evaluation of credit models

7/29/2019 Evaluation of Credit Models

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EVALUATION OF CONSISTENCY OF RATING BY CREDIT RATING AGENCIES

AND CHECKING THE RELIABILTY OF CREDIT RATING MODEL:

A LITERARTURE REVIEW

OJAL SAHU

Abstract

We report on the current state and important older findings of empirical studies on corporate creditratings and their relationship to ratings of other entities. Specially, we view the consistency of creditrating models and comparing the performance (ii) to check the reliability of credit score model oncompany And we consider the results of three lines of research: The correlation of credit ratings andcorporate default, the influence of ratings on capital markets, and the determinants of credit ratingsand rating changes. Results from each individual line are important and relevant for the constructionand interpretation of studies in the other two fields, e.g. the choice of statistical methods. Moreover,design and construct of credit ratings and the credit rating scale are essential to understand empiricalfindings.

Keywords: Rating agency; Credit Ratings; Through-the-cycle rating methodology; CorporateGovernance

INTRODUCTION

CRAs have been in operation since the late 1890s, signifying an existence of over 100 years. Rating

standards by Moodys and S&P were known to be stringent. From 1970 onwards, financial literaturehas been commenting on the superior information efficiency of the markets, in comparison to

information disseminated by the CRAs. Lack of corporate governance standards and vigilance by

accountants were identified as the root cause, while the CRAs were accused of abetting the intricate

structures with high credit ratings. It is said that CRAs once again overestimated the credibility of the

contracting parties to honour the structured obligations.

The situation in India is different on account of conservative origin standards and lower complexity

levels in securitized transactions with very little systemic implications. There is, however, the

possibility of asymmetric information between the issuers and all others due to reasons mentioned in

this study. CRAs have been operating in India since 1988. CRISIL, ICRA and Fitch India have

collaborative arrangements with S&P, Moodys and Fitch respectively. CARE is promoted by IDBI &

Canara Bank. Most of the ratings by CRAs relate to Bank Loans, on account of ascertaining the

Credit-related capital adequacy.

CRISIL:

It was incorporated in 1987 and was promoted by Industrial Credit and Investment

Corporation of India Ltd. (ICICI) and Unit Trust of India (UTI).

CRISIL has its association with internationally recognized rating agency Standard and Poors

(S&P) since 1996.

CRISIL is a group of businesses which offers the following diversified services: Rating andRisk Assessment, Infrastructure Advisory, and Business Research.


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INVESTMENT INFORMATION AND CREDIT RATING AGENCY OF INDIA LTD. (ICRA):

It was incorporated in 1991 and was jointly sponsored by Industrial Finance Corporation of

India (IFCI) and other Financial Institutions and banks as an independent and professionalinvestment information and credit rating agency. ICRA is an associate of the international

rating agency Moodys Investors Services which is ICRAs largest shareholder. ICRA has

been granted registration with SEBI under the Securities & Exchange Board of India (Credit

Rating Agencies) Regulations, 1999. ICRA provides information products, ratings and

solutions to different businesses and investors.

ICRA Online Limited (ICRON)

It is a wholly-owned subsidiary of ICRA Limited. ICRON was incorporated in January 1999

and is providing software and outsourcing solutions since then. ICRON has a wholly-owned

subsidiary M-Serve Business Solutions Private Limited, a KPO services company which is

headquartered in Kolkata, India. ICRON has two Strategic Business Units.

CREDIT ANALYSIS & RESEARCH LTD. (CARE)

Credit Analysis & Research Ltd. was incorporated in 1993 by consortium of Banks/financial

institutions in India. The three largest shareholders of CARE are IDBI Bank, Canara Bank and State

Bank of India. CAREs Ratings are recognized by Govt. of India and all regulatory authorities like

RBI and SEBI. CARE has been granted registration with SEBI under the Securities & Exchange

Board of India (Credit Rating Agencies) Regulations, 1999. CARE is a founder member of

Association of Credit Rating Agencies in Asia (ACRAA).

CARE is set up with two divisions:

CARE RatingsCARE Ratings offers a wide range of rating and grading services across sectors. Types of debtinstruments rated by CARE Ratings include commercial paper, fixed deposit, bonds, debentures,hybrid instruments, structured obligations, preference shares, loans, etc. CARE Ratings provideinvestors and risk managers with credit opinions based on detailed in-depth research, whichencompasses detailed analysis of risks that affect credit quality of an issuer.

CARE Research and Information Services

CARE Research & Information Services is an independent division of CARE. The research division

undertakes two activities, i.e., providing an in-house support to the ratings division and providing

sectoral research to financial intermediaries, corporate, analysts, policy-makers, etc. as an aid to their

decision-making process. CARE Research & Information Services offers both subscription based

reports and also customised reports on request.

Literature Review

The study is a proactive initiative, with a view to assess the preparedness of the CRAs to

communicate signals and reduce the informational asymmetries that generally exist between issuers

and investors. CRAs have been rating instruments and subjecting them to periodic review, sometimes

necessitating a transition to a lower or higher grade. Thus far, CRAs have obtained the approval of

SEBI, giving them the status of approved rating agencies. The RBI also has put regulations in place

with reference to credit rating agencies and credit information companies. There are four Credit

Rating Agencies registered with SEBI, viz. CRISIL, ICRA, CARE. The study presents a timely

opportunity for introspection by all concerned entities policy makers, regulators, investors, ratingagencies, issuers and intermediaries.


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The relevance of credit ratings changes for capital markets, i.e. the efficient market hypothesis, canonly be measured effectively if they are conditioned on the respective change in default probability.

Moreover, market reactions around rating changes are asymmetrical. Specially, markets react

stronger to downgrades than to upgrades (even after incorporating the corresponding default

probability). This discrepancy might have multiple reasons. It might be caused by the way

rating agencies operate or specific features of the market for ratings. On the other hand itmight be caused by the behavior of corporations and how they release relevant information.

One important feature of the agencies' approach that might cause the asymmetrical

information content of credit ratings is the so called through-the-cycle method. Rating

agencies try to estimate the long term creditworthiness of a corporation independent of short-

term business cycle effects. Nevertheless, ratings do correlate with the business cycle.

Therefore macroeconomic variables along with financial ratios and corporate governance

characteristics are determinants of credit ratings.

Research objective

The objective of this study is to gauge the robustness of the operations of the CRAs with a view to

consistency of credit rating models and comparing the performance (ii) to check the reliability of

credit score model on company X (iii) CRAs in India are more subjective in their assessment and (iv)

the deterioration in ratings is not captured in time by CRAs, if compared with financial information in

the public domain. The objective is to place a simple tool in the hands of the public that will enable a

cross-verification of the reports by CRAs in a cost-effective manner and raise the quality standards bar

of the CRAs. The study also suggests practical ways in which the CRAs can improve their rating

processes and help reduce the information gap.

Under study, a simple model, built around Net Worth, Leverage and Interest Cover, was used to

detect deteriorations in creditworthiness. When compared with the actual ratings, it was found that the

actual ratings did not always reflect the falling creditworthiness in a timely manner. A survey of

CRAs and their Analysts revealed that there was a very low level of awareness among Indian

accountants of International Financial Reporting Standards (IFRS) which Indian corporations need to

comply with effective from financial year 2011. The outcome of such research could result in inputs to

strengthen the financial markets and CRAs in particular

The objective of the study is as follows.

Assessment of the performance of CRAs in India in terms of parameters like transition data.

How far CRAs assessment helps financial regulation.

Accountability, corporate governance issues of CRAs.

Disclosures of methodologies of rating.

Rating of complex products like structured obligation.

Consistency of rating data with accounting data.

Overall evaluation of what CRAs have done in terms of value addition or the Indian

economy.

Place a simple tool in the hands of the public that will enable a cross-verification of the

reports by CRAs in a cost-effective manner and raise the quality standards bar of the CRAs.

A comparative analysis of the operations carried out by the various credit rating agencies

under study, viz. CRISIL, ICRA, CARE and FITCH has been presented and check the

consistency.


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To check the reliability of credit score model used.

RATING PROCESS

Rating is a multi-layered decision-making process which requires interactive dialogue with the issuer.

The rating process is a fairly detailed exercise that starts with a rating request from the issuer, the

signing of a rating agreement and continues up to the surveillance of rating. It involves among other

things, analysis of published financial information, visits to issuers offices and work places, and

intensive discussions with issuers auditors, bankers, creditors, etc. It also involves an in -depth study

of the industry itself and a degree of environment scanning. The rating process is explained below:

1. Request for Rating: The rating process starts with the issuers request for rating. Then the rating

agreement is signed between the client and the rating agency. The rating agency assigns a rating team

for the purpose, and the client provides the relevant information to the rating team along with the

rating fees.

2. Analysis of Information: The rating team conducts the preliminary analysis of the informationprovided by the client. The team also conducts the site visits for the purpose of analysis.

3. Meeting: Then the meetings between the rating team and management of the issuer are conducted

and the rating team does the final analysis of the information after clarification of any doubts in the

management meeting.

4. Assignment of Rating: The rating team presents its analysis to the rating committee which assigns

the rating to the given instrument and communicates the same to the issuer. The rating is then

accepted by the issuer or the issuer may appeal the rating agency to further refine the rating.

5. Dissemination of Rating: In case the rating is accepted by the issuer it is disseminated to CRISIL's

subscriber base, and to the local and international news media. Rating information is also updated on

line on the website of rating agency.

6. Continuous Surveillance: All ratings are kept under continuous surveillance throughout its validity

by the rating agency

METHODOLOGY ADOPTED

The financial credit score is calculated for company X in the following steps.

1. The first step is obtaining the ratio NW/ TL . This ratio usually ranges between 0 and 1.

2. In the next step, TD/ TA is calculated. A negative relationship is postulated between TD/ TAratio and the FCS. This is because higher is the debt in relation to the assets, greater is the risk

in lending to such a company, other things remaining the same.

3. The third step in calculating FCS is obtaining inverse of interest coverage ratio, i.e.,1/ PBDIT

. This ratio ought to range between zero and one for a credit-worthy company.

4. The Financial Credit Score has been defined as stated in below equations for FCS.

FCS = NW/ TL TD/ TA 1/ PBDIT, (if I/ PBDIT 0)

FCS = NW/ TLTD/ TA + 2 1/ PBDIT, (if 1/ PBDIT < 0)

For Company X, the FCS is calculated from 2007- 2012

Dec 2007 March 09 March 10 March 11 March 12

Net worth 1,439.24 2,061.51 2,583.52 2,633.92 3,512.93


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TotalLiabilities

1,527.77 2,483.46 2,583.52 2,633.92 3,512.93

NW/TL 0.9420 0.8300 1.00 1.00 1.00

Total Debt 88.53 421.95 0.00 0.00 0.00

Total Assets 1,527.76 2,483.46 2,583.52 2,633.92 3,512.93

TD/TA 0.057 0.169 0.00 0.00 0.00PBDIT 2,504.80 3,241.48 2,997.43 3,103.97 3,688.52

1/PBDIT 3.992 3.085 3.336 3.221 2.711

FCS -3.107 -2.424 -2.336 -2.221 -1.711

Higher will be the NW/TL ratio, better is the FCS for company which can be seen in this case

in 2012.

A negative relationship is postulated between TD/ TA ratio and the FCS. This is because

higher is the debt in relation to the assets, greater is the risk in lending to such a company,

other things remaining the same.

Due to the fact that this ratio can take values which are both positive and negative, anasymmetric treatment to this ratio is given.

For calculating credit score, following ratios are also taken into consideration.

The current ratio declined from 2011 to 2012, the current assets to current liabilities have

declined.

The debt equity ratio is zero which means that total debt is financed by equity only.

The inventory turnover ratio increased from 2011 to 2012 which means that stock piling is not

there and more inventory is converted into sales. The debtors turnover ratio has also increased which means that how quickly debtors are

converted into cash.

The net profit margin has also increased which means that net profit has increased from 2011

to 2012.

The instruments rated by different credit rating agencies and calculation of mean are as follows.

The instruments rated by CRISIL.

RATIOS Dec 07 March 09 March 10 March 11 March 12

CurrentRatio

0.68 0.92 0.84 0.86 0.83

DebtequityRatio

0.06 0.20 - - -

Inventory

TurnoverRatio

7.20 9.26 8.99 7.91 9.93

DebtorsTurnoverRatio

31.41 41.83 29.24 24.28 27.27

Net profitmargin

12.58 12.09 12.29 11.56 12.07


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Year Long term instruments

Medium

term Short term

Debentures

Preference

shares

Loan FD CD CP STL Others Total

2006 197(24.72)

89(11.17)

153(19.20)

42(5.27)

22(2.76)

131(16.44)

60(7.53)

103(12.92)

797(100)

2007 205(24.79)

79(9.55)

169(20.44)

47(5.68)

22(2.66)

145(17.53)

53(6.41)

107(12.94)

827(100)

2008 213(24.51)

92(10.59)

161(18.53)

55(6.33)

35(4.03)

137(15.77)

66(7.59)

110(12.66)

869(100)

2009 420(28.26)

107(7.20)

496(33.38)

63(4.24)

48(3.23)

149(10.03)

79(5.32)

79(5.32)

1486(100)

2010 429(22.63)

123(6.49)

705(37.18)

69(3.64)

50(2.64)

175(9.23)

67(3.53)

278(14.6)

1896(100)

2011 436(21.06)

117(5.65)

728(35.17)

88(4.25)

59(2.85)

192(9.28)

80(3.86)

370(17.87)

2070(100)

2012 487(20.66)

138(5.85)

855(36.27)

97(4.12)

69(2.93)

177(7.51)

89(3.78)

445(18.88)

2357(100)

Total 2231(13.31)

1039(6.20)

6595(39.34)

675(4.03)

465(2.77)

465(2.77)

1492(8.90)

682(4.07)

16672(100)

Overall %

58.85 6.80 12.97 21.38

Mean

381.22 115.44 599.44 75.00 51.67 165.78 75.78 398.11

The instruments rated by ICRA

Year Long term instrume

nts

Mediu

m

Term Short term

Debentures

Preferenceshares


2006 42(22.22)

24(12.70)

33(17.46)

14(7.41)

4(2.12) 27(14.29)

13(6.88)

32(16.93)

189(100.00)

2007 38(19.59)

26(13.40)

35(18.04)

15(7.73)

5(2.58) 29(14.95)

14(7.22)

32(16.49)

194(100.00)

2008 53(22.75)

28(11.72)

38(16.31)

17(7.30)

8(3.43) 35(15.02)

15(6.44)

39(16.74)

233(100.00)

2009 59(24.69)

28(11.72)

38(15.90)

17(7.11)

8(3.35) 35(14.64)

15(6.28)

39(16.32)

239(100.00)

2010 67(23.6

7)

34(12.0

1)

45(15.9

0)

22(7.7

7)

10(3.5

3)

42(14.8

4)

19(6.7

1)

44(15.5

5)

283(100.0

0)

2011 68(21.79)

41(13.14)

50(16.03)

23(7.37)

12(3.85)

47(15.06)

21(6.73)

50(16.03)

312(100.00)

2012 98(21.12)

58(12.50)

78(16.81)

35(7.54)

19(4.09)

74(15.95)

33(7.11)

69(14.87)

464(100.00)

Total 630(21.51)

379(12.94)

480(16.39)

229(7.82)

116(3.96)

433(14.78)

198(6.76)

464(15.84)

2929(100.00)

Overall %

50.84 11.78 21.54 15.84


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Mean

70.00 42.11 53.33 25.54 12.89 48.11 21.00 51.56

The instruments rated by CARE

Year Long term instruments

Medium

Term Short term

Debentu

res

Prefere

nceshares


2006 40(21.51)

24(12.90)

35(18.82)

7(3.76) 7(3.76) 30(16.13)

18(9.68)

25(13.44)

186(100.00)

2007 45(21.13)

29(13.62)

40(18.78)

9(4.23) 8(3.76) 34(15.96)

19(8.92)

29(13.62)

213(100.00)

2008 50(19.84)

35(13.89)

46(18.25)

9(3.57) 10(3.97)

48(19.05)

22(8.73)

32(12.70)

252(100.00)

2009 61(21.03)

37(12.76)

58(20.00)

11(3.79)

13(4.48)

50(17.24)

27(9.31)

33(11.38)

290(100.00)

2010 73(21.10)

45(13.01)

64(18.50)

13(3.76)

15(4.34)

56(16.18)

34(9.83)

46(13.29)

346(100.00)

2011 82(21.52)

49(12.86)

70(18.37)

15(3.94)

17(4.46)

61(16.01)

37(9.71)

50(13.12)

381(100.00)

2012 93(21.68)

56(13.05)

76(17.72)

18(4.20)

19(4.43)

70(16.32)

41(9.56)

56(13.05)

429(100.00)

Total 653(21.24)

400(13.01)

570(18.54)

122(3.97)

136(4.42)

505(16.42)

292(9.50)

397(12.91)

3075(100.00)

Overall %

52.78 8.89 25.92 12.91

Mean

72.56 44.44 63.33 13.56 15.11 56.11 32.44 44.11

These are different ratings given by different credit rating agencies. Through SPSS, data is analyzed

to know whether there is significant difference between rating agencies evaluation or not. Thus, our

null hypothesis is that there is no significant difference in evaluation process of credit rating agencies

and our alternate hypothesis is that there is significant difference in evaluation process.

ANALYSIS

Ho: there is no significant difference between evaluation processes of CRISIL, ICRA

and CARE.

Ha: there is significant difference between evaluation processes of CRISIL, ICRA and

CARE.

As we can see in annexure, output sheet the probability i.e. 2 tailed test between CRISIL and

ICRA is 0.023 which is less than 0.05, thus the null hypothesis is rejected and there is

significant difference between evaluation process of CRISIL and ICRA.


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Another test for CRISIL and ICRA is also done which is Wilcoxon test according to which

the probability is 0.012 which is also less than 0.05, thus according to this test also our null

hypothesis is rejected.

Similar tests are done for CRISIL and CARE which gives probability value of 0.023 with

paired sample t- test and 0.012 with Wilcoxon test which rejects our null hypothesis.

When the same tests was performed for ICRA and CARE, it was found that the probability

value is greater than 0.05 i.e. 0.486 for t test and 0.401 for Wilcoxon test which tells that the

null hypothesis is retained and there is no significant difference between evaluation process of

ICRA and CARE.

The financial credit score for company X is calculated using the model which is negative.Higher will be the NW/ TL , better will be the financial credit score of company which can

be seen in 2012.According to the research done , the model could be taken as somewhat

reliable.

CONCLUSION

According to analysis done, it could be concluded that there is no significant difference between

evaluation process of CARE and ICRA. It could also be concluded that there is significant difference

between ICRA and CRISIL. The financial credit score model used could be considered as somewhatreliable.

CRAs have assigned very poor ratings to Collective Investment Schemes and some IPOs, hence

driving poor quality issuers out of the market. The basic accounting figures: Total Income and PBDIT

are contaminated due to the influx of other income being merged into the Total Income.

There are several instances where the Interest Coverage ratio has deteriorated but the ratings have

remained the same, without any downgrade, despite adverse business prospects, mergers &

acquisitions and forays into diversified areas.

All CRAs reveal the processes flows. But they do not disclose the actual methodologies. Unacceptedratings are not published; hence information is asymmetric to that extent.

CRAs generally give information based on Credit risk. Markets factor in other risks also.

ANNEXURE

T-Test


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Paired Samples Statistics

Mean N Std.

Deviation

Std. Error

Mean

Pair 1CRISIL 232.8050 8 201.56886 71.26535

CARE 42.7075 8 21.43987 7.58014

Paired Samples Correlations

N Correlation Sig.

Pair 1 CRISIL & CARE 8 .738 .036

Paired Samples Test

Paired Differences t df Sig. (2-

tailed)Mean Std.

Deviation

Std. Error

Mean

95% Confidence Interval

of the Difference

Lower Upper

Pair 1 CRISIL - CARE 190.09750 186.30345 65.86822 34.34392 345.85108 2.886 7 .023

Wilcoxon Signed Ranks Test

Ranks

N Mean Rank Sum of

Ranks

CARE - CRISIL

Negative Ranks 8a

4.50 36.00

Positive Ranks 0b

.00 .00

Ties 0c

Total 8

a. CARE < CRISIL

b. CARE > CRISIL

c. CARE = CRISIL


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Test Statisticsa

CARE -

CRISIL

Z -2.521b

Asymp. Sig. (2-tailed) .012

a. Wilcoxon Signed Ranks Test

b. Based on positive ranks.

T-Test


Mean N Std.

Deviation

Std. Error

Mean

Pair 1CRISIL 232.8050 8 201.56886 71.26535

CARE 42.7075 8 21.43987 7.58014

Pair 2ICRA 40.5675 8 19.21314 6.79287

CARE 42.7075 8 21.43987 7.58014


N Correlation Sig.

Pair 1 CRISIL & CARE 8 .738 .036

Pair 2 ICRA & CARE 8 .924 .001

Paired Samples Test

Paired Differences t df Sig. (2-tailed)Mean Std.

Deviation

Std. Error

Mean


of the Difference

Lower Upper

Pair 1 CRISIL - CARE 190.09750 186.30345 65.86822 34.34392 345.85108 2.886 7 .023

Pair 2 ICRA - CARE -2.14000 8.23035 2.90987 -9.02074 4.74074 -.735 7 .486



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Ranks

N Mean Rank Sum of

Ranks

CARE - CRISIL

Negative Ranks 8a

4.50 36.00

Positive Ranks 0b

.00 .00

Ties 0c

Total 8

CARE - ICRA

Negative Ranks 2d

6.00 12.00

Positive Ranks 6e

4.00 24.00

Ties 0f

Total 8

a. CARE < CRISIL

b. CARE > CRISIL

c. CARE = CRISIL

d. CARE < ICRA

e. CARE > ICRA

f. CARE = ICRA

Test Statisticsa

CARE -

CRISIL

CARE -

ICRA

Z -2.521b

-.840c

Asymp. Sig. (2-tailed) .012 .401



c. Based on negative ranks.

T-Test


Mean N Std.

Deviation

Std. Error

Mean

Pair 1CRISIL 232.8050 8 201.56886 71.26535

ICRA 40.5675 8 19.21314 6.79287



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N Correlation Sig.

Pair 1 CRISIL & ICRA 8 .761 .028

Paired Samples Test

Paired Differences t df Sig. (2-

tailed)Mean Std.

Deviation

Std. Error

Mean


of the Difference

Lower Upper

Pair 1 CRISIL - ICRA 192.23750 187.35887 66.24136 35.60157 348.87343 2.902 7 .023

Descriptive Statistics

N Mean Std.

Deviation

Minimum Maximum

CRISIL 8 232.8050 201.56886 51.67 599.44

ICRA 8 40.5675 19.21314 12.89 70.00


Ranks

N Mean Rank Sum of

Ranks

ICRA - CRISIL

Negative Ranks 8a

4.50 36.00

Positive Ranks 0b

.00 .00

Ties 0c

Total 8

a. ICRA < CRISIL

b. ICRA > CRISIL

c. ICRA = CRISIL

Test Statisticsa

ICRA -

CRISIL

Z -2.521b

Asymp. Sig. (2-tailed) .012


13/13



REFERENCES

1)Alexander B. Matthies, 2013-2003 ,Empirical Research on Corporate Credit- Ratings: A

Literature Review, Discussion paper

2) Altman E.I., (1968), Financial Ratios, Discriminate Analysis and the Prediction ofCorporate Bankruptcy, Journal of Finance, Sept., pp. 589-609.

3) Financial Times, (1996), Emerging Markets - Credit Ratings, Financial Times Publishing,

Pearson Professional Ltd.

evaluation of credit models

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