Pro-cyclical Effect of Sovereign Rating Changes on Stock Returns: … ANNUAL MEETINGS... ·...
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aSuleman Dawood School of Business (SDSB), Lahore University of Management Sciences (LUMS),
Opposite Sector U, DHA., Lahore 54792, Pakistan. Email: [email protected] bSuleman Dawood School of Business (SDSB), Lahore University of Management Sciences (LUMS),
Opposite Sector U, DHA., Lahore 54792, Pakistan. Email: [email protected] cCollege of Business, Zayed University, United Arab Emirates. Email: [email protected]
*Corresponding author: Yasir Riaz, Suleman Dawood School of Business (SDSB), Lahore University of
Management Sciences (LUMS), Opposite Sector U, DHA., Lahore 54792, Pakistan; Email:
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
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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.
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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).
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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-
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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
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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
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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
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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.
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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.
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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
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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:
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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.
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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.
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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.
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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.
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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