Analysis of Credit Ratings in India - Spidispidi2.iimb.ernet.in/~networth/CCS/2006/004- CCS...

Analysis of Credit Ratings in

India

Contemporary Concerns Study

Submitted to Prof. Ashok Thampy

On

August 28, 2007

Prepared by:

Manoj Kumar Chitlangia 0611172

Pankaj Periwal 0611176

Indian Institute of Management Bangalore

[2]

Acknowledgements

We would like to thank Prof. Ashok Thampy for giving us the opportunity to work under his guidance.

We are thankful for the never ending support and inspiration received from him and for the time he

took out of his busy schedule to discuss with us and provide us directions during the course of this

study. His insights were instrumental in giving directions to this study.

We would also like to thank Neela, Asst. Librarian, for helping us find necessary data required for this

study.

Last but not the least we would like to thank the PGP Office for allowing such a study in the curriculum.

[3]

Table of Contents

Executive Summary ....................................................................................................................................... 4

Evolution of Credit Ratings ........................................................................................................................... 5

Understanding Credit Ratings and its process .............................................................................................. 6

CRISIL ......................................................................................................................................................... 6

ICRA ........................................................................................................................................................... 7

Credit Rating analysis of Banks in India ...................................................................................................... 14

Credit Rating analysis of corporate sector in India ..................................................................................... 15

Prediction of a company being rated D in 1 year time using Discriminant and Logit Analysis ............... 15

International Scenario ................................................................................................................................. 20

Comparison of Indian and Korean Banks ................................................................................................ 20

Study of Defaults ......................................................................................................................................... 22

Decreasing number of rated companies? ................................................................................................... 24

Conclusion ................................................................................................................................................... 30

Problems faced during the study ................................................................................................................ 31

Appendix ..................................................................................................................................................... 32

Average One‐Year Transition Rates ........................................................................................................ 32

Number of corporate sector ratings by CRISIL from 1992 onwards ....................................................... 32

Statistical tests for difference across AA and AAA rated Indian Banks ................................................... 33

Statistical tests for comparison of Indian and Korean Banks. ................................................................ 35

SPSS Outputs for Discriminant and Logit Analysis .................................................................................. 36

Credit Rating Scales ................................................................................................................................. 38

Comparison of bond market growth in Asia‐Pacific region .................................................................... 39

References .................................................................................................................................................. 40

[4]

Executive Summary

Credit ratings came into being because of the huge debt issue by US railroad companies that triggered

the need for information about the creditworthiness of these companies. The first ever rating was

published in 1909 and since then the rating business has come a long way with credit rating agencies

actively providing independent opinions on the creditworthiness of the entities spread across the world.

In this study we explored the process followed by the various credit ratings agencies for the

determination of credit ratings. The rating process differs across the rating agencies and the same entity

may get a different rating from different rating agencies. The rating agencies evaluate different

parameters for different industries and the evaluation process also depends on the type of rating (long

term, short term, etc) being given. As a proxy to evaluate the capability to payback the resources raised,

these agencies also check whether the company can sustain or improve its profitability in the future and

whether the industry in which it works would be favorable.

We performed an analysis of the credit ratings in India and came up with some interesting insights. For

example, the number of ratings has been consistently decreasing over the years. The present

outstanding ratings list of CRISIL has only a few number of companies rated below the investment grade.

Have all companies been giving robust performance or have the probable lower rated companies said

good‐bye to credit ratings? We have also explored this aspect in the study.

An analysis of the credit ratings of banks and corporate sector companies in India has been performed.

We have derived the differentiating financial parameters across the AAA and AA rated banks. We have

also tried to derive a model using Discriminant (for financial factors) and Logit analysis (also including

non‐financial factors) which could help to predict whether a particular corporate sector company would

be categorized in the default category in the following year, which would become important once banks

come under the purview of Basel‐II norms and start following Internal Credit Rating options. An

international perspective has also been provided with a comparison of the financial ratios of Indian and

Korean banks.

The very basic purpose of credit ratings is to predict the probability of default. We performed a study of

the defaults happening in India and found that the default rate has been decreasing. A possible

explanation can be the decreasing number of rated companies or the current credit rating up‐cycle due

to booming economy. We have identified some recent developments which explain the decrease in the

number of rated companies. We finally present the problems that we have faced while pursuing this

study.

[5]

The first credit rating was published in the year 1909 by John Moody. It included an opinion on the creditworthiness of corporate debt papers issued by railroad

companies.

Evolution of Credit Ratings

The history of credit ratings began long back in 1850 when the US railroad companies had to raise funds

which where beyond the capacity of banks or the equity investors to provide. Prior to this issue all major

debt issues were from the government which were considered as safe by the investors. The companies

raised amount either through bank loans or by way of issue of stock.

This huge issue of debt, the investors of which spanned

across continents, led to a high demand of accurate and

reliable information about the issuing company in a

simplified form. The investors wanted information from a

third party based upon which they could make the required

pricing and investing decisions.

Henry Varnum Poor sensed this business opportunity and

started publishing systematic information about railroad’s

properties and other financial details. It became very popular

and led to the annual publication Poor’s Manual of the

Railroads of the United States that remained a standard and authentic source of

information for several decades. This statistics contained in this manual was the birth of the credit

ratings as we see today.

Thus to sum up credit ratings were created because of the following needs, requirement of large

investments, globalization leading to an expanse of investor base and the need for a comparable and

independent information. In India, CRISIL was setup in 1987. ICRA was founded in 1991.

[6]

Understanding Credit Ratings and its process

In the very basic sense a credit rating is an opinion on the creditworthiness of an issuer indicating the

probability that the issuer will not default on the payment obligation of the issue. It is important to

understand that the credit rating is just an OPINION by someone and should not be taken as a fact.

However, through experience the rating agencies have been able to predict defaults with reasonable

accuracy levels.

Following are the leading credit rating agencies of India:

• Credit Rating Information Services of India Limited (CRISIL)

• Investment Information and Credit Rating Agency of India (ICRA)

• Credit Analysis & Research Limited (CARE)

• ONICRA Credit Rating Agency of India Ltd

Amongst the above, CRISIL is associated with Standards & Poors and ICRA with Moody’s.

The credit rating processes followed by these agencies differ from one another and thus it is useful to

explore and understand the methodologies followed by them or credit rating.

CRISIL

CRISIL is India's leading Ratings, Research, Risk and Policy Advisory Company. CRISIL’s majority

shareholder is Standard & Poor’s, the world's foremost provider of independent credit ratings, indices,

risk evaluation, investment research and data1.

The exact methodology details were not available for CRISIL, but by and large they assess a bank,

financial institution on their Market position on following factors (They follow the modified version of

classical CAMEL rating process to access market position called CRAMEL2):

1. Capital Adequacy

2. Resource raising ability

3. Asset quality

1 http://www.crisil.com/about-crisil/about-crisil.htm 2 Rating Criteria for Banks and Financial Institutions, CRISIL

[7]

4. Management and systems evaluation

5. Earning potential

6. Liquidity/Asset liability management

All factors are treated equally in term of importance or weightage.

Capital Adequacy is judged on factor like Size of capital, Quality of capital (Tier I capital), and

sustainability, of capital rations and flexibility to raise Tier I capital and growth plans.

Resource‐raising ability depends on size of deposit case, diversity in deposit base and the geographical

spread, deposit mix, growth in deposits, cost of deposits, diversity in investor base, funding mix and cost

of funds and retail penetration.

Asset quality is adjudged on geographical diversity and diversity across industries, client profile of the

corporate asset portfolio, quality of non‐industrial, lending, NPA levels, movement of provisions and

write‐offs and growth in advances.

Management and systems evaluation is based on goals and strategies, systems and monitoring,

appetite for risk and motivation levels of staff.

Earning potential is evaluated on factors Level of earnings, diversity of income sources, efficiency

measures

Liquidity/Asset liability management position is assessed through factors like liquidity risk, liquid

assets/total assets, proportion of small deposits, Interest rate risk,

They also look at the amount of Government support for specialized entities in the financial sector.

Further public sector banks benefit from backing of government ownership.

ICRA

ICRA Limited (an Associate of Moody's Investors Service) was incorporated in 1991 as an independent

and professional company. ICRA is a leading provider of investment information and credit rating

services in India. ICRA’s major shareholders include Moody's Investors Service and leading Indian

financial institutions and banks3.

3 http://icra.in/profile.aspx

[8]

ICRA under Moody’s framework has specified it rating mechanism to detailed levels and following is the

summary of their methodology4.

Moody’s Bank Financial Strength Ratings (BFSR): Global Methodology

Bank’s credit risk can be divided as a function of three broad factors

1. Bank's intrinsic financial strength,

2. The likelihood that it would benefit from external support in the case of need

3. The risk that it would fail to make payments owing to the actions of a sovereign.

Moody's assigns credit risk ratings to banks and their debt obligations using a multi‐step process that

incorporates both a bank's intrinsic risk profile and specific external support and risk elements that can

affect its overall credit risk. These include bank‐specific elements such as financial fundamentals,

franchise value, and business and asset diversification, as well as risk factors in the bank's operating

environment, such as the strength and prospective performance of the economy, the structure and

relative fragility of the financial system, and the quality of banking regulation and supervision.

The following diagram shows how BFSRs fit into Moody's overall approach to assigning bank credit

ratings (The left side of the diagram shows the principal factors that are used to determine a bank's

BFSR. The right side summarizes the specific external support and risk elements that are combined with

the BFSR to determine Moody's local currency and foreign currency deposit and debt ratings).

4 Moody’s Bank Financial Strength Ratings (BFSR): Global Methodology, http://www.moodys.com

[9]

The focus is on five key rating factors that Moody’s believe are critical to understanding a bank’s

financial strength and risk profile. They are:

1. Franchise Value

2. Risk Positioning

3. Regulatory Environment

4. Operating Environment

5. Financial Fundamentals

To dampen the cyclical nature of the industry, most of the financial metrics they use are three‐year

averages.

The relative importance of the different key rating factors cannot be same across banks globally since

banks in developing markets face a substantially different set of challenges than banks in mature

markets. On the other hand, the higher degree of economic volatility in developing markets, as well as

the potential for weaker regulatory oversight and less reliable financial reporting, indicates the relative

riskiness of relying heavily on the current disclosed financial numbers for banks in developing markets.

Therefore, while Moody’s puts a heavy emphasis on financial fundamentals in assigning BFSRs to banks

in mature markets, this is significantly less the case for banks in developing markets. In the BFSR

scorecard they assign a 50% weighting to financial fundamentals for banks in mature markets, with the

four other key rating factors receiving a combined weighting of 50%. However, for banks in developing

markets, this weighting is changed, so that financial fundamentals are only weighted at 30%, with the

four other key rating factors receiving a combined weighting of 70%.

Key Rating Factors for the BFSR

Rating Factor 1: Franchise Value

Franchise Value is about the solidity of a bank's market standing in a given geographical market or

business niche. A solid and defensible Franchise is a key element underpinning the ability of an

institution to generate and sustain recurring earnings, to create economic value and, thus, to preserve

or improve risk protection in its chosen markets.

Four sub‐factors to assess an institution’s Franchise Value:

1. Market Share and Sustainability: Large market shares suggest an entrenched market positioning with

strong brand name recognition that tends to come hand in hand with high pricing power. These

[10]

elements act as barriers to entry to other players and as such are indicative of the likely sustainability of

a bank’s positioning and its ability to defend itself from competition.

2. Geographical Diversification: Excessive concentration on lending in a single geographic area with

relatively undiversified economies heightens an institution’s credit risk profile and plays an important

role in weakening asset quality.

3. Earnings Stability: In this regard, retail‐based institutions are favored than banks with

wholesale/corporate banking given their highly predictable risk‐adjusted earnings stream which is an

invaluable asset in times of volatility or stress. This earnings stability is usually a result of strong

customer relationships, higher switching costs for customers, and highly granular loan portfolios

4. Earnings Diversification: Excessive reliance on one business line can make an institution highly

vulnerable to potential changes in market dynamics which could be sudden and unpredicted with no

offsetting earnings stream to protect the institution's economic solvency.

Rating Factor 2: Risk Positioning

Risk management should aim to reduce or control the risks that banks face – be these customary (day‐

today activities), cyclical or event‐driven – or take advantage of them, when beneficial to the bank.

Taken together, these risks impact the core profitability and earnings predictability and may even, at an

extreme, severely damage a bank’s credit standing in a matter of days if they are not managed

appropriately

Six subfactors in assessing Risk Positioning

1. Corporate Governance: Focuses not only on the relationship between the boards of directors (also

known as supervisory boards, hereinafter referred to as the “board”), management and shareholders,

but also on the degree to which the board and management team have shown that they effectively

balance shareholder and creditor interests. Factors like Ownership and Organizational Complexity, Key

Man Risk, Insider and Related‐Party Risks are used to evaluate the scores.

2. Controls and Risk Management: Well‐functioning and deeply imbedded system of controls and

internal checks and balances are typical means of reducing operational risk and the overall risk profile of

the bank.

[11]

3. Financial Reporting Transparency: Factors like Global Comparability of Reported Financial

Information, Frequency and Timeliness of Reporting, Quality of Financial Information Reported by Banks

are important in this case

4. Credit Risk Concentration: As with any concentration risk, large exposures to single obligors,

industries, or regions are a potential source of earnings volatility.

5. Liquidity Management: It starts with an assessment of the degree to which a bank’s illiquid assets

(primarily loans) are funded by core liabilities that are stable (primarily customer deposits, long‐term

debt and equity). Banks with stable core funding in excess of their illiquid assets generally face low

liquidity risk. Liquidity risk increases to the extent that illiquid assets are being funded by more

confidence‐sensitive funding sources such as short‐term capital markets funding or interbank funding.

6. Market Risk Appetite: Focus is to assess the sensitivity of both the trading and non‐trading (i.e.

banking) books to major changes in key financial variables (including interest rates, equity prices, foreign

exchange rates, and credit spreads). Typically assessed through the results of a bank's own stress tests

or economic capital measures, or if not available, other measures of market risk such as VaR or interest

rate sensitivity analyses.

Rating Factor 3: Regulatory Environment

A bank's financial strength is often improved with the existence of an independent bank regulator with

credible and demonstrated enforcement powers and an adherence to standards of effective regulation

and supervision consistent with global best practices.

The key parameters of evaluation are Independence, Regulatory standards, supervision and

enforcement, maturity of regulatory framework and health of banking system

Rating Factor 4: Operating Environment

A bank’s performance is frequently constrained by its operating environment and, where conditions are

particularly difficult, banks could often be said to be the victims of their environments. Violent economic

cycles, business damaging political decisions, weak legal systems and irrational competitive

environments can all act singly or in combination to impair a bank’s creditworthiness. Main factors are

Economic Stability, Integrity and Corruption, Legal System.

Rating Factor 5: Financial Fundamentals

[12]

The use of financial metrics helps to verify or falsify performance assumptions that were based on past

trends. These following sub‐factors are all components of the classical CAMEL approach to bank credit

analysis:

1. Profitability

2. Liquidity

3. Capital Adequacy

4. Efficiency

5. Asset Quality

Adjustments

The scorecard is designed taking into account global availability of information, global comparison and

reasonable fit for all banks rated by Moody’s Investors Service. However, given that we rate banks in

over 85 countries, with different market environments, regulations and business models, this basic

scorecard can not perfectly fit all of them and can not permit perfect global comparability. For example

the efficiency ratio of an investment bank ratio established in a market with loose labor regulations

would be well lower than that of a nationwide retail bank.

Therefore Moody’s analysts and rating committees will consider making additional adjustments to one

or more sub‐factors in the scorecard, or consider additional metrics to improve comparability.

[13]

BFSR Scorecard Weights for Banks in Mature Markets

BFSR Scorecard Weights for Banks in Developing Markets

Results

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[15]

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[16]

analyze some of the main factors. Apart from our understanding, a similar technique would also be

useful to most of the Banks, since with Basel norms getting implemented at various levels, Banks would

be required to perform this task to some extent themselves, and hence there is a definite need for such

models.

Literature Survey

We found the following studies relevant to this analysis during our literature survey:

1. Z‐score formulation for predicting bankruptcy by Altman (1968). He employed working

capital/total assets ratio, retained earnings/total assets ratio, earning before interest and

taxes/total assets ratio, market value of equity/book value of total debt ratio, and sales/total

assets ratio as predictor of financial health of a company. The indicator variable Z‐score

forecasted the probability of a firm entering bankruptcy in a period of two years (the cut‐off

score was below 1.81).

2. Altman et al. (1977) constructed a 2nd generation model with several enhancements to the initial

Z‐score model. The new model was called ZETA and it was effective in predicting bankrupt

companies up to five years prior to failure. The sample considered was of corporations

consisting of manufacturers and retailers. The ZETA model tests were based on non‐linear like

quadratic as well as linear discriminate models. Although the non‐linear model was more

accurate in the original test sample results but at the same time less accurate and reliable in

holdout or out‐of‐sample forecasting.

3. In Altman et al. (1995), they modified the Z‐score model to fit for emerging market corporations,

especially Mexican firms that had issued Eurobonds denominated in US dollars. This alternate Z‐

score model for emerging markets dropped sales/total assets and used book value of equity for

the fourth and final variable. This was modified to suit better to private firms.

4. In Arindam Bandyopadhyay (2004), the above model was tweaked more to fit better for India as

an emerging market. His study focused on predicting probability of default of Indian corporate

bonds. He used an MDA (Multivariate Discriminate Analysis) model to predict corporate default

using a balanced panel data of 104 Indian corporations for the period of 1998 to 2003.

[17]

Since quite a few literature has suggested that the credit rating, companies’ average performance and

hence a quantitative model are all quite dynamic in nature over various time periods and are dependent

on the state of economy. The previous study was done during the period 1998 to 2003, which also

included jittery phases due to 9/11 and export linked economy and at the same time IT bubble bursting.

Therefore we have tried to study the effects more to a context where Indian economy has been by far

growing. We have included the most recent data available to for both Credit Rating issued by CRISIL and

the financial & other factors.

We have looked at 2 different analyses:

1. Multivariate Discriminate Analysis for developing the Z‐score model for the current Indian

economy state. This would help us to predict the probability of being rates as default grade.

2. Logistic Regression model to estimate the probability of being rated in the default grade and

factoring in non‐financial factors like age of company, group company (if it is past of some

conglomerate/business house) and industry in which a firm operates. This particular model has

a potential to predict credit risk capital for Indian Banks.

Discriminant and Logit analysis

This section details the discriminant and logit analysis performed to arrive at the model for predicting

the probability of a corporate sector company being rated in Default category.

Assumptions and limitations

For this study we have taken the credit ratings done by CRISIL. CRISIL has the largest market share in the

credit rating industry and the companies rated by CRISIL provide us an acceptable reference set for the

study. However, since the ratings provided by different agencies can differ even for the same firm, the Z‐

Score model developed in this study might not reflect the ratings provided by other rating agencies.

We have taken the data from 2001 onwards, which is a period of up‐swing rating cycle. Hence the

companies having a default grade rating is low as compared to the number of companies during early

90s. The number of companies which are rated by CRISIL has also been going down which further limits

the data set that we get for the analysis limiting the accuracy of the model.

Selection of Data and Data Sources

[19]

Results of Discriminant Analysis

A summary of the data set used for the analysis has been included in the Appendix. A total of 197 valid

data points were included in the analysis out of which 20 randomly chosen data points (10 each from

Default and Non‐Default grades) were kept as Holdout sample.

The following discriminating function was derived using SPSS software for discriminant analysis:

Z‐Score = 0.830 * Solvency Ratio + 0.602 * (Cash Profits / Total Assets) + 0.582 * (Working Capital/Total

Assets) + 0.523 * (Operating Profit/Total Assets) + 0.516 * (Sales / Total Assets)

The Z‐score for default grade was ‐0.612 and for non‐default grade was 0.619.

The above model successfully predicted the rating grade for only 77.4% of the cases in the analysis

sample and 70% of the cases in the holdout sample. A detailed SPSS output has been given in the

appendix.

Results of Logit Analysis

As explained before three other non‐financial factors were included for logit analysis. The natural log of

the age of the firm, the affiliation with the top 50 business groups of India and the category of industry

the company is in. The results obtained through this analysis too were not conclusive. This model was

successful only for 81.9% of the cases in the analysis sample and 75% of the cases in the holdout

sample. A detailed output has been provided in the appendix.

One interesting result from logit analysis is that only the following ratios were included in the final

equation to attain the classification of default/non‐default grade:

• Solvency Ratio

• Sales/Total Assets

• Ln(Age)

[21]

Indian Banks Rating

Bank Of Baroda AAA Bank Of India AA Canara Bank AAA H D F C Bank Ltd. AAA I C I C I Bank Ltd. AAA Indian Overseas Bank AA Punjab National Bank AAA State Bank Of India AAA Source CRISIL Credit Ratings and S&P Credit Ratings

It is relevant here to mention that the sovereign rating of Korea (A+) is higher from that of India (BBB‐)

as per S&P ratings. The following table shows the means and variances of the various financial

parameters of the chosen banks of India and Korea. The data is of financial year 2006 (ending 31st March

2007 for India and 31st December 2006 for Korea) and the exchange rate as of these dates have been

used to convert the total assets to equivalent Dollar value.

Mean Variance

India Korea India Korea CAR 12.44 12.36 0.75 1.62

Tier1 Capital 7.95 8.58 0.84 1.28 NIM 3.17 3.01 0.71 0.52 ROE 17.60 19.56 5.12 3.51 NPL 0.83 1.00 0.36 0.32

Total Assets (USD bn) 48.93 119.48 37.86 80.97

From the look at the above table it is seen that the various financial parameters have similar values in

both the nation. Further statistical tests confirm that the means of these financial parameters are the

same at a 90% confidence level. The details of the test can be found in the appendix. It should be noted

here that the data set is very small here; hence the statistical tests are not very accurate.

The above analysis immediately poses a question in one’s mind, that if the above parameters are almost

the same for both the Indian and the Korean banks then why are the Korean banks given a higher

international credit rating than the Indian Banks. One explanation to it can be the fact that the sovereign

rating of Korea is higher than that of India, as already mentioned before. Country risk is one of the

parameters taken as input by the rating agencies for the assessment of international credit ratings.

Korean Banks Rating

Daegu Bank A3 Industrial Bank of Korea A1 Kookmin Bank A1 Korea Exchange Bank A3 Pusan Bank A3 Woori Bank A3 Shinhan Bank A3 Hana Bank A3

[22]

Study of Defaults

A prominent concern in the minds of every investor is whether he would get his money invested back or

not. The probability of default as depicted by the credit ratings helps every investor to choose

appropriate investment instruments as per his risk appetite. High risk investors would prefer the

speculative grade instruments which have higher return along with a high probability of default.

CRISIL defines default as “Even a single day’s delay, or a shortfall of even a single rupee, in terms of the

promised payment schedule”5.

A few important terms have been defined below to provide a better understanding of the study:

Default Rate: It is the percentage of companies defaulting in a particular rating category. It is

calculated as the number of companies defaulting in a rating category divided by the total number of

ratings outstanding in that category.

Transition Rate: It captures the probability with which a company’s rating moves between different

credit rating categories.

Importance of the default and transition rates: The pricing of the instruments in the debt

markets depends on the credit risk of the company issuing the debt. The default and transition rates

indicate the probability of the future payments by a company and thus help in the pricing of the debt

instruments. Any other product dependant or influenced by the credit risk of the company also keeps

these rates as critical input parameters. Certain quantitative models for determine credit risk include

these rates as input. Finally, both these rates can be used to validate the scales used for ratings since as

the rating degrades the default probability should decrease and thus the transition probability to default

grade would also increase.

5 CRISIL Credit Rating Default Study 2006

[23]

CRISIL Default and Transition Rates

The figure on the right shows that the default

rates have declined over the years after 1998.

The default rates have also exhibited trends

similar to that of the S&P’s global default

rates. The default rate for the period 2000‐06

has been 1.7%. The declining default rates

also indicate that the current period has been

of a rating up‐cycle. The stricter regulations in

the current scenario making fraudulent and

manipulative accounting policies difficult have also added to the trend.

The decline in the default rates is further illustrated by comparing the cumulative default rates for the

current period and historical rates.

Source: CRISIL Ratings

It is evident from the above two charts that the cumulative default rates for the period 2002‐2006 has

been lower than the historical average, further indicating that the present period is a rating up‐cycle

period. The average 1‐year transition rates for CRISIL have been given in the appendix.

[24]

0100200300400500600

No of Rated Companies by CRISIL

No of Rated Companies

Decreasing number of rated companies?

The adjoining table shows that the number of rated

companies by CRISIL has been decreasing since 1997. It

poses an interesting question to one’s mind. Have the

companies vanished? Or are these ratings not worth to

be carried forward?

In this section we explore the possible reasons for this

phenomenon.

The following developments contribute to a large extent to the above phenomenon. We explain them

one by one in this section.

1. Buoyant Equity Markets

2. Private Placements

3. Low risk appetite for risky corporate bonds in Indian debt market

4. Primary and Secondary Markets for bond in India

5. Decrease in participation from Mutual funds

6. Structured finance product picking up fast

7. Effects of regulatory norms like Basel‐II

1. Buoyant Equity Markets As a widely know fact that Indian equity markets have been fairly buoyant in past couple of years and

this has also been a reason for many corporate houses to follow the equity route for resource

mobilization. This is also evident from the data in table below which clearly shows that the “Percentage

share of debt in total resource mobilization” has been declining steeply in the recent years from 98.08%

in 2002‐03 to 73.51% in 2004‐05.

[25]

Table: Resource mobilization by the corporate sector (INR billions)

F‐Year Public equity issues

Debt issues

Total resources (2+5)

Share of private placements in total debt (4/5*100)

Share of debt in total resource mobilization FY (5/6*100)

Public issues

Private placements

Total (3+4)

% %

1 2 3 4 5 6 7 8 2000‐01 24.79 41.39 524.34 565.73 590.52 92.68 95.8 2001‐02 10.82 53.41 462.2 515.61 526.43 89.64 97.97 2002‐03 10.39 46.93 484.24 531.17 541.56 91.16 98.08 2003‐04 178.21 43.24 484.28 527.52 705.73 91.8 74.75 2004‐05 214.32 40.95 553.84 594.79 809.11 93.12 73.51 Note: Financial Year (April – March). Sources: Prime Database; Indian Securities Market Review, National Stock Exchange (NSE).

2. Private Placements

Private placements have been quite popular in India, primarily because of the ease of issuance in the

underdeveloped primary as well as secondary debt markets and cost efficiency. Again from the table

above it is quite clear that private placements share more than 90% market share in the total debt

market.

It has been observed that a significant proportion of bank's investments in non‐statutory liquidity ratio

(SLR) securities are through the private placement route. Other than the banks and financial institutions

bond issues are privately placed with several small players such as provident funds, mutual funds and

co‐operative banks and regional rural banks (RRBs)6.

Besides the general benefits for private placements, until 29th December, 2003 rating was not

mandatory for private placements. It was only after this date that the regulations were drafted and

issued to make the corporate debt securities under private placement void from this rule as per the

notification on BSE India issued on 29th December 2003.

6 Rating may be mandatory for private placement, http://www.blonnet.com/2002/03/26/stories/2002032601860100.htm

[26]

Conditions to be complied with in respect of private placement of debt securities include following

clause: “The debt securities shall carry a credit rating from a Credit Rating Agency registered with SEBI”7.

3. Low risk appetite for risky corporate bonds in Indian debt market

The data on ratings suggests that lower‐quality credits have difficulty issuing bonds. The concentration

of turnover in the secondary market also suggests that investors’ appetite is mainly for highly rated

instruments, with nearly 84% of secondary market turnover in AAA‐rated securities8.

4. Primary and Secondary Markets for bond in India

Both primary and secondary debt markets in India have not been developed as equity markets and their

has been fair amount of criticism over this but off late quite a few steps have been taken by SEBI to

develop these markets to a state of maturity.

Some limiting features often quoted are9:

1. “buy and hold” strategies legitimately followed by most institutional investors in corporate debt

securities;

2. small issue sizes that fulfill the specific needs of the issuer or investor;

3. stringent investor protection guidelines in the primary market;

4. imperfections in the tax structure;

5. mandatory investment in government bonds;

6. lack of proper market infrastructure; and

7. The inability of small and medium‐sized enterprises to access the debt markets.

5. Decrease in participation from Mutual funds

It has been discussed in literature that corporate debt market is guided from the total asset managed by

mutual funds and it fluctuates with fluctuations in them. Over and above that aggregate figures shows

that MF has been consistently decreasing their investments under the debt pie.

7 Secondary Market for Corporate Debt Securities, http://www.bseindia.com/whtsnew/secondarymkt.asp 8 V K Sharma and Chandan Sinha, ‘The corporate debt market in India’ 9 V K Sharma and Chandan Sinha, ‘The corporate debt market in India’

[27]

Table: Assets under management by mutual funds (% of total)

Instrument End‐March 2003

End‐March 2004

End‐March 2005

Debt 59.9 44.8 31.6Equity 12.4 16.9 25.6Money market instruments

17.3 29.9 35.9

Government securities 4.9 4.3 3Others 5.5 4.1 3.9Total 100 100 100Source: Association of Mutual Funds in India.

6. Structured finance product picking up fast

With Securitization and other innovative products catering to customized requirements for market

participants and other benefits like tax savings, their market volumes have seen a steep increase in

volumes of trade. The table below show that the total volumes have increased almost 8 times from 2002

to 2005. This is again a disturbing trend from corporate debt opportunities point of view.

Table: Trends in issuance volumes (INR billions)

Structure 2002 2003 2004 2005Asset‐backed securities 12.9 36.4 80.9 222.9Mortgage‐backed securities

0.8 14.8 29.6 33.4

Corporate debt obligations/

19.1 24.3 28.3 25.8

loan sell‐offs Partial guarantee structures

4 1.9 – 16

Others 0 0.4 0.5 10Total 36.8 77.7 139.2 308.2Source: Investment Information and Credit Rating Agency of India.

7. Effects of Regulatory norms like BaselII

Under Basel II the risk weights assigned to securities rated below A (BBB and below) are all given 100%

or more risk weights, while at the same time if a security is not rated its default weight is considered to

be 100%. This clearly reduces incentive for firms to get rated unless they are sure of being rated an A or

above rating.

[28]

Table: Proposed Risk Weights based on External Risk Assessment10

Credit Rating

Sovereigns

Banks Corporate Option 1 Option 2

AAA to AA‐ 0 20 20 20A+ to A‐ 20 50 50* 100BBB+ to BBB‐ 50 100 50* 100BB+ to B‐ 100 100 100* 100Below B+ 150 150 150 150Un‐rated 100 100 50* 100Note: (i) * denotes claims on banks of short‐term maturity, e.g., less than 6 months would receive a weighting that

is one category more favorable than usual risk weight on the bank’s claim. (ii) Option 1: Based on risk weighting of

sovereign where bank is incorporated. (iii) Option 2: Based on assessment of the individual bank.

Other relevant changes in the norm are11:

• Credit rating is mandatory for issuance of debt instruments by listed companies with

maturity/convertibility of 18 months and above.

• SEBI along with stock exchanges made ratings mandatory for debt instruments placed under

private placement basis and having a maturity of one year or more, which are proposed to be

listed.

• Requirement for certain investors to invest not more than a stipulated part of their portfolio in

unrated bonds.

• RBI has made it mandatory for all commercial banks to make fresh investment only in rated non‐

SLR securities.

Other Trends covered through RBI Annual Report (200506) Decreased investments in Corporate bond from Banks

In addition to liquidating a part of their gilt portfolio, banks also reduced their non‐SLR investments

(especially, investments in the bonds/ debentures issued by various corporate entities) by Rs.10,256

crore during 2005‐06. A reason could be again because of following regulations being brought in:

10 D. M. Nachane , Saibal Ghosh, “Credit Rating And Bank Behaviour In India: Possible Implications Of The New Basel Accord”

11 Business Outlook: Basel‐II a big boon in the offing http://www.moneycontrol.com/india/news/ipoissuesopen/networthstockbrokingicraipo/subscribetoicra/market/stocks/article/272850

[29]

With effect from July 26, 2005, the risk weight for credit risk on certain capital market exposures was

increased from 100 percent to 125 percent. Capital market exposures subject to higher risk weights

included:

1. Direct investment by a bank in equity shares, convertible bonds and debentures and units of

equity oriented mutual funds;

2. Advances against shares to individuals for investment in equity shares [including Initial Public

Offerings (IPOs)/ Employee Stock Option Plans (ESOPs)], bonds and debentures and units of

equity oriented mutual funds; and

3. Secured and unsecured advances to stock brokers and guarantees issued on behalf of stock

brokers and market makers.

Significant capital being raised from international markets

Reflecting the increased domestic investment activity, demand for external commercial borrowings

(ECBs), including foreign currency convertible bonds (FCCBs), remained high during 2005‐06. Corporates

resorted to ECBs mainly for import of capital goods, project financing, capital investment, modernization

of plants and expansion of activity. Gross disbursements under ECBs increased from US $ 8.5 billion

during 2004‐05 to US $ 13.5 billion during 2005‐ 06. Net disbursements under ECBs were lower during

2005‐06 essentially on account of the one‐off effect of principal repayment of IMDs (US $ 5.5 billion).

Recourse to short‐term trade credits also increased during the year, reflecting rising import financing

requirements.

Buoyant stock markets also provided an opportunity to corporates to raise funds from international

capital markets for their investment requirements. Resources raised by Indian corporates from

international capital markets during 2005‐06 increased substantially by 238.7 per cent to Rs.11,358

crore (see Table 1.59). Out of these, Rs.9,779 crore were mobilized in the form of Global Depository

Receipts (GDRs), followed by American Depository Receipts (ADRs) (Rs.1,573 crore) and Foreign

Currency Convertible Bonds (FCCBs) (Rs.6 crore). Most of the euro issues were made by private non‐

financial companies. During 2006‐07(April‐June), resources raised through euro issues by Indian

corporates at Rs.5,786 crore were substantially higher than those of Rs.1,834 crore during the

corresponding period of 2005‐06.

All these reason are in a way making the debt market a little unattractive within the Indian geographical

territory for the corporate houses here and hence could be a reason for the not many corporate being

[30]

willing to incur the costs & time for being rated through a rating agency and in the process releasing

significant business information to an external agency.

Conclusion

In this report we saw that the credit rating agencies follow a detailed methodology to arrive at on

opinion on the creditworthiness of an entity. The process varies amongst agencies and the type of

entity. CAMEL model is generally used for banks. The credit ratings of banks in India are skewed towards

A category and an analysis of the financial ratio’s of AAA and AA rated banks showed that a number of

ratios were significantly different across the banks in these categories. The model developed using

discriminant and logit analysis to predict the possibility of a corporate being rated into the default

category in the following year showed that the following factors, Sales/Total Assets, Solvency Ratio and

Ln(Age).

The international ratings differ from the domestic ratings as we see in this report. The highest

international rating for any Indian bank is BBB‐. The comparison of financial ratios of Indian and Korean

banks showed no difference amongst them, but the Korean banks were rated higher internationally than

Indian banks implying that there are some country related factors too that are taken into account for

international ratings.

The present booming economic scenario in India has led to a decrease in the number of defaults

happening in the country. During our study we found a disturbing trend being coming up in terms of the

number of companies getting rated. This number is constantly decreasing over the past 7 years. We

found that this trend has links with the buoyant equity markets, rising number of debt being raised

through private placements, low risk appetite for risky corporate bonds in Indian debt market, the

maturity level of primary and secondary markets for bond in India, substantial decrease in participation

from Mutual funds, and uptrend in structured finance products rather than the debt products.

[31]

Problems faced during the study

We would like to document the problems that we have faced while doing this study so as to provide

some information on the difficulties encountered to anyone else who would be pursuing a similar or

related study. The study is heavily dependent on the availability of data and thus the first and prime

concern is the availability of data. The development of an accurate z‐score model and statistical tests

require larger number of data points which was again not present because the number of companies

rated in India are very less and the number of defaults even more less. The rating history of India is also

not very old.

We also faced problems while collecting data on international organization. IIMB doesn’t have license

for linking excel with Bloomberg database making it difficult to extract data about a large number of

companies, and the information available from ISI Emerging market is limited. We also could not find

the exact details of the rating methodology of CRISIL but could get only a summary of steps.

The credit ratings of banks are highly skewed towards AAA in India, which again leads to a very few data

points for lower ratings, restricting the scope of study. The study has been done in a period of rating up‐

cycle where the number of defaults is lesser.

[32]

Appendix

Average OneYear Transition Rates

Number of corporate sector ratings by CRISIL from 1992 onwards

The following table lists the number of ratings as done by CRISIL from 1992 onwards and the number of

defaults in each year. The ratings have been categorized industry‐wise.

[33]

Statistical tests for difference across AA and AAA rated Indian Banks

The test was performed on the financial ratios of the following banks:

Long Term : AAA Rated Banks Long Term: AA Rated Banks Central Bank of India Standard Chartered Bank Citibank N.A HDFC Bank ICICI Bank ABN‐AMRO Bank of Baroda Canara Bank Punjab National Bank

I N G Vysya Bank Ltd. Bank Of Maharashtra Indian Overseas Bank Oriental Bank Of Commerce Syndicate Bank Uco Bank Union Bank Of India Vijaya Bank Allahabad Bank Bank of India Central Bank of India

Source: CRISIL Monthly Rating Scan: April 2007 Issue

To test the whether the means of the various ratios are significantly different or not we used the following steps:

Step 1 An F‐Test was performed at 90% confidence interval to determine whether the variances of the individual ratios are same or not so that the tests for the difference of the means could be performed. Null hypothesis was that the variances are equal.

F = s12/s22

Fcrictical upper = 3.14

Fcritical down = 0.15

The following ratios were not rejected for the null Hypothesis that their variances are same. Hence further tests can be performed to check if their means are the same or different.

Investment / Deposit (%) Cash / Deposit (%) Interest Expended / Interest Earned (%) Other Income / Total Income (%) Operating Expenses / Total Income (%) Interest Income / Total Funds (%) Interest Expended / Total Funds (%) Net Interest Income / Total Funds (%) Non Interest Income / Total Funds (%) Operating Expenses / Total Funds (%) Profit before Provisions / Total Funds (%) Net Profit / Total funds (%) RONW (%) Capital Adequacy Ratio Tier1 Capital Tier2 Capital Efficieny Ratio Net NPA/Net Advances

[34]

Step 2 In order to test whether the mean of these ratios is statistically different or not across AAA and AA banks, a two‐tailed T‐test was performed at 90% confidence level.

Tcritical = 1.74

The ratio’s for which t‐statistic value was greater than this critical value did not satisfy the null hypothesis that the means of the ratio is equal at 90% confidence level across AAA and AA credit ratings. We are interested in these set of ratio’s since they are the among the reasons for the difference between the ratings of AAA and AA.

The ratio’s for which means across AAA and AA can be termed as significantly different are the following:

Efficieny Ratio Tier1 Capital Tier 2 Capital Net Profit / Total funds (%) Profit before Provisions / Total Funds (%) Operating Expenses / Total Funds (%) Operating Expenses / Total Income (%) Investment / Deposit (%)

However, there were some ratios for which these tests could not be performed since the standard deviations were statistically different for the two ratings. These ratios should be tested using some other measure. These ratios are:

Interest Expended / Total Funds (%) Net Interest Income / Total Funds (%) Non Interest Income / Total Funds (%) Interest Expended / Interest Earned (%) Other Income / Total Income (%) Credit‐Deposit (%)

A 2‐tailed t‐test, applicable to samples having different variances, was performed on these ratios. The results of the test suggest that the mean of the following ratios are significantly different across the credit ratings AAA and AA.

Interest Expended / Total Funds (%) Net Interest Income / Total Funds (%) Non Interest Income / Total Funds (%) Interest Expended / Interest Earned (%) Other Income / Total Income (%)

[35]

Statistical tests for comparison of Indian and Korean Banks.

F‐test followed by T‐test was performed in order to determine whether the means of the different

financial parameters of Korean banks were statistically different or not from the corresponding ratios of

Indian Banks.

Bank Country Rating CAR(%)

Tier1(%)

NIM(%)

ROE (%)

NPL (%)

Total Assets

(USD bn) Daegu Bank Korea A3 11.3 8.4 3.61 23.4 1 21.40978

Industrial Bank of Korea

Korea A1 11.7 8.4 2.66 21.9 1.2 93.30167

Kookmin Bank Korea A1 15.1 11.1 3.73 20.4 1.7 195.9367

Korea Exchange Bank Korea A3 14.8 9.8 3.4 16.7 0.9 69.67224

Pusan Bank Korea A3 11.1 8.1 3.09 16 0.8 23.68637

Woori Bank Korea A3 11.6 7.1 2.67 19.3 0.9 231.2891

Shinhan Bank Korea A3 12 7.8 2.55 24.1 0.8 193.8982

Hana Bank Korea A3 11.3 7.9 2.4 14.7 0.7 126.6718

Bank Of Baroda India AAA 11.8 8.74 2.95 12.15 0.6 32.96657

Bank Of India India AA 11.58 6.54 2.71 20.6 0.74 32.71112

Canara Bank India AAA 13.5 7.17 2.71 18.77 0.94 38.25262

H D F C Bank Ltd. India AAA 13.08 8.57 4.5 17.66 0.43 21.03083

I C I C I Bank Ltd. India AAA 11.69 7.42 2.22 10.98 1.02 79.52546

Indian Overseas Bank India AA 13.27 8.2 3.62 27.32 0.55 18.95972

Punjab National Bank India AAA 12.29 8.93 3.59 17.9 0.76 37.43046

State Bank Of India India AAA 12.34 8.01 3.02 15.41 1.56 130.5355Source: Capitaline databases for Indian banks, Hyundai Securities and Woori Investments and Securities research reports from the ISI Emerging

Markets database. Ratings India: Domestic ratings by CRISIL ratings, Korea: International ratings by S&P.

The null hypothesis for the F‐test was that the variances are equal for Indian and Korean banks and for

the T‐test the null hypothesis was that the means are equal for Indian and Korean banks. The following

table gives the p‐values for each test and also mentioned whether the hypothesis was accepted (not

rejected) or rejected after each test.

CAR Tier1 NIM ROE NPL

F‐Test 0.060186 0.28937 0.413743 0.340088 0.792184 Rejected Accepted Accepted Accepted Accepted T‐Test 0.900368 0.264337 0.633832 0.385699 0.318832 Accepted Accepted Accepted Accepted Accepted

[36]

The results show that we cannot infer that the values of these financial parameters are significantly different for Indian and Korean banks.

SPSS Outputs for Discriminant and Logit Analysis

Discriminant Analysis

Group Statistics

Group Statistics Mean Std. Deviation Valid N (listwise) Unweighted Weighted Default Grade WorkingCapital_TotalAssets -0.121280899 0.360965215 89 89 CashProfits_TotalAssets -0.031539326 0.169138664 89 89 Solvency 1.243707865 0.462946055 89 89 OperatingProfits_TotalAssets -0.035696629 0.154917676 89 89 Sales_TotalAssets 0.60741573 0.360084296 89 89 Non-Default Grade WorkingCapital_TotalAssets 0.097829545 0.23769367 88 88 CashProfits_TotalAssets 0.064465909 0.06962262 88 88 Solvency 2.1875 1.225545386 88 88 OperatingProfits_TotalAssets 0.042784091 0.075034467 88 88 Sales_TotalAssets 0.893375 0.526486059 88 88 Total WorkingCapital_TotalAssets -0.012344633 0.32426259 177 177 CashProfits_TotalAssets 0.01619209 0.137903323 177 177 Solvency 1.712937853 1.036122778 177 177 OperatingProfits_TotalAssets 0.003322034 0.127794149 177 177 Sales_TotalAssets 0.749587571 0.4716014 177 177

Standardized Canonical Discriminant Function Coefficients

Function1WorkingCapital_TotalAssets 0.082478CashProfits_TotalAssets 0.449033Solvency 0.729321OperatingProfits_TotalAssets -0.24362Sales_TotalAssets 0.395827

Functions at Group Centroids

Function1 Default -0.61191 Non-Default

0.618863

[37]

Classification Results

Predicted Group Membership Total

Default Non-Default Cases Selected Original Count Default 75 14 89 Non-

Default 26 62 88

% Default 84.27 15.73 100 Non-

Default 29.55 70.45 100

Cases Not Selected

Original Count Default 6 4 10

Non-Default

2 8 10

% Default 60 40 100 Non-

Default 20 80 100

Logit Analysis

Classification Table

Observed Predicted Selected Cases(a) Unselected

Cases(b)

DRAT Percentage Correct

DRAT Percentage Correct

0 1 0 1 Step 1 DRAT 0 76 13 85.39 6 4 60 1 18 70 79.55 1 9 90 Overall Percentage 82.49 75 Step 2 DRAT 0 75 14 84.27 6 4 60 1 18 70 79.55 1 9 90 Overall Percentage 81.92 75 Step 3 DRAT 0 76 13 85.39 6 4 60 1 20 68 77.27 1 9 90 Overall Percentage 81.36 75 Step 4 DRAT 0 74 15 83.15 6 4 60 1 18 70 79.55 1 9 90 Overall Percentage 81.36 75 Step 5 DRAT 0 73 16 82.02 6 4 60 1 19 69 78.41 1 9 90 Overall Percentage 80.23 75 Step 6 DRAT 0 77 12 86.52 6 4 60 1 20 68 77.27 1 9 90 Overall Percentage 81.92 75 Note: DRAT Variable: 0: Default grade and 1: Non‐default grade

[38]

Variable in the equation

The final step of the logistic regression is shown in the following table:

B S.E. Wald df Sig. Exp(B)

Step 6(a) Solvency 2.192904 0.44593 24.18274 1 8.76E-07 8.961198

Sales/Total Assets

1.207459 0.457587 6.963009 1 0.008321 3.344975

Ln (Age) 1.716624 0.685592 6.269296 1 0.012285 5.565708

Constant -6.83474 1.338742 26.06455 1 3.3E-07 0.001076

Credit Rating Scales

The credit rating scales broadly are the same for all credit rating agencies with minor differences in the notation for each category. The investment grade ratings are BBB (LBBB) and above for CRISIL (ICRA). The following table describes convention for CRISIL.

CRISIL’s Long term credit rating scale Investment Grade Ratings AAA

Highest degree of safety with regard to timely payment of financial obligations. Any adverse changes in circumstances are most unlikely to affect the payments on the instrument

AA

High degree of safety with regard to timely payment of financial obligations. They differ only marginally in safety from `AAA' issues.

A Adequate degree of safety with regard to timely payment of financial obligations. However, changes in circumstances can adversely affect such issues more than those in the higher rating categories.

BBB Moderate safety with regard to timely payment of financial obligations for the present; however, changing circumstances are more likely to lead to a weakened capacity to pay interest and repay principal than for instruments in higher rating categories.

Speculative Grade Ratings BB Inadequate safety with regard to timely payment of financial obligations; they are

less likely to default in the immediate future than other speculative grade instruments, but an adverse change in circumstances could lead to inadequate capacity to make payment on financial obligations.

B Greater likelihood of default; while currently financial obligations are met, adverse business or economic conditions would lead to lack of ability or willingness to pay interest or principal.

C Factors present that make them vulnerable to default; timely payment of financial obligations is possible only if favorable circumstances continue.

D Instruments rated 'D' are in default or are expected to default on scheduled payment dates. Such instruments are extremely speculative and returns from these instruments may be realized only on reorganization or liquidation.

Source: Long term Ratings Scale from CRISIL (http://www.crisil.com/credit‐ratings‐risk‐assessment/rating‐scales‐long‐term.htm)

[39]

Comparison of bond market growth in AsiaPacific region

[40]

References

1. Credit Rating Information Services of India Limited (CRISIL, www.crisil.com)

2. Investment Information and Credit Rating Agency of India (ICRA, www.icra.in)

3. Credit Analysis & Research Limited (CARE, www.careratings.com)

4. ONICRA Credit Rating Agency of India Ltd. (www.onicra.com)

5. Prowess companies database

6. Capitaline Databases (www.capitaline.com)

7. ISI Emerging markets database

8. EBSCO Research Databases

9. Research report from Hyundai securities and Woori investments for Korean markets

10. Evolution of credit ratings - Part 1.

(http://www.caricris.com/index.php?option=com_caricris&Itemid=41&id=5)

11. Evolution of credit ratings – Part 2.

(http://www.caricris.com/index.php?option=com_caricris&Itemid=41&id=6)

12. Nikitin L. and Romashov A, The Global Geography of Credit Ratings in Past and Present: Exploratory

Research

13. V K Sharma and Chandan Sinha, ‘The corporate debt market in India’

14. D. M. Nachane , Saibal Ghosh, “Credit Rating And Bank Behaviour In India: Possible Implications Of

The New Basel Accord”

15. Arindam Bandyopadhyay, “Predicting probability of default of Indian corporate bonds: logistic and Z-

score model approaches”

16. Reserve Bank Of India Annual Report, 2005-06

17. CRISIL Annual Report, 2005-06

18. Insights - CRISIL default study 2005-06

19. Rating Roundup, FY 2006-07, CRISIL

20. Bank Financial Strength Ratings: Global Methodology, Moody’s Investor Services

21. Arturo Estrella, Sangkyun Park, and Stavros Peristiani, “Capital Ratios as Predictors of Bank Failure”

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