Rating Propensity Indicator:

Methodology for Estimating Company Credit Ratings

by

Doruk Ilgaz. Ph.D.*

Strategist

Fixed Income Research

FactSet Research Systems

Dated: September 25th , 2015

* Comments should be directed to Doruk Ilgaz. (dilgaz@factset.com), Fixed Income Research, FactSet Research Systems Inc., 311 South Wacher Dr, 63rd Floor, Chicago, IL 60606.

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Rating Propensity Indicator:

Methodology for Estimating Company Credit Ratings

Abstract

Rating changes have more implications than the mere distinction of investment or speculative grades in risk management. A portfolio manager should be able to make informed decisions and manage the risk exposure of his portfolio based on the anticipated changes. We developed Rating Propensity Indicator (RPI) to assist portfolio managers with this task. RPI, using fundamental company data, computes a probability score for US Industrial companies based on the likeliness of an upgrade/downgrade in case a rating change occurs in the following year. RPI sectorial rankings give supplemental information about the company’s standing among its peers.

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I. Introduction

The 2008 financial crises have shown that there is a fairly continuous need for

credit institutions and other investors to carefully monitor the financial outlook and

credit-worthiness of industrial and financial enterprises. The importance of credit risk

assessment has relevance not only to asset prices in credit and debt markets, but also in

equity and in many types of derivative markets, particularly the credit default swap

market.

We developed Rating Propensity Indicator (RPI) in order to assess the changes in

credit risk of non-financial enterprises by developing up-to-date probability scoring for

the next period rating upgrade/downgrade of public companies. Using a sample period

of 1985-2014 US Industrial companies, involving quarterly and annual firm financial

statements, market prices and macroeconomic data, we have jointly estimated a

company manager’s choice of issuing debt/equity and rating agencies’ choice of credit

rating upgrade/downgrade. We have assigned companies to upgrade, downgrade, and

no rating change groups to compare and contrast the characteristics that yield to such

credit events. We have further dissected our sample to high-yield and investment grade

based on the company ratings after having observed that there are differences in

dynamics of the company leverage-rating relationship.

The model is modified then to work both with quarterly and annual data. The

quarterly RPI uses one year of history, whereas the annual RPI uses five year of history.

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Both RPI scores are calculated on-the-fly, meaning a new RPI score for a company will

be available for the next period as soon as the company fiscal-quarter or fiscal-year end

data is available. We plot Annual-RPI scores to show the historical performance of the

company over a long-term. On the other hand for the Quarterly-RPI scores, we consider

the most recent twelve quarters and also regress over them to see whether there is a

consistent short-term trend in these numbers. When the Annual- and Quarterly-RPI

used together, they will give a good idea where the company is headed as far as rating

agents see it.

Section II explains the logic behind the model and our approach to the rating

estimations and introduces the model. Section III shows how the results can be used as

a risk management tool. Section IV looks at the performance statistics. Section V

concludes.

II. RPI Model

1. Theory

A company’s credit rating affects its cost of debt and subsequently its overall cost

of capital. A firm with a higher credit rating can issue lower-yield debt and vice versa.

Graham and Harvey (2001)1 find that maintaining financial flexibility and good credit

ratings are the two most important factors that firms consider when deciding to issue

1 Graham, John R. and Campbell R. Harvey (2001). “The Theory and Practice of Corporate Finance: Evidence from the Field.” Journal of Financial Economics 60: 187-243.

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additional debt. They mention that 57.1% of CFOs consider credit ratings as a very

important indicator for them in how they choose the appropriate amount of debt for

their firms.

Consider a Reuters report about S&P’s (Standard & Poor’s Ratings Services)

decision of downgrading Barneys New York, a US based luxury retailer, from CCC to

CC in 2012. In the report, S&P mentions that “we assess the company’s financial risk

profile as ‘highly leveraged’ under our criteria because of its substantially leveraged

capital structure and very thin cash flow protection measures.” 2 Following this change

in credit rating, Barney’s management recruited a restructuring advisor to immediately

resolve its existing problem in capital structure. In a contrasting case, S&P has assigned

the Walt Disney Co.’s proposed issuance of 5- and 10-year debt an issue-level rating of

‘A’ in 2012 because of the company’s strong business risk profile and modest financial

risk which is supported by their conservative capital structure and good discretionary

cash flows. 3 According to Reuters, this credit rating is indeed good news for Walt

Disney to persistently raise sufficient debts with minimal costs. Both incidences provide

two critical messages for us. In the first case, a weak financial profile due to excessive

leverage results in a downgrade in credit rating by S&P, which in turn forces Barney to

restructure its existing capital structure. In the second case, healthy fundamentals

enable Walt Disney to achieve a high rating on issuance, which enables them to

2 See “Text-S&P cuts Barneys New York to CC” in Reuters dated February 9, 2012. http://www.reuters.com/article/2012/02/09/idUSWNA986020120209 3 See “Text-S&P rates Walt Disney debt A” in Reuters dated February 9, 2012. http://www.reuters.com/article/2012/02/09/idUSWNA983920120209

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generate external funds more economically. Therefore, credit rating changes are not

fully exogenous. Furthermore, managers might act either immediately or slowly to

adjust their leverages following credit rating changes.

Conventional wisdom suggests that credit downgrades would cause the cost of

debt to rise as firms become riskier, and credit upgrades would imply an opposite

effect. If firms are conscious of the cost of debt and subsequent financial distress, they

should downwardly adjust leverage ratios following credit downgrades, and upwardly

adjust leverage ratios following upgrades. We could expect this behavior to be non-

linear and asymmetric: firms that have undergone credit downgrades could be more

likely to adjust their capital structures than firms that have undergone credit upgrades,

and firms that are close to speculative grade ratings as a result of credit downgrades

could be more wary of further credit downgrades, and are therefore more likely to take

preventive measures to avoid further downgrades that would effectively put them in

the speculative category.

In developing Rating Propensity Indicator, we model the leverage-rating relation

using a simultaneous equation system. This allows for the feedback between the

manager making the leverage decision and the rating agency making the credit rating

decision. We calibrate the model separately for speculative grade and investment grade

firms. This allows for the different dynamics of the leverage-rating relation in these

groups of firms.

2. Methodology

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The literature models ratings based on some basic fundamental company data

such as leverage, profitability, liquidity, solvency, asset tangibility. New literature,

however, points out to the endogeneity of leverage in this type of modeling. The

endogeneity4 (in econometric terminology) occurs here as the other variables in the

model that are used to estimate ratings, also do affect leverage, and this suppresses the

relative impact of leverage on ratings (underestimation of leverage). There have been

studies that address the underleverage problem; firms seem systematically not to lever

up enough to take full advantage of the tax benefits of using debt. 5,6 However, when

you model appropriately to control for the endogeneity of leverage, this under-leverage

puzzle suddenly is not as much. Let’s take a look at the example below to illustrate this.

Example:

We will try to estimate a possible rating change for Goodyear Tire & Rubber Co.

in 2007 using a simplified model that is very similar to the ones used in the literature.

The table below shows the data from the FactSet Fundamentals database scaled by

coefficient estimates for the single and two-stage models. A two-stage model is one that

4 In a statistical model, an endogenous parameter or endogenous variable is one that is correlated with

the error term. Endogeneity can arise as a result of measurement error, auto-regression with auto-correlated errors, simultaneity (which is the case in our model) and omitted variables. For example, in a simple supply and demand model, when predicting the quantity demanded in equilibrium, the price is endogenous because producers change their price in response to demand and consumers change their demand in response to price. In this case, the price variable is said to have total endogeneity once the demand and supply curves are known. In contrast, a change in consumer tastes or preferences would be an exogenous change on the demand curve. 5 Molina, C. A. (2005). “Are Firms Underleveraged? An Examination of the Effect of Leverage on Default

Probabilities.” Journal of Finance 3: 1427-1459. 6 If you are interested about the underleverage puzzle you can check out the article “Underleverage: A Corporate Finance Puzzle and an Alternative Explanation” at the FactSet risk blog.

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controls for endogeneity in the first stage, whereas, a single-stage model does not. The

second column from the right shows the cumulative value, and the right-most column

converts that value from probit model estimates to probability using z-table. 7 The

single-stage model estimates a 53% increase in the rating. The two-stage model

estimates a rating increase with a 66% probability. In 2007, Goodyear experienced a

rating increase to BB- from its prior level B+.

As seen in the first column of the table, the largest contribution comes from the

change in leverage in the two-stage model. If a firm’s leverage has more impact on a

firm’s rating change than previously estimated using single-stage models, this may

explain why firms shy away from increasing their leverage and prefer to stay “under-

leveraged”.

The above example shows that the endogeneity of leverage is a serious issue that

needs to be addressed in estimating ratings. In modeling the RPI, not only have we

7 Probit model is an econometric method that allows estimating an outcome with categorical values such as ratings. It allows for a different distribution than the classical ordinary least squares method. Since we are estimating ratings here, using a probit model is the appropriate way. The by Molina (2005) is an example where probit model is used in a two-stage setup to predict ratings.

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addressed the above mentioned endogeneity, but have also considered the endogeneity

of rating in leverage estimation.

When we were building the RPI, we modeled rating and leverage changes jointly

(Simultaneous Equation System (SES)). This allows for feedback between the two

decision makers, the firm manager making the capital structure decision (Equation 1),

and the rating agency making the rating decision (Equation 2). The translation of this

econometric setup is that when a manager is making a decision regarding the

company’s capital structure (lever-up or lever-down), he is actually considering what

will happen to the company rating as a result; and vice versa when the rating agent is

assigning a rating to the company, he is considering the impact that the new rating will

have on the company’s capital structure. Both endogenous variables turned out to be

significant.

Let’s take a closer look at the RPI model set-up. The model to be estimated here

is a system of structural equations, where some equations contain endogenous variables

among the explanatory variables. The leverage equation (Equation 1) and the rating

equation (Equation 2) are the structural equations in the system. The dependent

variables are the left-hand-side variables, namely firm’s leverage change and firm’s

rating change, here. Both dependent variables are explicitly taken to be endogenous to

the system and are treated as correlated with the disturbances in the system’s equations.

∆𝐿𝑒𝑣𝑖,𝑡 = 10 + 𝛽11∆𝑅𝑎𝑡𝑖,𝑡 + 𝛽12𝐼𝐿𝑖,𝑡 + + 𝛽14∆𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛽15∆𝑀𝑎𝑐𝑟𝑜𝑠𝑡 + 𝜀1,𝑖,𝑡 (1)

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∆𝑅𝑎𝑡𝑖,𝑡 = 20 + 𝛽21∆𝐿𝑒𝑣𝑖,𝑡 + 𝛽22𝐼𝑅𝑖,𝑡 + 𝛽24∆𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛽25∆𝑀𝑎𝑐𝑟𝑜𝑠𝑡 + 𝜀2,𝑖,𝑡 (2)

Endogenous Variables: Firm’s Leverage ratio (debt to asset ratio), and firm’s credit

rating (𝑅𝑎𝑡𝑖,𝑡).

Instruments: Instrument for leverage (𝐼𝐿𝑖) and Instrument for rating (𝐼𝑅𝑖). Instruments

are included in the model for econometric reasons. Instruments are exogenous to the

endogenous variables. Using better instruments that are more representative of what

they proxy for is the key to the quality of the estimation.

Exogenous Variables: 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖 are the firm specific ratios and fundamental

information that are commonly used in estimating both leverage and rating decisions.

Some examples would be profitability, solvency, liquidity, asset quality, and asset size.

𝑀𝑎𝑐𝑟𝑜𝑠𝑡 are the macroeconomic variables that might affect firm’s leverage and rating,

such as output gap, rate of inflation, and unemployment rate. While some of the

variables are directly used in the model as point in time measures as they are reported,

some are used in trends or volatility.

Our sample covers 1985-2014 period US Industrials, and data is from FactSet

Fundamentals dataset. Following Fama and French (1997), we exclude the firms from

the financial sector (SICs 6000-6999), from non-classifiable establishments (SICs 9995-

9999), and from the regulated sector or utilities (SICs 4900-4999). To avoid outliers, we

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trim the observations that correspond to the top and bottom 0.5% values of the

variables. We do not restrict the dataset to be balanced. Firms may exit and reappear in

the sample.

Our basic finding is that both endogenous variables are significant. This means a

rating change is a significant factor for the manager when making a capital structure

choice, and the impact of a rating change on the capital structure is also a significant

factor for the rating agent. We calibrate the model for annual and quarterly data as well

as speculative and high grade companies. Results are then used to calculate the annual

and quarterly RPI scores on-the-fly. This way, as soon as a new financial statement is

published by the company, a new RPI score will be available to our users.

III. Using RPI scores

As an example, below are the historical annual RPI values for Nordstrom (JWN –

US). The blue line is the RPI scores, and it is the estimate for next year’s probability of

upgrade/downgrade for the company.

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The RPI value by the end of 2000 is calculated as -0.26, so it indicates a potential

downgrade in 2001. In the beginning of 2001, the rating was downgraded from A to A- .

By the end of 2005, Nordstrom's RPI was 1.41, indicating a potential rating upgrade.

The rating was upgraded from A- to A in April 2006. An RPI score that is positive

(negative) and higher (lower) in magnitude would indicate it is more likely that the

company will be upgraded (downgraded).

In addition to the Annual-RPI values, one can also check out the trend in the

Quarterly-RPI values in forming expectations regarding the company of interest. The

RPI values may be used in forming expectations about a company, as well as

determining portfolio weights. The table below shows 2012 year end RPI scores for the

computers sector. Green indicates the company has an RPI score greater than zero, and

red indicates the company has an RPI score of less than zero. If one would like to attain

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portfolio weight in the computer sector, but are holding securities that fall in the red

shaded area and does not want to take the risk of getting downgraded, he/she may

want to switch positions on the security that is being held with one of the companies

that fall in the green shaded area.

In 2013, in line with the RPI estimates, Xerox and Brocade have received

upgrades, and Hewlett-Packard has received a downgrade. A portfolio holding HP

would benefit from switching to Xerox or Brocade.

IV. Performance Statistics

First, let’s take a look at the distribution of the RPI scores by years.

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Following the 2008 financial crisis, there is a positive skew in the RPI scores in

2009 and 2010 (more downgrades than upgrades). However, this changes after 2011 and

in line with the economic recovery, we observe a negative skew in the RPI scores (more

upgrades than downgrades). This tells us the distribution of RPI scores is also an

indicator of the trend in overall financial creditworthiness of the US industrials.

The table below summarizes the performance statistics for the RPI over the last

eight years to include the 2008 financial crisis, and later as well. The first three columns

show the total number of upgrades, downgrades and all of the rating changes that have

occurred by year for US Industrials (non-financial, non-utility, non-governmental) . The

fourth column shows the coverage, the percent of the companies that model has

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estimated an RPI score for that universe. The last two columns show the success rate of

whether an upgraded (downgraded) company had an RPI score greater (less) than zero

(among firms that are covered by the model).

Total

Upgrades

Total

Downgrades All

RPI

Coverage

Upgrade

Success

Downgrade

Success

2007 128 205 333 49% 83% 45%

2008 116 321 437 49% 71% 72%

2009 115 319 434 54% 37% 79%

2010 220 94 314 60% 42% 73%

2011 185 122 307 58% 72% 51%

2012 128 133 261 57% 82% 56%

2013 153 99 252 66% 74% 52%

2014 147 90 237 58% 73% 74%

Average 149 173 322 56% 67% 63%

Median 138 128 311 57% 73% 64%

Per year, the model covers about 50-60% of the US Industrial firms as of 2015.8

The coverage will increase on the coming rounds of enhancements. The success rate of

the model is about 65-70% on average. One main factor that affects the performance is

the long held discussion regarding the timeliness versus the stability of ratings. When a

8 For a company to be covered by the model, it has to have a minimum of five year fundamental financial

data history and past ratings data available.

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rating change is supposed to take place, the rating agent may subjectively delay

assigning a new rating. This will suppress the accuracy of the model which does not

have subjectivity.

"If over time new information reveals a potential change in an issuer's relative

creditworthiness, Moody’s considers whether or not to adjust the rating. It manages the tension

between its dual objectives – accuracy and stability – by changing ratings only when it believes

an issuer has experienced what is likely to be an enduring change in fundamental

creditworthiness. For this reason, ratings are said to 'look through-the-cycle'.”

”Under consideration are more aggressive ratings changes - such as downgrading a rating by

several notches immediately in reaction to adverse news rather than slowly reducing the rating

over a period of time - as well as shortening the rating review cycle to a period of weeks from the

current period of months” 9

“…the value of it’s rating products is greatest when it’s ratings focus on the long term

and do not fluctuate with near term performance”

“Clearly, such judgments are highly subjective; indeed, subjectivity is at the heart of every

rating”. Standard and Poor’s (2003). 10

To summarize, the rating agencies favor stability of their ratings and may

subjectively delay a rating change to maintain that long term stability. The RPI, on the

9 The Financial Times, 19 January 2002, "Moody's mulls changes to its ratings process".

10 Standard & Poor’s, 2003, "Corporate ratings criteria", www.standardandpoors.com

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other hand, is an econometric model based on company fundamentals and assigns an

upgrade/downgrade probability with an average of a 65-70% accuracy. The subjectivity

factor regarding the timeliness of the ratings is the main limitation to the success of the

RPI.

V. Conclusion

Ratings are a backward-looking evaluation of a company’s creditworthiness

issued by independent rating agencies. We present the Rating Propensity Indicator, an

econometric model that is based on company fundamentals, to assist portfolio

managers in monitoring the companies they are interested in from a ratings perspective,

and prepare their portfolio accordingly using a forward-looking risk management tool.