1. 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 + 𝛽12 𝐼𝐿𝑖,𝑡 + + 𝛽14∆𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝛽15∆𝑀𝑎𝑐𝑟𝑜𝑠𝑡 + 𝜀1,𝑖,𝑡 (1)

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∆𝑅𝑎𝑡𝑖,𝑡 = 20 + 𝛽21∆𝐿𝑒𝑣𝑖,𝑡 + 𝛽22 𝐼𝑅𝑖,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.