are fallen angels special

8/18/2019 Are Fallen Angels Special

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Are Fallen Angels special?

This paper investigates the extent to which several firm-specific and macroeconomic factors can

explain the downgrade from investment to speculative grade, as well as their subsequent predictive

accuracy.

More specifically, a logistic model containing five variables selected on a stepwise basis, is tested on

three samples of non-financial companies spanning 37 countries and 7 industries, with quarterly data

ranging from 1986 to 2011.

Consistent with previous literature, after controlling for industry, I find that market value,

profitability , leverage, operating efficiency and the yield spread have a significant impact on “fallen

angels”, while the logistic model has an out-of-sample predictive overall accuracy between 70 and

85%.

The results are robust across different samples and model specifications.


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1.Introduction

Ratings provided by Credit Rating Agencies (CRAs) gained over time a tremendous popularity, being

currently used as a benchmark for measuring different obligors’ creditworthiness. In spite of being

defined as “specialized opinions” by the CRAs, ratings are being currently “hardwired” to a vastnumber of financial regulations, instruments and transactions (Gonzalez et al, 2004). Their importance

was even further enhanced by the “Basel 2” rules, which established bank capital adequacy

requirements according to the corresponding credit ratings of their asset portfolio.

Among all rating transitions, there is one of particular importance due to its threshold effects: the

downgrade from investment to speculative grade, which results in a “fallen angel” - as Moody’s

define the companies that go through such a process. Even if the investment-speculative cutoff was

arbitrarily established by CRAs (Harold, 1938), speculative-rated bonds are much more prone to

default than investment ones according to Moody’s (appendix 1). Hence, it is hardly surprising that

there are regulations which restrict heavily the ability of different investor groups to hold speculative

grade securities1, which further leads to decreased access to debt for the downgraded entities with all

its subsequent consequences.

Rating research has a long history, following promptly the first public issuance of ratings. Yet, the

number of studies focusing on ratings is considerably low , in comparison with the papers related to

bankruptcy prediction - which might seem surprising given the fact that downgrades can be

considered as an early signal of default.

The existent studies relate either to ratings’ impact, quality or prediction.

The first stream of literature is concerned with investigating the informational effect of ratings, which

is typically captured by the announcement effect of credit events on the price of different instruments.

Most of these studies find an immediate and rather strong effect in case of downgrades, in particular

for “fallen angels”

2

- revealing that ratings do have a significant informational content.Several studies investigate various aspects related to ratings’ quality, such as timeliness (Ederington

et al,1998), or the actual usefulness of ratings for predicting default (Gutler&Wahrenbourg,2007).

Again, these studies find a differentiating effect between investment and speculative graded

companies.

1 commercial banks were prohibited from holding junk bonds since 1936, while the Financial Institutions Reform Recovery

and Enforcement Act of 1989 extended the ban to other speculative-rated credit instruments as well(West, 1973); in US,

state insurance regulations follow the guidelines established by the National Association of Insurance Commissioners,

which requires higher risk premia on holdings of speculative-grade bonds.

2 See Norden and Weber (2004) for a detailed literature review


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Lastly, the most prolific literature strand, to which this paper is also related, refers to the use of

publicly available information for explaining and predicting ratings. The most common type of rating

prediction models rely on various statistical techniques, similar with the ones used for default

prediction. Given the ordinal nature of ratings, order probit is a natural methodological choice, which

is discussed in detail in Kaplan et al (1979) and Ederington (1985), who also prove its empirical

superiority as compared to discriminant analysis and least squares. In spite of the development of more

complex techniques, ordered probit has also been used in more recent studies, such as Hwang(2008)

and Hwang(2010), which use a semiparametric specification for the probit function.

With regards to fallen angels, even if most of the previously mentioned studies control for speculative-

rated companies, there has been only one study focusing specifically on fallen angels:

Chernenko&Suderam (2011), which investigate the market segmentation at the investment-to-

speculative boundary, and having therefore a different focus than this paper.

On the other hand, given the fact that the accuracy of rating prediction models is inferior to the ones of

bankruptcy prediction models (many of which achieved accuracies of over 85%3), and their volatility

is rather high, it is worth investigating whether these differences are of methodological nature, or due

to the informational advantage of CRAs.

Firstly, using one single model for all rating transitions leads to spurious results, since the impact of a

downgrade and the factors taken into account in the rating methodology vary throughout the rating

scale. Moody’s state clearly that “fallen angels” have their own specific characteristics, while different

ratios weight heavier when assessing such companies (Moody’s Rating methodology, 2000)

Secondly, the meaning of “downgrade” is overlooked in most, if not all, studies. More specifically,

researchers focus on creating a model which differentiates between rating groups, without capturing

the downgrade process per se

4

- which presumably leads to a model with higher explanatory power, but “slower ” in predicting downgrades. In other words, there is an “aging effect”, which is being

neglected in these studies.

Thirdly, similarly with the default prediction literature, most studies focused on rating prediction

neglect the sensitivity of the cutoff score to classification errors, as first mentioned in Ohlson (1980).

While the “real” cost of type 1 error can’t be estimated, a more accur ate out-of-sample cutoff score

could be estimated according to a representative sample distribution.

3

Belovari et al(2006) provide the most recent literature review 4 In other words, their models are based on the average differences in financial ratios of companies from two rating groups,

rather than on the immediate effect of the rating change


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Further, I argue that using issuer as opposed to issue-based ratings leads to more accurate results, since

several other instrument-specific variables such as maturity or seniority should be taken into account

for instrument-specific ratings.

Lastly, taking into account the controversy surrounding ratings’ t imeliness, it would be more

interesting to use quarterly, as opposed to annual data, which is employed throughout the existent

literature.

While some of these issues are partially addressed in few studies, the research design and data

structure used in multiple rating class analysis makes it virtually impossible to rule out simultaneously

the aforementioned pitfalls.

For this reason, but also because of the importance of the investment-to-speculative grade cutoff, this

paper attempts to develop a model focusing specifically on fallen angels, taking into account the

financials from the quarter prior to the actual downgrade. I find that several firm-specific factors

commonly used in default prediction such as size, profitability, leverage, operating efficiency, as well

as industry and macroeconomic factors can explain as much as 35%5 of the downgrades, being

significant and robust across different specifications. Moreover, the model has an average out-of-

sample prediction accuracy ranging from 70 to 85%. While the results should be interpreted with

caution due to the limited sample size , the consistent significance and relatively high prediction

accuracy, especially given the sample heterogeneity , strengthens considerably this papers’ findings.

The remainder of the paper is organized as follows. The next section discusses the methodology and

model, after which the findings are presented and compared with previous literature. Lastly, several

suggestions for improvements and further research are mentioned.

2.The logistic model

Since this paper focuses exclusively on fallen angels (which will result in a dependent dichotonomous

variable), the methodology will be similar, to some extent, with default studies. Since both rating and

default literature have been largely using different types of logistic regression, the same method is a

straightforward choice for the purpose of this research. A brief description of the function will

follow6, as well as its benefits in comparison to other techniques.

5

This number represents McFadden R-squared , which is specific to logistic regressions, and is therefore notdirectly comparable to OLS r-squared6 For a more detailed discussion on ML estimation and logit , see McFadden (1973)


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In short, logit is a classification model, which uses a logistic function to convert a binary outcome into

probabilities, given a specific set of predictors.

Let’s assume Xi as a vector of predictors for the ith observation, and ε be a vector of unknown

parameters. Then, P(X1, ε) denotes the probability of downgrade for any given Xi and ε, with 0 < P <1. The logarithm of the likelihood of any specific outcome, given the binary alternative (downgrade vs

not downgraded), is then given by:

( )

where N is the set of non-downgraded firms and D is the set of downgraded.

Given a specified function P, the maximum likehood estimates of ε1,ε2….are obtained by max (l(ε)).

In the absence of an adequate theoretical background for the distribution of P, a reasonable alternative

could be based on computational and interpretive simplicity (Ohlson, 1980). One such function is

logistic function, which has the following form:

P=(1+ exp (-yi)-1

), where yi =∑ (2)

One important implication of (2) is that y=P/(1-P), which means that the interpretation is rather

straightforward, and it has more informational content as compared, for example, with MDA.7

Moreover, one major benefit of logit over all other statistical models is its unbiasedness when used for

choice-based sampling. As detailed in Cram et al (2009), the fact that the “control” and “treatment”

group are known and chosen apriori represents a potential threat to external validity - which means

that the results can’t be generalized. Nevertheless, according to the authors, logit represents the sole

exception in this case, generating even with choice-based samples unbiased coefficients, except for the

intercept.8 Furthermore, as Ohlson (1980) argues, logit performs well when predictors’ normality

assumption is violated.

On the other hand, in the context of going-concern literature, one critical aspect to validating statistical

techniques such as logit is their predictive accuracy, and in particular the occurrence of type I error

(i.e. misclassifying a bankrupt company as a survivor or, respectively, a fallen angel as an investment

graded company).

8 See Palepu (1986) for a more detailed discussion on how the intercept can be changed, and Maddala (1986) for a textbook

discussion of the logit exception to the choice-based sample bias; Zmijevski (1984) explains that other statistical modelscan only be used with WESML (weighted maximum likelihood estimation), but this estimation requires information about

the population/sample ratio, which is usually unknown in default/credit risk studies


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Following Palepu(1986) and Cram et al (1999), this paper tackles one largely ignored, albeit important

methodological concern related to the choice of cutoff scores, which determines the trade-off between

type I and type II errors.

As shown in Palepu(1986), the standard cutoff score of 0,5 , which is used in the overwhelmingmajority of prediction studies, is intended for a balanced sample, where both types of errors are

equally important. . Yet, rare-event studies imply a non-balanced sample (unless matching is done),

and type I error is more costly, therefore “..the use of arbitrary cut-off probabilities in prediction tests

makes the computed error rates difficult to interpret.” (Palepu, 1986).

The current paper is the first to address this methodological concern in the context of predicting rating

changes, by proposing a cutoff rate equal with the sample distribution. By comparing the error rates

against a range of cutoff scores (as done first in Ohlson, 1986), for 3 different samples, I show that the

proposed cutoff rate minimizes the total error rate.

3. Data

The sample of companies was collected from Datastream and S&P public database on the basis of

three criteria, in the following order:

1. having S&P long term issuer rating, with at least 1 year (4 consecutive financial quarters) within

BBB group9 rating

2. not being a financial services company

3. having data for Total Assets item between 1986 and 2011

The first criterion was the most important, firstly because of being the raw data for the dependent

variable, and secondly because it lead to a drastic sample reduction10

, following which the other

selection criteria, as well as the research method as a whole, had to be adjusted.

Further, companies from both databases were merged, which resulted in a further reduction due to a

considerable number of duplicates. Since the “fallen angels” were of interest, all the companies which

had ratings outside BBB-BB group, as well as the ones that were rated BB, but have never been

9 Includes BBB-, BBB and BBB+10

The lack of issuer rating data is also documented in Hwang(2008,2010): 20% of the companies they found on Compustat

have such rating


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investment grade, were removed. The notches outside BBB-BB were excluded in order to rule out

high differences in creditworthiness (appendix 2), while only-BB were excluded as they might have

biased upwards the results11

. Finally, the 1-year BBB criterion was intended to confer a certain

“sta bility” within this rating gr oup, especially since CRAs claim to have a more thorough review on a

yearly basis.

Second criterion is common throughout the literature, as financial companies have very different

accounting and are subject to different regulations.

One natural criterion for data collection is the availability of all required data for a pre-determined

period. Furthermore, a typical methodology involves the initial collection of a large number of

explanatory variables, which are then filtered with various statistical methods (e.g. factor analysis or

stepwise regression) in order to achieve a more parsimonious model which best fits the dataset.

However, due to the already limited amount of data, the only variable on which we imposed

availability was total assets – since this item will be used as a denominator for most accounting ratios.

Consequently, the choice of certain variables will result in different sample sizes, with a significant

decrease for most of the quarterly data. As with regards to 1986-2011 timeline, this is the resulting

timeframe of the companies with rating data. As will be discussed in a later section, the rest of

variables have been collected for the time they were available, and the specification of the final

model(s) will consequently be dependent on the data availability. After applying these criteria, the

final sample consisted of 450 companies, spanning different industries and countries, with quarterly

data ranging from 1986 to 2011 (see Appendix 3 for general company characteristics)

4. The dependent variable

The variable of interest in my analysis is a dummy which attempts to capture in a timely manner the

downgrade from investment to speculative grade, ruling out thus the “aging” effect mentioned in the

introduction. More specifically, the value of 1 is assigned to the financial quarter following the

downgrade , and the value of 0 is assigned to the investment-grade companies for all the quarters

during which they had a BBB rating12.

This approach differs therefore from the other studies focusing on rating prediction, which consider

the entire period during which a company is speculative rated.

11 i.e. the speculative-only companies presumably have poorer credit quality as compared to the ones just downgraded, but

the same effect is not relevant for the investment ones, firstly because of the downgrade vs upgrade asymmetrymentioned in the literature review section, and secondly because we are solely concerned with the downgrade effect12

These companies were never downgraded to speculative grade


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The key assumption is that there are significant differences between a company which was rated BB

for one year, as opposed to one which was just downgraded to BB. This assumption relies on the

empirical evidence concerning an important announcement effect, especially in the case of fallen

angels.13

This prompt market reaction will result in a domino effect, which should trigger an

accelerated deterioration in that company’s financials. In a sense, this effect is underlying the so-called

“aging” effect, documented in many studies, such as Altman &Kao(1992).

The outcome of such a coding is: 164 values of 1 and 12417 null values , out of an initial number

457.100 observations 14, which means a highly unbalanced sample.

As formerly argued, such a data structure fits bets our research question; however, given the fact that

the downgrade observations represent only 0,0132 of the total , the choice of the non-downgrade

observations becomes an important methodological concern.

The default prediction literature, which I follow with regards to method, failed so far to reach a

consensus regarding the most appropriate sampling/matching techniques - therefore different sample

specifications are compared and tested.

Since bankruptcies are rare events, taking the entire population of companies would translate intoincreased data collection costs, but a decreased informational content (since extra observations will

mostly represent companies which survived) - and thus potentially weaker results. As a solution to

this issue, but also in order to reduce the omitted variable bias, first default prediction studies used

matched samples for survivors (Altman, 1968; Beaver,1966).

Later on, this method was sharply criticized for inducing further biases, while not being representative

for the entire population (and therefore lacking validity). Zmijevski(1984) was the first to address

matching issues, while Palepu (1986) has a thorough discussion of the biases introduced by non-

random sampling, referring this time to M&A literature:

“First, the use of non-random samples in the model estimation stage, without appropriate

modifications to the estimators, leads to inconsistent and biased estimates.. This results in overstating

the model’s ability to predict. Second, the use of nonrandom samples in prediction tests leads to error

rate estimates that fail to represent the model’s performance in the population.”

13 This was already mentioned in the introduction; typically in this category of studies, there is a variable capturing the“age” of a company in a certain rating group 14

457 companies across 100 financial quarters


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As stated in Part 2, logistic regression should alleviate the choice-based sampling biases. However,

according to Cram et al (2009), there are other types of biases arising whenever a representative

sample can’t be used.

Since neither using the entire population of rated companies nor random sampling is possible in this papers’ research context15, the final regression will be tested on samples based on different choices of

the “control” group16

- in order to address and compare the potential biases.

The main specification – which will be referred to as sample (1) – will include a “0” observation for

each control company with available data, following the same time distribution as the downgrades (see

fig.1C). Therefore, this model will include distinct companies, with time-matched observations17.

While this sampling choice is random to some extent and arguable representative for the “fallen -

angels” to “investment only” population18

, the fact that exact year matching is not possible, as well

as the limited number of observations might affect the outcome.

For this reason, I choose two other samples to test the robustness of the outcome.

The second model (2) is using a balanced sample, resulted after matching by time, country sovereign

rating and industry19

. Hence, the number of observations is even lower, while, even in this case, exact

matching is not possible - thus the coefficients might be biased as well.

The final specification (3) takes into account the full sample, including same-company observations.Consistent with Ohlson (1980), logistic regression should lead to unbiased estimates for this

specification. However, due to its construction, our sample will not be comparable with Ohlson’s.

Firstly, there is a large amount of same-company observations, which means that the time variation of

accounting variables wouldn’t be extremely high across the same company, while the “treated” group

should have much less influence on the outcome 20. Secondly, since the ratio of fallen angels to

investment companies is 1:3, this sample is not representative for the entire population. Nevertheless,

this specification is merely used as a robustness check, providing a strong argument for the variable

choice if the results are significant in this context as well.

The summary statistics for the variables used in the final logistic model, as well as the number of

observations with available data are shown in Table. 2.

15 Treated and control group are selected based on the outcome, therefore there is at least a choice-based bias

16 Investment-graded companies which were never downgraded to speculative.

17 e.g. in 2003 there are 20 downgrade-observations, which means that 20 non-downgrade observations from 2003 will be

selected from the company year-observations; however, due to data scarcity, exact matching is not possible. 18

According to Moody’s Investor Sevice, 2013, the average ratio is approx. 1:3 during 30 year period

19 The non-downgrade observations are all different from model (1)20

However, if there is a very strong difference between downgraded and non-downgraded companies, the results should

be robust in a full sample specification context as well – which we use therefore only as a robustness check.


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5.Explanatory variables

Previous literature on credit rating prediction shows that the models and factors used in default

prediction perform very well for credit ratings as well, which is not surprising. Therefore, this study

will follow the same pattern, taking also into account more recent studies focusing on credit ratings

21

.We consider three types of factors: firm-specific (accounting), industry-specific and country/

macroeconomic . The financial variables were collected from Datastream, and the macroeconomic

factors from OECD, both on quarterly and annual basis, resulting in 39 variables for each frequency.

There are several reasons why we consider quarterly data as more accurate for the purpose of our

investigation - however, for robustness purposes, we also collect annual data.

One first argument relates to the “timeliness” of the rating process, or , otherwise said, to the extent to

which ratings are “point-in-time” or “through-the-cycle” (Allen & Saunders, 2001). Although CRA’s

claim to update their ratings quarterly or on upon the receipt of new information for old bonds22

(

Ederington , 1985), there has been some evidence that these updates are less thorough (Ederington &

Yawitz, 1998). Further, Blume et al (1998) find that the CRA’s became more strict with overall rating

process recently, while Johnson(2003) suggests that S&P23

ratings are more timely for investment –

to-speculative downgrades. On the other hand , as mentioned in the introduction , the rating

announcement literature seems to support immediate effects on bond returns (with several exceptions),

which means that ratings have an informational content which is not available to investors, and which

is transmitted in a timely manner. Lastly, since the empirical evidence is rather mixed, and timeliness

seems to be the result of a tradeoff between stability and accuracy, finding the correct lag in the

prediction model becomes extremely difficult. However, since most studies employ annual financial

data, the practical approach – to use the financial information from former financial year – means that

the considered lag is of roughly one year (Hwang et al., 2010).

However, since most interim reports are unaudited, the questionable reliability of quarterly data lead to

little empirical evidence: Baldwin&Gezel (1992) are among the few who use an extensive sample withsuch data, and find support for its superiority compared with annual data for default prediction.

Moreover, in the context of credit ratings, higher observation frequency becomes even more

important, given that CRAs sometimes make drastic rating changes throughout a year 24

.

21 Dimitras et al (1996) and Belovari et al (2001) provide extensive reviews of the factors used in bankruptcy prediction ,

while Cram et al (2009), Huang et al (2010); Figliweski et al (2012) refer to factors used for ratings.22

For newly issued bonds the rating is simultaneous; however, this situation is ruled through the coding of our dummy

variable23

this study refers in particular to S&P data)

24 In our sample, 10% of the fallen angels were “bouncing” the investment-speculative boundary more than once in a years’

time


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Therefore, quarterly financial data was preferred and consequently collected from Datastream. Since

the quarterly in interim data was indeed more scarce compared to the annual one, the corresponding

annual values were also collected, but the query type remained quarterly, as well as the coding of the

dependent variable. Non-calendaristic financial quarters were matched with the rating dummy and the

macroeconomic variables, by converting them to a calendaristic order (as shown in appendix 4)

Following both the rating changes and the default prediction literature, the 31 firm-specific variables

are divided into 6 groups: size, profitability, leverage, cash flow adequacy, operating efficiency , and

liquidity. Each group contains several ratios which are correlated, since they have been used as proxy

for the aforementioned measures.

Most variables are normalized by total assets, while the size variables are normalized by using the

logarithm. One particular ratio is problematic according to the previous literature : interest coverage.

Indeed, the variable is not normally distributed, having quite a few extreme values. For addressing this

issue, I test the quadratic value of the ratio, as well as its conversion to a piecewise linear function

following Blume et al(1998) - however, the statistics and output for the converted ratio are not

reported, since they lack significance for all specifications.

Size is considered to be the single most significant ratio in the context of credit ratings (Blume,1998),

being always included both in rating change and default prediction models. Bigger companies are

considered less risky, with more stable cash flows – therefore the probability of downgrade should be

negatively related to size.

High profitability, cash flow adequacy, operating efficiency and liquidity are all indicators of a

healthy, revenue-generating company- thus these measures should be also negatively correlated with

the probability of downgrade.

Leverage and interest coverage ratios are indicators of financial distress – thus they should be

positively related to the downgrade variable.

Since the sample spans 39 countries and 25 years, it is important to include macroeconomic variables.

Empirical evidence shows that both defaults and credit ratings are procyclical (Allen & Saunders,2003); however, since CRA’s claim to rate “through the cycle”, while there is an obvious cyclicality

in firms’ financials, the extent to which ratings are - or should be – dependent on the firms’ external

environment is a still debated topic (Nickel et all, 2000).

Three of the country-specific variables relate to economic growth. While measures of economic

growth are commonly used in related literature, the impact on rating downgrades is not very

straightforward - firstly because of the arguments formerly mentioned, secondly because , usually, the

time when the economy grows fastest, is also the time when there is a lot of slack – typically right

after a crisis period. Therefore, even if GDP growth is considered as a candidate for the final model -


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consistently with previous literature, we argue that leading indicators would be more appropriate.

Furthermore, using such indicators makes more sense when developing a model for prediction

purposes.

Thus, apart from GDP, two other variables are considered: output gap - which represents the

difference between the potential and realized GDP, and consumer leading indicator – which is an

aggregated measure of consumer sentiment calculated by OECD 25. Consistent with the literature

supporting the procyclicality of credit ratings, we would expect that these measures are negatively

correlated with the probability of downgrade.

Other factors which could im pact ratings’ relate to the financial markets. Here,I choose as well two

leading indicators: aggregated returns for all stock’s traded in the “national” stock market, and the

yield spread - which represents the difference between the long and short term interest rates). In a

rational market, we would expect that a market with volatile stocks would also have more volatile

bond returns , which means lower ratings. Ceteris paribus, long term instruments would be preferred

over the short term ones in a risky market.

Lastly, the sovereign credit rating is taken into account, since the rating of the domicile countries of

firms is an important threshold in the rating methodology - however, since we have observations only

for investment-graded sovereigns, it is very probably that this variable lacks any explanatory power.

Since most macroeconomic variables are considered leading indicators, there is no need to lag the

data. Furthermore, it is very difficult to decide on a specific lag. However, we test a weighted-average

lagged sum following Figliewski et al (2011) for GDP, output gap and yield spread - but the results

are not significant.

Furthermore, since there is strong empirical evidence for industry effects (see Chava&Jarrow, 2001,

for a more detailed discussion), an industry dummy based on GICS sector classification26

is used in

the “baseline” model. Alternatively, a ranking based on industry’s sensitivity to external shocks27

is

tested as well.

The variable description, as well as references to studies using them, can be found in table 1.

25 http://www.oecd.org/std/leading-indicators/41629509.pdf

26 http://www.msci.com/resources/factsheets/MSCI_Global_Industry_Classification_Standard.pdf , the industry group

distribution is shown in appendix 3B; the standard SIC classification used in related literature would generate more groupswith fewer observations, which is why GICS general group classification is used instead27

Variable constructed using Gaguin (2000)’s classification, pg.25

http://www.oecd.org/std/leading-indicators/41629509.pdfhttp://www.oecd.org/std/leading-indicators/41629509.pdfhttp://www.oecd.org/std/leading-indicators/41629509.pdfhttp://www.msci.com/resources/factsheets/MSCI_Global_Industry_Classification_Standard.pdfhttp://www.msci.com/resources/factsheets/MSCI_Global_Industry_Classification_Standard.pdfhttp://www.msci.com/resources/factsheets/MSCI_Global_Industry_Classification_Standard.pdfhttp://www.msci.com/resources/factsheets/MSCI_Global_Industry_Classification_Standard.pdfhttp://www.oecd.org/std/leading-indicators/41629509.pdf


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6.Univariate analysis and variable selection

The descriptive statistics and selection procedures’ outcome for all variables except the industry

dummy are shown in table 1.

The summary statistics for the downgrade and the non-downgrade groups 28 show significant

differences for most variables, consistent with our initial assumptions.

Due to data restrictions and multicolinearity issues, I follow the literature by performing a variable

filtering. The most common approach involves various statistical techniques (such as stepwise

regression), which are however flawed by the fact that the selection is done solely based on best

fit/significance. To overcome this potential pitfall, I have used a “manual” stepwise approach, by

choosing the variable(s) within each (sub)group that is significantly different between the 2 groups

(according to the mean t-test) and performs best when being added to the regression.29

The selection

criteria are based on the following statistics:

- McFadden R-squared: similarly to the R-squared in OLS, it measures the fit of the model;

however, it is not directly comparable with its OLS counterparty;

- Akaike information criterion: provides a measure of relative quality of the model, dealing with

the trade-off between goodness of fit and model complexity; the lower the AIC, the better the

model;

- LR statistics is the equivalent of regression F-stat for OLS, representing the overallsignificance of the model; thus, higher LR value is desirable (which is equivalent to lower p

values)

- Standard error of the regression;

- P-value of the added coefficient.

After all these filtering steps, the final model includes 6 factors, and the final regression is thus:

Downgrade_dummy = c + industry dummy + log(mv) + ebita/ta + td/ta + sales/(ta-ca) + spread

The variables we chose as proxy for liquidity and cash flow adequacy did not improve any of the

aforementioned statistic tests, and were therefore excluded from the model.

28 Unless otherwise specified, the sample used is (1), i.e. where the non-downgraded group is matched solely by time.

29 The “baseline” model for our stepwise procedure is a logistic regression including only the intercept and industry effects;to the baseline model we add each of the significant variables from a subgroup, and we keep the one that performs best

according to 5 different statistics.


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7. Logistic model estimates

As argumented in section 4, the main specification is considered the time-matched sample (1). The

results are compared with the fully matched sample(2) and full sample(3), which have been discussed

in section

The regression output for all samples is shown below.

Table 3. Regression estimates - quarterly data

Sample Time matched sample(1) Fully matched sample(2) Full sample(3)

Obs with Dep=0 224 112 6332

Obs with Dep=1 106 106 106

Variables

LOG(MV) -0,801 -0,749 -0,824

(0,114)*** (0,151)*** (0,098)***

EBITA/TA -10,114 -9,598 -9,664

(4,350)** (3,258)*** (1,331)***

TD/TA 3,062 2,921 3,824

(1,060)** (1,319)** (1,223)**

SALES/(TA-CA) -0,612 -0,663 -0,680

(0,144)*** (0,128)*** (0,137)***

SPREAD 0,311 0,231 0,231

(0,110)** (0,131)* (0,076)**

Industry effects Yes*** Yes*** Yes***

Regression tests

McFadden R 0,357 0,321 0,262

Akaike 0,893 1,069 0,128

LR statistic 146,753*** 96,906*** 283,716***

S.E.of regression 0,361 0,408 0,118

The variables are selected according to the stepwise procedure described in section .. *** denotes 1%

significance level, ** 5% and * 10%. The standard errors are homoscedasticity-adjusted using White

method


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The coefficients are all significant and robust across different specifications. From a statistical

perspective, the full sample model seems to be superior - which is not surprising given a much larger

number of observations. Additionally, the fact that all variables in (3) are significant given the highly

unbalanced nature of the sample, is a proof of their strong explanatory power 30

. However, as discussed

in section 4, from an economic standpoint, using sample (3) for estimating the model has serious

drawbacks. Consistent with Zmijevski (1984) and Palepu(1986), sample (2) is weaker in comparison

to (1) -confirming our hypothesis that the time-matched sample should lead to the best outcome.

More important than the statistical quality of the model is, of course, its economic significance. First,

the coefficient signs are as expected, confirming our original hypotheses. One further aspect is the

magnitude of the coefficients - which is not as straightforward as in OLS models. Therefore, in order

to compare the magnitude of different factors, we computed below the marginal effects for model (1).

Table 4. Economic magnitude.

30 The outcome is also consistent with Ohlson (1980), who argues that logistic regression should perform well

even for highly unbalanced samples

Variables

Marginal

effects

LOG(MV) -0,18905

EBITA/TA -2,38719

TD/TA 0,722621

SALES/(TA-CA) -0,14437

SPREAD 0,073403

Basic Materials 0,976603

Consumer Goods 1,089732

Consumer Services 1,228993

Health Care 0,757763

Industrials 0,927857

Oil & Gas 1,052082

Technology 1,311143

Telecommunications 1,32375

The marginal effects are calculated

using the mean values of the variables.


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Consistent with previous literature both focused on rating changes and default prediction, profitability

has the highest impact relative to the other factors. More specifically, according to our model, an

increase of one in ebita/ta will lead to a decrease of 2,38 % in the probability of downgrade to

speculative grade.

Interestingly, the magnitude of all industry coefficients is much higher than the spread, meaning that

the industry matters more than the business cycle conditions. However, given that some industries are

procyclical as well, drawing a conclusion in this context can be difficult.

Lastly, since related literature employed ordered probit for the analysis of entire rating scale (while

taking into account the full period in a certain rating scale, as formerly discussed), a direct comparison

of the models is not possible.

8.Predictive power

In contrast with other rating prediction models, this paper proposes a cutoff score based on the sample

distribution, following the arguments discussed in section (2).

By comparing the sample-specific cutoff scores with the range of cutoffs for the 3 samples, I find

evidence that choosing such a cutoff minimizes indeed total error rates.

Below, the trade-off between the 2 errors is shown for the cutoff range for model (1):

0

0,1

0,2

0,3

0,4

0,5

0,6

0 0,2 0,4 0,6 0,8 1

Error trade-offType II

Type I


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However, as type I is more costly for the market participants31, an optimal cutoff score in this case

won’t be the one minimizing the total error, but just the type I.32

Since determining the optimal cutoff

score is practically impossible since the costs of the 2 errors should be known (the method is presentedin Hsieh, 1993), we acknowledge this issue as a limitation , but focus on discussion the type I error

rates. However, the cutoff scores we use correspond as well to type I error values which are close to

the minimal ones.

As shown in table 6, the holdout sample classification tables reveal that model (1) performs best

compared to the other specifications, with type 1 error of 22% and an average error rate of 17%.

9. Further robustness checks

We argued before that quarterly observations could lead to a better explanatory and predictive

performance - given that the rating process is timely enough.

However, doubts can be cast with regards to the quality of the interim financial reports, which are

often unaudited . Furthermore, if performance compensations are set on a longer

timeframe (typically one year), there is a higher incentive to “pump” the financial reports by the end

of the financial years.

Lastly, interim data is not available for all years and companies, which lead to a further decrease in our

sample size. For these reasons, we re-estimate our model and out-of-sample classification tables with

annual data.(table 5 and 6)

Consistent with initial assumptions, quarterly data performs slightly better both in terms of

explanatory and predictive power, but the estimates using annual data are not significantly different,

confirming the robustness of the model.

31 Type I error’s cost can be considered as, for example, the opportunity cost of holding a long position in a security whichwas downgraded to speculative grade32

Minimizing one of the errors is done at the expense of maximizing the other


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The variables represent annual values. Industry effects coefficients are not reported for brevity. However, they

are similar to the ones in the quarterly regression. *** denotes 1% significance level, ** 5% and * 10%. The

standard errors are homoscedasticity-adjusted using White method. Overall, the coefficients and their

significance are similar to the quarterly regressions, having slightly lower standard errors. The regression

statistics show a slightly poorer fit and information criteria, compared to the quarterly observations.

Table 6. Out-of-sample error rates

frequency quarterly observations annual observations

sample Smpl(1) Smpl(2) Smpl(3) Smpl(1) Smpl(2) Smpl(3)

cutoff 32% 47% 0,17% 34% 49% 0,16%

type I 22% 25% 25% 21% 27% 27%

type II 11% 20% 18% 15% 20% 22%

average 17% 23% 22% 18% 24% 25%

Smpl (1), (2), (3) represent the 3 different specifications based on different choices of treatment groups – we

repeat the same sampling technique for annual observations as well. The cutoff score is obtained by dividing

the “1” observations to the total sample.

Table 5. Regression estimates - annual data

Sample Time matched sample(1) Fully matched sample(2) Fullsample(3)

Obs with Dep=0 229 114 7269

Obs with Dep=1 122 122 1022

LOG(MV) -0,779 -0,746 -0,875

(0,108)*** (0,152)*** (0,095)***

EBITA/TA -11,115 -10,734 -9,496

(4,517)** (3,579)*** (1,159)***

TD/TA 2,488 2,799 3,442

(1,063)** (1,278)** (1,159)**

SALES/(TA-CA) -0,171 -0,188 -0,164

(0,110)*** (0,034)*** (0,016)***

SPREAD 0,266 0,174 0,143

(0,110)** (-0,125) (0,076)**

INDUSTRY EFFECT Yes*** Yes*** Yes***

McFadden R 2 0,347 0,314 0,254

Akaike 0,922 1,068 0,127

LR statistic 157,55*** 102,744*** 316,533***



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Given the nature of the dataset, and the small sample size (which doesn’t allow a large number of

variables), omitted variable bias is an important concern.

Since we cannot possible account for all the potential omitted variables, we consider using fixed

effects as a reasonable alternative. However, given the unbalanced nature of our dataset and the non-

linear estimation model, using FE with our model would generate an error.

Therefore, we estimate an OLS model using the same variables, and by adding year fixed effects. For

brevity, we report only the results for model (1) regression with quarterly data.

Lastly, a small sample is particularly sensitive to outliers. We run all regressions without several

observations which we label as outliers according to the interquartile range rule33 and we don’t find

significant differences.

Although OLS and logit coefficients cannot be compared directly, the results are comparable in terms

of signs and relative magnitude. Therefore, in spite of potential omitted variables, the results are

consistent.

10.Conclusions and further research

By focusing only on fallen angels and the quarter immediately following their downgrade, this paper

shows that a parsimonious logistic model performs well both in terms of explanatory and predictive

power. All our variables are significantly both statistically and economically, and robust throughout

different specifications, while the holdout prediction accuracy for type I error ranges between 78% and

75%.

This paper can be considered as a prerequisite for a further research on issuer rating prediction in

general, and “fallen angels” in particular. Through the impact of rating changes, especially around the

aforementioned boundary, all stakeholders involved in the credit market should benefit from such a

research.

A follow-up study could analyze the presumed asymmetry between downgrades and upgrades aroundinvestment-to-speculative grade boundary, at a variable level (i.e. using factor analysis to investigate

whether the extent to which the two rating changes are influenced by different factors). The same

rationale could be extended further to analizing the entire rating spectrum (e.g. showing that different

variables are more relevant for certain rating groups only).

33 Rule of thumb which considers as an outlier observations outside the “median” quartile range

(Q 1 - 1,5* IQR; Q 3 + 1,5* IQR), where IQR= Q 3 – Q 1 , and Q are the quartiles.


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Table 7. Robustness tests for outliers and omitted variables

Specification Model(1) Model(1) excluding outliers OLS with fixed effects

Obs with Dep=0 224 223 224

Obs with Dep=1 106 105 106

Variables

LOG(MV) -0,801 -0,789 -0,107

(0,114)*** (0,115)*** (0,016)***

EBITA/TA -10,114 -10 -0,903

(4,350)** (4,395)** (0,197)***

TD/TA 3,062 3,044 0,394

(1,060)** (1,082)** (0,166)**

SALES/(TA-CA) -0,612 -0,6 -0,078

(0,144)*** (0,145)*** (0,020)***

SPREAD 0,311 0,365 0,009

(0,110)** (0,162)** -0,025

Industry effect Yes*** Yes*** Yes***

Time effect No No Yes***

Regression tests

McFadden R 0,357 0,358 0,496

Akaike 0,893 0,889 1,017

LR statistic 146,753*** 147,439*** -


First column represents the main regression output - the same as in first column of table..2nd column shows the

regression output without 2 observations which were considered as outliers according to the quantile thumb

rule. Last column is an OLS estimation including year fixed effects- thus, the coefficients and the test statistics

from 3rd column are not directly comparable with the other estimations.


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Ruey-Ching, K. F. Cheng, Cheng-Few Lee, On multiple-class prediction of issuer credit

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Web Resources:

http://www.bis.org/statistics/index.htm

http://www.msci.com/resources/factsheets/MSCI_Global_Industry_Classification_Standard.pdf

http://www.oecd.org/std/leading-indicators/41629509.pdf

http://www.sec.gov/spotlight/xbrl/nrsro-implementation-guide.shtml

http://www.sec.gov/spotlight/dodd-frank/creditratingagencies.shtml

http://www.moodys.com/ratings-process/Ratings-Definitions/002002

http://www.msci.com/resources/factsheets/MSCI_Global_Industry_Classification_Standard.pdf


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Table 1. Initial variable set - descriptives and selection

measure variable descriptionexpected

signObs(1) obs(0) Mean(1) Mean(0)

Std,

Dev,(1)

Std,

Dev,(0)p(mean) Rsq Akaike LR SE p(coeff)

Size

Altman(1968)

Shumway(2001)

Blume(1998)

log(ta) log(total assets) - 122 272 15,550 15,962 1,207 1,291 0,003 0,07 1,20 34,42 0,45 0,00

log(sales) log(sales) - 124 270 13,853 14,295 1,323 1,412 0,003 0,09 1,19 43,83 0,44 0,00

log(mv) log(market value) - 141 260 7,557 8,648 1,652 1,492 0,000 0,17 1,13 88,18 0,43 0,00

Profitability

Altman&Kao(1977),

Ohlson(1981),

Hwang et al(2010)

ni/ta net income/total assets - 117 258 -0,030 0,046 0,147 0,068 0,000 0,24 1,01 101,39 0,39 0,04

oi/ta operating income/total assets - 121 267 0,005 0,022 0,029 0,054 0,000 0,20 1,05 85,66 0,40 0,08

re/ta retained earnings/total assets - 103 224 0,171 0,202 0,254 0,225 0,300

ebit/ta earnings before interest and taxes/total assets - 117 262 -0,011 0,021 0,073 0,055 0,000 0,21 1,04 88,11 0,40 0,00

ebita/ta earnings before interest, taxes and amortization/ta - 117 254 0,050 0,135 0,149 0,093 0,000 0,26 0,99 111,70 0,38 0,00

Leverage

Beaver (1966),

Platt&Platt(1990)

Amato&Furfine(2004)

td/ta total debt/total assets + 122 272 0,359 0,304 0,160 0,133 0,001 0,29 0,96 121,82 0,38 0,00

tl/ta total liabilities/total assets + 122 272 0,654 0,616 0,161 0,143 0,027 0,27 0,98 114,16 0,38 0,03

nd/ta net debt/total assets + 121 271 0,275 0,239 0,188 0,166 0,069

ni/(ta-tl) net income/equity book - 116 258 -0,253 0,175 1,373 0,819 0,002 0,26 0,99 111,95 0,39 0,07

oi/(ta-tl) operating income/equity book - 120 267 0,017 0,080 0,147 0,329 0,011 0,26 1,00 110,52 0,38 0,09

td/mv market value of equity/total debt + 111 250 3244 3902 16634 35509 0,811

tl/mv market value of equity/total liabilities + 111 250 5461 9326 26720 90284 0,537

nd/mv market value of equity/net debt + 110 249 2753 3645 14753 34735 0,733

ic_a interest coverage + 135 255 -9,030 7,210 144,944 10,104 0,196

CF adequacy

Beaver(1966)

Altman&Katz(1974)

Hwang et al(2010)

ncf/ta net cash flow/total assets - 117 257 0,073 0,104 0,053 0,070 0,000 0,29 0,96 125,10 0,37 0,32

(ncf+capex)/ta (net cash flow+capital expenditures)/total assets - 115 252 0,121 0,169 0,080 0,097 0,000 0,30 0,95 124,07 0,37 0,33

ncf/td net cash flow/total debt - 117 257 1,107 1,733 8,407 13,597 0,587

ncf/tl net cash flow/total liabilities - 117 257 0,124 0,184 0,107 0,142 0,000 0,29 0,97 122,3 0,38 0,72

ncf/Nd net cash flow/net debt - 116 256 0,274 1,747 1,672 13,526 0,089

ncf/sales net cash flow/sales - 119 255 0,463 0,663 0,463 0,528 0,000 0,30 0,96 125,20 0,38 0,45

Liquidity

Altman(1977)

Shumway(2000)Blume(1998)

ca/cl current ratio - 120 272 1,564 1,364 0,962 0,714 0,044 0,28 0,97 119,44 0,38 0,10

(ca-cl)/ta working capital/total assets + 120 272 0,099 0,071 0,171 0,129 0,113

(cash+ar)/cl quick ratio (cash + accounts receivable/current liabilities) - 75 195 0,872 0,851 0,624 0,653 0,806ca/ta current assets/total assets - 122 272 0,337 0,309 0,191 0,195 0,188

cl/ta current liabilities/total assets - 120 272 0,239 0,238 0,119 0,135 0,914

operating efficiency

Altman(1968)

Laitinen(1992)

Hwang et al(2010)

sales/(ta-ca) sales/fixed assets - 122 270 0,452 0,551 0,485 1,306 0,000 0,33 0,92 140,14 0,37 0,00

sales/ta sales/total assets - 122 270 0,237 0,246 0,176 0,201 0,649

ca/sales current assets/sales - 122 270 1,763 1,487 1,284 0,821 0,031 0,30 0,96 125,55 0,38 0,15

macro variables

Chava&Jarrow(2001)

Allen&Saunders(2000)

Amato&Furfine(2004)

spread spread between 10 year treasuries and 3-month bills + 157 274 -1,132 -1,676 1,362 1,506 0,000 0,36 0,89 147,75 0,36 0,00

gdp real gross domestic product growth (seasonally adj.) - 157 273 0,285 0,484 1,163 0,856 0,062

og output gap - 157 276 -1,027 -1,643 2,488 2,837 0,020 0,34 0,92 138,63 0,37 0,32

si Total stock market index return - 156 275 2,931 -1,124 8,517 9,539 0,000 0,35 0,91 141,41 0,37 0,01

cli confidence leading indicator(consumer sentiment) - 156 274 99,454 100,15 1,450 1,396 0,000 0,34 0,91 139,92 0,37 0,01

sovereign sovereign credit rating - 149 275 9,779 9,687 0,743 0,738 0,228industry effects industry_risk* 1,2,3 ranking made by S&P according to industry risk + 161 284 1,739 1,919 0,905 0,961 0,079

First part of the table represents descriptive statistics for the downgrade (group 1, “treated” ) versus non-downgrade (group 0, “control” ) groups, for the time matched sample – model 1 . All the variables except the industry dummy are included. P(mean) is the t-test for equal means between the

treated and control groups ; the last 5 columns show various tests and statistics obtained when adding each of the variables to the model including the intercept and industry dummy: mcfadden r-squared, akaike information criterion , maximum likelihood, standard error of regression and

coefficient p values. Below the variable groups there are references to the models which use either of the group variables; last reference for each group refers to a rating change model, while the first ones refer to default prediction; the references for macro variables include all industry effects.


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Quarterly data

Variables DOWNG log (MV) EBITA/T TD/TA SALES/(T SPREAD DOWNGlog (MV) EBITA/T TD/TA SALES/(T SPREAD DOWNGlog (MV) EBITA/T TD/TA SALES/( SPREA

Mean 0,321 8,341 0,107 0 ,319 0,547 -1,455 0,486 8,040 0,090 0,327 0,539 -1,070 0,016 8,573 0,139 0 ,300 0 ,537 -1,448

Median 0,000 8,452 0,111 0,311 0,309 -1,510 0,000 8,236 0,106 0,325 0,315 -0,800 0,000 8,719 0,127 0,294 0,299 -1,520

Maximum 1,000 11,929 0,435 0,844 17,666 8,230 1,000 10,952 0,414 0,844 19,291 2,220 1,000 12,749 0,669 0,895 23,678 11,970

Minimum 0,000 0,223 -0,769 0,001 0,041 -3,820 0,000 -0,083 -0,769 0,001 0,023 -4,220 0,000 -0,083 -0,769 0,000 0,015 -9,660

Q-baseline:obs.nr

Mean 0,323 8,333 0,098 0 ,320 0,600 -1,427 0,470 8,019 0,080 0,329 0,610 -1,080 0,017 8,580 0,138 0 ,300 0 ,544 -1,418

Median 0,000 8,444 0,108 0,311 0,322 -1,480 0,000 8,292 0,106 0,319 0,345 -0,800 0,000 8,719 0,127 0,293 0,296 -1,490

Maximum 1,000 11,391 0,415 0,844 17,666 8,230 1,000 10,952 0,414 0,844 19,291 2,220 1,000 12,749 0,669 0,889 23,678 11,970

Minimum 0,000 0,223 -0,769 0,001 0,041 -3,820 0,000 -0,083 -0,769 0,001 0,023 -4,220 0,000 -0,083 -0,769 0,000 0,015 -8,460

Std, Dev, 0,469 1,580 0,136 0,150 1,426 1,577 0,501 1,659 0,156 0,151 1,642 1,455 0,127 1,376 0,082 0,127 1,188 1 ,481

Q-reduced:obs.nr

Annual

Variables DOWN log (MV) EBITA/T TD/TA SALES/(TSPREAD DOWN log (MV EBITA/T TD/TA SALES/( SPREAD DOWN log (MV EBITA/ TD/TA SALES/ SPREA

Mean 0,348 8,289 0,099 0 ,320 2,036 -1,451 0,517 7,982 0,082 0,325 2,045 -1,078 0,017 8,559 0,132 0 ,298 2 ,026 -1,368

Median 0,000 8,403 0,107 0,312 1,181 -1,510 1,000 8,168 0,103 0,333 1,228 -0,800 0,000 8,697 0,124 0,292 1,128 -1,350 Maximum 1,000 11,929 0,391 0,884 73,227 8,230 1,000 10,952 0,348 0,884 71,638 2,220 1,000 12,749 0,848 0,898 76,585 11,970

Minimum 0,000 0,223 -0,769 0,001 0,129 -3,820 0,000 -0,083 -0,769 0,001 0,110 -4,220 0,000 -0,386 -0,769 0,001 0,021 -9,660

Std, Dev, 0,477 1,602 0,122 0,141 4,583 1,511 0,501 1,624 0,133 0,146 4,962 1,383 0,127 1,387 0,076 0,131 4,565 1 ,506

A-baseline:obs.nr

Mean 0,345 8,289 0,091 0 ,321 2,227 -1,404 0,494 7,969 0,074 0,329 2,283 -1,058 0,016 8,566 0,132 0 ,297 2 ,035 -1,337

Median 0,000 8,411 0,106 0,304 1,228 -1,450 0,000 8,164 0,105 0,331 1,251 -0,820 0,000 8,702 0,123 0,291 1,123 -1,300

Maximum 1,000 11,391 0,355 0,884 73,227 8,230 1,000 10,952 0,348 0,884 71,638 2,220 1,000 12,749 0,848 0,898 76,585 11,970

Minimum 0,000 0,223 -0,769 0,001 0,129 -3,820 0,000 -0,083 -0,769 0,001 0,110 -4,220 0,000 -0,083 -0,769 0,001 0,021 -8,460

Std, Dev, 0,476 1,603 0,134 0,145 5,441 1,549 0,502 1,678 0,151 0,146 5,926 1,429 0,126 1,364 0,077 0,130 4,613 1 ,504

A-reduced:obs.nr

Table 2. Descriptive statistics for all specifications

The first table contains the summary statistics for the final regression variables, using quarterly data, Q-baseline: obs,nr, represents the total observation number for the

sample used for the main regression specification, Q-reduced represents a random 70% of Q-baseline, being the subsample used for estimating the model for prediction

purposes ( the predictive accuracy is tested on the remaining 30% of the sample), The second table is analogous to the first one, except for the fact that annual data is used,

229 160 4886

217 149 4238

Pa rti a l ly m atch ed s a mp le (1) F ul l y m atch ed s a mp le (2) F ul l s a mp l e(3)

351 236 7391

Pa rti a l ly m atch ed s a mp le (1) F ul l y m atch ed s a mp le (2) F ul l s a mp l e(3)

330 218 6438


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Number of defaults from 1920 to 2010 for investment and speculative graded companies. (Moody’s

Investor Service, 2010)

Appendix 2. . Rating migration rates 1970-2009

FROM/TO: Aaa Aa A Baa Ba B Caa-C Default Wr

Aaa 87.65% 8.48% 0.61% 0.01% 0.03% 0.00% 0.00% 0.00% 3.22%

Aa 1.01% 86.26% 7.82% 0.34% 0.05% 0.02% 0.01% 0.02% 4.47%

A 0.06% 2.78% 87.05% 5.21% 0.48% 0.09% 0.03% 0.05% 4.24%

Baa 0.04% 0.19% 4.65% 84.41% 4.20% 0.79% 0.20% 0.17% 5.35%

Ba 0.01% 0.06% 0.38% 5.66% 75.74% 7.25% 0.61% 1.13% 9.16%

B 0.01% 0.04% 0.13% 0.35% 4.81% 73.52% 6.35% 4.37% 10.42%

Caa-C 0.00% 0.02% 0.02% 0.14% 0.42% 7.52% 62.40% 16.68% 12.80%

Figure 2. Average One-Year Broad Rating Migration Rates, 1970 -2009(Moody's investor report, Special

Comment); highlighted percentage represents the "fallen angels" from 1970 to 2009

0

50

100

150

200

250

300

Appendix 1. Number of defaults 1920-2010

Investment

speculative


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A.Country distribution

SWEDENAUSTRALIA

FINLAND

GERMANY

NORWAY

MEXICO

FRANCE

UK

JAPAN

CANADA

USA

OTHER

B.Industry distribution

BASIC

MATERIALS

CONSUMER

GOODS

CONSUMER

SERVICES

HEALTH CARE

INDUSTRIALS

TECHNOLOGY

TELECOMMUNI

CATIONS

UTILITIES

0

5

10

15

20

25

1980 1985 1990 1995 2000 2005 2010 2015

C.Downgrades to speculative grade

DOWNGRADES

Appendix 3.General sample characteristics

Figure A represents the vountry distribution for the initial sample of 450 companies. The total

number of countries is 37,however the figure shows only the countries with more than 5 companies in

the sample; the rest of the countries are aggregated as “other”. Figure B shows the General Industry

Classification Standard (GICS) industry distribution across the initial sample. Figure C represents the

time distribution of the number of downgrades to speculative grade during the analyzed period for

the sample companies.


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Appendix 4. Dependent variable coding

type of companies fallen angel ("treated" group) investment-grade ("control" group)

date converted quarter rating current paper other research rating current paper other research

2009-09-05 q3 2009 A- n/a n/a AA n/a n/a

q4 2009 n/a n/a n/a n/a

2010-01-30 q1 2010 BBB n/a 0 A- n/a n/a

q2 2010 n/a 0 n/a n/a

q3 2010 n/a 0 n/a n/a

2010-12-25 q4 2010 BBB- n/a 0 BBB n/a n/a

q1 2011 n/a 0 0 0

2011-06-20 q2 2011 BB n/a 1 0 0

q3 2011 1 1 0 0

q4 2011 n/a 1 0 0

2012-01-15 q1 2012 B n/a n/a 0 0

The "date" and the "rating" columns represent raw data for the rating changes. The dates were converted to financial quarters, adjusting for non-

callendaristic quarters and matching with sovereigns financial year ends. For simplification, in this example the same dates and calendaristic

financial year ends are assumed for the 2 companies. The fallen angels receive "1" only for the quarter following the downgrade - as opposed to

other studies, which take the entire BB period into account. The investment-grade companies are the ones which were never downgraded to

speculative during the period of this study. They get a "0" during the entire period of BBB rating, except for the first BBB quarter. The one quarter

delay is consistent with the fact that it is non-realistic to assume that companies are downgraded exactly when their financials start to deteriorate.

are fallen angels special

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