evaluation of credit models
TRANSCRIPT
-
7/29/2019 Evaluation of Credit Models
1/13
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.
-
7/29/2019 Evaluation of Credit Models
2/13
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.
-
7/29/2019 Evaluation of Credit Models
3/13
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.
-
7/29/2019 Evaluation of Credit Models
4/13
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
-
7/29/2019 Evaluation of Credit Models
5/13
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
-
7/29/2019 Evaluation of Credit Models
6/13
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
Loan FD CD CP STL Others Total
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
-
7/29/2019 Evaluation of Credit Models
7/13
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
Loan FD CD CP STL Others Total
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.
-
7/29/2019 Evaluation of Credit Models
8/13
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
-
7/29/2019 Evaluation of Credit Models
9/13
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
-
7/29/2019 Evaluation of Credit Models
10/13
Test Statisticsa
CARE -
CRISIL
Z -2.521b
Asymp. Sig. (2-tailed) .012
a. Wilcoxon Signed Ranks Test
b. Based on positive ranks.
T-Test
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
Pair 2ICRA 40.5675 8 19.21314 6.79287
CARE 42.7075 8 21.43987 7.58014
Paired Samples Correlations
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
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
Pair 2 ICRA - CARE -2.14000 8.23035 2.90987 -9.02074 4.74074 -.735 7 .486
Wilcoxon Signed Ranks Test
-
7/29/2019 Evaluation of Credit Models
11/13
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
a. Wilcoxon Signed Ranks Test
b. Based on positive ranks.
c. Based on negative ranks.
T-Test
Paired Samples Statistics
Mean N Std.
Deviation
Std. Error
Mean
Pair 1CRISIL 232.8050 8 201.56886 71.26535
ICRA 40.5675 8 19.21314 6.79287
Paired Samples Correlations
-
7/29/2019 Evaluation of Credit Models
12/13
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
95% Confidence Interval
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
Wilcoxon Signed Ranks Test
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
-
7/29/2019 Evaluation of Credit Models
13/13
a. Wilcoxon Signed Ranks Test
b. Based on positive ranks.
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.