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Indian Credit Rating Model:

Developing a Non-Convertible Bond

Rating Model for the Indian Debt Markets

by

Shabari Nayak

An honors thesis submitted in partial fulfillment

of the requirements for the degree of

Bachelor of Science

Undergraduate College

Leonard N. Stern School of Business

New York University

May 2004

Professor Marti Subrahmanyam Professor Marti Subrahmanyam

Faculty Adviser Thesis Adviser


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Table of Contents

Executive Summary....................................................................................................................3

Areas of Study........................................................................................................................ 5

Correlation of Financial Ratios with Current Credit Ratings..................................................... 5

Methodology...........................................................................................................................5Correlation Analysis and Observations .................................................................................. 8

Developing the Indian Credit Rating Model ............................................................................ 10Contributory Financial Ratios .............................................................................................. 10

Computing ICRM.................................................................................................................11

Converting ICRM to a Lettered Rating................................................................................ 12Results and Findings.............................................................................................................14

Multiple Discriminant Analysis on ICRM ............................................................................... 18

Methodology.........................................................................................................................18Results and Findings.............................................................................................................19

The Ratings-Change Prediction Power of ICRM.....................................................................23Sample Determination and Composition.............................................................................. 23

Methodology.........................................................................................................................24

Results and Analysis.............................................................................................................25

Summary and Conclusions ....................................................................................................... 28

Appendices ............................................................................................................................... 29

Appendix 1: Correlation Matrices for CRISIL8 and CRISIL18 .......................................... 29

Appendix 2: ICRM8 and ICRM18 Calculations .................................................................. 33

References ................................................................................................................................ 34

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Executive Summary

Credit risk is commonly defined as the possibility that an issuer of a financial obligation, such

as a debenture, fixed deposit, commercial paper, or structured obligation, will not be able torepay interest and principal in a timely manner, or in accordance with the terms of the

borrowing agreement. Credit rating agencies, such as the United States-based Moodys

Investor Services (Moodys) and Standard and Poors (S&P), are responsible foranalyzing the credit quality of various issuers and assigning a rating to these issuers

obligations that corresponds to their perceived degree of credit risk. Associated with each

rating, or risk bucket, is a probability of default that is derived from historical observationsof the default behavior of companies within each ratings class. As such published ratings

clearly contain significant information concerning the quality and marketability of various

fixed income issues, it is of little surprise that credit ratings are considered a primary source ofinvestor information in investment decision-making.

In India, four primary agencies provide such credit ratings to the public: Credit Rating

Information Services of India, Ltd (CRISIL), Investment Information and Credit RatingAgency (ICRA), Credit Analysis and Research (CARE) and Duff & Phelps. The

importance of the services of these agencies in the Indian debt market cannot be

underestimated, especially considering the noteworthy growth in the past decade in thenumber of Indian companies raising funds through long-term borrowings, which was

accompanied by growth in the volume of trade of debt instruments in secondary markets in

India. Their role becomes doubly important after taking into consideration the Indian financialmarkets inefficiency, much like that of most developing countries, as information relevant to

determining creditworthiness may not be publicly available.

The degree of perceived credit risk is also an important facet in the development of debt

markets. India, between 1991 and 1992, experienced a recession that was accompanied by

three consecutive country risk ratings downgrades of two notches each within a span of nine

months, by both primary American ratings agencies. However, a subsequent deregulatory andde-licensing government movement (deregulation of industry as well as loosening of

restrictions on foreign investment) spurred a period of declining credit risk, mainly due to an

infusion of equity funds from both domestic and international sources. Though this periodlasted through the mid-nineties, more recently, factors specific to the Indian economic system

and the governments control of corporate entities have caused uncertainty and

unpredictability among investors, a situation which has negatively influenced credit risk.

These factors include low growth rates (possibly a result of a controlled money supplyintended to check inflation), uncertainty in the exchange rate for Indian currency, and the

effect of decreased government expenditure on the growth of the Indian economy

(specifically a reduction in development expenditures which has adversely affected demandfor core industries such as cement and steel). All of these factors have conspired to drive

Indian credit risk higher since 1996. More recently, however, flux in the American economy

and the dwindling faith of American investors in financial statements and earnings forecastsof domestic companies have served to drive funds into foreign markets, including that of

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India. In recognition of an improving risk profile, S&P upgraded Indian country risk fromNegative to Stable at the end of 2003.

Clearly, the credit risk history of India displays a quality of instability, with major changes incredit risk occurring within a few years of one another. In such a climate, the role of the credit

rating agency becomes increasingly important as a source of current information concerningthe creditworthiness of the corporations under watch. Recently, credit rating agencies have

come under sharp criticism for failing to respond to events and downgrade suspect companieswith sufficient speed. Enron is perhaps the most well-known example, as credit rating

agencies in the United States maintained investment-grade ratings for that company until as

late as a month prior to its Chapter 11 filings.

In India, CRISIL, the countrys oldest and perhaps most reliable credit ratings provider,

performed a comparably unreliable action when it downgraded BPL Ltds long-termdebenture from A to D after the company had already defaulted on that rated obligation. This

incident occurred a mere few years following a mass downgrade of nearly one hundred Indian

companies by CRISIL and ICRA in reaction to public criticism of their ratings practices in2000. In a country in which illiteracy is high, and in which a significant portion of common

investors do not know how to correctly interpret and analyze the information contained within

public financial statements, the reliability of credit ratings as a means of evaluating potential

fixed income investments becomes increasingly important. In addition to the commoninvestor, Indian commercial banks often use published credit ratings as a step in a new loan

evaluation, provided that the borrower in question is tracked by a ratings agency.

In part, the problem of the Indian credit rating agencies may lie within the rating process

itself, which is by nature highly dependent on historical data, perhaps largely blind tomacroeconomic complexities and potentially limited in knowledge of industries and

businesses. However, blame lies with the agencies as well, as the data indicates that ratingsagencies substantially overestimated the financial flexibility of traditional corporate houses inthe aforementioned mass downgrade. Past research has also shown that the ratings provided

by the two primary Indian bond rating agencies, CRISIL and ICRA, are becoming extremely

variable over time. The majority of these ratings changes are on the downside, with potentialprice risk implications for investors. The consistency of determinant financial ratios between

rating classes also points to probable weakness in rating methodologies, as the significant

financial factors fail to discriminate across rating classes. That is, while the key financial

ratios desirably do not vary for companies belonging to the same ratings class, they also donot vary across companies belonging to different ratings classes.

These factors conspire to the question of whether an individual Indian investor can self-ratedebt issues using only the financial statements of Indian corporations available to the public.

If such a model rates instruments on par with the ratings provided by the reigning credit rating

agencies in India, then it can be used to rate companies for which ratings information isunavailable. Furthermore, if the model can predict ratings changes (both upgrades and

downgrades) earlier than changes published by major Indian ratings houses, then it can be

used as a fixed-income investment decision-making tool in lieu of publicly available ratings.

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Areas of Study

The purpose of this paper is to develop a model with which to rate the outstanding non-

convertible debt of Indian corporations, from the perspective of an Indian investor (that is,

without taking into account the country risk of India).

The first section of this study examines the extent of the association between the ratings

published by credit ratings agencies and the financial ratios of companies within a sample set.The purpose of such an investigation is to potentially determine which ratios, if any, are

closely correlated with the ratings of the credit agencies so that they can subsequently be

used in the development of a new ratings model. In the second section of this paper, a debt

scoring model called the Indian Credit Rating Model (ICRM) is introduced and calculatedfor the various companies included in the sample. The ratings determined by ICRM are then

compared to the ratings published by CRISIL. The third section of this study uses multiple

discriminant analysis to attempt to make ICRM more efficient through the inclusion ofcoefficients. Finally, the final section of this paper tests the predictionary power of the ICRM

model with respect to upgrades and downgrades.

Correlation of Financial Ratios with Current Credit Ratings

Perhaps the most important source of information concerning the creditworthiness of acorporation can be found in the publicly available financial statements issued by said

corporation. As credit rating agencies and relevant credit risk research has focused on the

information contained within financial ratios derived primarily from financial statements, anovel credit rating model may be developed after close analysis of the correlation between

these ratios and the current ratings of companies in a sample. A model that rates debt issues

on par with the ratings of the leading ratings agency in India, which is one of the primarygoals of this paper, can only be developed by first conducting a correlation analysis between

those ratings and a plethora of potentially influential financial ratios.

For the purposes of this research, the ratings published by CRISIL have been chosen as a

basis for the building of the new model. CRISIL is the oldest of the four primary credit rating

agencies, an important criterion when considering that assigning and monitoring credit ratings

is largely dependent on historical information concerning credit history and default risks. Inaddition, CRISIL seems to be the better choice for a comparative tool because past studies

(such as Raghunathan and Varma in 1993 and Chaudhury in 1999) have shown that the

ratings published by CRISIL surpass that of ICRA and CARE based on international

comparability and internal consistency. Also worthy of mention is that the ratings publishedby CRISIL seem to be more widely regarded in India as the standard, and are more readily

available to the investing public.

Methodology

The ratings used to analyze correlation matrices are taken from CRISILs quarterly-published

Rating Scan, which lists all the firms rated by CRISIL and their ratings at the time of

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publication, from March 2002. The financial statements used to determine the financial ratiosin this analysis have been sourced in the form available on Bloomberg, from the fiscal year

ending March 1999 to the fiscal year ending March 2003.

The design of this paper necessitates that the first and main restriction on the potential sample

for this study be that each included company have an outstanding non-convertible debentureissue that is rated by CRISIL. Therefore, the raw initial sample of companies included in this

study is simply the one hundred and eight Indian companies covered by CRISIL that haveoutstanding non-convertible debt. The importance to this study of available financial

information cannot be understated, as the necessary financial ratios simply cannot be

calculated in the absence of financial statements. As a result, the next restriction placed on thesample set was the availability of the above five years worth of financial data for all included

companies. The fifty-nine companies remaining after the enforcement of this restriction also

were required to have fiscal years ending in March (which is the prevalent fiscal year end inIndia) for the sake of conformity within the data set. In addition, commercial banking

organizations have been excluded, as the standard financial ratios applicable all other included

firms do not apply to the financial statements of commercial banks. A list detailing the samplefor this study, along with the rating of the companies outstanding non-convertible debentures

and the sector within which each corporation falls, can be found in the following table.

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Companies in Study Sample

Company Name Rating Sector

Asian Paints (India) Limited AAA Paints

BASF India Limited AAA Chemicals

Bharat Petroleum Corporation Limited AAA Oil and Gas

Great Eastern Shipping Company Limited AAA Shipping

Hero Honda Motors Limited AAA Two-WheelersHindalco Industries Limited AAA Non-Ferrous Metals

Hindustan Petroleum Corporation Limited AAA Oil and Gas

Larsen & Toubro Limited AAA Diversified

National Aluminium Company Limited AAA Non-Ferrous Metals

Sun Pharmaceutical Industries Limited AAA Pharmaceuticals

Tata Power Company Limited AAA Power

Century Enka Limited AA+ Textiles

Dabur India Limited AA+ Consumer Goods

Electrosteel Castings Limited AA+ Engineering

Raymond Limited AA+ Textiles

Tata Iron & Steel Company Limited AA+ Steel

BOC India Limited AA Diversified

Cadila Healthcare Limited AA Pharmaceuticals

Carborundum Universal Limited AA Engineering

Colour-Chem Limited AA DyesCoromandel Fertilisers Limited AA Fertilizers

Finolex Industries Limited AA Petrochemicals

Glenmark Pharmaceuticals Limited AA Pharmaceuticals

Madras Cements Limited AA Cements

Mahindra & Mahindra Limited AA Two-Wheelers

Sterlite Industries (India) Limited AA Non-Ferrous Metals

Tata Chemicals Limited AA Chemicals

Tube Investments Of India Limited AA Diversified

VST Industries Limited AA Tobacco

Ahmedabad Electricity Company Limited AA- Power

Apollo Hospitals Enterprise Limited AA- Diversified

Ashok Leyland Limited AA- Automobiles

E.I.D. Parry (India) Limited AA- Diversified

Finolex Cables Limited AA- Telecommunications

India Glycols Limited AA- ChemicalsIndian Petrochemicals Corporation Limited AA- Petrochemicals

Mahavir Spinning Mills Limited AA- Textiles

Vardhman Spinning & General Mills Limited AA- Textiles

Aarti Industries Limited A+ Chemicals

Apollo Tyres Limited A+ Tires

Chambal Fertilisers & Chemicals Limited A+ Fertilizers

Excel Industries Limited A+ Chemicals

Sudarshan Chemical Industries Limited A+ Dyes

Max India Limited A Diversified

DCM Shriram Consolidated Limited A- Diversified

Thirumalai Chemicals Limited A- Petrochemicals

Gabriel India Limited BBB- Auto Ancillary

Tata Finance Limited BBB- Non-Banking Finance Corporation

Atul Limited BB+ Dyes

Bharat Gears Limited C Auto AncillaryAmforge Industries Limited D Engineering

Flex Industries Limited D Packaging

Garware Polyester Limited D Textiles

Hindustan Organic Chemicals Limited D Petrochemicals

Jain Irrigation Systems Limited D Plastics

Jindal Iron & Steel Company Limited D Steel

Jindal Vijayanagar Steel Limited D Steel

Mukand Limited D Steel

RPG Transmission Limited D Power

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As is clear from the preceding list, there exists a bias in the sample toward investment-gradeddebt. This is largely due to two factors, the first of which is that corporations that choose to

have new issues rated by the agencies tend to have generally stable financials it is rare in

India for a financially infirm company to seek ratings for an upcoming borrowing. The secondcontributory factor to the bias in the sample set is that companies that are non-investment

grade (including companies that have already defaulted on obligations) may delay the releaseof financial statements while undergoing debt restructuring proceedings. As the availability of

five years worth of financial data was integral to the pursuit of this study, companies that havefiled inconsistently have been eliminated from the sample. As a result, it is important to keep

in mind that the results of this study will be influenced by a bias towards investment-grade

debt.

Correlation Analysis and Observations

The potentially determinant financial ratios whose correlation with the credit ratings of

CRISIL could be analyzed were found in past research on credit ratings. Edward Altman in

1968, using multiple discriminant analysis, developed a model commonly known as theAltman Z-Score with which one can determine a companys propensity to default. The

financial ratios which were included in the Altman Z-Score model are: Working Capital to

Total Assets, Retained Earnings to Total Assets, Earnings before Interest and Taxes (EBIT)

to Total Assets, Sales to Total Assets, and Market Value of Equity to Book Value of Debt.For the purposes of this paper, the financial ratio Market Value of Equity to Book Value of

Debt will not be considered, as this ratio fluctuates on a daily basis with stock price.

Alexander Bathory in 1984 performed similar studies in predicting corporate collapse using

ratios relevant to debt-service ability, profitability, adequacy of reserves, and liquidity. The

ratios included in Bathorys final model are: Earnings Before Taxation to Capital Employed(Return on Capital Employed or ROCE), Equity to Current Liabilities, Tangible Net Worth

to Total Liabilities, and Working Capital to Total Assets (which has already been included inthis study after the consideration of Altmans ratios).

S&P uses the following financial ratios in the determination of their published ratings in the

United States: Pre-tax Interest Coverage Ratio (EBIT/Interest), Cash Flow from Operations to

Long-Term Debt, Cash Flow from Operations to Total Debt, Earnings Before Taxes toPermanent Capital (Return on Net Worth or RONW), Operating Income to Sales, Capital to

Long-Term Debt, Capital Plus Short Term Debt to Total Debt, and Equity to Total Liabilities.

Other financial ratios tested in this analysis include Cash Flow to Total Assets, Cash Flow toTotal Debt, Debt to Equity, Interest to Average Debt, Interest to EBITDA (inverse of

EBITDA interest coverage ratio), and Long-Term Debt to Total Assets.

The following table displays the results of an initial correlation analysis of all of the above

financial ratios against the ratings provided by CRISIL, where ratings subcategories have

been eliminated. That is, the scale of potential CRISIL ratings is: AAA, AA, A, BBB, BB, B,

C, and D. As the ratings published by CRISIL are merely rankings, for the purposes of thiscorrelation analysis, the ratings have been converted to a numerical scale in which AAA-graded debt receives the highest ranking, 1, and D receives the lowest ranking, 8. Going

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forward, the CRISIL ratings system exclusive of ratings subcategories is referred to asCRISIL8, where eight represents the number of possible ratings rankings.

CRISIL Without Subcategories

Financial RatioA Priori Signs of

CorrelationCorrelation p-value Significant at 1% Significant at 5%

Working Capital to Total Assets - -0.200 0.132

Retained Earnings to Total Assets - -0.652 0.000 Yes Yes

EBIT to Total Assets - -0.592 0.000 Yes Yes

Sales to Total Assets - -0.371 0.004 Yes Yes

EBT to Capital Employed (Return on Capital Employed) - -0.061 0.651

Equity to Current Liabilities - -0.314 0.016 Yes

Tangible Net Worth to Total Liabilities - -0.512 0.000 Yes Yes

Interest Coverage Ratio + 0.074 0.582

Cash Flow from Operations to Long-Term Debt - -0.378 0.003 Yes Yes

Cash Flow from Operations to Total Debt - -0.305 0.020 Yes

Earnings before Taxes to Permanent Capital (RONW) + 0.192 0.150

Operating Income to Sales - -0.477 0.000 Yes Yes

Capital to Long-Term Debt - -0.276 0.036 Yes

Capital Plus Short-Term Debt to Total Debt - -0.232 0.079

Equity to Total Liabilities - -0.450 0.000 Yes Yes

Cash Flow to Total Assets + 0.014 0.915

Cash Flow to Total Debt + 0.007 0.959

Debt to Equity + 0.592 0.000 Yes YesInterest to Average Debt + 0.221 0.095

Interest to EBITDA + 0.474 0.000 Yes Yes

Long-Term Debt to Total Assets - -0.371 0.004 Yes Yes

If the rating subcategories are included (making the CRISIL Ratings System inclusive of

ratings AAA, AA+, AA, AA-, A+, A, A-, BBB+, BBB, BBB-, BB+, BB, BB-, B+, B, B-, C,and D), then correlations of the various financial ratios with the CRISIL ratings can be seen in

the following table. In this correlation analysis, AAA is again assigned the highest rating of 1,

while D is assigned the lowest rating of 18. From here on, the CRISIL ratings systeminclusive of ratings subcategories is referred to as CRISIL18, where eighteen again represents

the number of possible ratings classes.

CRISIL with Subcategories

Financial RatioA Priori Signs of

CorrelationCorrelation p-value Significant at 1% Significant at 5%

Working Capital to Total Assets - -0.217 0.102

Retained Earnings to Total Assets - -0.674 0.000 Yes Yes

EBIT to Total Assets - -0.601 0.000 Yes Yes

Sales to Total Assets - -0.367 0.005 Yes Yes

EBT to Capital Employed (Return on Capital Employed) - -0.059 0.661

Equity to Current Liabilities - -0.323 0.013 Yes

Tangible Net Worth to Total Liabilities - -0.528 0.000 Yes Yes

Interest Coverage Ratio + 0.068 0.612

Cash Flow from Operations to Long-Term Debt - -0.380 0.003 Yes Yes

Cash Flow from Operations to Total Debt - -0.303 0.021 Yes

Earnings before Taxes to Permanent Capital (RONW) + 0.179 0.180

Operating Income to Sales - -0.489 0.000 Yes Yes

Capital to Long-Term Debt - -0.285 0.030 Yes

Capital Plus Short-Term Debt to Total Debt - -0.237 0.074Equity to Total Liabilities - -0.462 0.000 Yes Yes

Cash Flow to Total Assets + 0.033 0.808

Cash Flow to Total Debt + 0.017 0.901

Debt to Equity + 0.596 0.000 Yes Yes

Interest to Average Debt + 0.226 0.089

Interest to EBITDA + 0.469 0.000 Yes Yes

Long-Term Debt to Total Assets + 0.669 0.000 Yes Yes

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As can be seen from the above analysis, the ratios that are significant at a 1% level includeRetained Earnings to Total Assets, EBIT to Total Assets, Sales to Total Assets, Tangible Net

Worth to Total Liabilities, Cash Flow from Operations to Long-Term Debt, Operating Income

to Sales, Equity to Total Liabilities, Debt to Equity, Interest to EBITDA, and Long-TermDebt to Total Assets. In addition, Equity to Current Liabilities, Cash Flow from Operations to

Total Debt, and Capital to Long-Term Debt are significant at a 5% alpha. These results areessentially as expected: capital structure ratios (such as Debt to Equity and Long-Term Debt

to Total Assets), interest coverage ratios (such as EBITDA Interest Coverage), company sizefactors (Tangible Net Worth to Total Liabilities), and profitability indicators (EBIT to Total

Assets), are highly correlated with the ratings for these instruments. That is, as expected, the

ratings of CRISIL do not depend on ratios in which the common equity investor would beinterested such as Return on Capital Employed or Return on Net Worth, but do instead

depend on those with which a fixed income investor is concerned, such as those detailed

above.

Developing the Indian Credit Rating Model

The main objective of this paper, the development of a new and potentially more effective

credit rating model designed with the Indian debt markets in mind, to be defined here as the

Indian Credit Rating Model (ICRM), will now be addressed. The most important facet ofthis new model should be its ability to outperform current means of rating Indian debt

instruments, and the model has been developed keeping this goal in mind. It is reasonable to

impute that a credit scoring model that performs as well as outlined above cannot excludefactors that indicate a companys ability to service its debt, its profitability, and its size.

Unfortunately, there are several financial ratios that both correlated highly with CRISIL

ratings and that represent such ideas about creditworthiness, and so only the best-performingratios are selected from these.

Contributory Financial Ratios

Though many antecedent models were created in this study as stepping stones toward the final

version of ICRM, the final inclusion of financial ratios from the potential list of significant

ratios was dependent on prediction power as proven in past studies and in my research as wellas on ease of computation from publicly filed financial statements. As stated earlier, the ratios

that are significant at a 1% level are Retained Earnings to Total Assets, EBIT to Total Assets,

Sales to Total Assets, Tangible Net Worth to Total Liabilities, Cash Flow from Operations toLong-Term Debt, Operating Income to Sales, Equity to Total Liabilities, Debt to Equity,

Interest to EBITDA, and Long-Term Debt to Total Assets. A look at the correlation matricesin Appendix I shows clearly that the ratios Retained Earnings to Total Assets, EBIT to TotalAssets, Sales to Total Assets, Cash Flow From Operations to Long-Term Debt, and Long-

Term Debt to Total Assets are all highly correlated with one another; nearly all of these

correlations are significant at a 1% significance level. As a result, one of these ratios can well-

represent the other four in a new model, and this study, after several trials, has selected Long-Term Debt to Total Assets.

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Furthermore, Tangible Net Worth to Total Liabilities seems to be significantly correlated withseveral other financial ratios, such as Cash Flow from Operations to Long-Term Debt,

Operating Income to Sales, Equity to Liabilities, and Debt to Equity. However, there seems to

be little internal correlation between the other ratios, and so Tangible Net Worth to TotalLiabilities seems to be a good representation of the remaining four ratios.

Both Operating Income to Sales and Interest to EBITDA (the inverse of an EBITDA interest

coverage ratio) display few strong correlations with other financial ratios, while beingstrongly correlated with CRISIL ratings themselves, making them robust potential

contributors to the final version of ICRM. Therefore, ICRM will be calculated using those

four financial ratios, specifically, Long-Term Debt to Total Assets, Tangible Net Worth toTotal Liabilities, Operating Income to Sales, and Interest to EBITDA.

Computing ICRM

When reading this section concerning the exact composition of ICRM and the exact method

for its calculation, it is important to keep in mind that ICRM is organized such that the higherthe computed value for ICRM, the higher the ranking when converted from a number system

to the standard lettered ranking system. That is, a company with a positive ICRM value will,

according to the organization of the model, be ranked higher or more creditworthy than a

company with a negative ICRM value.

The first step in calculating ICRM for a company is taking the negative of the value of the

Long-Term Debt to Total Assets ratio. This must be so, as when this ratio is high, it willindicate a high level of debt, making the company in question riskier to potential lenders, and

thereby making the desired value of ICRM lower.

The second step in calculating ICRM is adding the value of Tangible Net Worth to Total

Liabilities to the value from the first step (or the negative of the value of Long-Term Debt toTotal Liabilities). This value is added because the value of ICRM and Tangible Net Worth toTotal Liabilities should be positively correlated. That is, when the value of Tangible Net

Worth to Total Liabilities is high, a low level of liabilities in comparison to net worth of the

company is indicated, and so the value of ICRM should be correspondingly high.

The third step in the calculation of the ICRM value of a company is the addition of the

Operating Income to Sales value to the previous sum. Companies that have high Operating

Income to Sales ratios are companies that manage their costs well, and are therefore morelikely to have discretionary profits with which to service outstanding debt. Therefore, a high

Operating Income to Sales ratio indicates a lower risk level for debt, and such a company

would likewise command a higher ICRM value.

The final step in the calculation of ICRM is the inclusion of Interest to EBITDA into the

equation. If this ratio is positive, then it should be subtracted from the output of the previous

step. This is because if the annual interest payment is high in comparison to EBITDA, ametric commonly substituted for incoming cash flow, the risk of the company being unable toservice its outstanding fixed income obligations is higher. However, if a company has a

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negative Interest to EBITDA ratio, due to a negative EBITDA, subtracting that value willonly serve to increase the value of ICRM, which is the polar opposite of the desired effect. In

order to compensate for this potential inaccuracy, if the Interest to EBITDA ratio is negative,

then twice the absolute value of the ratio is subtracted from the output of the previous step inorder to calculate ICRM.

The rationale for this calculation is best explained through an example: consider that the

output from the previous step in the calculation of the ICRM is zero; that is, the sum ofnegative Long-Term Debt to Total Assets plus Tangible Net Worth to Total Liabilities plus

Operating Income to Sales is zero. When calculating the ICRM for a company with an

Interest to EBITDA ratio of 1, the value of ICRM will be negative one when one is subtractedfrom zero. If, when calculating the ICRM for a second company with an Interest to EBITDA

ratio of -1, the value of the ratio is simply subtracted, the value of the ICRM will be positive

one, which ensures that the second company will be rated higherthan the first company in theexample. Even simply subtracting the absolute value of the Interest to EBITDA ratio of the

second company returns an ICRM value of negative one, which only serves to ensure that the

ICRM model will recognize no difference between a company that has an Interest to EBITDAratio of positive one and a second company that has an Interest to EBITDA ratio of negative

one. In order to shift the value of ICRM further to the left by an equal value, that is, to make

the ICRM value for the second company negative two, twice the absolute value must be

subtracted for companies having a negative Interest to EBITDA value.

In summary, in the case of a company that has a positive value for the Interest to EBITDA

value, the ICRM is calculated as follows:

EBITDA

I

Sales

OI

TL

TNW

TA

LTDICRM ++=

If the Interest to EBITDA value is negative, then the ICRM is calculated as follows:

EBITDA

I

Sales

OI

TL

TNW

TA

LTDICRM ++= 2

Converting ICRM to a Lettered Rating

The fact that the four ratios that contribute to ICRM all tend to fall within a general range of

possible values, as compared to the excluded financial ratios, is noteworthy. In fact, the

reason that the EBITDA Interest Coverage ratio was inverted was so that it would also fallwithin a predictable and reasonable range and could therefore be a contributory factor to

ICRM. The following two tables show the general ranges within which the values for each

financial ratio that contributes to ICRM fall, both intuitively and empirically. Thepreponderance of the values of the financial ratios for the companies in the sample for this

study fall within the ranges described below, though data points do exist outside of the

specified ranges.

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ICRM Contributory Financial Ratios

(Positive Inverse Inte rest Cove rage )

Low Value for

Financial Ratio

High Value for

Financial Ratio

Negative Long-Term Debt to Total Liabilities -1 0

Total Net Worth to Total Liabilities -1 2

Operating Income to Sales -1 1

Negative Interest to EBITDA -1 0

ICRM -4 3

ICRM Contributory Financial Ratios

(Ne gative Inve rse Inte rest Cove rage )

Low Value for

Financial Ratio

High Value for

Financial Ratio

Negative Long-Term Debt to Total Liabilities -1 0

Total Net Worth to Total Liabilities -1 2

Operating Income to Sales -1 1

Negative Twice Absolute Value of Interest to EBITDA -2 0

ICRM -5 3

Though the above tables show that the lowest and highest potential values for ICRM are

negative five and positive three, respectively, empirical data from the sample set for this studyshows that the values will generally fall between a low value of negative two and a high value

of positive one. As a result, these endpoints have been used as guidelines for converting the

value given by ICRM to a lettered rating system. The following two tables show the valueswithin which ICRM must fall in order to be converted into each CRISIL8 and CRISIL18

ratings class, respectively.

ICRM Conversion with Subcategories

ICRM Value ICRM Rating

0.60 and above AAA0.30 to 0.59 AA+

0.00 to 0.29 AA

-0.10 to -0.01 AA-

-0.20 to -0.11 A+

-0.30 to -0.21 A

-0.40 to -0.31 A-

-0.50 to -0.41 BBB+

-0.60 to -0.51 BBB

-0.70 to -0.61 BBB-

-0.80 to -0.71 BB+

-0.90 to -0.81 BB

-1.00 to -0.91 BB--1.10 to -1.01 B+

-1.20 to -1.11 B

-1.30 to -1.21 B-

-1.40 to -1.31 C

-1.41 and below D

ICRM Conversion without Subcategories

ICRM Value ICRM Rating

0.60 and above AAA

0.30 to 0.59 AA

-0.20 to 0.29 A

-0.50 to -0.21 BBB

-0.80 to -0.51 BB

-1.10 to -0.81 B

-1.4 to -1.11 C

-1.41 and below D

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The conversions displayed in the two tables above were determined in the majority by evenlydividing the values in the range within which ICRM values normally fall, which was earlier

determined to be between negative two and positive one. Theoretically, it should be more

difficult for a company to obtain a rating of AA+ and AAA; that is, a company should proveits worthiness to be rated such, and so a larger gap is left in ICRM values for those two ratings

classes.

Results and Findings

By following the enumerated guidelines for calculating ICRM, ICRM ratings were calculated

for all fifty-nine companies in the sample for this study and then compared to outstandingCRISIL ratings for the same companies. ICRM ratings were calculating by first calculating

the values of the determinant financial ratios for all fifty-nine sample companies for the fiscal

years ended March 2002, March 2001, and March 2000, then determining a three-yearaverage for each ratio, and finally using these three-year averages to calculate an ICRM valid

for March 2002. This averaging of years worth of financial data was performed in order to

smooth over potential year-to-year volatility in the values of the ratios, as well as to allowICRM to include a historical credit-risk perspective, as is common amongst the practices of

ratings agencies.

The results of the comparison between both the CRISIL8 and ICRM8 ratings (ratings withoutsubcategories) can be found in the table on the following page.

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Company Name

CRISIL8

Lettered

Rating

CRISIL8

Numbered

Rating ICRM Value

ICRM8

Lettered

Rating

ICRM8

Numbered

Rating Difference

Ahmedabad Electricity Company Limited AA 2 0.672 AAA 1 1

Ashok Leyland Limited AA 2 0.138 A 3 -1

Amforge Industries Limited D 8 -1.763 D 8 0

Apollo Hospitals Enterprise Limited AA 2 0.674 AAA 1 1

Asian Paints (India) Limited AAA 1 0.736 AAA 1 0

Apollo Tyres Limited A 3 0.032 A 3 0

Aarti Industries Limited A 3 0.038 A 3 0

Atul Limited BB 5 -0.744 BB 5 0

Basf India Limited AAA 1 0.938 AAA 1 0

Bharat Gears Limited C 7 -1.572 D 8 -1

Bharat Petroleum Corporation Limited AAA 1 -0.005 A 3 -2

Cadila Healthcare Limited AA 2 1.710 AAA 1 1

Century Enka Limited AA 2 1.274 AAA 1 1

Chambal Fertilisers & Chemicals Limited A 3 -0.222 BBB 4 -1

Colour-Chem Limited AA 2 0.713 AAA 1 1

Coromandel Fertilisers Limited AA 2 0.535 AA 2 0

Carborundum Universal Limited AA 2 0.729 AAA 1 1

Dabur India Limited AA 2 0.451 AA 2 0

Dcm Shriram Consolidated Limited A 3 -0.416 BBB 4 -1

E.I.D. Parry (India) Limited AA 2 -0.139 A 3 -1Electrosteel Castings Limited AA 2 0.828 AAA 1 1

Excel Industries Limited A 3 -0.549 BB 5 -2

Flex Industries Limited D 8 -0.998 B 6 2

Finolex Cables Limited AA 2 2.166 AAA 1 1

Finolex Industries Limited AA 2 0.782 AAA 1 1

Gabriel India Limited BBB 4 -0.491 BBB 4 0

Great Eastern Shipping Company Limited AAA 1 0.598 AA 2 -1

Glenmark Pharmaceuticals Limited AA 2 2.382 AAA 1 1

Garware Polyester Limited D 8 -1.807 D 8 0

Hero Honda Motors Limited AAA 1 1.052 AAA 1 0

Hindalco Industries Limited AAA 1 4.926 AAA 1 0

Hindustan Organic Chemicals Limited D 8 -1.407 D 8 0

Hindustan Petroleum Corporation Limited AAA 1 0.525 AA 2 -1

India Glycols Limited AA 2 0.374 AA 2 0

Boc India Limited AA 2 0.005 A 3 -1

Indian Petrochemicals Corporation Limited AA 2 -0.222 BBB 4 -2Jindal Vijayanagar Steel Limited D 8 -1.957 D 8 0

Jain Irrigation Systems Limited D 8 -2.517 D 8 0

Jindal Iron & Steel Company Limited D 8 -3.337 D 8 0

Larsen & Toubro Limited AAA 1 -0.337 BBB 4 -3

Max India Limited A 3 0.914 AAA 1 2

Madras Cements Limited AA 2 -0.291 BBB 4 -2

Mahindra & Mahindra Limited AA 2 0.126 A 3 -1

Mahavir Spinning Mills Limited AA 2 0.081 A 3 -1

Mukand Limited D 8 -2.728 D 8 0

National Aluminium Company Limited AAA 1 2.031 AAA 1 0

Raymond Limited AA 2 0.185 A 3 -1

Rpg Transmission Limited D 8 -7.915 D 8 0

Sudarshan Chemical Industries Limited A 3 -0.089 A 3 0

Sterlite Industries (India) Limited AA 2 -0.014 A 3 -1

Sun Pharmaceutical Industries Limited AAA 1 3.473 AAA 1 0

Tube Investments Of India Limited AA 2 0.621 AAA 1 1Tata Iron & Steel Company Limited AA 2 -0.110 A 3 -1

Thirumalai Chemicals Limited A 3 -0.275 BBB 4 -1

Tata Power Company Limited AAA 1 0.251 A 3 -2

Tata Chemicals Limited AA 2 0.471 AA 2 0

Tata Finance Limited BBB 4 -0.478 BBB 4 0

Vardhman Spinning & General Mills Limited AA 2 -0.360 BBB 4 -2

Vst Industries Limited AA 2 0.176 A 3 -1

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As can be seen from the previous table, ICRM rates twenty-four out of fifty-nine companies,or 40.7% of the sample, exactly on par with the ratings of CRISIL. Of the remaining thirty-

five companies in the sample, ICRM rates fifteen companies just one class lower than

CRISIL, and another eleven companies just one class above the CRISIL rating. That is,ICRM8 rates 50 companies, or 84.7% of the sample, within one ratings class of published

CRISIL ratings.

A similar analysis was performed using ICRM18 and CRISIL18 ratings, the results of whichcan be found in the table on the following page. When expanded to include ratings

subcategories, ICRM rates nineteen companies out of the fifty-nine company sample, or

32.2%, exactly on par with the ratings of CRISIL. Of the remaining forty companies, ICRMrates eight companies just one notch lower than the corresponding CRISIL rating, and another

eight companies just one notch above the CRISIL rating. Furthermore, ICRM rates an

additional thirteen companies two notches above or below the corresponding CRISIL rating.This means that ICRM18 rates 81.4% of the sample set of companies within two notches of

their CRISIL rating.

A detailed computation of both ICRM8 and ICRM18 can be found in Appendix II.

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Company Name

CRISIL18

Lettered

Rating

CRISIL18

Numbered

Rating ICRM Value

ICRM18

Lettered

Rating

ICRM18

Numbered

Rating Difference

Ahmedabad Electricity Company Limited AA- 4 0.672 AAA 1 3

Ashok Leyland Limited AA- 4 0.138 AA 3 1

Amforge Industries Limited D 18 -1.763 D 18 0

Apollo Hospitals Enterprise Limited AA- 4 0.674 AAA 1 3

Asian Paints (India) Limited AAA 1 0.736 AAA 1 0

Apollo Tyres Limited A+ 5 0.032 AA 3 2

Aarti Industries Limited A+ 5 0.038 AA 3 2

Atul Limited BB+ 11 -0.744 BB+ 11 0

Basf India Limited AAA 1 0.938 AAA 1 0

Bharat Gears Limited C 17 -1.572 D 18 -1

Bharat Petroleum Corporation Limited AAA 1 -0.005 AA- 4 -3

Cadila Healthcare Limited AA 3 1.710 AAA 1 2

Century Enka Limited AA+ 2 1.274 AAA 1 1

Chambal Fertilisers & Chemicals Limited A+ 5 -0.222 A 6 -1

Colour-Chem Limited AA 3 0.713 AAA 1 2

Coromandel Fertilisers Limited AA 3 0.535 AA+ 2 1

Carborundum Universal Limited AA 3 0.729 AAA 1 2

Dabur India Limited AA+ 2 0.451 AA+ 2 0

Dcm Shriram Consolidated Limited A- 7 -0.416 BBB+ 8 -1

E.I.D. Parry (India) Limited AA- 4 -0.139 A+ 5 -1Electrosteel Castings Limited AA+ 2 0.828 AAA 1 1

Excel Industries Limited A+ 5 -0.549 BBB 9 -4

Flex Industries Limited D 18 -0.998 BB- 13 5

Finolex Cables Limited AA- 4 2.166 AAA 1 3

Finolex Industries Limited AA 3 0.782 AAA 1 2

Gabriel India Limited BBB- 10 -0.491 BBB+ 8 2

Great Eastern Shipping Company Limited AAA 1 0.598 AA+ 2 -1

Glenmark Pharmaceuticals Limited AA 3 2.382 AAA 1 2

Garware Polyester Limited D 18 -1.807 D 18 0

Hero Honda Motors Limited AAA 1 1.052 AAA 1 0

Hindalco Industries Limited AAA 1 4.926 AAA 1 0

Hindustan Organic Chemicals Limited D 18 -1.407 D 18 0

Hindustan Petroleum Corporation Limited AAA 1 0.525 AA+ 2 -1

India Glycols Limited AA- 4 0.374 AA+ 2 2

Boc India Limited AA 3 0.005 AA 3 0

Indian Petrochemicals Corporation Limited AA- 4 -0.222 A 6 -2Jindal Vijayanagar Steel Limited D 18 -1.957 D 18 0

Jain Irrigation Systems Limited D 18 -2.517 D 18 0

Jindal Iron & Steel Company Limited D 18 -3.337 D 18 0

Larsen & Toubro Limited AAA 1 -0.337 A- 7 -6

Max India Limited A 6 0.914 AAA 1 5

Madras Cements Limited AA 3 -0.291 A 6 -3

Mahindra & Mahindra Limited AA 3 0.126 AA 3 0

Mahavir Spinning Mills Limited AA- 4 0.081 AA 3 1

Mukand Limited D 18 -2.728 D 18 0

National Aluminium Company Limited AAA 1 2.031 AAA 1 0

Raymond Limited AA+ 2 0.185 AA 3 -1

Rpg Transmission Limited D 18 -7.915 D 18 0

Sudarshan Chemical Industries Limited A+ 5 -0.089 AA- 4 1

Sterlite Industries (India) Limited AA 3 -0.014 AA- 4 -1

Sun Pharmaceutical Industries Limited AAA 1 3.473 AAA 1 0

Tube Investments Of India Limited AA 3 0.621 AAA 1 2Tata Iron & Steel Company Limited AA+ 2 -0.110 A+ 5 -3

Thirumalai Chemicals Limited A- 7 -0.275 A 6 1

Tata Power Company Limited AAA 1 0.251 AA 3 -2

Tata Chemicals Limited AA 3 0.471 AA+ 2 1

Tata Finance Limited BBB- 10 -0.478 BBB+ 8 2

Vardhman Spinning & General Mills Limited AA- 4 -0.360 A- 7 -3

Vst Industries Limited AA 3 0.176 AA 3 0

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Multiple Discriminant Analysis on ICRM

Following the development and analysis of ICRM, the question of whether the model can be

made more efficient through the inclusion of coefficients arises. It is possible that the model

will more exactly rate companies, in comparison to CRISIL ratings, should certain financialratios be weighted more than others in the calculation of ICRM.

Methodology

In order to test this theory, the four financial ratios that contribute to ICRM are regressedagainst CRISIL8 and CRISIL18 ratings. First, however, the Interest to EBITDA ratio is split

into two separate contributing factors, in order to make the regression more consistent withthe idea of the ICRM formula. The first factor is +I/EBITDA, which has a value of zero for

any company that has a negative Interest to EBITDA ratio, and has a value equal to Interest to

EBITDA for any company that has a positive value for the ratio. The second factor is -I/EBITDA, which has a value of zero for any company that has a positive Interest to

EBITDA ratio and a value equal to Interest to EBITDA for any company that has a negativevalue for the ratio. As a result, the five independent variables that are regressed againstCRISIL ratings in this portion of the study are Long-Term Debt to Total Assets, Tangible Net

Worth to Total Liabilities, Operating Income to Sales, +I/EBITDA, and I/EBITDA.

ICRM8 inclusive of Multiple Discriminant Analysis (ICRM8 MD) was created based onthe following regression data, including variance analysis:

The regression equation is

CRISIL8 = 0.307 + 0.277 TNW/TL - 5.75 OI/Sales + 7.78 LTD/TA + 1.34 +I/EBITDA MD

- 1.69 -I/EBITDA MD

Predictor Coef SE Coef T P

Constant 0.3066 0.5696 0.54 0.593TNW/TL 0.2766 0.2658 1.04 0.303

OI/Sales -5.754 2.008 -2.86 0.006

LTD/TA 7.783 1.511 5.15 0.000

+I/EBITDA MD 1.3449 0.3609 3.73 0.000

-I/EBITDA MD -1.6875 0.3445 -4.90 0.000

S = 1.22817 R-Sq = 74.8% R-Sq(adj) = 72.5%

Analysis of Variance

Source DF SS MS F P

Regression 5 237.784 47.557 31.53 0.000

Residual Error 53 79.945 1.508

Total 58 317.729

Source DF Seq SS

TNW/TL 1 67.353

OI/Sales 1 30.364

LTD/TA 1 95.716

+I/EBITDA MD 1 8.149

-I/EBITDA MD 1 36.202

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From the above information, it is clear that the regression is significant and that the R-Squared adjusted is sufficiently high to continue this analysis.

ICRM18 inclusive of coefficients, or weightings on the included financial ratios (ICRM18MD) was created based on the following regression data:

The regression equation is

CRISIL18 = - 1.03 + 0.656 TNW/TL - 13.6 OI/Sales + 19.7 LTD/TA

+ 3.25 +I/EBITDA MD - 4.21 -I/EBITDA MD

Predictor Coef SE Coef T P

Constant -1.026 1.461 -0.70 0.486

TNW/TL 0.6565 0.6820 0.96 0.340

OI/Sales -13.580 5.153 -2.64 0.011

LTD/TA 19.712 3.878 5.08 0.000

+I/EBITDA MD 3.2502 0.9259 3.51 0.001

-I/EBITDA MD -4.2056 0.8838 -4.76 0.000

S = 3.15115 R-Sq = 73.5% R-Sq(adj) = 71.0%

Analysis of Variance

Source DF SS MS F P

Regression 5 1462.57 292.51 29.46 0.000

Residual Error 53 526.28 9.93

Total 58 1988.85

Source DF Seq SS

TNW/TL 1 421.11

OI/Sales 1 171.95

LTD/TA 1 599.08

+I/EBITDA MD 1 45.58

-I/EBITDA MD 1 224.86

Again, it is clear that the regression is significant at a 1% alpha level and that the R-Squaredadjusted is sufficiently high to continue this investigation.

Results and Findings

One of the main benefits of this analysis lies in the fact that no conversion tables are

necessary, as the regression equation yields a value for ICRM8 MD that is already in the form

of a ranked (numbered) credit rating. The results of applying the ICRM8 MD regressionequation to the sample set and then comparing the output to CRISIL8 ratings can be found in

the table on the following page. The values in the column labeled ICRM8 MD Numbered

Rating are simply the output from the ICRM8 MD regression equation, rounded such that

there are zero decimal places.

The following table shows that of fifty-nine companies in the sample, ICRM8 MD ratedtwenty-three companies, or 40.0% of the sample, exactly on par with CRISIL8 ratings. Of the

remaining thirty-six companies, ICRM8 MD rated twelve companies one ratings class above

and another twelve companies one ratings class below their respective CRISIL8 ratings. Thatis, of fifty-nine companies, ICRM8 MD rated forty-seven, or 79.7% of the sample, within one

ratings class of CRISIL8.

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Company Name

CRISIL8

Lettered

Rating

CRISIL8

Numbered

Rating

ICRM8 MD

Lettered

Rating

ICRM8 MD

Numbered

Rating Difference

Ahmedabad Electricity Company Limited AA 2 AAA 1 1

Ashok Leyland Limited AA 2 AA 2 0

Amforge Industries Limited D 8 BB 5 3

Apollo Hospitals Enterprise Limited AA 2 AA 2 0Asian Paints (India) Limited AAA 1 AAA 1 0

Apollo Tyres Limited A 3 AA 2 1

Aarti Industries Limited A 3 A 3 0

Atul Limited BB 5 BBB 4 1

Basf India Limited AAA 1 AA 2 -1

Bharat Gears Limited C 7 BB 5 2

Bharat Petroleum Corporation Limited AAA 1 AA 2 -1

Cadila Healthcare Limited AA 2 AA 2 0

Century Enka Limited AA 2 AA 2 0

Chambal Fertilisers & Chemicals Limited A 3 A 3 0

Colour-Chem Limited AA 2 AA 2 0

Coromandel Fertilisers Limited AA 2 AA 2 0

Carborundum Universal Limited AA 2 AAA 1 1

Dabur India Limited AA 2 AA 2 0

Dcm Shriram Consolidated Limited A 3 BBB 4 -1

E.I.D. Parry (India) Limited AA 2 A 3 -1

Electrosteel Castings Limited AA 2 AAA 1 1

Excel Industries Limited A 3 A 3 0

Flex Industries Limited D 8 B 6 2

Finolex Cables Limited AA 2 AAA 1 1

Finolex Industries Limited AA 2 AAA 1 1

Gabriel India Limited BBB 4 A 3 1

Great Eastern Shipping Company Limited AAA 1 A 3 -2

Glenmark Pharmaceuticals Limited AA 2 AA 2 0

Garware Polyester Limited D 8 B 6 2

Hero Honda Motors Limited AAA 1 AAA 1 0

Hindalco Industries Limited AAA 1 AAA 1 0

Hindustan Organic Chemicals Limited D 8 BB 5 3

Hindustan Petroleum Corporation Limited AAA 1 AAA 1 0

India Glycols Limited AA 2 A 3 -1

Boc India Limited AA 2 BBB 4 -2Indian Petrochemicals Corporation Limited AA 2 A 3 -1

Jindal Vijayanagar Steel Limited D 8 C 7 1

Jain Irrigation Systems Limited D 8 C 7 1

Jindal Iron & Steel Company Limited D 8 D 8 0

Larsen & Toubro Limited AAA 1 A 3 -2

Max India Limited A 3 BBB 4 -1

Madras Cements Limited AA 2 BBB 4 -2

Mahindra & Mahindra Limited AA 2 A 3 -1

Mahavir Spinning Mills Limited AA 2 A 3 -1

Mukand Limited D 8 D 8 0

National Aluminium Company Limited AAA 1 AAA 1 0

Raymond Limited AA 2 A 3 -1

Rpg Transmission Limited D 8 D 10 -2

Sudarshan Chemical Industries Limited A 3 A 3 0

Sterlite Industries (India) Limited AA 2 A 3 -1Sun Pharmaceutical Industries Limited AAA 1 AAA 1 0

Tube Investments Of India Limited AA 2 AA 2 0

Tata Iron & Steel Company Limited AA 2 A 3 -1

Thirumalai Chemicals Limited A 3 AA 2 1

Tata Power Company Limited AAA 1 A 3 -2

Tata Chemicals Limited AA 2 AA 2 0

Tata Finance Limited BBB 4 A 3 1

Vardhman Spinning & General Mills Limited AA 2 BBB 4 -2

Vst Industries Limited AA 2 AA 2 0

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The results of applying the ICRM18 MD regression equation to the sample set and thencomparing the output to CRISIL18 ratings can be found in the following table. The values in

the column labeled ICRM18 MD Numbered Rating are simply the output from the ICRM18

MD regression equation, rounded such that there are zero decimal places.

The table shows that out of a fifty-nine company sample, ICRM18 MD rates twelvecompanies, or 20.3% of the sample, exactly on par with CRISIL ratings. Fourteen companies

are just rated one notch away from CRISIL18 (above and below), and another twelvecompanies are rated two notches away from CRISIL. That is, ICRM18 MD rates a total of

thirty-eight companies, or 64.4% of the sample set, within two notches of CRISIL18 ratings.

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Company Name

CRISIL18

Lettered

Rating

CRISIL18

Numbered

Rating

ICRM18 MD

Lettered

Rating

ICRM18 MD

Numbered

Rating Difference

Ahmedabad Electricity Company Limited AA- 4 AAA 1 3

Ashok Leyland Limited AA- 4 AA 3 1

Amforge Industries Limited D 18 BBB- 10 8

Apollo Hospitals Enterprise Limited AA- 4 AA- 4 0

Asian Paints (India) Limited AAA 1 AAA 1 0

Apollo Tyres Limited A+ 5 AA- 4 1

Aarti Industries Limited A+ 5 A- 7 -2

Atul Limited BB+ 11 BBB 9 2

Basf India Limited AAA 1 AA+ 2 -1

Bharat Gears Limited C 17 BBB- 10 7

Bharat Petroleum Corporation Limited AAA 1 AA- 4 -3

Cadila Healthcare Limited AA 3 AA 3 0

Century Enka Limited AA+ 2 AA 3 -1

Chambal Fertilisers & Chemicals Limited A+ 5 A 6 -1

Colour-Chem Limited AA 3 AA+ 2 1

Coromandel Fertilisers Limited AA 3 AA+ 2 1

Carborundum Universal Limited AA 3 AAA 1 2

Dabur India Limited AA+ 2 AA+ 2 0

Dcm Shriram Consolidated Limited A- 7 BBB 9 -2

E.I.D. Parry (India) Limited AA- 4 A 6 -2Electrosteel Castings Limited AA+ 2 AA+ 2 0

Excel Industries Limited A+ 5 A 6 -1

Flex Industries Limited D 18 B+ 14 4

Finolex Cables Limited AA- 4 AAA 1 3

Finolex Industries Limited AA 3 AAA 1 2

Gabriel India Limited BBB- 10 A- 7 3

Great Eastern Shipping Company Limited AAA 1 A+ 5 -4

Glenmark Pharmaceuticals Limited AA 3 AA 3 0

Garware Polyester Limited D 18 B+ 14 4

Hero Honda Motors Limited AAA 1 AAA 1 0

Hindalco Industries Limited AAA 1 AAA 1 0

Hindustan Organic Chemicals Limited D 18 BB 12 6

Hindustan Petroleum Corporation Limited AAA 1 AAA 1 0

India Glycols Limited AA- 4 A+ 5 -1

Boc India Limited AA 3 BBB+ 8 -5

Indian Petrochemicals Corporation Limited AA- 4 A 6 -2

Jindal Vijayanagar Steel Limited D 18 B- 16 2

Jain Irrigation Systems Limited D 18 B- 16 2

Jindal Iron & Steel Company Limited D 18 D 19 -1

Larsen & Toubro Limited AAA 1 A- 7 -6

Max India Limited A 6 BBB 9 -3

Madras Cements Limited AA 3 BBB+ 8 -5

Mahindra & Mahindra Limited AA 3 A+ 5 -2

Mahavir Spinning Mills Limited AA- 4 A 6 -2

Mukand Limited D 18 C 17 1

National Aluminium Company Limited AAA 1 AAA 1 0

Raymond Limited AA+ 2 A 6 -4

Rpg Transmission Limited D 18 D 22 -4

Sudarshan Chemical Industries Limited A+ 5 A 6 -1

Sterlite Industries (India) Limited AA 3 A+ 5 -2

Sun Pharmaceutical Industries Limited AAA 1 AAA 1 0Tube Investments Of India Limited AA 3 AA+ 2 1

Tata Iron & Steel Company Limited AA+ 2 A+ 5 -3

Thirumalai Chemicals Limited A- 7 AA- 4 3

Tata Power Company Limited AAA 1 A+ 5 -4

Tata Chemicals Limited AA 3 AA 3 0

Tata Finance Limited BBB- 10 A+ 5 5

Vardhman Spinning & General Mills Limited AA- 4 BBB+ 8 -4

Vst Industries Limited AA 3 AA- 4 -1

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Although ICRM8 MD and ICRM18 MD are both valid and significant credit rating models oftheir own right, the original ICRM8 and ICRM18 models seem to have more explanatory

power in rating on par with the credit ratings published by CRISIL. For this reason, this study

will consider only the original ICRM8 and ICRM18 models in further areas of investigation.

The Ratings-Change Prediction Power of ICRM

It is undoubtedly remarkable that ICRM has such a strong ability to rate non-convertible

debentures on par with the ratings of CRISIL, especially considering that it is composed of

four simple financial ratios while the ratios of CRISIL take into consideration both a widerarray of financial indicators as well as qualitative factors such as the state of the Indian

economy and that of the specific industry within which the firm in question operates.

However, the next test which ICRM must survive, for it to fulfill the requirements of thisstudy, is the investigation of its forecasting power with respect to ratings upgrades and

downgrades published by CRISIL. In order to maintain the integrity of this study, an entirelydifferent sample has been created in order to test the predicting ability of ICRM, with respect

to both the companies included and the time during which ratings are tested.

Sample Determination and Composition

As the purpose of this section of the study is to determine whether ICRM can predict ratings

changes published by CRISIL before their actual publication, it is imperative that the

companies in any sample for this section have been upgraded or downgraded within the timeframe of the sample. As the earlier section of this analysis compared CRISIL and ICRM

ratings as of March 31, 2002, this section of the study will explore changes in ratings since

that time that is, this section will focus on ratings changes during the Indian fiscal years

ended March 2003 and March 2004. As the sample must include companies that haveexperienced ratings changes, the initial raw sample for this section was determined by

gathering information from CRISILs semiannually-published Ratings Roundup, a pamphlet

that details all ratings changes that occurred in the six months prior to publication.

Again, the initial sample size of forty-three was restricted by the availability of financial

statements, which were obtained from Bloomberg as before. After additional restrictions thatexcluded companies with fiscal years that did not end in March and banking institutions, the

final sample size consisted of nineteen companies, twelve of which are companies that have

been upgraded by CRISIL and seven of which are firms that have been downgraded byCRISIL. The final companies included in the sample, including their sector, the date of the

associated ratings change, and the actual ratings changes themselves, are shown in the tablebelow.

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Upgrades

Company Name Sector Upgrade Date Upgrade From Upgrade To

Gujarat Gas Company Limited Oil and Gas Aug-03 AA+ AAA

Tata Finance Limited Non-Banking Finance Corporation May-03 BBB- BBB

The Arvind Mills Limited Textiles Jun-03 D BB

The Tata Iron and Steel Company Limited Steel Aug-03 AA+ AAA

Apollo Tyres Limited Tires Mar-03 A+ AA-

DCM Shriram Consolidated Limited Diversified Oct-03 A- A

Finolex Industries Limited Petrochemicals Dec-03 AA- AA

Gabriel India Limited Auto Ancillary Nov-03 BBB- BBB+

Indian Petrochemicals Corporation Limited Petrochemicals Jan-04 AA- AA

Kalyani Steels Limited Steel Oct-03 D BB

Tata Chemicals Limited Chemicals Apr-04 AA AA+

Century Enka Limited Textiles Feb-04 AA AA+

Downgrades

Company Name Sector Downgrade Date Downgrade From Downgrade To

BPL Limited Consumer Durables May-03 A- D

Excel Industries Limited Chemicals Dec-03 A+ A-

Madras Cements Limited Cements Jan-04 AA AA-

Max India Limited Diversified Mar-04 A BB

Sterlite Industries (India) Limited Non-Ferrous Metals Feb-04 AA AA-

Tata Power Company Limited Power Mar-04 AAA AA+

Thirumalai Chemicals Limited Chemicals Nov-03 A- BBB+

As can be seen from the table above, the sample seems to have a bias towards upgrades as

opposed to downgrades. This may be due to the fact that the two fiscal years considered forthe determination of this sample were characterized by improving credit fundamentals, strong

capital-market conditions and low interest costs, allowing companies to improve their capital

structures by replacing high-cost borrowings with significantly cheaper loans. Thiswidespread improvement in the overall credit profile of Indian companies has led CRISIL to

upgrade the long-term ratings of fourteen firms and downgrade those of just five in the year to

March 2004, compared to fourteen upgrades and nineteen downgrades in the previous year.

Essentially, there were more upgrades than downgrades during the sample period. In addition,as before, financial statements are difficult to obtain for companies that have been

downgraded, especially to default (D) ratings, and so these companies were excluded on thebasis of unavailability of information. Regardless, it is important to keep in mind that theresults of the data have been based on a sample that is slightly biased towards upgrades as

opposed to downgrades.

Methodology

As all of the ratings actions taken by CRISIL in the sample set above occur after March of2003, financial statements from the fiscal year ended March of 2003 are used for the exercise

of this research. Although quarterly statements would be a better substitute, Indian regulatory

agencies are relatively lax when compared to the Securities and Exchange Commission (SEC)

of the United States, and as a result full and accurate historical quarterly data is somewhatdifficult to obtain, especially for one outside of India.

Because many of the CRISIL ratings changes are for upgrades or downgrades one or twonotches within a ratings class, ICRM18 (ICRM with ratings subcategories) is used for this

analysis. ICRM18 values are calculated for all nineteen companies in the above sample by

calculating financial ratios for the fiscal years ended March 2003, March 2002, and March2001, and averaging those ratios in order to determine an ICRM value that is valid for the

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March 2003 time period. These ICRM values are then converted to ICRM ratings, as detailedpreviously, and compared to the ratings upgrades and downgrades published by CRISIL

during that time period. The following table details this calculation and comparison for each

of the nineteen companies included in the sample set. The table also includes the ratings ofeach company as of March 2002, that is, the ratings as per the previous study examining if

ICRM rates companies on par with CRISIL ratings, for the sake of comparison purposes.

Upgrades

Company Name Date From To

Lettered

ICRM18 2002

Lettered

ICRM18 2003 Predict Upgrade

Gujarat Gas Company Limited Aug-03 AA+ AAA AA AAA Yes

Tata Finance Limited May-03 BBB- BBB BBB+ BBB- No

The Arvind Mills Limited Jun-03 D BB D D No

The Tata Iron and Steel Company Limited Aug-03 AA+ AAA A A+ Yes

Apollo Tyres Limited Mar-03 A+ AA- A+ AA Yes

DCM Shriram Consolidated Limited Oct-03 A- A BBB BBB+ Yes

Finolex Industries Limited Dec-03 AA- AA AAA AA Yes

Gabriel India Limited Nov-03 BBB- BBB+ BBB- BBB+ Yes

Indian Petrochemicals Corporation Limited Jan-04 AA- AA A- A+ Yes

Kalyani Steels Limited Oct-03 D BB D BB- YesTata Chemicals Limited Apr-04 AA AA+ AA- AA+ Yes

Century Enka Limited Feb-04 AA AA+ AA+ AA+ Yes

Downgrades

Company Name Date From To

Lettered

ICRM18 2002

Lettered

ICRM18 2003 Predict Downgrade

BPL Limited May-03 A- D BBB+ D Yes

Excel Industries Limited Dec-03 A+ A- BB+ BBB- No

Madras Cements Limited Jan-04 AA AA- BBB+ BBB+ No

Max India Limited Mar-04 A BB BBB B+ Yes

Sterlite Industries (India) Limited Feb-04 AA AA- A A Yes

Tata Power Company Limited Mar-04 AAA AA+ A+ A Yes

Thirumalai Chemicals Limited Nov-03 A- BBB+ BBB+ BBB+ Yes

Results and Analysis

The results of the examination of the prediction power of the ICRM model, to be fully

understood, must be analyzed on a company-by-company basis. The reason for the entries in

the last column of the table above, Predict Upgrade and Predict Downgrade areenumerated in this analysis.

Gujarat Gas Company Limited: Although in March of 2002, ICRM predicted a rating forGujarat Gas that was one notch below CRISIL ratings, not only did ICRM predict the upgrade

for this company, but it predicted the new rating exactly (zero difference between CRISIL

upgraded rating and ICRM rating in March 2003). In addition, ICRM predicted this ratings

upgrade by March of 2003, a full five months prior to the CRISIL upgrade.

Tata Finance Limited: In the case of this company, the initial ratings provided by CRISIL and

ICRM differed by two notches: ICRM rated the company BBB+ whereas CRISIL rated thecompany BBB-. The May 2003 ratings upgrade action by CRISIL is accompanied by a March

2003 downgrade prediction to BBB- by ICRM.

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The Arvind Mills Limited: Though CRISIL upgrades Arvind Mills from default (D rating) to aBB rating in June of 2003, ICRM in March of 2003 maintains its D rating for the company.

Though it is possible that the companys financials significantly improve in the quarter

between March and June, it is more likely that CRISIL upgrades the company out of defaultbased on qualitative characteristics such as the completion of deals with lenders common to

restructuring practices.

The Tata Iron and Steel Company Limited: Though ICRM did not agree with CRISILconcerning the initial rating for the firm (before any ratings changes), ICRM did predict an

increase in the rating of the company by one notch. In fact, ICRM predicts this upgrade five

months before action is taken by CRISIL.

Apollo Tyres Limited: In April of 2003, CRISIL upgraded the rating of Apollo Tyres one

notch from A+ to AA-. ICRM predicts a ratings upgrade in March of 2003, but foresees aratings upgrade of two notches from A+ to AA.

DCM Shriram Consolidated Limited: Although CRISIL and ICRM disagree concerning therating of DCMS, ICRM predicts an upgrade of one notch (from BBB to BBB+) in March of

2003 that is followed by an actual ratings upgrade of one notch (from A- to A) by CRISIL in

October of 2003.

Finolex Industries Limited: CRISIL and ICRM disagree widely concerning the credit risk of

this company in March of 2002, but a ratings upgrade by CRISIL in December of 2003 and a

ratings downgrade prediction by ICRM in March of 2003 ensure that the two models see eyeto eye both models rate Finolex AA by the end of December 2003.

Gabriel India Limited: CRISIL upgraded this company from BBB- to BBB+ in November of

2003, a ratings action that was exactly predicted by ICRM in March of 2003, a full eightmonths in advance.

Indian Petrochemicals Corporation Limited: Although the two models disagree on the initial

rating of the company, ICRM does indeed predict a ratings upgrade in March 2003 of twonotches that is followed by an actual ratings upgrade by CRISIL in January 2004.

Kalyani Steels Limited: In March of 2003, ICRM predicts a ratings upgrade for this company

from default (D rating) to BB-. This prediction is valid, as in October of 2003, CRISILupdates the company out of default to a BB rating.

Tata Chemicals Limited: ICRM predicts a credit upgrade for Tata Chemicals in March 2003to AA+, a prediction that sees reality when CRISIL upgrades the company to AA+ in April of

2004. This prediction is especially remarkable as it comes thirteen months in advance.

Century Enka Limited: In the case of Century Enka, ICRM maintains a rating of AA+ for this

company since March of 2002. Although in March of 2002, CRISIL rates Century Enka AA,

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it is upgraded to AA+ in February of 2004, making the CRISIL rating correspond with therating that ICRM has maintained for a substantial period of time.

BPL Limited: BPL is a special case in that Indian investors were infuriated when CRISILdowngraded the companys outstanding long-term debentures from A- to default a week

following the companys announced default. In this case, ICRM predicts the default of BPL inMarch of 2003, a full two months before the company actually defaulted on its rated

obligations.

Excel Industries Limited: This is a case in which ICRM and CRISIL again disagree with the

initial ratings of the company. However, with respect to Excel Industries, ICRM predicts anupgrade from BB+ to BBB-, while CRISIL later downgrades the rated obligations of the

company from A+ to A-.

Madras Cements Limited: ICRM rates this company BBB+ and maintains that rating over the

years in consideration. However, in January of 2004, CRISIL downgraded the long-term

borrowings of this company from AA to AA-.

Max India Limited: Although CRISIL and ICRM ratings disagree initially, ICRM does

predict a ratings downgrade of five notches in March of 2003 from BBB to B+. This is

followed in March of 2004 by a ratings downgrade action by CRISIL of five notches from Ato BB+.

Sterlite Industries (India) Limited: ICRM has maintained a rating of A for Sterlite for the twoyears in consideration in this study. However, in February of 2004, CRISIL downgrades

Sterlite from AA to AA-. What is not shown in the above table is that in April of 2004,CRISIL again upgraded the Sterlite debentures back to AA, thereby effectively maintaining a

steady rating for the company over the time period in question, as predicted by ICRM.

Tata Power Company Limited: Although ICRM does not believe that Tata Power deserves a

rating of AAA initially, it does predict a ratings downgrade of one notch, from A+ to A. This

prediction is seen to reality when CRISIL downgrades Tata Power from AAA to AA+ (onenotch) in March of 2004.

Thirumalai Chemicals Limited: In this final case, ICRM predicts and maintains a rating of

BBB+ for the company in the years under study. Although CRISIL originally rates theobligations of the company A-, in November of 2003 the debt is downgraded to BBB+,

thereby fulfilling the prediction of ICRM.

Out of nineteen companies that experienced ratings upgrades and downgrades, ICRM was

able to predict either the exact ratings change, or at the very least the direction and extent of

the ratings change in the case of fifteen companies. This indicates that not only does ICRMrate obligations on par with the ratings of CRISIL as shown in the earlier portion of the study,

but also has predictionary power for ratings changes.

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Summary and Conclusions

The purpose of this paper was to develop a model with which to rate the outstanding non-

convertible debt of Indian corporations, from the perspective of an Indian investor. The model

should ideally rate companies on par with the ratings of the leading Indian credit ratingagency, CRISIL, and be able to predict ratings upgrades and downgrades before published by

the agency.

The first section of this study examined the extent of the association between the ratingspublished by credit ratings agencies and the financial ratios of companies within a sample set.

The purpose of such an investigation was to determine which ratios are closely correlated

with the ratings of the credit agencies, so that they could subsequently be used in thedevelopment of a new ratings model. In the second section of this paper, a debt scoring model

called the Indian Credit Rating Model (ICRM) was introduced and calculated for the

various companies included in the sample. The ratings determined by ICRM were then

compared to the ratings published by CRISIL, with strong results. The third section of thisstudy used multiple discriminant analysis to attempt to make ICRM more efficient through the

inclusion of coefficients, an attempt that was unsuccessful. Finally, the final section of thispaper tested the predictionary power of the ICRM model with respect to upgrades and

downgrades, and yielded favorable results.

This study developed a model that was not only able to rate companies on par with the ratingsof the primary credit ratings agency in India, CRISIL, but was also able to predict the

direction and degree of ratings changes before such ratings changes were published by

CRISIL. These findings indicate that CRISIL may give significant weight to financialstatements (as opposed to qualitative company-specific, industry, and economic information)

when determining its credit scores, as its ratings can be matched by a model that uses onlyfinancial statements. In addition, as ICRM can predict ratings changes far in advance of theirannouncement, CRISIL may adopt a type of wait-and-watch policy in publishing ratings

upgrades and downgrades.

As a result of this study, I find that ICRM is a good means of determining credit scores forcompanies that are unrated by any of the primary credit ratings agencies of India.

Nonetheless, with respect to investment decision-making, it remains important to supplement

an ICRM analysis with a scrutiny of a variety of qualitative variables that can affect thecreditworthiness of a firm, such as the economic climate, the situation of the industry in which

the company operates, the political environment that exists during the time of the analysis, the

quality and mindset of the firms management, etc.

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Appendices

Appendix 1: Correlation Matrices for CRISIL8 and CRISIL18

Correlation Matrix: CRISIL8 and Financial Ratios, Part IFinancial Ratio RISIL8 Ratin WC/TA RE/TA EBIT/TA Sales/TA ROCE E/CL

WC/TA -0.088

0.506

RE/TA -0.676 0.219

0.000 0.095

EBIT/TA -0.598 0.179 0.405

0.000 0.176 0.001

Sales/TA -0.343 -0.052 0.096 0.498

0.008 0.696 0.468 0.000

ROCE -0.550 0.075 0.316 0.968 0.613

0.000 0.571 0.015 0.000 0.000

E/CL -0.303 0.182 0.656 0.124 -0.259 0.0010.020 0.167 0.000 0.350 0.048 0.995

TNW/TL -0.454 0.270 0.854 0.343 -0.055 0.223 0.857

0.000 0.039 0.000 0.008 0.682 0.089 0.000

IC 0.004 0.241 0.067 -0.325 -0.370 -0.486 0.177

0.979 0.066 0.614 0.012 0.004 0.000 0.179

CFO/LTD -0.314 -0.082 0.340 0.712 0.501 0.793 0.079

0.016 0.535 0.008 0.000 0.000 0.000 0.553

CFO/TD -0.302 0.141 0.425 0.635 0.308 0.607 0.213

0.020 0.286 0.001 0.000 0.018 0.000 0.105

RONW 0.195 -0.052 0.136 -0.180 0.089 -0.161 0.011

0.138 0.697 0.306 0.173 0.501 0.223 0.931

OI/Sales -0.462 0.150 0.248 0.620 -0.117 0.488 0.333

0.000 0.258 0.058 0.000 0.376 0.000 0.010

C/LTD -0.191 0.569 0.445 0.290 0.003 0.173 0.295

0.148 0.000 0.000 0.026 0.985 0.189 0.023

C+STD/TD -0.195 0.476 0.434 0.324 0.037 0.215 0.273

0.138 0.000 0.001 0.012 0.778 0.102 0.037

E/L -0.423 0.282 0.841 0.304 -0.080 0.188 0.859

0.001 0.031 0.000 0.019 0.549 0.154 0.000

CF/TA 0.014 0.270 0.015 0.327 -0.002 0.330 0.106

0.915 0.038 0.913 0.011 0.986 0.011 0.425

CF/TD -0.035 0.188 0.106 0.129 0.019 0.096 0.102

0.792 0.155 0.426 0.332 0.887 0.471 0.444

D/E 0.588 -0.202 -0.678 -0.283 -0.272 -0.256 -0.290

0.000 0.125 0.000 0.030 0.037 0.050 0.026

I/AD 0.087 0.364 0.149 -0.102 -0.116 -0.176 0.011

0.514 0.005 0.259 0.442 0.381 0.182 0.936

I/EBITDA 0.091 -0.095 -0.114 -0.106 -0.146 -0.079 -0.015

0.493 0.473 0.390 0.424 0.271 0.551 0.910

LTD/TA 0.663 -0.063 -0.765 -0.458 -0.451 -0.476 -0.258

0.000 0.636 0.000 0.000 0.000 0.000 0.048

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Correlation Matrix: CRISIL8 and Financial Ratios, Part II

Financial Ratio TNW/TL IC CFO/LTD CFO/TD RONW OI/Sales C/LTD

IC 0.160

0.227

CFO/LTD 0.302 -0.737

0.020 0.000

CFO/TD 0.492 -0.226 0.783

0.000 0.085 0.000

RONW 0.009 -0.015 -0.006 -0.019

0.946 0.912 0.967 0.885

OI/Sales 0.382 0.104 0.191 0.275 -0.513

0.003 0.435 0.148 0.035 0.000

C/LTD 0.604 0.257 0.172 0.528 0.005 0.198

0.000 0.049 0.193 0.000 0.969 0.132

C+STD/TD 0.598 0.226 0.246 0.623 -0.011 0.207 0.981

0.000 0.085 0.060 0.000 0.934 0.116 0.000

E/L 0.992 0.166 0.296 0.514 0.024 0.323 0.618

0.000 0.209 0.023 0.000 0.859 0.013 0.000

CF/TA 0.107 -0.124 0.267 0.243 0.008 0.144 0.273

0.420 0.349 0.041 0.064 0.953 0.277 0.037

CF/TD 0.229 -0.022 0.058 0.057 0.017 0.070 0.628

0.082 0.867 0.661 0.670 0.896 0.597 0.000

D/E -0.417 0.013 -0.243 -0.233 -0.136 0.014 -0.197

0.001 0.924 0.063 0.076 0.304 0.916 0.135

I/AD 0.232 0.282 -0.212 -0.053 0.136 -0.067 0.651

0.077 0.030 0.106 0.693 0.304 0.616 0.000

I/EBITDA -0.111 0.022 -0.115 -0.109 0.031 -0.005 -0.101

0.404 0.871 0.386 0.410 0.815 0.970 0.445

LTD/TA -0.590 0.104 -0.499 -0.458 -0.070 -0.107 -0.315

0.000 0.435 0.000 0.000 0.596 0.419 0.015

Correlation Matrix: CRISIL8 and Financial Ratios, Part III

Financial Ratio C+STD/TD E/L CF/TA CF/TD D/E I/AD I/EBITDA

E/L 0.617

0.000

CF/TA 0.251 0.095

0.055 0.476

CF/TD 0.600 0.216 0.372

0.000 0.100 0.004

D/E -0.191 -0.403 0.037 -0.035

0.147 0.002 0.779 0.792

I/AD 0.600 0.218 0.151 0.676 -0.035

0.000 0.097 0.252 0.000 0.795

I/EBITDA -0.105 -0.095 -0.039 -0.024 0.167 -0.065

0.428 0.475 0.772 0.857 0.205 0.623

LTD/TA -0.341 -0.574 0.028 -0.053 0.645 -0.105 0.330

0.008 0.000 0.833 0.688 0.000 0.428 0.011

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Correlation Matrix: CRISIL18 and Financial Ratios, Part I

Financial Ratio

CRISIL18

Rating WC/TA RE/TA EBIT/TA Sales/TA ROCE E/CL

WC/TA -0.104

0.431

RE/TA -0.683 0.219

0.000 0.095

EBIT/TA -0.604 0.179 0.405

0.000 0.176 0.001

Sales/TA -0.349 -0.052 0.096 0.498

0.007 0.696 0.468 0.000

ROCE -0.554 0.075 0.316 0.968 0.613

0.000 0.571 0.015 0.000 0.000

E/CL -0.301 0.182 0.656 0.124 -0.259 0.001

0.021 0.167 0.000 0.350 0.048 0.995

TNW/TL -0.455 0.270 0.854 0.343 -0.055 0.223 0.857

0.000 0.039 0.000 0.008 0.682 0.089 0.000

IC -0.001 0.241 0.067 -0.325 -0.370 -0.486 0.1770.991 0.066 0.614 0.012 0.004 0.000 0.179

CFO/LTD -0.309 -0.082 0.340 0.712 0.501 0.793 0.079

0.017 0.535 0.008 0.000 0.000 0.000 0.553

CFO/TD -0.297 0.141 0.425 0.635 0.308 0.607 0.213

0.022 0.286 0.001 0.000 0.018 0.000 0.105

RONW 0.173 -0.052 0.136 -0.180 0.089 -0.161 0.011

0.189 0.697 0.306 0.173 0.501 0.223 0.931

OI/Sales -0.457 0.150 0.248 0.620 -0.117 0.488 0.333

0.000 0.258 0.058 0.000 0.376 0.000 0.010

C/LTD -0.198 0.569 0.445 0.290 0.003 0.173 0.295

0.133 0.000 0.000 0.026 0.985 0.189 0.023

C+STD/TD -0.200 0.476 0.434 0.324 0.037 0.215 0.2730.129 0.000 0.001 0.012 0.778 0.102 0.037

E/L -0.423 0.282 0.841 0.304 -0.080 0.188 0.859

0.001 0.031 0.000 0.019 0.549 0.154 0.000

CF/TA 0.014 0.270 0.015 0.327 -0.002 0.330 0.106

0.916 0.038 0.913 0.011 0.986 0.011 0.425

CF/TD -0.038 0.188 0.106 0.129 0.019 0.096 0.102

0.775 0.155 0.426 0.332 0.887 0.471 0.444

D/E 0.591 -0.202 -0.678 -0.283 -0.272 -0.256 -0.290

0.000 0.125 0.000 0.030 0.037 0.050 0.026

I/AD 0.087 0.364 0.149 -0.102 -0.116 -0.176 0.011

0.513 0.005 0.259 0.442 0.381 0.182 0.936

I/EBITDA 0.084 -0.095 -0.114 -0.106 -0.146 -0.079 -0.0150.525 0.473 0.390 0.424 0.271 0.551 0.910

LTD/TA 0.666 -0.063 -0.765 -0.458 -0.451 -0.476 -0.258

0.000 0.636 0.000 0.000 0.000 0.000 0.048

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Correlation Matrix: CRISIL18 and Financial Ratios, Part II

Financial Ratio TNW/TL IC CFO/LTD CFO/TD RONW OI/Sales C/LTD

IC 0.160

0.227

CFO/LTD 0.302 -0.737

0.020 0.000

CFO/TD 0.492 -0.226 0.783

0.000 0.085 0.000

RONW 0.009 -0.015 -0.006 -0.019

0.946 0.912 0.967 0.885

OI/Sales 0.382 0.104 0.191 0.275 -0.513

0.003 0.435 0.148 0.035 0.000

C/LTD 0.604 0.257 0.172 0.528 0.005 0.198

0.000 0.049 0.193 0.000 0.969 0.132

C+STD/TD 0.598 0.226 0.246 0.623 -0.011 0.207 0.981

0.000 0.085 0.060 0.000 0.934 0.116 0.000

E/L 0.992 0.166 0.296 0.514 0.024 0.323 0.618

0.000 0.209 0.023 0.000 0.859 0.013 0.000

CF/TA 0.107 -0.124 0.267 0.243 0.008 0.144 0.273

0.420 0.349 0.041 0.064 0.953 0.277 0.037

CF/TD 0.229 -0.022 0.058 0.057 0.017 0.070 0.628

0.082 0.867 0.661 0.670 0.896 0.597 0.000

D/E -0.417 0.013 -0.243 -0.233 -0.136 0.014 -0.197

0.001 0.924 0.063 0.076 0.304 0.916 0.135

I/AD 0.232 0.282 -0.212 -0.053 0.136 -0.067 0.651

0.077 0.030 0.106 0.693 0.304 0.616 0.000

I/EBITDA -0.111 0.022 -0.115 -0.109 0.031 -0.005 -0.101

0.404 0.871 0.386 0.410 0.815 0.970 0.445

LTD/TA -0.590 0.104 -0.499 -0.458 -0.070 -0.107 -0.315

0.000 0.435 0.000 0.000 0.596 0.419 0.015

Correlation Matrix: CRISIL18 and Financial Ratios, Part III

Financial Ratio C+STD/TD E/L CF/TA CF/TD D/E I/AD I/EBITDA

E/L 0.617

0.000

CF/TA 0.251 0.095

0.055 0.476

CF/TD 0.600 0.216 0.372

0.000 0.100 0.004

D/E -0.191 -0.403 0.037 -0.035

0.147 0.002 0.779 0.792

I/AD 0.600 0.218 0.151 0.676 -0.035

0.000 0.097 0.252 0.000 0.795

I/EBITDA -0.105 -0.095 -0.039 -0.024 0.167 -0.065

0.428 0.475 0.772 0.857 0.205 0.623

LTD/TA -0.341 -0.574 0.028 -0.053 0.645 -0.105 0.330

0.008 0.000 0.833 0.688 0.000 0.428 0.011

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Appendix 2: ICRM8 and ICRM18 CalculationsThree-Year Average of ICRM8 ICRM18

Company Name LTD/TA TNW/TL OI/Sales I/EBITDA

ICRM8

Standardized

ICRM8

Letter

ICRM18

Standardized

ICRM18

Letter

Ahmedabad Electricity Company Limited 0.083 0.920 0.048 0.213 0.672 1 AAA 1 AAA

Ashok Leyland Limited 0.181 0.691 0.084 0.456 0.138 3 A 3 AA

Amforge Industries Limited 0.366 0.154 -0.010 -0.771 -1.763 8 D 18 D

Apollo Hospitals Enterprise Limited 0.273 1.119 0.159 0.331 0.674 1 AAA 1 AAAAsian Paints (India) Limited 0.132 0.841 0.135 0.108 0.736 1 AAA 1 AAA

Apollo Tyres Limited 0.203 0.567 0.086 0.417 0

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