aSuleman Dawood School of Business (SDSB), Lahore University of Management Sciences (LUMS),

Opposite Sector U, DHA., Lahore 54792, Pakistan. Email: 14080001@lums.edu.pk bSuleman Dawood School of Business (SDSB), Lahore University of Management Sciences (LUMS),

Opposite Sector U, DHA., Lahore 54792, Pakistan. Email: tanveer.shehzad@lums.edu.pk cCollege of Business, Zayed University, United Arab Emirates. Email: zaghum.umar@zu.ac.ae

*Corresponding author: Yasir Riaz, Suleman Dawood School of Business (SDSB), Lahore University of

Management Sciences (LUMS), Opposite Sector U, DHA., Lahore 54792, Pakistan; Email:

14080001@lums.edu.pk.

Pro-cyclical Effect of Sovereign Rating Changes on Stock Returns: A Fact or Factoid?

Yasir Riaza *, Choudhry Tanveer Shehzada, Zaghum Umarb

This Version: December 18, 2017.

Abstract

This paper examines the effect of changes in sovereign credit ratings and their outlook on the stock

market returns of European countries at different phases of business cycle. Using standard four

factors model, study records a significant average marginal effect of credit rating announcements

on stock market returns. Both magnitude and significance of the effect varies with business cycle

and across announcement types. However, we do not find evidence of pro-cyclical effect of

sovereign rating and outlook changes on stock returns. Our results show that stock markets react

more negatively to rating downgrades in recovery phases and more positively to rating upgrades

in contractionary period. Both results are statistically significant and robust to various sensitivity

tests.

JEL Classifications: C23, E44, F30, G15

Keywords: Sovereign Ratings, Stock Returns, Business cycle, and Asset pricing

2

1. Introduction

European debt and subprime mortgage crises have highlighted the critical debate on the role of

credit rating agencies in financial markets. Sovereign credit ratings are observed to have a

significant impact on financial markets and influence private firm ratings in both developing and

developed economies (Poon et al., 2017; Borensztein et al., 2013; Almeida et al., 2016; Banerjee

et al. 2016 and Williams et al., 2013). This influence is primarily induced by country ceilings on

sovereign ratings applied by the Credit Rating Agencies (CRAs) on private sector ratings (Weigel

and Gemmill, 2006 and Purda, 2008) and through foreign exchange rate channel1 (Alsakka and ap

Gwilym, 2013). The impact of sovereign ratings and outlook changes is asymmetric on the

financial markets. Downgrades generally have a negatively significant effect; while upgrades have

been reported to have an insignificant effect on the stock markets returns (Ferreira and Gama,

2007). Afonso et al. (2014) shows sovereign rating announcements have a comparable impact on

the volatilities of both equity and debt markets. Additionally, there is evidence on pro cyclical

(Ferri et al., 1999 and Kaminsky and Schmukler, 2002), counter cyclical (Bar-Isaac and Shapiro,

2013) and sticky (Mora, 2006) nature of sovereign ratings. Broto and Molina (2016) finds

downgrades procyclical and upgrades sticky in nature. The existing literature focus either on the

impact of sovereign ratings and outlook changes on stock markets or on the cyclical nature of the

sovereign ratings. However, to the best of our knowledge the issue of conjoint effect of these two

interconnected problems is yet to be documented. Therefore, this study attempts to address the

issue of conjoint effect of sovereign ratings and outlook changes on the stock markets.

1 Alsaka and ap Gwilym (2012a) and Brooks et al. (2004) show the significant effect of sovereign

rating on the foreign exchange rates, its spillover effect on economies and its impact on the global

firm competitiveness.

3

The main contribution of this study is to analyze whether changes in sovereign ratings and outlook

status have a similar effect on stock market returns at different phases of business cycle.

Understanding of this effect is critical to perceive the economic role of CRAs. For example, a

rating downgrade can lead to a self-enforcing mechanism of stock market downfall which may

lack any fundamental basis. We extend the literature on cyclic nature of sovereign ratings, such

as, Broto and Molina (2016) and investigate the impact of these cyclical downgrades and upgrades

on the stock returns over the business cycle. This paper contributes to literature in four other ways.

Firstly, this study focusses on the impact of sovereign outlook and ratings changes on the domestic

stock markets of European countries by controlling for business cycle effect. The sovereign debt

crisis of the European economies was the second major financial catastrophe that hit the financial

markets after the global financial crisis of 2007-8. In particular, the European sovereign debt crisis

put the role of CRAs under discussion as many subsequent downgrades by CRAs might have

exacerbated the crisis. Consequently, studying the European stock markets over the period that

includes both crisis and tranquil times provide an excellent testing ground to investigate proposed

effect. This is primary reason, we focus on European countries for the analysis of conditional

impact of sovereign rating and outlook changes on the stock markets over the phases of business

cycle. In a related study, Alsakka et al. (2017) also examines the European markets but for the

differences in opinions across CRAs, their influence on subsequent rating announcements and the

effect of new European rating regulatory regime on both ratings and stock markets. However, we

study the effects of sovereign rating and outlook changes on the stock market at different level of

business cycle. In addition, we provide marginal effects of sovereign rating and outlook changes

on the stock returns conditional on business cycle that are not offered in Alsakka et al. (2017).

4

Secondly, this study improves upon the technique used for the calculation of abnormal returns,

which shapes the premise of any sensible investigation of the effect of sovereign outlook and rating

changes. Previously studies calculate abnormal stock returns using global CAPM model which

uses global market excess returns as the only predictor variable. However, Fama and French (1992,

1993) showed a significant improvement over the global CAPM model by using global three

factors. Bissoondoyal-Bheenick and Brooks (2015) provides a comparison of different asset

pricing models in case of sovereign rating and outlook changes and finds that assessment of the

impact of sovereign rating changes is not sensitive to a multi-factor model specification. They used

global factors in the Fama and French model and used US factors as a proxy for these global

factors. However, it has been documented that local factors show additional improvement over

the global factors and global CAPM model in estimations of abnormal stock returns (Griffin, 2002

and Fama and French, 2012). A direct consequence of this methodological improvement is that

past studies on the association between changes in sovereign outlook and rating and financial

markets may have biased results because of biases embedded in the calculations of abnormal

returns. For instance, CAPM intercepts are found to be positive always for extreme value portfolios

and negative for extreme growth portfolios (Fama and French, 2012). Furthermore, instead of

estimating and comparing the returns across the sovereign rating announcements we directly

included the sovereign rating announcement in the Fama and French four factor model to test if it

can explain up and above the four factors. Our model is superior to Bissoondoyal-Bheenick and

Brooks (2015) as we use local factors and these factors are estimated from the local European

markets instead of using proxy factors from US or any other country. Specifically, in case of

Europe, Fama and French (2012) showed that local factors are preferred over the global factors.

Here, this paper makes two distinct contributions, first, replacing CAPM with Fama-French-

5

Carhart four factor model and second, using local factors for the estimation of abnormal returns

instead of global factors. As a result, we believe that estimates provided by this paper are more

precise and reliable as compared to previous papers.

Finally, paper provides average marginal effect at different phases of business cycle for the effect

of each type of individual rating and outlook change on the stock returns. We believe that this is

the first paper that provides average marginal effects for the changes in sovereign ratings and

outlook on the stock returns at different phases of business cycle. It offers separate estimates for

the effect of both upgrades and downgrades in both credit rating and outlook status by CRAs at

different levels of the regional European business cycle.

Using Fama-French-Carhart four factor model of Carhart (1997) and Fama and French (2012) as

a baseline procedure we estimate the effect of sovereign rating and outlook changes on the stock

market returns of the 16 European countries at different phases of business cycle. The full sample

of the study consists of 16 European states over the period of July 1, 1990 to June 30, 2016 and

includes Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy,

Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom. The credit

ratings and outlook history consists of announcements by three leading CRAs namely, Moody’s,

Fitch and Standards & Poor’s (S&P). We estimate the average marginal effects of the sovereign

rating and outlook changes to evaluate impact at different levels of business cycle. We find no

evidence on pro cyclical effect of sovereign rating and outlook changes on the stock returns;

however, the effect is positive (negative) and strong, at lower (higher) stages of business cycle for

upgrades (downgrades) for both sovereign rating, it decreases (increases) with the increasing level

of economic activity. The significance and effect size not only varies over business cycle but also

across announcement types. Furthermore, estimated average marginal effect show a significant

6

positive effect of ratings upgrades and a significant negative effect of ratings downgrades

conditional on business cycle; however, average marginal effect of both outlook upgrades and

downgrades are insignificant in our primary estimations but turn out to be significant when we

exclude crisis period in sensitivity analysis. It could be because of a large number of outlook

changes simultaneously in the markets during financial crisis which may be perceived as not

providing any additional information by the market participants. Our results show that a one notch

upgrade in sovereign ratings leads to 0.174 percent more returns on the stock market and a one

notch downgrade in sovereign ratings leads to negative 0.138 percent returns on the stock market

of rated country. The results are robust to a number of specifications. We account for the global

financial crisis, Euro currency period and the potential endogeneity by re-estimating the primary

model under Generalized Method of Moment (GMM).

The rest of the paper is structured as follows: section 2 offers a detailed literature review, section

3 describes econometric methodology, section 4 explains data collection and procedures, section

5 provides discussion on the empirical results and section 6 concludes the paper by offering a

summary and brief discussion of the results.

2. Literature Review

Traditional scope of activities performed by CRAs included information supply (Akerlof, 1970),

certification facility (White, 2010) and monitoring services (Holthausen and Leftwich, 1986) for

corporations, CRAs rate sovereign debt as well. Any change in sovereign ratings results in

significant impact on private firms, financial markets and overall economic conditions (Kaminsky

and Schmukler, 2002). Almeida et al. (2016) argues that a downgrade of sovereign debt raises the

cost of borrowing for the private sector and leads the firms to decrease investments and confidence

7

in debt markets. This is primarily identified based on the country ceilings applied by the CRAs on

the private sector ratings. An event study analysis by Kiff et al. (2012) shows that sovereign ratings

carry additional information not available publicly in the markets, and undesirable credit warnings

are taken quite seriously by the markets as exhibited by the reaction of CDS spreads. Credit rating

may also affect the bond maturity of municipal bonds (Daniels et al., 2010). Some studies also

show that changes in sovereign credit rating have a direct effect on the private sector ratings (see

e.g. Borensztein et al., 2013). Ferreira and Gama (2007) shows the cross-country impact of rating

and outlook changes on the returns of stock markets. It finds a significant 51 basis points negative

reaction to a one-notch downgrade while no significant impact of the upgrade in a information

spillover to other markets. This effect is elevated due to geographical proximity and development

status of the economies. Literature also reports an aggregate negative wealth impact of sovereign

downgrades on the stock market and currency dollar values (Brooks et al., 2004).

Kaminsky and Schmukler (2002) provides an evidence of a significant effect of sovereign rating

and outlook changes in the emerging bonds and stock markets. It also observes the presence of

cross-country contagion effect and in opaque markets a more substantial effect of outlook changes

during the crisis. It shows a pro-cyclical nature of the CRAs. However, it does not control for the

business cycle effect and controls only for monetary shocks (US interest rate). It does not

differentiate between the ratings and outlook announcements of the event country as well. Ferri et

al. (1999) studies the impact of CRAs sovereign rating announcements during the Asian crisis of

1997 and finds a pro-cyclical effect of ratings issued by CRAs aggravating the crisis. The study

proposes that more weights assigned by CRAs to their qualitative judgments rather than the

economic fundamentals are one of the main reasons for the pro-cyclical nature of the sovereign

ratings. These ratings adjustments may act as trigger that exacerbates market instability during

8

times of turmoil and their effect is more severe in more vulnerable countries. Potential spillover

effects of actions by CRAs across financial markets and countries worsens the situation. On the

other hand, Bar-Isaac and Shapiro (2013) incorporates macroeconomic factors and reputational

concerns of CRAs and found a counter cyclical effect of ratings. After accounting for business and

financial risks and macroeconomic conditions, Amato and Furfine (2004) observes that credit

ratings of US firms are not excessively sensitive to the business cycle. Mora (2006) also finds that

ratings are reactive to non-macroeconomic factors and are sticky rather than pro-cyclical.

Asymmetric effect of ratings upgrades and downgrades has been reported in literature as well. For

example, Sy (2002) studies 17 emerging markets for the relationship between ratings and yield

spread of sovereign bonds. It found a significant and larger effect of negative news on the markets

as compared to positive news. In another study, Gande and Parsley (2005) investigates the cross-

country impact of sovereign rating changes. It explores the sovereign credit spreads for 34

developing and developed economies over the period of 1991 to 2000. They found significant

asymmetric effects across countries. The negative rating changes increased the credit spreads while

positive changes did not have any significant impact. Ismailescu and Kazemi (2010) identify that

positive events have a significant and substantial impact on the CDS market of rated country and

spillover to other markets but react weakly to negative news. Arezki et al. (2011) also supports the

similar conclusion. It analyzes the European economies during the European debt crisis during

2007 to 2010 for the spillover effect of rating changes of the sovereign debt. It proposes a

significant spillover effect of the downgrades across economies, but the magnitude and sign of the

spillover was dependent on the characteristics of the economies, announcement types, and

announcing CRA.

9

Focusing on the relative effect of ratings and outlook changes, Boot et al. (2006) argues that price

effects of rating changes will be more informative after a credit watch procedure than in the

absence of it. More precisely, companies with the most effective recovery efforts show an initial

small decline after the negative credit watch status and a small positive effect after rating

confirmation. Nevertheless, if the same firms face a credit rating downgrade after credit watch,

they will exhibit a significant negative dip in stock prices. Similar results are reported by Bannier

and Hirsch (2010).

Credit ratings and outlook change decisions are not symmetric for all three CRAs. For example,

Tran et al. (2014), analyzing the linkages between index option and sovereign debt ratings, claims

a highly significant impact of rating changes by S&P and Moody’s while less significant for Fitch.

It proposes that both sovereign rating and outlook are significant for S&P and Fitch and only

ratings for Moody’s. The authors attribute this difference to variations in the ratings procedures of

CRAs. Another similar stream of literature concentrates on the leadership and followership of

CRAs in making rating announcements. Güttler and Wahrenburg (2007) analyzes lead-lag

relationship and biases in S&P and Moody’s ratings of close-to-default entities using the data from

1997-2004. It shows that Moody’s adjusts timelier than S&P to default risk. Alsakka and ap

Gwilym (2010) study the lead-lag relationship between the CRAs in the case of sovereign ratings.

It finds Moody’s as a first mover in most cases of upgrades and S&P depends least on other CRAs.

Japanese CRAs are found to lag bigger CRAs while they downgraded ahead of Moody’s

downgrades in some cases.

10

3. Methodology

We estimate the effect of sovereign rating and outlook changes on the stock market returns of 16

European countries at different phases of business cycle. Capital Asset Pricing Model (CAPM)

with global market excess returns as the only explanatory variable has been extensively used for

the calculation of abnormal returns (Ferreira and Gama, 2007; Correa et al., 2014; Jorion and

Zhang, 2010; Baum et al. 2016 and Pelizzon et al., 2016). The CAPM can be specified as:

𝑅𝑖𝑡 − 𝑅𝐹𝑅𝑡 = 𝛼 + 𝛽1(𝑅𝑚𝑡 − 𝑅𝐹𝑅𝑡) + 𝜀𝑖𝑡 … … … (1)

Where the subscript i represents country and t represents daily observations, Rit represent stock

index returns for the country i at time t, Rmt stock return index of global market at time t and RFRt

is a risk-free rate of USA at time t and εit is the error term.

Fama and French (1992, 1993) proposes a three-factor model for explaining the stock returns. By

using three global factors of which one is global market excess returns, it shows a significant

improvement over the global CAPM model. In addition to three factors, Carhart (1997) proposes

a fourth factor known as momentum. This brought in additional improvements in estimation.

Recently, Fama and French (2012) proposes using local factors instead of global factors and

showed a significant improvement in estimations of abnormal stock returns over both the global

three and four factors and global CAPM model. It also finds CAPM intercepts positive for extreme

value portfolios and negative for extreme growth portfolios which may imply that past studies on

the association between changes in sovereign outlook and rating and securities market may have

biased results because of bias embedded in the calculations of abnormal returns.

This paper makes two distinct contributions in methodology for stock returns estimation. First, it

substitutes CAPM with Fama-French-Carhart four factors model and second, it uses local factors

11

not global factors for the estimation purposes. Therefore, this study adopts the standard form of

Fama-French-Carhart four factor model of Carhart (1997) and Fama and French (2012) given as:

𝑅𝑖𝑡 − 𝑅𝐹𝑅𝑡 = 𝛼 + 𝛽1(𝑅𝑚𝑡 − 𝑅𝐹𝑅𝑡) + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝛽4𝑊𝑀𝐿𝑡 + 𝜀𝑖𝑡 … … … (2)

Where the subscript i represents country, t is daily frequency of observations, Rit represent stock

return index of country i at time t, Rmt stock return index of market i.e. Europe, RFRt risk free rate

of USA, SMBt difference of returns in small and big firms of Europe, HMLt difference of returns

in high worth and low worth companies in Europe, WMLt is difference of returns in companies

labelled as winners and losers on stock markets and εit is the error term.

To study daily stock market returns model is modified as follows:

𝑅𝑖𝑡 − 𝑅𝐹𝑅𝑡 = 𝛼 + 𝛽1(𝑅𝑚𝑡 − 𝑅𝐹𝑅𝑡) + 𝛽2𝑆𝑀𝐵𝑚𝑠 + 𝛽3𝐻𝑀𝐿𝑚𝑠 + 𝛽4𝑊𝑀𝐿𝑚𝑠 + 𝜀𝑖𝑡 … … … (3)

Where the subscript i represents country, m represents market, t represents daily and s is monthly

observations, Rit represent stock index returns of country i at time t, Rmt is regional stock market

index returns i.e. Europe, RFRt is risk free rate of USA, SMBms is difference of returns in small

and big firms of the Europe, HMLms is difference of returns in high worth and low worth companies

in Europe, WMLms is difference of returns in companies labelled as winners and losers on stock

markets in Europe and εit is the error term.

To estimate the impact of sovereign rating and outlook changes and to include the impact of

business cycle in the analysis, variables for the sovereign rating and outlook changes, business

cycle and their interaction terms are added to the model as specified below:

12

𝑅𝑖𝑡 − 𝑅𝐹𝑅𝑡 = 𝛼 + 𝛽1(𝑅𝑚𝑡 − 𝑅𝐹𝑅𝑡) + 𝛽2𝑆𝑀𝐵𝑚𝑠 + 𝛽3𝐻𝑀𝐿𝑚𝑠 + 𝛽4𝑊𝑀𝐿𝑚𝑠 + 𝛽5𝐸𝑈𝐸𝑆𝐼𝑚𝑠

+ ∑ 𝛽6,𝑗

4

𝑗=1

∑ ∑ 𝑋𝑖𝑡𝑗

𝑇

𝑡=1

𝑁

𝑖=1

+ ∑ 𝛽7,𝑗

4

𝑗=1

∑ ∑ 𝑋𝑖𝑡𝑗

𝑇

𝑡=1

𝑁

𝑖=1

∗ 𝐸𝑈𝐸𝑆𝐼𝑚𝑠 + 𝜀𝑖𝑡 … … … (4)

Subscript i represents country, m represents market, t represents daily frequency, s monthly

observations and j represent rating and outlook variables, Rit represent stock index returns of

country i at time t, Rmt is regional stock market index returns i.e. Europe, RFRt is risk free rate of

USA, SMBms is difference of returns in small and big firms of the Europe, HMLms is difference of

returns in high worth and low worth companies in Europe, WMLms is difference of returns in

companies winners and losers on stock markets, EUESIms is European business cycle indicator

and εit is the error term.

Xitj is a vector containing rating and outlook variables j at time t for country i. The four rating and

outlook variables are: rating upgrades (R_UPit or Xit1) is a dummy variable which is equal to 1 for

a day before and after the current rating upgrade and 0 otherwise, outlook upgrades (O_UPit or

Xit2) is a dummy variable which is equal to 1 for a day before and after the current outlook upgrade

and 0 otherwise, rating downgrades (R_DOWNit or Xit3) is a dummy variable which is equal to 1

for a day before and after the current rating downgrade and 0 otherwise, and outlook downgrade

(O_DOWNit or Xit4) is a dummy variable which is equal to 1 for a day before and after the current

outlook downgrade and 0 otherwise, The last term in equation 4 represent the interaction term for

the rating upgrades, outlook upgrades, rating downgrades and outlook downgrades with the

business cycle. Precise variable definitions and their sources are given in Table A1. We estimate

the proposed model using ordinary least squares estimator; however, we also provide estimates

under the GMM technique to control for any possible endogeneity bias.

13

Conditional effect of a downgrade or an upgrade in rating or outlook can be estimated by taking a

derivative of the equation (4) and can be calculated as:

𝜕(𝑅𝑖𝑡 − 𝑅𝐹𝑅𝑡)

𝜕𝑋𝑖𝑡𝑗= 𝛽6𝑗 + 𝛽7𝑗 ∗ 𝐸𝑈𝐸𝑆𝐼𝑚𝑠 … … … (5)

Marginal effect is a natural choice for estimation. Therefore, this paper estimates average marginal

effect for the upgrades and downgrades individually conditional on the business cycle. Two

accepted methodologies to estimate the conditional effects are marginal effects at mean and

average marginal effect. This study prefers average marginal effect over marginal effect at means

because of the ordinal nature of the rating and outlook variables. Calculation of marginal effects

at means for ordinal variables results in estimating conditional effects at nonexistent observations.

Marginal effect provides the change in the adjusted predictions for the binary categories for

dummy explanatory variables.

4. Data

The full sample of the study constitutes 16 European states over the period of July 1, 1990 to June

30, 2016. The countries under study include Austria, Belgium, Denmark, Finland, France,

Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and

the United Kingdom, as used in Fama and French (2012) for the calculation of European market

factors. The use of only 16 European countries as in Fama and French (2012) allows us to compare

our results with the literature (specifically with the Fama and French, 2012) and to test if the

inclusion of sovereign ratings and outlook changes in Fama and French four factor model adds to

the explanatory power of the model.

14

The data for the study is collected from multiple sources. The daily stock price indices and risk-

free rate (RFRt) are extracted from DataStream. Stock returns are calculated by taking a log

difference of each country’s (Rit) and European market (Rmt) stock price index. RFRt is the US 3-

month T-bill rate. US risk-free rate contrary to local risk-free rate is used because of the two

reasons. First, Bernoth et al. (2012) argued that if a local authority can default with some positive

probability then it can reduce the investor’s return by taking actions like taxing interest income

retained at the source. This makes local security exposed to partial default risk. Second, Fama and

French (2012) also used US T-bill rate as a proxy for interest free rate and using it in this study

will increase comparability with the literature. Further, Fama-French European factors SMBms,

HMLms and WMLms are downloaded from the Fama-French online data-library as used in Fama

and French (2012). All the factors are defined in appendix Table A1.

We use European regional economic sentiment indicator (ESI) given by the Economic and

Financial affairs of the European Commission as a business cycle indicator (EUESIms). Our choice

of ESI as business cycle indicator is motivated from its broad scope, forward looking nature,

monthly data frequency and similar use in previous literature such as Dewachter et al. (2015) and

Boffelli et al. (2015). European Commission conducts periodic business and consumer surveys to

record economic activity in the region. ESI is a monthly weighted average based on such individual

national economic surveys and includes surveys from: consumers (20%), retail trade (5%),

services (30%), construction (5%) and industry (40%). Moreover, to assure comparability across

national confidence indicators, European Commission has a harmonization program of the national

surveys in place. For instance, all confidence indicators are standardized for a mean of 100 and a

standard deviation of 10. Therefore, ESI is an overall, broader, forward looking indicator

calculated based on all the five sectorial indices.

15

Data for sovereign rating developments is collected from three CRAs: S&P, Moody’s and Fitch.

CRAs assign rating notations to the sovereign securities. These notations show the assessment of

the ability and willingness of the issuing government to pay back interest and principal amount.

Generally, a triple-A rating shows the most credible issuer. For estimation purposes sovereign

credit rating is transformed into a linear variable with values ranging from 1 to 22. A maximum

value of 22 denotes the highest triple-A rating category while 1 indicates the lowest near to default

bonds (see Table A2 for further details). Credit outlook is also transformed into a linear variable

with values ranging from 1 to 5. A lowest value of 1 represents negative outlook and 5 represents

positive outlook (see Table A2). This study favors linear transformation to make results

comparable with the recent literature2. Similar coding scheme is used in Afonso et al. (2012),

Alsakka and ap Gwilym, (2012b), and Ferreira and Gama (2007).

To study the impact of changes in sovereign rating and outlook by CRAs we defined an event

window of 3 days. New dummy variable R_UPit is generated for the rating upgrades which is equal

to 1 for the one day before and one day after the announcement by any one of the three CRAs and

zero otherwise and New dummy variable O_UPit is generated for outlook upgrades which is equal

to 1 for the one day before and one days after the announcement by any one of the three CRAs and

zero otherwise. Similarly, R_DOWNit is a dummy variable which is equal to 1 for one day before

and one day after a downgrade in ratings by any one of the three CRAs and zero otherwise and

O_DOWNit is a dummy variable which is equal to 1 for one day before and one day after a

2 In literature both logistic (Reisen and Maltzan, 1999) and exponential transformations (Afonso,

2003) have been used, however, Afonso et al. (2011) showed little improvement provided by such

transformations over the linear transformation.

16

downgrade in outlook by any one of the three CRAs and zero otherwise (Gande and Parsley, 2005;

Hooper et al., 2008 and Afonso et al. 2012).

Descriptive statistics for variables and data are presented in Table 1. Mean of Rit and Rmt are 0.013

and 0.015 with 1.46 and 1.16 of the standard deviations respectively. The minimum and maximum

values of Rit lie within 11 standard deviations. To account for outliers, we winsorized the data at

1 and 99 percent for all the variables, except the rating and outlook variables. Business cycle

indicator has a mean value of 99.95 with a standard deviation of 9.85. Comparing the statistics of

the rating and outlook variables S&P has a lowest mean rating score and highest outlook score

(20.43 and 2.95 respectively) followed by Moody’s (20.45) and Fitch (20.49) in ratings and Fitch

(2.84) and Moody’s (2.86) in case of outlook assignments. However, rating assigned by S&P has

less spread as compared to other two CRAs. Lowest mean value and standard deviation shows the

rating strictness by a CRA. The number of observations varies depending on the data availability

of the respective variable. Correlation between the variables is shown in appendix Table A3.

Table 1: Descriptive Statistics

(1) (2) (3) (4) (5) (6)

Variables Mean Median

Standard

Deviation

5th Percentile

95th

Percentile

Obs.

Rit 0.013 0.04 1.46 -2.25 2.12 108,544

Rmt 0.015 0.06 1.16 -1.80 1.66 108,544

RFRt 0.01 0.01 0.01 0.00 0.04 108,544

SMBms -0.04 0.01 2.18 -3.76 3.58 108,544

HMLms 0.33 0.28 2.23 -3.30 4.39 108,544

17

WMLms 0.99 1.23 3.70 -6.63 6.75 107,136

EUESIms 99.95 102.00 9.85 78.60 113.50 108,544

SP-RATit 20.43 22.00 2.87 13.00 22.00 108,544

SP-OUTit 2.95 3.00 0.90 1.00 5.00 108,544

M-RATit 20.45 22.00 3.02 14.00 22.00 108,531

M-OUTit 2.86 3.00 0.76 1.00 3.00 80,282

F-RATit 20.49 22.00 2.85 15.00 22.00 90,868

F-OUTit 2.84 3.00 0.69 1.00 3.00 66,148

Note: This table presents the descriptive stats for the full sample over July 1, 1990 to June 30,

2016 for the 16 European markets. Mean, median, standard deviation, 5th percentile, 95th

Percentile and numbers of observations for all the variables used in the analysis are presented.

Rit is the country i stock index returns, Rmt is the European market returns, RFRt is the risk-free

rate, SMBms is Small minus big returns factor, HMLms is High minus low return factor, WMLms

is winner minus loser returns factor, EUESIms is a European regional business cycle indicator.

SP-RATit, M-RATit and F-RATit are transformed ratting variables and SP-OUTit, M-OUTit and

F-OUTit are transformed outlook variables for S&P, Moody’s and Fitch respectively as shown

in appendix Table A2.

5. Empirical Results

Study provides estimates for average marginal effect of CRAs announcements on stock returns

conditional on business cycle. To study the impact of sovereign rating and outlook changes on the

stock market returns at different phases of business cycle we regress stock returns on upgrades and

downgrades in sovereign ratings and outlook and their interaction terms with the proxy for

business cycle. Table 2 presents the regression coefficients of the estimated equations in Panel A,

18

marginal effects of the upgrades and downgrades in rating and outlook in Panel B and model

statistics in Panel C. The dependent variable is excess stock returns of a country i at time t.

Independent variables include Fama-French three factors, momentum factor, business cycle and

sovereign rating and outlook variables. The specification (1) shows the estimations of the simple

CAPM model in which local market returns is the only independent variable. Market risk beta is

found significant at 1% with a coefficient of 0.979. Fama and French estimates separate CAPM

beta for the local factors from Europe. The beta coefficients estimated by Fama and French lies

between 0.83 and 1.20 and is significant at 1%. Coefficient and significance of our estimates are

analogous to their estimations. Beta coefficient shows that a one percent increase in market excess

returns leads to 0.979 percent increase in the country i excess stock return. The Adjusted R-squared

of our estimated model is 0.609. The results are also in line with Tauscher and Wallmeier (2016).

Specification (2) presents coefficients for Fama-French four factors (FF4F) model. In FF4F model

we found three factors Rmt - RFRt, SMBms and HMLms significant at 1%; however, WMLms is

significant yet only at 10% level which are also in line with Fama and French (2012) model

estimates. The coefficient estimate of Rmt - RFRt is 0.981. It implies that a 1 percent increase in

European Markets excess return (Rmt - RFRt) leads to 0.981 percent increase in individual country

market returns. Coefficients on SMBms and HMLms are similar at magnitude of 0.009. The

estimates show that a one percent increase in difference between returns of small and big firms

and high value and low value firms leads to a 0.009 percent increase in the individual country

market excess returns. The fourth factor WMLms has a negative effect of -0.001. The effect is

negative and small but significant. The Adjusted R-Squared of 0.614 of the regression models also

shows a marginal improvement over the CAPM. F-test of the models is also significant at 1% for

both specifications.

19

To test for the business cycle effect on stock returns business cycle variable is added to the model

(3) and it is further modified in (4) to account for sovereign rating and outlook effect on stock

returns. Four separate variables are used each for the rating and outlook upgrades and downgrades

as follows: R_DOWNit variable shows downgrades in ratings, O_DOWNit variable represents

downgrades in outlook, R_UPit variable represents upgrades in ratings and O_UPit variable shows

upgrades in outlook. In the specification (4) the R_DOWNit variable is negatively significant at

1% level while all other rating and outlook variables are insignificant even at 10% level. The

coefficient or average marginal effect of R_DOWNit is - 0.122 which shows that a downgrade in

sovereign credit ratings or outlook leads to 0.122 percent less returns for the downgraded country.

Coefficients on R_UPit, O_UPit and O_DOWNit are insignificant with a magnitude of 0.076, -

0.027 and –0.045 respectively. Nevertheless, the average marginal effect of R_DOWNit is

significant at 1% level. Fama-French factors Rmt - RFRt, SMBms and HMLms are all significant at

1% level of significance while WMLms at 10% level. The magnitudes on all the four factors remain

same from specification (2) to (4).

The main contribution of the paper is specification (5) where we estimate the impact of sovereign

ratings and outlook change on the stock markets at different phases of business cycle. Model (4)

is modified to include interaction terms of the ratings and outlook variables with the European

business cycle. The three factors Rmt - RFRt, SMBms and HMLms remain significant in this new

model with a coefficient of 0.981, 0.009 and 0.009 respectively. All of them are significant at 1%

level of significance. WMLms is significant at 10% level with a magnitude effect of -0.001. A direct

attempt to interpret coefficients of interactive terms in regression models may result in wrong

interpretation. For this purpose, we calculate marginal effects to measures the effect on excess

returns of a change in ratings or outlook variables conditional on business cycle. This study prefers

20

average marginal effect over marginal effect at means because all the rating variables are ordinal

and making estimations at means for ordinal variables results in estimations at nonexistent

observations. Hence, average marginal effects of R_UPit, O_UPit, R_DOWNit and O_DOWNit

conditional on EUESIms are provided in Panel B of Table 2. Average marginal effect of both

ratings upgrades and downgrades are 0.174 and -0.138 at a 1% level of significance respectively.

It shows that a one notch upgrade in sovereign ratings leads to 0.174 percent more excess returns

on the stock market and a one notch downgrade in sovereign ratings leads to -0.138 percent less

excess returns on the stock market of that specific country depending on the level of business cycle.

Marginal effects of outlook upgrades (-0.029) and downgrades (-0.062) are, however,

insignificant.

Overall, the model F-stat is significant at 1% level with an Adjusted R-squared of 0.614. Arezki

et al. (2011) also finds insignificant effect of outlook changes in assorted data specifications.

Moreover, it claims that magnitude and sign of spillover effect of sovereign rating and outlook

changes on stocks is contingent on type of announcement, rated country and particular CRA.

Hooper et al. (2008) also shows an insignificant effect of outlook changes for stock volatility.

Similar results are also available in case of CDS market, for e.g. information content is found

insignificant in both actual rating downgrades and outlooks; however, only review downgrades are

significant (Hull et al. 2004). Similarly, Afonso et al. (2012, Table 4) also reports insignificant

effect of both positive and negative outlook changes on sovereign yields for Moody, Fitch and

cumulative outlook variable; though, only S&P outlook changes has significant effect on sovereign

yield. On the other hand, we also estimate for tranquil period, showing a significant effect of

outlook downgrades at 5% level and the significance of rating downgrade falls at 10% level.

Significance of rating and outlook upgrades remains unchanged from the original estimated model.

21

Hooper et al. (2008) also finds significant effect of both rating and outlook changes for returns;

however, shows an insignificant effect of outlook changes for stocks volatility.

Table 2: Regression Coefficients and Average Marginal Effects for Full Sample

(1) (2) (3) (4) (5)

Panel: A

Rmt - RFRt 0.979*** 0.981*** 0.981*** 0.981*** 0.981***

(0.002) (0.002) (0.002) (0.002) (0.002)

SMBms

0.009*** 0.009*** 0.009*** 0.009***

(0.001) (0.001) (0.001) (0.001)

HMLms

0.009*** 0.009*** 0.009*** 0.009***

(0.001) (0.001) (0.001) (0.001)

WMLms

-0.001* -0.001* -0.001* -0.001*

(0.001) (0.001) (0.001) (0.001)

EUESIms

0.021 0.019 0.027

(0.025) (0.025) (0.026)

R_UPit

0.076 2.682***

(0.055) (0.856)

O_UPit

-0.027 0.346

(0.046) (0.503)

R_DOWNit

-0.122*** 0.480

(0.047) (0.508)

O_DOWNit

-0.045 0.546

(0.042) (0.382)

22

R_UPit*EUESIms

-2.511***

(0.828)

O_UPit*EUESIms

-0.376

(0.498)

R_DOWNit*EUESIms

-0.618

(0.517)

O_DOWNit*EUESIms

-0.609

(0.392)

Constant -0.005** -0.006** -0.026 -0.024 -0.032

(0.003) (0.003) (0.025) (0.025) (0.026)

Panel: B

EUESIms

0.021 0.019 0.015

(0.025) (0.025) (0.025)

R_UPit

0.076 0.174***

(0.055) (0.062)

O_UPit

-0.027 -0.029

(0.046) (0.046)

R_DOWNit

-0.122*** -0.138***

(0.047) (0.048)

O_DOWNit

-0.045 -0.062

(0.042) (0.044)

Panel: C

Adj. R-sq 0.609 0.614 0.614 0.614 0.614

23

F-Stat 169340*** 42581*** 34065*** 18928*** 13107***

Log-likelihood value -133391 -130779 -130779 -130772 -130763

RMSE 0.827 0.820 0.820 0.820 0.820

Obs. 108,544 107,136 107,136 107,136 107,136

Note: This table presents the coefficient estimates and average marginal effect of sovereign

rating or outlook change on the stock returns over the full sample estimated using ordinary least

squares. Rmt - RFRt is the excess market returns, SMBms is small minus big factor, HMLms is

high minus low factor, WMLms is winners minus losers, and EUESIms is the regional European

business cycle. R_UPit, O_UPit, R_DOWNit and O_DOWNit are the main variables of interest.

R_UPit is a dummy variable set equal to one for one day before and after the rating upgrade,

O_UPit is a dummy variable set equal to one for one day before and after the outlook upgrade,

R_DOWNit is a dummy variable set equal to one for one day before and after the rating

downgrade and O_DPWNit is a dummy variable set equal to one for one day before and after

the outlook downgrade. Adj. R-sq is adjusted R-squared (coefficient of determination). RMSE

represents root mean square error. Standard errors in parentheses while *, **, and *** denote

10%, 5%, and 1% levels of statistical significance.

To further analyze the effect of changes in sovereign ratings and outlook on the stock returns at

different phases of business cycle we use marginal plots to draw the marginal effects estimated

using the equation (5). Marginal plots are graphed in Figure 1. Each graph in the figure shows

average marginal effect of sovereign rating and outlook change respectively on the stock returns

and a 95% confidence interval at different level of business cycle. Panel (a) shows the effect of

positive sovereign ratings changes on the stock returns during different phases of European

business cycle. As shown by the graph average marginal effect of upgrades is significant at lower

24

levels of business cycle and turns insignificant with increasing level of business cycle. Panel (b)

shows the effect of negative sovereign ratings changes on the stock returns during different phases

of European business cycle. The average marginal effects of downgrades are negatively significant

at 1% level (Table 2, Panel B). The graph also shows a significant the average marginal effect of

downgrades n ratings at higher level of business cycle and insignificant at lower levels. It turns

significant after the business cycle crosses 95. The mean and median value of the business cycle

indicator is 99.95 and 102 respectively. Panel (c) shows the effect of positive sovereign outlook

changes on the stock returns during different phases of European business cycle. The graph shows

an insignificant average marginal effect of upgrades in sovereign outlook but it also decreases with

the increasing economic activity. Panel (d) shows the effect of downgrades in sovereign outlook

on the stock returns during different phases of business cycle. As shown by the graph average

marginal effect of downgrades is insignificant at lower levels of business cycle and turns

significant with increasing level of business cycle. Average marginal effect of sovereign outlook

downgrades also turns significant after business cycle level of 95. Therefore, effect of sovereign

ratings and outlook varies with the level of business activity and is not pro cyclical in nature. This

may be due to the reason that during bad (good) business conditions investors’ expectations are

low (high) and an unexpected upgrade (downgrade) during depression (recovery) are expected to

have an augmented positive (negative) effect on the countries financial markets. Our results are in

line with the implications of the theoretical model of Bar-Isaac and Shapiro (2013).

[Insert Figure 1 here]

5.1. Sensitivity Tests

We run various sensitivity tests to examine if our results remain robust to changes in sample,

specifications and estimation technique.

25

Firstly, we test for rating and outlook variable in two separate specifications. Table 3 Specification

(1) presents the estimated average marginal effect for a full sample model including only rating

variables, four factors and business cycle. Both rating upgrades and downgrades remain significant

at 5% and 1% with positive and negative average marginal effects respectively. Coefficient of

marginal effect of rating upgrade is 0.149 and rating downgrade is -0.166. To test for average

marginal effects for sovereign outlook changes individually in the full sample model is re-specified

as in (2) Table 3. Sovereign outlook variables replace sovereign ratings in this specification. We

find upgrades have a positive but insignificant effect while downgrades have a negatively

significant average marginal effect of -0.079 at 10% significance. Formerly in our primary

estimation in Table 2 specification (5) panel B the average marginal effects of outlook upgrade

and downgrade are insignificant. When we used only sovereign outlook changes in the model

downgrades turns significant.

Secondly, a stream of literature also asserts that CRAs rating opinions disagree more frequently

and material heterogeneity also exists between CRAs ratings (Hill et al. 2010). Consequently, we

also modeled the individual effect of each CRA’s announcements on the stock returns at different

levels of business cycle. Table 3 presents the estimated average marginal effects for each rating

agency from model (3) through model (5). Model (3) presents the average marginal effects for the

rating announcements by S&P, model (4) displays estimated model for Moody’s and model (5)

for Fitch ratings. From Table 3, we find the average marginal effect of S&P rating downgrades

and Fitch rating upgrades while the effect of Moody’s announcements remains insignificant.

Estimates are comparable with the literature. For example, Hill and Faff (2010) finds that S&P

ratings are more timely, informative and is more active agency as compared to Moody’s and Fitch.

26

Alsakka and ap Gwilym (2010) also proposes that S&P depends least on the other rating agencies

and Japanese CRAs lag other rating agencies; however, they lead Moody’s downgrades.

Thirdly, another primary issue that can concern biasness might be the presence of endogeneity in

one of the regressors. Following Fama and French (2012), this study implies OLS estimator. OLS

is regularly used to estimate the Fama-French-Carhart model (for e.g. Fama and French, 2012) and

the relationship between returns and sovereign ratings (for e.g. Afonso et al., 2012 and Bannier

and Hirsch, 2010). However, the business cycle indicator may possibly be related to excess returns

but in this specific model we used regional business cycle rather than local business cycle.

Regional business cycle is a collective performance of all the European countries included in the

regional business cycle calculation and it is not the cycle indicator of an individual country, is

thus exogenous to the local stock returns of an individual country. Nevertheless, we also test to

control for the possible effect of endogeneity on our estimated model by using GMM. The

estimated results are presented in model (6) in Table 3. Rmt - RFRt, SMBms, and HMLms are all

significant at 1% level while WMLms is significant at 10% level. Average marginal effects of

R_UPit and R_DOWNit are also significant at 1% with coefficients of 0.192 and -0.132; however,

O_UPit and O_DOWNit remains insignificant. Adjusted R-squared of the model is 0.612 and F-

statistic is 12808 at 1% level of significance. The results are comparable with our primary model

presented in the Table 2 model (5).

Table 3: Average Marginal Effect for full sample, individual CRAs and GMM Estimator

(1) (2) (3) (4) (5) (6)

Rmt - RFRt 0.981*** 0.981*** 0.980*** 0.986*** 0.999*** 0.980***

27

(0.002) (0.002) (0.002) (0.003) (0.003) (0.002)

SMBms 0.009*** 0.009*** 0.009*** 0.008*** 0.010*** 0.009***

(0.001) (0.001) (0.001) (0.001) (0.002) (0.001)

HMLms 0.009*** 0.009*** 0.009*** 0.009*** 0.008*** 0.009***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

WMLms -0.001* -0.001* -0.001* -0.001 -0.002** -0.001*

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

EUESIms 0.015 0.020 0.018 0.008 -0.007 0.005

(0.025) (0.025) (0.026) (0.031) (0.034) (0.026)

R_UPit 0.149** 0.157 0.101 0.267** 0.192***

(0.060) (0.103) (0.128) (0.120) (0.063)

O_UPit -0.057 -0.002 -0.065 -0.063 -0.034

(0.043) (0.069) (0.086) (0.092) (0.046)

R_DOWNit -0.166*** -0.328*** 0.117 -0.117 -0.133***

(0.044) (0.076) (0.098) (0.087) (0.049)

O_DOWNit -0.079* -0.136** -0.128 -0.009 -0.071

(0.041) (0.069) (0.084) (0.086) (0.045)

Adj. R-Sq 0.614 0.614 0.612 0.644 0.687 0.612

F-Stat 18932*** 18927*** 12809*** 10990*** 10933*** 12808***

Log-likelihood -130765 -130774 -129174 -96768 -76551 -129176

RMSE 0.820 0.82 0.821 0.825 0.789 0.821

Obs. 107,136 107,136 105,728 78,874 64,740 105,728

28

Note: This table presents the average marginal effect of sovereign rating and outlook changes

on the stock returns for each individual CRA and for the full sample using GMM. Rmt - RFRt is

the excess market returns, SMBms is small minus big factor, HMLms is high minus low, WMLms

is winners minus losers, and EUESIms is the European business cycle. R_UPit is a dummy

variable set equal to one for one day before and after the rating upgrade, O_UPit is a dummy

variable set equal to one for one day before and after the outlook upgrade, R_DOWNit is a

dummy variable set equal to one for one day before and after the rating downgrade and

O_DOWNit is a dummy variable set equal to one for one day before and after the outlook

downgrade. Adj. R-sq is adjusted R-squared (coefficient of determination). RMSE represents

root mean square error. Specification (1) shows the marginal effects for full sample including

only sovereign ratings and controls, specification (2) shows the marginal effects for full sample

including only sovereign outlook and controls, specification (3) shows the marginal effects for

full sample for sovereign ratings and outlook changes by S&P, specification (4) shows the

marginal effects for full sample for sovereign rating and outlook changes by Moody’s,

specification (5) shows the marginal effects for full sample for sovereign rating and outlook

changes by Fitch, specification (6) presents the marginal effects for the full sample for all the

three rating agencies using GMM estimator. Standard errors in parentheses while *, **, and

*** denote 10%, 5%, and 1% levels of statistical significance.

Fourthly, to account for any possible effect of global financial crisis, we re-estimate all models for

the duration excluding the global financial crisis period (July 1, 2007 to June 30, 2011). Estimated

average marginal effects of tested specifications are presented in Table 4. Specification (1)

represents the impact of both sovereign rating and outlook changes on the stock markets at

different phases of business cycle over period excluding the crisis epoch. The marginal effects of

29

the rating and outlook variables conditional on the business cycle turn out to be significant. R_UPit

carries a coefficient of 0.174 significant at a 1% while R_DOWNit is -0.117 significant at 10%

level. It shows that an upgrade in sovereign ratings leads to a positive impact of 0.174 and a

downgrade in sovereign ratings leads to a negative effect of -0.117 percent in stock returns of the

rated country. In case of the impact of changes in outlook a positive change is insignificant while

negative changes are significant at a level of 5% with an average marginal effect of -0.117 with

each announcement. It shows that a one notch downgrade in sovereign outlook leads to 0.117

percent less returns on the stock market of that specific country. We also tested for rating and

outlook changes separately for the tranquil period. Specification (2) presents the impact of

sovereign rating changes on the stock markets at different phases of business cycle while

specification (3) shows the impact of sovereign outlook changes on the stock markets at different

phases of business cycle. The average marginal effect of rating upgrade is 0.130 at a 5%

significance and for downgrade it is -0.135 significant at 5% in model specification (2). The

average marginal effects of sovereign outlook changes are -0.007 and -0.102 for upgrades and

downgrades respectively, however, only downgrades are significant at 5%. Adjusted R-Squared

for all the three specifications is 0.55 and a significant F-Stat at 1% level.

Fifthly, it can be argued that prior to 1999 i.e. before the introduction of single currency in Europe,

financial systems and markets in EU may have behaved differently. So, to consider the potential

influence of Euro and to account for the effect of monetary unification from 01 January 1999, we

re-estimate the impact of sovereign ratings and outlook changes on the stock returns excluding the

period before January 1, 1999. The results are presented in Table 4 specifications (4), (5) and (6).

Specification (4) shows the impact of both sovereign rating and outlook changes on the stock

markets at different phases of business cycle, specification (5) shows the impact of only sovereign

30

rating changes on the stock markets at different phases of business cycle and specification (6)

shows the impact of sovereign outlook changes on the stock markets at different phases of business

cycle respectively. Rating upgrades and rating downgrades remain significant over the three

contrasting specifications at 10 % and 5% levels while we find insignificant effect of outlook

upgrades and downgrades during this period. We also find similar results in our primary

specification in Table 2 specification (5). Outlook changes are insignificant while rating changes

significant. We can attribute insignificant effect of outlooks to global financial crisis as it records

more frequent outlook changes particularly downgrades. So, the essence of outlook for being an

indicator for future rating change diminishes and does not provides unique information to the

market. Estimates over the monetary union period shows, a downgrade in sovereign rating lead to

0.164 percent less returns to stock market of the downgraded country and an upgrade in sovereign

ratings lead to positive returns of 0.126% in the stock markets of the rated country. The adjusted

R-squared of the regression is .67 with a significant F-stat at 1% level.

Table 4: Average Marginal Effects Subject to Global Financial Crisis and Euro Period

Variables (1) (2) (3) (4) (5) (6)

Rmt - RFRt 0.948*** 0.948*** 0.948*** 0.993*** 0.993*** 0.993***

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

SMBms 0.007*** 0.007*** 0.007*** 0.010*** 0.010*** 0.010***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

HMLms 0.008*** 0.008*** 0.008*** 0.007*** 0.007*** 0.007***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

WMLms -0.000 -0.000 -0.000 -0.001 -0.001 -0.001

31

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

EUESIms -0.009 -0.006 -0.003 0.004 0.003 0.009

(0.030) (0.030) (0.030) (0.032) (0.032) (0.032)

R_UPit 0.174*** 0.130**

0.126* 0.119*

(0.060) (0.057)

(0.070) (0.067)

R_DOWNit -0.117* -0.135**

-0.164*** -0.183***

(0.062) (0.059)

(0.053) (0.047)

O_UPit -0.001

-0.007 -0.019

-0.063

(0.050)

(0.047) (0.050)

(0.046)

O_DOWNit -0.117**

-0.102** -0.020

-0.055

(0.051)

(0.048) (0.049)

(0.044)

Adj. R-Sq 0.550 0.550 0.550 0.669 0.669 0.669

F-Stat 8460*** 12217*** 12216*** 11355*** 16402*** 16397***

Log-likelihood -109908 -109914 -109916 -87859 -87860 -87868

RMSE 0.820 0.820 0.820 0.806 0.806 0.806

Obs. 90,096 90,096 90,096 73,040 73,040 73,040

Note: This table presents the average marginal effect of sovereign rating and outlook changes

on the stock returns for varied data samples and specifications estimated using ordinary least

squares. Rmt - RFRt is the excess market returns, SMBms is small minus big factor, HMLms is

high minus low, WMLms is winners minus losers, and EUESIms is the European business cycle.

R_UPit is a dummy variable set equal to one for one day before and after the rating upgrade,

O_UPit is a dummy variable set equal to one for one day before and after the outlook upgrade,

32

R_DOWNit is a dummy variable set equal to one for one day before and after the rating

downgrade and O_DOWNit is a dummy variable set equal to one for one day before and after

the outlook downgrade. Adj. R-sq is adjusted R-squared (coefficient of determination). RMSE

represents root mean square error. Specification (1) shows the marginal effects for both

sovereign rating and outlook changes excluding crisis period, , specification (2) presents the

marginal effects for the period excluding crisis period for only sovereign ratings and controls ,

specification (3) shows the marginal effects for the same period for sovereign outlook and

controls , specification (4) shows the marginal effects for both sovereign rating and outlook

changes over the period after introduction of Euro as an official single currency , specification

(5) shows the marginal effects for sovereign ratings and controls only for the Euro period and

specification (6) shows the marginal effects for sovereign outlook and controls for the Euro

period. Standard errors in parentheses while *, **, and *** denote 10%, 5%, and 1% levels of

statistical significance.

6. Conclusion

This paper studies the impact of sovereign rating and outlook changes on the stock markets

conditional on business cycle. Literature provides evidence in support of both pro and counter

cyclical as well as sticky nature of the sovereign ratings and outlook announcement and changes

by the CRAs. However, the effect of ratings and outlook change on the stock markets at different

phases of business cycle had not been explored yet. Moreover, despite large literature on the topic

the relationship between sovereign rating changes and stock market returns is still ambiguous. We

study European countries for the impact of sovereign outlook and ratings on the domestic and

regional stock markets at different phases of business cycle effect. This study also improves upon

the technique for the calculations of abnormal returns measure by using Fama-French four factor

33

model instead of CAPM or market adjusted model and local factors instead of global factors among

other improvements.

A panel of 16 European countries over a period of July 1, 1990 to June 30, 2016 is explored. The

main findings of the paper are as follows: average marginal effect of sovereign rating and outlook

changes is not pro cyclical. A downgrade in sovereign ratings has a negative and significant

average marginal effect conditional on business cycle on the stock returns of the rated country

while effect of upgrade is significantly positive. Conversely, upgrades and downgrades in

sovereign outlook are significant in the main model for the full sample. Comparatively, outlook

changes turn significant when we test for outlook separately and during tranquil period. At large,

the results are robust to different model and data specifications. Testing only for the period after

the introduction of Euro shows similar results to our primary specification and for the period

excluding crisis period we find significant conditional effect of both upgrades and downgrades in

sovereign ratings and only for downgrades in sovereign outlooks. Estimates also remained

unaffected under after controlling for endogeneity under GMM estimates.

A significant impact of sovereign rating and outlook changes over the different phases of business

cycle explicates more detail on the asset pricing mechanisms. Significant average marginal effect

shows that CRAs announcements provide new valuable information to the market. This conclusion

has some important policy implications. As the sovereign rating and outlook changes occur,

spillover effects encroach to other segments of the economy through stock markets, policy makers

should be vigilant to such externalities and consider these factors while managing public debt

especially in the periods of downturns and low confidence in economic conditions by public.

Second, policy makers should be cautious in following and implementing credit rating based

regulations which can accelerate the performance of different indicators of business cycle. A

34

thorough analysis of suitability and consequences of such regulations must be made before

implementing such regulations.

35

Appendix

Table A1: Variable Definitions

Variable Name Definition Source

Stock Returns (Rit) Log difference of country i stock price index DataStream

Market Factor

(Rmt)

Log difference of regional level stock price

index

DataStream

Risk-Free Rate

(RFRt)

3 month US T-bill rate DataStream

Small Minus Big

(SMBms)

Regional Fama-French factor calculated as

difference of returns in small and big firms

listed on stock market

Fama-French Data

Library

High Minus Low

(HMLms)

Regional Fama-French factor calculated as

difference of returns in high worth and low

worth companies listed on stock markets

Fama-French Data

Library

Winner Minus

Loser (WMLms)

Regional Fama-French factor calculated as

difference of returns in companies winners

and losers listed on stock markets

Fama-French Data

Library

Business cycle

(EUESIms)

Regional economic sentiment indicator for

Europe collected from European Commission

European Commission

Rating Upgrade

(R_UPit)

A dummy variable which is equal to 1 for

three days; one day before and one after the

current ratings upgrade and 0 otherwise

Self-calculated using

Ratings data provided

by CRAs

36

Outlook Upgrade

(O_UPit)

A dummy variable which is equal to 1 for

three days; one day before and one after the

current outlook upgrade and 0 otherwise

Self-calculated using

outlook data provided

by CRAs

Rating Downgrade

(R_DOWNit)

A dummy variable which is equal to 1 for

three days before and after the current ratings

or outlook downgrade and 0 otherwise

Self-calculated using

Ratings data provided

by CRAs

Outlook

Downgrade

(O_DOWNit)

A dummy variable which is equal to 1 for

three days before and after the current ratings

or outlook downgrade and 0 otherwise

Self-calculated using

outlook data provided

by CRAs

37

Table A2: Long-Term Credit Rating and Outlook Transformation

Long-Term Credit Rating

Credit Outlook Status

S&P Moody’s Fitch Coding

S&P Moody’s Fitch Coding

AAA Aaa AAA 22

Positive Positive Positive 5

AA+ Aa1 AA+ 21

Watch +ve RUR+ Watch +ve 4

AA Aa2 AA 20

Stable Stable Stable 3

AA- Aa3 AA- 19

Watch –ve RUR- Watch –ve 2

A+ A1 A+ 18

Negative Negative Negative 1

A A2 A 17

A- A3 A- 16

BBB+ Baa1 BBB+ 15

BBB Baa2 BBB 14

BBB- Baa3 BBB- 13

BB+ Ba1 BB+ 12

BB Ba2 BB 11

BB- Ba3 BB- 10

B+ B1 B+ 9

38

B B2 B 8

B- B3 B- 7

CCC+ Caa1 CCC+ 6

CCC Caa2 CCC 5

CCC- Caa3 CCC- 4

CC Ca CC 3

C 2

SD C DDD 2

D

DD 1

D 1

39

Tab

le A

3:

Sp

earm

an

Ran

k C

orr

elati

on

s m

atr

ix

O_D

OW

Nit

1

This

tab

le p

rese

nts

the

coef

fici

ents

of

corr

elat

ion f

or

the

var

iable

s use

d i

n t

he

study. T

he

sam

ple

cover

s 16 E

uro

pea

n c

ountr

ies

over

a

per

iod o

f 01 J

uly

, 1990 t

o 3

0 J

une,

2016. A

ll v

aria

ble

s ar

e si

mil

ar a

s def

ined

in T

able

.

O_U

Pit

1

.026

R_D

OW

Nit

1

.025

.023

R_U

Pit

1

.024

.025

.022

F_O

UT

it

1

.0064

-.0017

.0032

-.003

F_R

AT

it

1

.12

-.0031

.00045

-.0013

-.00093

M_O

UT

it

1

.11

.23

.021

.008

.0087

.01

M_R

AT

it

1

.12

.76

.15

-.0064

-.0031

-.0061

-.0065

SP

_O

UT

it

1

.06

.2

.071

.23

.0062

-.0043

.0046

.001

SP

_R

AT

it

1

-.012

.74

.15

.75

.16

.00054

.0027

.0015

.0043

EU

ES

I ms

1

-.0063

.095

.03

.19

-.02

.081

2.3

e-06

.0019

.0026

-.00065

WM

Lm

s

1

.02

.0065

-.0024

.0039

.0052

-.0066

-.0051

.0085

.0043

.0015

.00073

HM

Lm

s

1

-.24

-.023

.026

.05

.032

.01

.024

.032

-.00017

-.0019

-.0018

-.0029

40

SM

Bm

s

1

-.017

.062

-.027

.0043

-.0064

.026

-.0037

.011

-.0075

-

.00094

.0

041

.0042

.0049

RF

Rt

1

-.081

.11

.0072

.28

.032

.18

-.0077

.14

.021

.11

.0027

-.008

-.0013

.0012

Rm

, it

1

-.0098

-.047

.041

-.03

.0031

.0029

-.0066

.00047

-.0051

.0015

-.00076

-.00036

-.0015

.0053

-.0037

Rit

1

.75

-.007

-.022

.049

-.028

.0047

.0041

-.005

.0025

-.0065

.0049

.0036

.002

.001

.0051

-.0018

Rit

Rm

t

RF

Rt

SM

Bm

s

HM

Lm

s

WM

Lm

s

EU

ES

I ms

SP

_R

AT

it

SP

_O

UT

it

M_R

AT

it

M_O

UT

it

F_R

AT

it

F_O

UT

it

R_U

Pit

R_D

OW

Nit

O_U

Pit

O_D

OW

Nit

41

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48

Figure 1: Marginal effect of sovereign credit ratings and outlook changes on stock returns

controlling for business cycle. This figure shows the marginal effect of upgrades and downgrades

in sovereign credit rating and outlook on the stock returns at different levels of business cycle.

Panel (a) and (b) presents the average marginal effect of sovereign credit rating changes on the

stock returns at different levels of level business cycle and panel (c) and (d) presents the marginal

effect of sovereign credit outlook changes on the stock returns at different levels of business cycle.

Left side panel represents upgrades and right side represents downgrades.

-.5

0.5

11.5

min p5

med

ian

p95m

ax

European Business Cycle

(a)

Rating Upgrade

-.4

-.2

0.2

.4

min p5

med

ian

p95m

ax

European Business Cycle

(b)

Rating Downgrade-.

4-.

20

.2.4

min p5

med

ian

p95m

ax

European Business Cycle

(c)

Outlook upgrade

-.4

-.2

0.2

.4

min p5

med

ian

p95m

ax

European Business Cycle

(d)

Outlook Downgrade