016)	−0.707*** (0.016)	−0.263*** (0.018)	−0.406*** (0.007)	−0.333*** (0.008)	−0.188*** (0.013)
Leverage	0.415*** (0.033)	0.232*** (0.029)	0.163*** (0.039)	0.145*** (0.012)	0.100*** (0.013)	0.111*** (0.028)
Profitability	−2.335*** (0.275)	−1.184*** (0.257)	−0.780*** (0.270)	−0.800*** (0.099)	−0.530*** (0.121)	−0.605*** (0.193)
Interest coverage	−0.002*** (0.000)	−0.001*** (0.000)	−0.002** (0.001)	−0.001*** (0.000)	−0.001*** (0.000)	−0.002** (0.001)
N	10,414	6,584	3,830	10,414	6,584	3,830
adj. R²	0.359	0.262	0.066
F	1,167.783	468.215	54.718

, **, and *** indicate significance at the .1, .05, and .01 levels, respectively .

Table 7.

Impact on Credit Rating of a Firm by CRA “A” Due to the Presence of CRA “3”.

Dependent variable: Rating code, $C R_{it}$
	OLS regression			Probit regression
	All ratings	Investment grade ratings	Non-investment grade ratings	All ratings	Investment grade ratings	Non-investment grade ratings
CRA³D_it	−0.449*** (0.145)	−0.438*** (0.125)	0.067 (0.173)	−0.209*** (0.053)	−0.221*** (0.060)	0.033 (0.126)
Coefficient Economic Size				−0.548	−0.539	−0.056
Sales	−1.121*** (0.016)	−0.703*** (0.016)	−0.264*** (0.018)	−0.405*** (0.007)	−0.331*** (0.008)	−0.189*** (0.013)
Leverage	0.419*** (0.033)	0.236*** (0.029)	0.163*** (0.039)	0.147*** (0.012)	0.103*** (0.013)	0.112*** (0.028)
Profitability	−2.349*** (0.275)	−1.194*** (0.256)	−0.785*** (0.270)	−0.808*** (0.099)	−0.538*** (0.121)	−0.612*** (0.193)
Interest Coverage	−0.002*** (0.000)	−0.001*** (0.000)	−0.002** (0.001)	−0.001*** (0.000)	−0.001*** (0.000)	−0.002** (0.001)
N	10,414	6,584	3,830	10,414	6,584	3,830
adj. R²	0.359	0.263	0.066
F	1,169.051	469.866	54.722

, **, and *** indicate significance at the .1, .05, and .01 levels, respectively.

For non-investment grade category ratings, β₁, the dummy variable coefficient, is positive and significant in OLS and ordered probit regression. However, the coefficient is negative and significant at a 1% level for investment grade. The negative coefficient of dummy variables in investment-grade category rating indicates that CRA “1” presence leads to CRA “A” inflating the issuer rating, that is, CRA “A” assigns better/higher ratings when faced with direct competition from CRA “1.” As discussed earlier, the investment category has more significance than the non-investment category from a risk perspective.

Table 6 shows whether the ratings given by CRA “A” are inflated when CRA “2” is also rating the same firm. The results show that for both investment and non-investment grade category ratings, the dummy variable coefficient related to the dual ratings is negative and significant at 1% in both OLS and oprobit regression. The negative coefficient of dummy variable related to dual ratings indicates that in the presence of CRA “2” rating a firm, CRA “A” rating for the firm is inflated.

Table 7 shows rating inflation by CRA “A” in the presence of CRA “3”. The results show that the dummy variable’s coefficient related to the dual ratings is negative and significant for all rating categories at a 1% level. Tables 5 to 7 show that CRA ‘A’ inflates the rating level if another CRA, CRA “1” or CRA “2” or CRA “3”, is rating the firm. As per OLS regression, the rating of a firm by CRA ‘A’ in the presence of other CRAs is 0.21-0.44 notches more than the rating of a firm in which only CRA “A” is assigning the rating, controlling for firm-specific characteristics.

However, to determine the economic impact of dual CRA on a firm’s credit rating, the paper employs the method used by Alp (2013). As per this method, the dual rating dummy coefficient estimates, β1 in the ordered probit regression, are in units of the latent variable CRit. The economic impact of the dummy variable on the credit ratings can be determined by converting the coefficient β1 to units of rating notches. This is done by dividing the estimated coefficient by the average distance (measured in terms of the latent variable) between the rating categories. The economic impact of the dummy variable for dual ratings, $CR A^{j} D_{it}$ measures average rating difference between firms rated by CRA “A” where other CRAs are present (i.e., $CR A^{j} D_{it} = 1)$ and where CRA “A” is the only CRA (i.e., $CR A^{j} D_{it} = 0)$ . It can be seen that in the case of CRA “1,”“2,” and “3” presence, CRA “A” rated firms on an average, are likely to have 0.38, 0.23, and 0.54 notches higher rating than those firms where CRA “A” is the only CRA rating the firms. This corresponds to a one-notch higher rating by CRA “A” for approximately one out of every four firms rated along with CRA “2,” one out of every three firms rated with CRA “1,” and one out of every two firms rated with CRA “3.” The range of dummy variable, $CR A^{j} D_{it}$ , coefficient’s economic value for different CRAs could be attributed to the reputational concerns of CRA “A” viz.a.viz other CRAs in the credit rating industry.

Thus, the results from Tables 5 to 7 indicate that hypothesis 2 is rejected, that is, a CRA rating for a firm is not influenced by another CRA rating the firm, especially in the more critical investment-grade category. The results show that the firm rating given by CRA “A” is inflated in the presence of other CRAs, as the dummy variable coefficient is negative and significant in all the cases. CRA “A” actions could be attributed to it providing a higher or equal rating than other CRA to prolong its existing relationship with the firm. In the absence of a higher or equal rating, the firm may drop CRA “A.” This corresponds to the multiple CRAs rating the firm, improving the firm’s rating to a higher but permissible level due to competition as compared to the rating of a single CRA-rated firm. Ultimately, the issuer-pays model makes CRA’s revenue dependent upon a firm’s relationship, and the competition forces CRA to inflate ratings. Our findings align with the existing literature that competition among CRAs leads to rating inflation (Bolton et al., 2012; Camanho et al., 2022; Lee & Schantl, 2019; Zhuo et al., 2017).

Impact on Initial Rating by a CRA Due to Competition

The tendency of rating shopping by firms can be analyzed using the initial rating assigned by a CRA to a firm when another CRA is already rating the firm. In the case of rating shopping due to competition, the rating assigned by a new CRA will invariably be higher than the current rating. For testing hypothesis 3, we compare the initial rating assigned by other CRAs to a firm rated by CRA “A” to understand whether other CRAs assign a higher initial rating than the current rating given by CRA “A”. In addition, we test hypothesis 3 by using regression to check whether CRA “A” assigns a higher initial rating to firms with existing ratings by other CRAs.

Initial Rating assigned by other CRAs to CRA “A” rated firms

We first convert the rating scale into a numerical scale for this analysis using Table 3. Then we compare the initial rating given by other CRAs and CRA “A”. We calculate the difference between the current rating given by CRA “A” and the initial rating assigned by other CRAs. If rating shopping occurs, the difference would invariably be positive. The summary statistics of this rating difference are present in Table 8. The mean and median difference between CRA “A” rating and initial rating by other CRAs are positive for all the CRAs. The difference is also positive for investment and non-investment grade categories with all the CRAs. Table 8 shows CRA 1, 2, and 3 assign a higher rating to a firm with an existing rating from CRA “A” in more than 50% of cases, while a lower rating is assigned in only less than 10% of cases. This lends credence to the argument that competition leads to rating shopping in the credit rating industry, that is, invariably a firm gets higher than the current rating when it approaches a new CRA for rating. Competition among CRAs to acquire new business from the firm and the Issuer-pays business model is the primary reason for the rating shopping occurring in the credit rating industry.

Table 8.

Summary of the Difference Between Credit Rating of a Firm by CRA “A” and Initial Rating by Other CRAs.

	N	Mean	Median	Percentage of observations where new CRA rating is
	N	Mean	Median	Higher	Equal	Lower
Comparison of initial rating of CRA “1” with rating of CRA “A”
All rating	367	0.89	1.00	61.04	30.52	8.45
Investment grade rating	175	0.61	1.00	50.86	41.14	8.00
Non-Investment grade rating	192	1.16	1.00	70.31	20.83	8.85
Comparison of initial rating of CRA “2” with rating of CRA “A”
All Rating	304	0.72	1.00	53.62	40.13	6.25
Investment grade rating	179	0.30	0.00	37.43	54.75	7.82
Non-Investment grade rating	125	1.31	1.00	76.80	19.20	4.00
Comparison of initial rating of CRA “3” with rating of CRA “A”
All rating	167	1.02	1.00	52.10	44.91	2.99
Investment grade rating	123	0.58	0.00	39.84	56.91	3.25
Non-investment grade rating	44	2.25	2.00	86.36	11.36	2.27

Table 9 shows CRA “A” assigns a higher initial rating in more than 50% of cases in which CRA “1” has an incumbent rating, while in around 10% of cases, it assigns a lower rating. If a firm already has an incumbent rating from CRA “2” and CRA “3”, CRA “A” assigns a higher initial rating in around 30% of cases. In comparison, it assigns a lower initial rating in only around 13% of cases. Thus, there is a skew toward CRA “A” assigning a higher or equal initial rating to firms where other CRA are already present.

Table 9.

Summary of the Difference Between the Initial Rating of a Firm by CRA “A” and Rating by Other CRAs.

	N	Mean	Median	Percentage of observations where new CRA rating is
	N	Mean	Median	Higher	Equal	Lower
Comparison of initial rating of CRA “A” with rating of CRA “1”
All Rating	503	0.75	1.00	52.3	37.0	10.7
Investment grade rating	288	0.73	1.00	53.5	40.6	5.9
Non-investment grade rating	215	0.77	1.00	50.7	32.1	17.2
Comparison of initial rating of CRA “A” with rating of CRA “2”
All Rating	251	0.42	0.00	28.3	58.2	13.5
Investment grade rating	199	0.43	0.00	26.6	64.8	8.5
Non-Investment grade rating	52	0.38	0.00	34.6	32.7	32.7
Comparison of initial rating of CRA “A” with rating of CRA “3”
All Rating	104	0.65	0.00	33.7	53.8	12.5
Investment grade rating	82	0.62	0.00	28.0	63.4	8.5
Non-Investment grade rating	22	0.77	1.00	54.5	18.2	27.3

Initial rating assigned by CRA “A” to other CRAs rated firms

We also test hypothesis 3 by testing, using regression, whether the initial rating assigned by CRA “A” to a firm rated by other CRAs is higher, controlling for firm-specific characteristics. The hypothesis is tested using the following equation:

$\begin{matrix} D . {CR}_{it}^{j} = β_{°} + β_{1} {ICR}_{it}^{j} + β_{2} Siz e_{it} + β_{3} Leverag e_{it} \\ + β_{4} Profitabilit y_{it} + β_{5} Interest Coverag e_{it} + ξ_{it} \end{matrix}$ (2)

Where, $D . {CR}_{A}^{i}$ is the difference in credit rating (converted to numerical scale) of a firm $i$ at time $t$ by CRA `j’ and CRA “A”

${ICR}_{it}^{j}$ is a dummy variable which takes a value “1” if a firm i gets a rating assigned by CRA “A,” when it was already being rated by CRA “j”

$Siz e_{it}$ , the natural logarithm of total assets of firm i at period t;

$Leverag e_{it},$ total debt divided by total debt and total equity of firm i at period t

$Profitabilit y_{it}$ , EBITDA divided by total assets of firm i at period t

$Interest Coverag e_{it}$ , EBITDA divided by total finance expenses of firm i at period t

Control variables are the same as in equation (1). Rating level “C” and “D” has been excluded from our analysis. The test employs Ordinary Least Squares (OLS) and Ordered Probit regression. In Case CRA “A” assigns a higher initial rating to a firm rated by other issuers, then the difference between credit rating by other CRAs and CRA “A,” that is, the dependent variable should be positive and the independent dummy variable, ${ICR}_{it}^{j}$ , should be positively correlated to it. The results of equation (2) are shown in Tables 10 to 12.

Table 10.

Rating Shopping: Initial Rating by CRA “A” to a Firm With CRA “1” as Incumbent CRA.

Dependent variable: Rating difference, $D . {CR}_{it}^{j}$
	OLS regression			Probit regression
	All ratings	Investment grade ratings	Non-investment grade ratings	All ratings	Investment grade ratings	Non-investment grade ratings
${ICR}_{it}^{j}$	0.612*** (0.098)	0.345*** (0.093)	0.887*** (0.207)	0.435*** (0.060)	0.380*** (0.076)	0.474*** (0.100)
Sales	−0.038* (0.020)	−0.085*** (0.019)	−0.343*** (0.067)	−0.016 (0.012)	−0.067*** (0.016)	−0.142*** (0.033)
Leverage	−0.020 (0.024)	−0.026 (0.018)	0.647 (0.484)	−0.018 (0.014)	−0.026* (0.015)	0.314 (0.230)
Profitability	1.108** (0.473)	−0.012 (0.395)	6.964*** (1.526)	0.708** (0.284)	0.268 (0.316)	2.836*** (0.732)
Interest coverage	−0.000 (0.001)	−0.000 (0.001)	−0.048** (0.021)	−0.000 (0.001)	−0.000 (0.001)	−0.020** (0.010)
N	1,659	1,067	592	1,659	1,067	592
adj. R²	.027	.031	.105
F	10.095	7.752	14.796

, **, and *** indicate significance at the .1, .05, and .01 levels, respectively.

Table 11.

Rating Shopping: Initial Rating by CRA “A” to a Firm With CRA “2” as Incumbent CRA.

Dependent variable: Rating difference, $D . {CR}_{it}^{j}$
	OLS regression			Probit regression
	All ratings	Investment grade ratings	Non-investment grade ratings	All ratings	Investment grade ratings	Non-investmentgrade ratings
${ICR}_{it}^{j}$	0.603*** (0.110)	0.329*** (0.111)	1.125*** (0.357)	0.531*** (0.081)	0.395*** (0.093)	0.793*** (0.185)
Sales	−0.009 (0.021)	−0.080*** (0.022)	−0.277*** (0.099)	−0.001 (0.015)	−0.070*** (0.018)	−0.057 (0.052)
Leverage	0.238*** (0.088)	0.202** (0.084)	−0.124 (0.937)	0.079 (0.064)	0.040 (0.069)	0.130 (0.442)
Profitability	−2.913*** (0.547)	−2.471*** (0.543)	−3.862 (2.901)	−1.318*** (0.397)	−0.933** (0.442)	−1.008 (1.207)
Interest Coverage	−0.001 (0.003)	0.005 (0.003)	−0.114 (0.102)	−0.001 (0.002)	0.002 (0.002)	−0.051* (0.026)
N	1,048	815	143	1,048	815	233
adj. R²	.052	.047	.133
F	12.494	9.060	5.372

, **, and *** indicate significance at the .1, .05, and .01 levels, respectively.

Table 12.

Rating Shopping: Initial Rating by CRA “A” to a Firm With CRA “3” as Incumbent CRA.

Dependent variable: Rating difference, $D . {CR}_{it}^{j}$
	OLS regression			Probit regression
	All ratings	Investment grade ratings	Non-investment grade ratings	All ratings	Investment grade ratings	Non-investment grade ratings
${ICR}_{it}^{j}$	1.126*** (0.267)	0.881*** (0.252)	1.552 (1.114)	0.668*** (0.130)	0.616*** (0.145)	0.808** (0.322)
Sales	0.209*** (0.045)	0.157*** (0.046)	0.160 (0.344)	0.108*** (0.022)	0.085*** (0.027)	0.032 (0.077)
Leverage	−0.021 (0.042)	−0.055 (0.037)	1.050*** (0.381)	−0.031 (0.020)	−0.047** (0.020)	0.353* (0.192)
Profitability	−0.089 (0.844)	−0.095 (0.738)	−12.211 (9.097)	−0.162 (0.394)	−0.162 (0.402)	−1.076 (2.392)
Interest Coverage	−0.000 (0.000)	−0.000 (0.000)	0.207* (0.104)	−0.000 (0.000)	−0.000 (0.000)	0.027 (0.021)
N	440	364	49	440	364	76
adj. R²	.069	.052	.203
F	7.540	4.957	3.441

, **, and *** indicate significance at the .1, .05, and .01 levels, respectively.

Table 10 shows the coefficient estimates of regression of dependent variable $D . {CR}_{it}^{j}$ , the difference between an existing rating of a firm “1” and initial rating by CRA “A,” on the dummy variable ${ICR}_{it}^{j}$ , while controlling for firm-specific factors, as per equation (2). Similarly, Tables 11 and 12 present coefficient estimates of regression 2 of CRA “1” with CRA “A” and CRA “2” with CRA “A”, respectively.

The coefficient of ${ICR}_{it}^{j}$ in the OLS regression varies from 0.603 to 1.126 at a significance level of 1% in Tables 10 to 12. The dummy variable coefficient is also positive (0.435–0.616) and significant in probit regression, which along with the positive mean and median rating difference in Table 9, implies that CRA “A” tends to assign a higher initial rating to a firm than its existing rating. The economic magnitude of rating shopping can be seen from Table 9, which indicates that the initial rating assigned by CRA “A” to a firm, on average, is 0.42 to 0.75 notches higher than its current rating by another CRA. Thus, based on these findings, hypothesis 3 is rejected. This could be explained because CRA “A” ability to increase its revenue depends on acquiring new firms for ratings due to the issuer-pay model. Therefore, CRA “A” assigns a rating higher than the existing rating of the firm from another CRA to ensure the firm extends the relationship beyond the initial rating. Consequently, the initial rating assigned by a CRA to a firm is generally higher than the firm’s existing rating from other CRAs. The results confirm rating shopping in the credit rating industry stemming from the competition among rating agencies.

The paper’s findings confirm how competition among CRAs worsens the quality of the ratings in the credit rating industry. The findings suggest that due to competition, the issues of rating inflation and rating shopping were prevalent in the Indian domestic credit rating industry during FY08–FY20. The study finds that all four major CRAs in India tend to assign a higher than the existing rating to a firm already rated by another CRA. However, the study primarily focuses on the impact of competition on credit ratings assigned by CRA “A”; therefore, credit ratings by other CRAs may not be affected similarly. The paper does not look into the benefits accruing to a CRA from such practices in the absence of relevant data on CRAs fees. The motivation behind CRAs’ actions, when faced with competition, is primarily attributed to the issuer-pays model followed in the credit rating industry.

The paper’s findings are consistent with existing literature that rating inflation and shopping are evident in the credit rating industry driven by the industry’s issuer-pays business model and competition among CRAs. Vu et al. (2022) found that S&P and Moody inflate sovereign credit ratings due to competition from Fitch. Becker et al. (2011) established that increased competition in the corporate bond market from Fitch resulted in inflated ratings from other incumbents. However, Bae et al. (2015) found no evidence of rating inflation due to competition from Fitch. Bae et al. (2019) presented evidence that the quality of bond ratings by DBRS deteriorated when faced with increased competition from S&P in Canada. Park and Lee (2018) showed the presence of rating shopping in the Korean bond market. Flynn and Ghent (2018) also reported rating shopping in the structured finance market in the US. Credit rating influences investors’ risk pricing and the regulator’s capital requirements prescribed for banks. Therefore, such issues in the credit rating industry are likely to impact efficient capital allocation.

Conclusion

The paper investigated how competition among CRAs in the credit rating industry impacts firms’ credit rating. The results of this paper indicate that the competition among CRAs influences firms’ credit rating. The paper focuses on empirical analysis of firms’ credit ratings by the top four CRAs in India, focusing on how competition by other CRAs impacts firm rating of CRA “A”. The paper found empirical evidence of rating inflation by a CRA due to competition from other CRAs. The paper’s findings also indicate that CRAs tend to be lenient when rating large-size firms in the face of competition. In addition, the paper results also indicate that competition leads to a firm getting a higher initial rating from a CRA than the firm’s existing rating, resulting in rating shopping in the credit rating industry. The findings of the study are consistent with prior literature, which supports that competition among CRAs worsens the quality of credit ratings (Becker et al., 2011; Bolton et al., 2012; Camanho et al., 2022; Flynn & Ghent, 2018; Park & Lee, 2018; Vu et al., 2022).

The paper findings have important implications for regulators and investors. In the past, regulators have sought to address the credit rating industry’s issues by increasing competition by allowing more players in the credit rating market. In the US, Congress passed the Credit Rating Agency Reform Act in 2006, while in Europe, CRA III regulation, enacted in 2013–14, had provisions for increasing competition in the credit rating industry. In India, three additional CRAs have been accredited for credit rating since 2008. The study’s findings indicate that the regulators need to be cautious while allowing more competition in the credit rating industry due to its adverse effects on rating quality. The study also informs investors about the hazards of relying solely on credit ratings for risk estimation and the upward bias in large issuers’ credit ratings.

Penalizing CRAs, through monetary fines or temporary suspension, for misrating could be a way to make CRAs more cautious and curb the practice of rating inflation and rating shopping. However, this is more of a reactive approach and needs to be complemented with closer regulatory supervision and increased disclosure requirements to improve transparency and consistency in ratings. Controlling the allocation of cases to different CRAs could help curb rating shopping, as this will restrict firms’ movement from one CRA to another for favorable ratings. Periodic rotation of a firm’s rating between CRAs could address the rating inflation issue in the industry, with CRAs being aware that their relationship continuance with a firm is independent of the firm’s rating. However, regulators also need to strike a balance between supervision and allowing CRA to rate firms freely, as a too-conservative approach by CRAs could raise the cost of capital for firms and result in inefficient allocation of resources.

There are certain limitations to the findings of the study. The existing competitive dynamics between CRAs may change in the future based on incentives for CRAs to indulge in such practices. The study considers only credit ratings for non-financial corporates, but domestic CRAs also assign ratings in the area of public finance, structured finance, and financial corporates. Future research can focus on how credit ratings in other areas are affected due to competition between CRAs. Future studies can also analyze how the relative market position in the industry impacts a CRA response to competition faced by it.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Chandan Sharma

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