The Market Impact of Relative Agency Activity in the Sovereign Ratings Market

Journal of Business Finance & Accounting, 37(9) & (10), 1309–1347, November/December 2010, 0306-686Xdoi: 10.1111/j.1468-5957.2010.02220.x

The Market Impact of Relative AgencyActivity in the Sovereign Ratings Market

PAULA HILL AND ROBERT FAFF∗

Abstract: Using a sample of 101 countries, over the period 1990 to 2006, we assess therelative credit-rating activity of the major agencies at the sovereign level. Informed by thispreliminary analysis, we then examine the market impact of rating actions (ratings changes,watch procedures and outlooks), allowing for various interactions across raters and ratingevents. Additionally, we carry out a separate analysis of crisis periods. We find that Standardand Poor’s tend to be more active, provide more timely rating assessments and offer morenew information than either Fitch or Moody’s. We find evidence of specialisation, however,among agencies, with Moody’s, for example, being the ‘leading’ agency among IMF ‘advancedeconomies’. In line with our rating activity analysis, we find some evidence of stronger reactionto changes in Standard and Poor’s rating assessments than in those of the other agencies. We alsofind evidence that credit-outlook and credit-watch events are more timely and more informativethan downgrades and upgrades, and that, as anticipated, reactions are stronger during crisisperiods, but that events remain informative outside crisis periods.

Keywords: credit rating, rating change, credit watch, information, sovereign

1. INTRODUCTION

In this paper, we document the relative rating actions of the three major players in thesovereign rating market: Fitch, Moody’s and Standard and Poor’s (S&P), utilising anextensive sample over a long time period, and we use this to inform a comprehensiveanalysis of the stock-market impact (information content) of sovereign rating events.In so doing, we extend prior work into the information content of changes in sovereigncredit quality (see, for example, Gande and Parsley, 2005).

Why is it important to improve our understanding and assessment of sovereignrisk and ratings? One important driver relates to the ever-growing globalisation ofeconomies and internationalisation of financial markets. As the extent of internationalinvestment has increased, research interest in this field has similarly intensified (see,for example, Erb et al., 1996; and Harvey and Zhou, 1993). While sovereign ratings

∗The first author is from the University of Bristol, UK. The second author is from the University ofQueensland, Australia. They thank Rob Brooks, Louis Ederington, David Hillier and staff at the FinanceDepartment of the University of Melbourne for their helpful comments and suggestions. (Paper receivedNovember 2008, revised version accepted July 2010)

Address for correspondence: Robert Faff, Professor of Finance, University of Queensland, 4072 Queens-land, Australia.e-mail: [email protected]

C© 2010 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UKand 350 Main Street, Malden, MA 02148, USA. 1309

Journal of Business Finance & Accounting

1310 HILL AND FAFF

impact directly on the cost of borrowing of a sovereign issuer, they are also connectedto corporate credit ratings via the sovereign ceiling principle (albeit, that somecompanies may pierce this ceiling), such that changes in the sovereign rating impactupon the creditworthiness of companies domiciled within the country. In turn, Aretzet al. (2010) show that aggregate corporate survival probability is associated witha significant and positive risk premium. Similarly, Chava and Purnanandam (2010)document a positive relationship between expected stock returns and default risk.1

As such, collectively, this literature highlights core linkages from sovereign ratings viacorporate creditworthiness (survival probability) through to corporate stock returns.2

The timeliness of rating assessments by the leading credit-rating agencies (CRAs)has received much press coverage in recent years. These agencies have recently beencriticised by, among others, German chancellor Angela Merkel and French presidentNicolas Sarkozy for acting precipitately in downgrading eurozone sovereigns, therebyexacerbating an impending crisis. Conversely, at the start of the credit crisis in2007, rating agencies were criticised for their failure to adequately assess the risk ofcollateralised debt obligations and for their slowness in the downgrading of bankratings. Similarly, Radelet and Sachs (1998) criticised the rating agencies for tardydowngradings of the countries involved in the Asian financial crisis of 1997, whichcaused further withdrawals of capital by creditors during that crisis.3,4 Thus, in ratingsovereign debt during turbulent times, the agencies are under great pressure to timethe announcement of any downgrades ‘perfectly’ – they tread a fine line betweendowngrading debt either ‘too early‘ or ‘too late‘, both of which have been arguedto contribute to a worsening of debt crises. Given this recent debate, a key feature ofour study is an investigation of the (relative) timeliness of rating assessments by theCRAs.

The aims of our paper can be summarised as follows. First, we assess the relativelevel of activity, type of activity, timeliness of events and extent to which rating actionsof each agency lead to a new high (upward trend) or low (downward trend) assessmentof credit quality. Second, we examine the information content of rating actions atthe sovereign level via both univariate and multivariate analyses, and we interpretthese results in the context of our observations regarding relative rating activity. Ourmultivariate analysis, in particular, allows us to examine whether events associatedwith a particular rating agency have more impact on stock markets after allowingfor sovereign and rating-specific variables and background macroeconomic effects.We deem such an effect a ‘reputation effect’, since other variables fail to capture theinfluence of the identity of the rating agency. A third aim is to specifically examineperiods of crisis and contagion in terms of both rating activity and the informationcontent of rating events.

1 It should be noted that the Aretz et al. (2010) and Chava and Purnanandam (2010) finding of a significantpositive association between default risk and returns is in contrast with earlier studies, such as those ofPetkova (2006) and Hahn and Lee (2006), who fail to observe a statistically significant result, or Campbellet al. (2008), who report negative linkages. The difference is likely due to alternative proxies employed fordefault risk (Aretz et al., 2010) and/or expected returns (Chava and Purnanandam, 2010).2 Further, Arteta and Hale (2006) show that country risk premia adversely affect the private sector’s accessto foreign capital markets.3 A re-assessment of the Asian crisis by Radelet and Sachs (1999, p.7) argues that the crisis ‘cannot easily beexplained by fundamentals’, and they conclude that a lack of appropriate financial regulation and the IMFresponse to the crisis were the key contributors to the depth of the crisis.4 Ferri et al. (1999), among others, argue that downgradings of non-high-income countries have historicallybeen both procyclical and excessive.

C© 2010 Blackwell Publishing Ltd

MARKET IMPACT OF RELATIVE AGENCY ACTIVITY 1311

By pursuing the above aims, we contribute to the existing body of work on creditratings in three major respects. First, there has been limited prior analysis of therelative rating actions of the three major agencies at the sovereign level, and weare aware of only one other study (Alsakka and ap Gwilym, 2010) that provides adetailed examination of the relative credit assessments at the sovereign level and ofthe relative timeliness of these assessments.5 Second, there is limited research intothe relative information content of the actions of different rating agencies,6 and ourcomprehensive analysis extends existing work along a number of dimensions. Third,we are aware of only one study (Kaminsky and Schmukler, 2002) that considers theimpact of crisis periods on the news content of sovereign rating assessments, and weextend their analysis of crisis periods. As we show, during crisis periods (in any eventwindow), there is a greater probability of a large negative return than during non-crisisperiods. Thus, by controlling for such crisis periods, we can undertake a more robustassessment of the general extent to which sovereign rating events are informative.

Prior studies of the information content of rating actions of the CRAs have primarilyfocused on upgrades and downgrades. Studies at both the corporate level (see, forexample, Goh and Ederington, 1993; Dichev and Piotroski, 2001; Abad-Romero andRobles-Fernandez, 2006; and Purda, 2007) and the sovereign level (see, for example,Brooks et al., 2004; and Gande and Parsley, 2005) find that rating downgrades doprovide news to the market, although most find that rating upgrades do not. Variousexplanations have been posited for this asymmetric effect. For example, Holthausenand Leftwich (1986) suggest that CRAs might have an asymmetric loss function,which leads to upgrades being less timely – a hypothesis that is supported by thefindings of Kim and Nabar (2007). Conversely, Gande and Parsley (2005) suggestthat rating agencies may be less keen to implement rating downgrades, for fear offoreign governments denying access to vital information, and the news element of adowngrade is therefore greater. Holthausen and Leftwich (1986) and Ederington andGoh (1998) suggest that firms/governments may have an incentive to leak positiveinformation to the market prior to a credit-rating upgrade, but have no incentive todo so prior to a downgrade. The news element of a downgrade is therefore greater.Kim and Nabar (2007), however, present evidence that pre-rating disclosures do notdiffer across upgrades and downgrades. Ederington and Goh (1998) also suggest thatCRAs expend more effort researching negative developments because their reputationrelies on identifying credit problems, and this increased effort produces information.

We undertake our analysis in two parts, focusing on (a) the relative actions ofthe CRAs and (b) the relative information content of their actions, which we assessvia their impact on stock-market returns.7 We summarise our findings as follows.Unsurprisingly, we find that negative rating events cluster around identified crisis

5 Recent studies at the corporate level investigate some interesting lines of research in the context ofmultiple credit ratings. For example, Livingston et al. (2010) compare the impact of split (Moody’s vsS&P) ratings on bond yields and find that investors do differentiate in such cases, placing more relianceon Moody’s. Mahlmann (2009) shows that it is worthwhile (in terms of lower debt costs) for some firms toseek a third ‘optional’ credit rating from Fitch, where they are unhappy with the rating assigned by Moody’sand S&P.6 Studies that examine the relative actions of different ratings agencies include Morgan (2002), whoconsiders the effect of divergent Moody’s and S&P ratings of banks, and Beaver et al. (2006), who considerthe informational advantage of certified (Moody’s) versus non-certified (Egan Jones) raters.7 In the preceding discussion, we highlighted the link from sovereign issuer ratings via corporate creditratings to corporate stock returns. We provide further discussion of the use of stock-market returns to assessinformation content in Section 3 (ii).


1312 HILL AND FAFF

periods, and our analysis shows that watch procedures or outlook announcements,rather than downgrades, are more likely to presage these crisis periods. During theseperiods we find that S&P is more active, provides more ‘new’ information (definedlater) and is more timely in its rating assessments than Fitch and Moody’s. We findsome evidence of specialisation, with Moody’s playing a ‘leading’ role among IMF‘advanced economies’ (see the Appendix; we also term these ‘advanced countries’here). Outside crisis periods, we again find that S&P is more active, and it tends to‘lead’ among IMF non-advanced economies.

Turning to the information content of the actions of the rating agencies, there areseveral findings worthy of note. First, we report evidence of significant mean pre-event(−10, −1) and event (0, +1) window returns in response to negative events in bothcrisis and non-crisis periods. We therefore confirm that the finding of a significantnegative reaction to negative events is robust to the exclusion of crisis periods, albeitthat during crisis periods the response in the 12-day (−10, +1) window is more thanthree times as great. Pre-event returns in response to negative rating activity havebeen reported by a number of authors (Wansley and Clauretie, 1985; Steiner andHeinke, 2001; Hull et al., 2004; and Purda 2007), who suggest that this is due to pre-announcement anticipation/speculation and/or information leakage/dissemination.We find that, outside periods of crisis and contagion, the event window (0, +1) returnin response to negative rating events is negatively related to the pre-event window(−10, −1) return, which would support these theories because, in the case that anevent is anticipated, the reaction to the event would be expected to be weaker.

We find evidence that the event window response to downgrades in crisis periods ispositively related to the pre-event window return. Stock-market reactions to downgradesin crisis periods in the pre-event and event windows are both significantly negative,and the finding that a large (small) reaction follows a large (small) reaction mightsuggest that other news impacting on the stock market is responsible for both thepre-event and the event window returns. However, the finding that post-event (+2,+11) returns are not significantly different from zero (and significantly negativelycorrelated with event window returns) suggests that the causality is from rating actionsto the stock market. Therefore, even though downgradings during crisis periods areuntimely, they tend to exacerbate an already downward trend in the stock market.In this vein, Radelet and Sachs (1998) argue that the untimely downgradings of thecountries involved in the Asian financial crisis of 1997 were unhelpful in that theycaused further withdrawals of capital by creditors during the crisis.

Second, we find that changes in a credit-outlook or credit-watch status tend to bemore informative than downgrades. This finding supports our analysis of crisis periodsdiscussed above, where we suggest that downgrades are less timely than changes inoutlook or credit-watch status.8 The significant negative reaction to downgrades is not,in fact, robust to the exclusion of crisis periods. Third, we also find evidence of anasymmetric reaction to rating events. Across positive events, the mean two-day (0,+1) cumulative abnormal return is +0.25% against −0.54% for (non-crisis period)negative events. The comparable 12-day (−10, +1) returns are 0.56% for positiveevents and −2.87% for negative events. Nonetheless, we do find evidence that positive

8 With regard to the impact of outlook and credit-watch rating events on the Asian Financial crisis, Radeletand Sachs (1999) argue that the rating agencies ‘continued to give positive outlooks’ as late as early1997. However, they concede that the agencies’ evaluation of these economies did not differ from thatof investment banks, the IMF and the World Bank.



events lead to significant event-day returns, and this result seems to be mostly drivenby S&P credit-outlook announcements.

Fourth, across all events we find no conclusive evidence of a ‘reputation’ effect.However, we find that the reaction to Moody’s downgrades is significantly less negativethan to Fitch or S&P downgrades. Since we are unable to find an alternativeexplanation for this result, we suggest that it may be driven by a reputation effect. Wefind evidence of a reputation effect across advanced countries where the reaction toFitch negative events is significantly less than to Moody’s or S&P events. We find someevidence of Moody’s leading the field across IMF advanced economies and we might,therefore, have expected a greater reaction to Moody’s events than to S&P events,but this is not the case. Finally, in line with previous studies, we find that positive ratingevents are difficult to model. However, we do find some evidence of a reputation effectin favour of S&P across non-advanced countries.

The remainder of the paper is structured as follows. Section 2 contains a descriptionof the credit-rating process and an analysis of rating activity across agencies. Section 3contains univariate and multivariate analyses of the reactions to sovereign ratingevents. Section 4 provides a concluding discussion.

2. DIFFERENCES IN RATING ACTIVITY ACROSS CREDIT-RATING AGENCIES

(i) Sovereign Ratings and the Credit-Rating Process

A number of alternative ratings are issued at the sovereign level, namely, countryceilings, issuer government ratings and individual bond issue ratings. We employ therating attached to the sovereign issuer. The rating agencies issue both foreign currencyand local currency sovereign issuer ratings, with the former placing additional em-phasis on external liquidity and the external debt burden. Given that most countrieshave a longer foreign currency rating history and the ready availability of full foreigncurrency rating histories across all three agencies, we choose to focus on foreigncurrency ratings for our empirical analysis.

We obtained Fitch and S&P sovereign issuer ratings data from their websites. Sincethe Moody’s website only provides sovereign ceiling ratings, its sovereign issuer ratingsare obtained directly from the agency. Our sample period extends from 1 January, 1990through to 30 June, 2006.9 We employ a sample of 101 countries for which there was acredit watch, longer term outlook change or rating change over our sample period.10

The Appendix provides a list of our sample countries organised by the UN HumanDevelopment Index (HDI), with the IMF classification of ‘Advanced Economies’ alsoindicated. A few features are noteworthy. First, it shows that, for our event study andmultivariate analyses, we have a useable sub-sample of 49 countries for which stockdata are available. Second, two-thirds (one-third) of the sample classify as ‘high rank’

9 It should be noted that Fitch did not rate sovereign issuers until 10 August, 1994. Also, Moody’s did notmake use of credit-watch procedures prior to the 1990s, and there was very little sovereign re-rating activityprior to 1990. This lack of re-rating activity reflects the fact that sovereign ratings were only issued for arelatively select group of countries with higher credit ratings, and few re-ratings were required.10 We exclude countries for which there were no rating actions across the three agencies in the sampleperiod, comprising a very small number of countries that tend to have (i) a high rating and long ratinghistory, such as Switzerland and the United Kingdom, and (ii) a lower rating and shorter rating history, suchas Nigeria and Mozambique.


1314 HILL AND FAFF

(‘medium rank’) in the UN HDI.11 Third, according to the IMF, a majority (55%) ofcountries for which stock data are available are not classified as ‘advanced economies’.Fourth, it is evident that the advanced economies in our sample are more likely to havea longer rating history than the other countries covered.

Three types of rating event can be actioned by these CRAs that affect the currentcredit standing of a given rated entity: a change in the credit outlook, the instigationof credit-watch procedures and a change in the rating.

An outlook takes a longer-term view of the credit worthiness of a bond issuer and isusually attached to all ratings.12 A credit outlook typically covers a period of up to twoyears ahead. Outlooks can be ‘positive’ (signalling the possibility that at some stageover the two-year horizon a rating may be raised), ‘stable’ (indicating that a rating isunlikely to be changed) or ‘negative’ (indicating that a rating may be lowered).

A credit watch is more short-term focused and is instigated where new developmentsbecome known that might affect the rating. For example, Fitch put a rating on reviewwhere new information comes to light that might affect the rating, and this review ofthe existing rating is normally undertaken within two business days. If a review is notconcluded, an entity will be put on credit watch – typically as ‘rating watch negative’.13

An entity placed on credit watch does not have a ratings outlook during thecredit-watch period. A credit watch is designated ‘positive’ (a rating may be raised),‘developing’ (a rating may either be raised or lowered, but inadequate information isavailable for this to be currently assessed) or ‘negative’ (a rating may be lowered). Itis important to note that credit-watch status need not lead to a rating change, anda rating change need not be preceded by an entity being placed on credit watch.For corporate bonds, Moody’s estimates that, historically, between 66% and 76%of all ratings have been changed in the same direction as indicated by the credit-watch review.14 For corporate bonds, the duration of credit-watch status is typicallyup to 90 days.15 Across our sample, watch procedures last for an average of 77 days(median = 62 days). Moody’s watch negative procedures consume about the samelength of time as watch positive procedures at 70 days on average, but Fitch watchnegatives take nearly twice as long as watch positives (104.4 days versus 56.5 days).Across all CRAs, the duration of the watch procedure is about twice as long before aconfirmation,16 at an average length of 117.9 days (median = 91.5 days), as before arating change, when a credit watch lasts, on average, 65.7 days (median = 56 days).

11 Of 22 countries that are classed as low rank, only one (Malawi) had a credit rating during our sampleperiod.12 See ‘Standard and Poor’s Primer on CreditWatch and Ratings Outlooks’, Standard and Poor’s RatingsDirect, April 2004.13 See p. 3, ‘The Rating Process’, FitchRatings (July 2006).14 See p. 7, ‘Understanding Moody’s Corporate Bond Ratings and Rating Process’, Moody’s InvestorsService (May 2002).15 Again, see, ‘Understanding Moody’s Corporate Bond Ratings and Rating Process’, Moody’s InvestorsService (May 2002), and ‘Standard & Poor’s Primer on CreditWatch and Ratings Outlooks’, Standard andPoor’s Ratings Direct (April 2004).16 Moody’s draws a distinction between rating confirmations (a rating is removed from the watch list) anda rating affirmation (the current rating remains in force following, for example, an informal review, therelease of new information or a major market event). See p. 36, ‘Moody’s Rating Symbols & Definitions’,Moody’s Investors Service (August 2003). We employ the term ‘confirmation’ throughout this paper to implythat ratings are affirmed/confirmed, rather than changed, at the close of a credit-watch procedure.



(ii) Rating Activity and Clustering

In Panel A of Table 1, we present summary statistics in relation to foreign currencycredit-rating changes across the three rating agencies, over our sample period. Itshould be noted that Fitch did not issue sovereign ratings until 10 August, 1994 andthat Moody’s did not attach outlooks to sovereign issuer ratings prior to 2002. The firstfour columns of Table 1 relate to all sovereign events. The second four columns relateto events for which we have stock data.

Since the number of rated countries and periods over which they are rated vary, wealso present a ‘Key Percentages of Event Types’ in Panel B. The (first four) columns,which relate to all sovereign events, show that Fitch (61%), S&P (55%) and Moody’s(59%) all have a higher proportion of positive rating events. Panels A and B ofTable 1 indicate that Moody’s employs credit-watch procedures to a much greaterextent than either S&P or Fitch. This suggests that Moody’s is prepared to commitconsiderably more resources to this activity than its competitors, or that it devotes lesseffort to each watch procedure (though the latter could lead to a potentially harmfulreputational effect for the agency in a competitive market setting). The second notablefeature is that Fitch favours watch negatives over watch positives, and S&P watches arevirtually exclusively negative (it instigates only one non-negative case: a ‘developing’watch procedure prior to an upgrade), whereas Moody’s watch procedures are equallycommon for rating upgrades as for downgrades. An examination of the CRAs’literature fails to suggest that watch procedures are defined differently across thethree. However, the differences in the extent to which, and circumstances in which,watch procedures are employed across the agencies suggest a contrary conclusion –it seems that different criteria are being employed across the three to determine theannouncement of watch procedures.17 The final four columns of Panel B, which relateto countries for which we have stock data, confirm that our stock-market sample isbroadly representative of the population for all event types across agencies.

Following, for example, Brooks et al. (2004), we examine returns within a windowof −10 to +11 trading days. Importantly, we define an ‘independent’ event as one inwhich no other event occurs within 21 trading days, thus ensuring that all independentevents are not contaminated by other events within the (−10, +11) window. Forcompleteness, we define a ‘clustered’ event as one that occurs within 21 trading daysof another event for that sovereign.

Of interest is the frequency with which clustering occurs across the 1,375 separaterating events (Table 1) in our sample. We find that there are 851 independent events(‘cluster’ size equals 1), 148 cases of ‘2-cluster’ events and 25 cases of ‘3-cluster’events. Furthermore, there are 25 cases in which ‘4-cluster’ or greater events occur.The largest cluster identified contains 20 separate events. Examination of our datasuggests that, as expected, events cluster particularly during crisis periods. Stock-market volatility during such periods is considerable, which further complicates ouranalysis of the stock-market reaction to rating events. Accordingly, we choose to analysethe relative rating behaviour of the agencies and the stock-market reaction to theirrating behaviour, both within and outside crisis periods.

17 Hill et al. (2010) provide evidence of material heterogeneity between agencies in their rating assess-ments.


1316 HILL AND FAFF

Table 1Credit-Rating and Watch Statistics: All Countries with Ratings Changes/Credit

Watches Over the Period January 1990 to June 2006

All Sovereign Events Events with Stock Data

Total S&P Moody’s Fitch Total S&P Moody’s Fitch

Panel A: Types of Rating EventNo. of event countries 101 89 55 76 49 47 36 40Downgrades (i) 270 119 78 73 172 76 54 42Upgrades (ii) 418 171 113 134 255 103 73 79Default (iii) 15 11 0 4 8 7 0 1Out of default (iv) 15 12 0 3 5 5 0 0

Negative Watch (v) 130 43 52 35 99 33 38 28

of whichAmalgamated events(vi) na na na na (28) (15) (3) (10)Net of amalgated (sample) na na na na 71 18 35 18Outcomes for watch negativeend in rating change 87 27 36 24 65 22 27 16end in confirmation (vii) 40 15 16 9 23 7 10 6not concluded 3 1 0 2 11 4 1 6Total (= (v) above) 130 43 52 35 99 33 38 28

Positive Watch (viii) 84 0 61 23 59∗ 0 44 15∗

Outcomes for watch positiveend in rating change 71 0 51 20 51 0 36 15end in confirmation (ix) 6 0 4 2 5 0 4 1not concluded 7 0 6 1 4 0 4 0Total ( = (viii) above) 84 0 61 23 60∗ 0 44 16∗

Negative Outlook (x) 158 115 8 35 100 75 4 21Positive Outlook (xi) 239 158 18 63 140 99 7 34

Total negative events(i) + (iii) + (v) + (ix) + 579 288 142 149 356 176 97 83

(x) − (vi)Total positive events(ii) + (iv) + (vii) + 796 356 208 232 482 214 134 134

(viii) + (xi)Total events 1,375 644 350 381 838 390 231 217

Panel B: Key Percentages of Events TypesPositive 57.9% 55.3% 59.4% 60.9% 57.5% 54.9% 58.0% 61.8%Negative 42.1% 44.7% 40.6% 39.1% 42.5% 45.1% 42.0% 38.2%Downgrade (incl default) 49.2% 45.1% 54.9% 51.7% 50.6% 47.2% 55.7% 51.8%Watch neg. 22.5% 14.9% 36.6% 23.5% 19.9% 10.2% 36.1% 21.7%Outlook neg. 27.3% 39.9% 5.6% 23.5% 28.1% 42.6% 4.1% 25.3%Confirm neg. 1.0% 0.0% 2.8% 1.3% 1.4% 0.0% 4.1% 1.2%Upgrade (incl. 54.4% 51.4% 54.3% 59.1% 53.9% 50.5% 54.5% 59.0%

out of default)Watch pos. 10.6% 0.0% 29.3% 9.9% 12.2% 0.0% 32.8% 11.2%Outlook pos. 30.0% 44.4% 8.7% 27.2% 29.0% 46.3% 5.2% 25.4%Confirm pos. 5.0% 4.2% 7.7% 3.9% 4.8% 3.3% 7.5% 4.5%



Table 1 (Continued)

Notes:This table presents a breakdown of our sample of rating events over the period January 1990 toJune 2006. The left-hand columns show data for the full sample of 101 countries and the right-handcolumns show data for the 49 countries for which we have stock data. These latter data are employed inour analysis of the information content of rating events. Stock data were not available for one Fitch watchpositive for which we have data for the outcome (denoted by an asterisk). Amalgamated watch eventsare those that occur on the same day as downgrades, and both events are combined for the stock-marketanalysis. Full details are given in Sections 4(ii) and 4(iii).

(iii) Relative Rating Activity in Crisis Periods

Our purpose is to take account of both event clustering and periods of high stock-market volatility, and we therefore assess both stock-market and rating data to createan indication of the existence and extent of a crisis. Specifically, we characterise acrisis period as one in which there is a cluster of four or more (negative) rating eventsaccompanied by a fall in the domestic stock-market index. Employing this definitionof a crisis period, we identify 13 crisis periods. During these periods, 196 rating actionsoccurred, which are set out in Table 2.18

We start by identifying the first rating action of a crisis period. To some extent,this process is reliant on judgment. However, the choice of the first rating action isrelatively non-contentious in nine out of 13 cases, since in eight cases we are able toidentify a non-negative rating action as the last action preceding the crisis, and in onecase (Pakistan) there is a long gap between the last event before the crisis and thefirst crisis event. This leaves four cases (Argentina, Brazil, Turkey and Uruguay) whereour designated first crisis event, by reference to the sequence of rating events and thestock-market index, may be open to question. The last rating event in the crisis periodis the last negative rating action before the identified first post-crisis event, which inturn is the first positive rating event following the stock-market low.

In Table 2 we document (a) the crisis periods (first event column G and lastevent column H); (b) the Datastream stock-market index value at the first crisis event(column B) and the stock-market low following this up to the end of our sample period(column C); (c) the number of crisis events – the figures in parentheses relate to thenumber of events included in the stock-market analysis (column D); (d) the lowestrating by any agency at the start of the crisis (column E); (e) the last rating eventbefore the crisis (column F); (f) the first post-crisis event (column I); (g) the meanreturn for each (0, +1)19 period during the crisis (column J); and (h) the standarddeviation of the event window returns during the crisis (column K).

From Table 2, three things are of particular note. First, S&P events presage the crisesin 10 out of 13 cases (77%), Moody’s in two cases and Fitch in one case. However, infour (one) cases, Fitch (Moody’s) did not rate the sovereign at the start of the crisisperiod. Second, the crises are presaged by a watch negative in six out of 13 cases (46%),

18 Kaminsky and Schmukler (2002) also identify the Mexican Peso crisis from December 1994 to March1995 and the Brazilian crisis from January 1999 to February 1999. These periods were not accompanied bysignificant re-rating activity, and only in the case of the Brazilian crisis did event clusters occur, with twoevents in September 1998 and two in January 1999. For this reason, we do not include a separate analysisof rating activity for these crises, but we do label three Mexican events in December 1994 and March 1995,and the two Brazilian events in January 1999 as crisis-period events for our stock-market analyses.19 This is the event window that we employ, as suggested by Gande and Parsley (2005). The pre-eventwindow is therefore (–10, –1) and the post-event window (+2, +11).


1318 HILL AND FAFF

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0216

/10/

02(5

)M

oody

’sS&

PFi

tch

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hFi

tch

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Dom

.Rep

.25

.92

8.20

13B

B−

UG

BB−

Wat

Neg

Def

ault

From

Def

−1.0

3%6.

87%

14/0

5/03

26/1

1/03

(6)

S&P

S&P

S&P

Fitc

hS&

P23

/10/

0123

/10/

0115

/05/

0305

/05/

0529

/06/

05In

done

sia

61.3

89.

8129

Baa

3U

GB

BB

DG

BB

B−

DG

CC

C+

OL

Sta

−0.1

7%6.

55%

09/1

0/97

20/0

4/01

(18)

Moo

dy’s

S&P

S&P

S&P

S&P

14/0

3/94

18/0

4/95

10/1

0/97

21/0

5/01

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Kor

ea15

1.71

39.0

015

AA−

UG

AA−

OL

Neg

DG

B−

Wat

Dev

−2.3

5%8.

75%

05/0

8/97

31/1

2/97

(10)

S&P

S&P

S&P

Fitc

hS&

P03

/05/

9503

/05/

9506

/08/

9723

/12/

9716

/01/

98M

alay

sia

544.

0015

5.47

11A+/

A1

OL

Sta

OL

Neg

DG

BB

B−

Aff

irm

−0.4

4%6.

93%

24/0

9/97

01/0

9/98

(10)

S&P/

Moo

dy’s

S&P

S&P

S&P

Moo

dy’s

29/1

2/94

(SP)

18/0

8/97

25/0

9/97

15/0

9/98

03/1

2/98

Paki

stan

165.

5856

.78

11B

2D

GB

2O

LN

egD

efau

ltFr

omD

ef−0

.53%

5.79

%13

/01/

9810

/07/

98(9

)M

oody

’sM

oody

’sS&

PS&

PS&

P06

/11/

9606

/11/

9614

/01/

9829

/01/

9921

/12/

99R

oman

ian/

an/

a10

BB−

1stR

atin

gW

atN

egD

GB−

OL

Sta.

n/a

n/a

(0)

All

Moo

dy’s

S&P

Fitc

hS&

P06

/03/

96(S

P/F)

04/0

6/97

23/0

1/98

24/0

3/99

04/0

8/00



Rus

sia

1074

.70

147.

6621

BB−

1stR

atin

gO

LN

egD

efau

ltU

GC

aa2

−1.1

4%5.

72%

18/1

2/97

02/1

0/98

(17)

S&P

Moo

dy’s

S&P

S&P

Moo

dy’s

04/1

0/96

22/1

1/96

19/1

2/97

27/0

1/99

05/0

1/00

Tha

iland

811.

7117

2.39

11A

/A2

UG

AW

atN

egD

GB

BB−

Wat

Pos

−0.9

8%5.

11%

12/0

2/97

03/0

9/98

(11)

S&P/

Moo

dy’s

S&P

Moo

dy’s

S&P

Fitc

h01

/08/

89(M

)29

/12/

9413

/02/

9708

/01/

9826

/04/

99Tu

rkey

139

8.11

135.

049

Baa

3O

LN

egW

atN

egD

GB

a3O

LSt

a−0

.73%

8.15

%07

/10/

932/

05/9

4(7

)M

oody

’sS&

PM

oody

’sM

oody

’sS&

P05

/05/

9203

/05/

9308

/10/

9302

/06/

9416

/08/

94Tu

rkey

213

68.3

424

4.77

11B+/

B1

Aff

irm

Wat

Neg

DG

BO

LSt

a−0

.42%

6.80

%18

/02/

008/

10/0

1(7

)S&

P/M

oody

’sM

oody

’sS&

PFi

tch

S&P

13/0

3/97

(M)

20/1

2/00

21/0

2/01

02/0

8/01

30/1

1/01

Uru

guay

n/a

n/a

22B

aa3/

BB

B−

OL

Neg

OL

Neg

Def

ault

From

Def

.n/

an/

a(0

)A

llFi

tch

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Fitc

h/S&

PS&

P23

/01/

97(F

)23

/07/

0111

/01/

0216

/05/

0302

/06/

03

Not

es:

Thi

sta

ble

prov

ides

data

inre

latio

nto

13id

entif

ied

cris

ispe

riod

s.W

edo

cum

ent

the

follo

win

g.(a

)T

heD

atas

trea

mst

ock-

mar

ket

inde

xva

lue

atth

efi

rst

cris

isev

ent(

colu

mn

B)

and

the

stoc

k-m

arke

tlow

follo

win

gth

isup

toth

een

dof

our

sam

ple

peri

od(c

olum

nC

).(b

)T

henu

mbe

rof

cris

isev

ents

(col

umn

D)

–th

efig

ures

inpa

rent

hese

sre

late

toth

enu

mbe

rof

even

tsin

clud

edin

the

stoc

k-m

arke

tana

lysi

s.T

his

num

ber

isad

just

edfo

ram

alga

mat

edev

ents

that

occu

ron

the

sam

eda

y(s

eeSe

ctio

n4(

ii)

for

full

deta

ils),

soth

eto

tals

ums

to11

8.In

the

stoc

k-m

arke

tan

alys

is,w

ead

dto

this

tota

lthe

five

addi

tiona

lcri

sis

even

tsof

Kam

insk

yan

dSc

hmuc

kler

(200

2)to

reac

h12

3ev

ents

.(c)

The

low

estr

atin

gby

any

agen

cyat

the

star

toft

hecr

isis

(col

umn

E).

Whe

retw

oor

mor

eco

untr

ies

have

the

sam

epr

e-cr

isis

ratin

glo

w,th

enbo

thar

ena

med

and

the

date

give

nis

the

earl

iest

date

atw

hich

this

ratin

gw

asas

sign

ed.(

d)T

hela

stra

ting

even

tbe

fore

the

cris

is(c

olum

nF)

.(e)

The

cris

ispe

riod

s(f

irst

even

tco

lum

nG

and

last

even

tco

lum

nH

).(f

)T

hefir

stpo

st-c

risi

sev

ent

(col

umn

I).

(g)

The

mea

nre

turn

for

each

(0,+1

)pe

riod

duri

ngth

ecr

isis

(col

umn

J).(

h)T

hest

anda

rdde

viat

ion

ofth

eev

ent

win

dow

retu

rns

duri

ngth

ecr

isis

(col

umn

K).

No

stoc

kda

taar

eav

aila

ble

for

Rom

ania

and

Uru

guay

,so

we

are

forc

edto

rely

sole

lyon

ratin

gda

tato

dete

rmin

eth

ere

leva

ntcr

isis

peri

ods.

No

stoc

kda

taar

eav

aila

ble

for

the

Dom

inic

anR

epub

licpo

st26

Dec

embe

r,20

03,s

ow

eem

ploy

thes

etr

unca

ted

data

inth

ista

ble.


1320 HILL AND FAFF

an outlook negative in five out of 13 cases (38%) and a downgrade in two out of 13cases (15%). Third, the volatility of event window returns is considerable. During crisisperiods, in any (0, +1) event window there is a greater probability of a large negativeor large positive return than during non-crisis periods.

We examine further the behaviour of the individual rating agencies during crisisperiods. An agency might be deemed a leader if an initial event triggers other eventsin the same rating direction, or if a rating action provides new information by taking asovereign rating to a new level. As such, we can examine leader-follower behaviouralong two dimensions, either via event clustering or via ‘new information’ versus‘copycat’ events. Specifically, we define these events, as follows.

New information rating events are:

(a) events that are in the opposite direction to the previous rating event; and

(b) events that take a rating to a new level, below the prevailing lowest rating byanother agency (downward trend) or above the prevailing highest rating byanother agency (upward trend). A priori, a rating change is deemed a strongersignal than a watch, which, in turn, is deemed to be a stronger signal than anoutlook. For example, if all agencies agree on the current rating level and oneagency then puts the sovereign on outlook followed a few days later by a watch,the rating is deemed to have changed both times to a new high or low.

Copycat rating events are events that:

(a) follow the trend set by the last event; and

(b) do not take the rating to a new rating level.

We employ the new information event/copycat event dichotomy to examine bothleader-follower behaviour among agencies and the relative informativeness of theseevents.

Along the cluster dimension, ‘leading’ actions are those that occur first in a clusterand are followed by rating actions in the same direction, with a gap of no more than 21trading days between each event. This analysis is in the spirit of the Cooper et al. (2001)analysis of the timeliness of analyst behaviour, where they assume that some analystsfree-ride on the information of lead analysts by revising their earnings forecasts shortlyafter the lead analyst.20

In Figure 1, for illustrative purposes, we present the timeline for the Argentineancrisis. In Table 3, we present our leader-follower analysis based on the two dimensions

20 Cooper et al. (2001) examine leader-follower behaviour among analysts during a one-year period withmany analysts and one type of forecast. They compute a leader-follower ratio based on the sum of cumulative(across all analysts) lead and follow times for each action. A similar ratio in our context would be misleadingsince the lead time for the first rating action can be very large if a sovereign is seldom re-rated, and thiswould distort results. The spirit of our analysis is similar, in that we assume a leading rating event has nopreceding events within 21 trading days and that a following event occurs within 21 trading days of thepreceding event. Later in this section, when we examine contagion during crisis periods, events are morefrequent and we are able to undertake the same analysis as Cooper et al. (2001).



Figure 1Time Line of Argentinean Crisis

Wat

ch (

S&

P)

Dow

ngra

de B

B–/

Wat

ch (

Fit

ch)

Dow

ngra

de B

+/W

atch

– S

&P

Dow

ngra

de B

+ &

B2/

Wat

ch (

Fit

ch &

M

oody

’s)

Dow

ngra

de B

/Wat

ch (

S&

P)

Con

firm

atio

n (S

&P

)

Con

firm

atio

n (M

oody

’s)

Dow

ngra

de B

– (F

itch

)

Dow

ngra

de B

– (S

&P

)

Dow

ngra

de B

3/W

atch

(M

oody

’s)

Dow

ngra

de C

aa1

(Moo

dy’s

)

Dow

ngra

de C

CC

+ (

S&

P)

Dow

ngra

de C

CC

– &

Caa

3 (F

itch

and

M

oody

’s)

Dow

ngra

de C

C (

S&

P)

Dow

ngra

de C

C/W

atch

(F

itch

)

Dow

ngra

de C

(F

itch

) D

efau

lt (

S&

P)

Def

ault

(Fi

tch)

Dow

ngra

de C

a (M

oody

’s)

19/0

3/01

20/0

3/01

26/0

3/01

28/0

3/01

08/0

5/01

04/0

6/01

06/0

6/01

11/0

7/01

12/0

7/01

13/0

7/01

26/0

7/01

09/1

0/01

12/1

0/01

30/1

0/01

02/1

1/01

06/1

1/01

03/1

2/01

20/1

2/01

N/C C 2xC 2xC 2xN(M) 2xC(F) 2xC N C N C 2xC N C

N(F) N(M) N 2xC

N(F) N(S) C C

(0,+1) Raw (1.2) (0.7) 4.2 (1.8) (1.8) 0.7 0.7 (7.9) (4.3) 0.9 (1.3) 0.3 0.1 1.2 1.0 0.6 1.4 11.2

Cluster 1 Cluster 2 Cluster 3 Cluster 4

Notes:This figure shows the timeline of events across all three agencies for the Argentinean crisis of 2001. Eachevent is designated either a new information event (N) or a copycat event (C). Raw returns for each (0, +1)event window are also indicated.

indicated, restricted to the eight crisis periods throughout which our sovereign samplewas rated by all three agencies.

During the eight crisis periods analysed, S&P undertook 51 rating events, of which33 (65%) were new information events; Fitch undertook 38 rating events, of which12 (32%) were new information events; and Moody’s undertook 29 rating events, ofwhich 14 (48%) were new information events. In general, S&P undertake more ratingactions and provide more new information to the market during crisis periods.

We can also examine all events in crisis clusters. As shown in Figure 1, theArgentinean rating events during the crisis period contain four clusters. Across alleight crisis periods, S&P events trigger other cluster events in 50% of clusters, Fitchin 29% of clusters and Moody’s in 21% of clusters. Employing a similar reasoning toCooper et al. (2001), we conclude that S&P is also the leading agency in crisis periodsin terms of timeliness.

Thus far we have considered crisis periods for each sovereign entity separately.We now consider the issue of contagion. Of the identified crisis periods, two largelyaffect only the country concerned: the 1993/94 Turkish crisis and the 2003 DominicanRepublic crisis. The other crisis periods are related to two global crisis periods – from1997 to 1999, and in 2001 – and a Latin and Central American crisis – from 2001 to2003. We consider relative rating activity of the agencies within each of these periods,where contagion occurs, by employing data for all sovereign events that occur withinthe crisis periods. Since we now have rating events that occur with greater frequency,we follow the method set out in Cooper et al. (2001) to calculate a leader-follower ratio(LFR). We adapt the method for our situation where we have only three agencies,and we consider only negative events (since these are the ones where contagion is


1322 HILL AND FAFF

Tab

le3

Ana

lysi

sof

Cri

sis

Clu

ster

s(C

ount

ries

Rat

edby

All

Age

ncie

sT

hrou

ghou

tCri

sis

Peri

od)

Lea

dEv

ents

inC

risi

sC

lust

ers

New

Info

rmat

ion

Even

tsin

Cri

sis

Cop

ycat

Even

tsin

Cri

sis

NS&

P%Fi

tch%

Moo

dy’s%

NS&

P%Fi

tch%

Moo

dy’s%

NS&

P%Fi

tch%

Moo

dy’s%

Arg

entin

a4

75.0

25.0

0.0

922

.233

.344

.412

50.0

33.3

16.7

Bra

zil

10.

010

00.

02

0.0

50.0

50.0

333

.067

.00.

0In

done

sia

475

.025

.00.

015

73.3

13.3

13.3

837

.550

.012

.5M

alay

sia

333

.333

.333

.35

60.0

20.0

20.0

633

.30.

066

.7R

oman

ia2

0.0

0.0

100.

04

75.0

0.0

25.0

60.

050

.050

.0R

ussi

a3

33.3

33.3

33.3

757

.10.

042

.912

25.0

58.3

16.7

Turk

ey2

367

.033

.30.

05

100.

00.

00.

03

0.0

100.

00.

0U

rugu

ay4

50.0

25.0

25.0

1241

.641

.616

.79

33.3

33.3

33.3

Tota

l12

75

3312

1418

2615

%To

tals

50.0

29.2

20.8

55.9

20.3

23.7

30.5

44.1

25.4

Not

es:

Thi

sta

ble

pres

ents

anan

alys

isof

the

exte

ntto

whi

chth

eth

ree

maj

orra

ting

agen

cies

prov

ided

(a)

timel

yin

form

atio

n,(b

)ne

win

form

atio

nan

d(c

)co

pyca

tin

form

atio

n.T

hean

alys

isre

late

sto

eigh

tcr

isis

peri

ods

thro

ugho

utw

hich

each

sove

reig

nis

rate

dby

allt

hree

agen

cies

.The

follo

win

gco

untr

ies

wer

eno

tra

ted

byal

lth

ree

agen

cies

thro

ugho

utso

me

ofth

ecr

isis

peri

od–

the

Dom

inic

anR

epub

lic,K

orea

,Pak

ista

n,T

haila

ndan

dTu

rkey

(Cri

sis

1).A

wat

chan

ddo

wng

rade

byth

esa

me

agen

cyon

the

sam

eda

yis

trea

ted

ason

eev

ent.



likely to occur). For each event, we calculate (a) the time from the preceding negativerating action by another agency and (b) the time to the succeeding negative actionby another agency. We sum these times across all events for each agency, and the LFRis given by the sum of preceding event times divided by the sum of succeeding eventtimes. Leading (following) actions have a long (short) lead time and a short (long)follow-up time, producing a LFR value exceeding (less than) unity.

Starting with the global crisis from 1997 to 1999, its trigger event (in terms ofrating activity) is taken to be S&P’s placement of Thailand on watch negative on 1August, 1997.21 From then until 31 March, 1999, negative rating events dominate,other than a short period in the middle of 1998, when there are a number of upgradesto (primarily) advanced economies. The crisis continued until Indonesia came outof default and Malaysia’s outlook was upgraded from negative to stable on 31 March,1999, both of which were actions by S&P.22

The trigger event for the 2001 crisis is when S&P put Turkey on watch negative on21 February, 2001. The end of this crisis period is harder to pin down, but by the startof 2002, the incidence of upgrades start to dominate downgrades, and we thereforeassume that the crisis continues until 30 November, 2001.

The 2001 global crisis runs into the 2001 to 2003 Latin and Central American crisis.We choose to examine this crisis separately because contagion appears to be limited toother Latin and Central American countries. We assume that the trigger event for thiscrisis is when Fitch allocates a default rating to Argentina on 3 December, 2001,23 andwe assume that the crisis is over when S&P bring Uruguay out of default on 2 June,2003 and Fitch assigns a positive outlook to Brazil on 3 June, 2003.

We investigate the relative rating activity of the three agencies across all countriesduring the first two periods of contagion, and across all relevant countries during thethird period of contagion, and the results are presented in Table 4.

The results in the table confirm those presented in Table 3 – that S&P tends tobe a ‘leader’, here having the highest LFR values in the two global crises of 1.10 and1.22, against 0.93 and 0.94 (1.07 and 0.78) for Moody’s (Fitch). Alsakka and ap Gwilym(2010) also document that S&P downgrades tend to lead those of Moody and Fitch.Moody’s has higher LFR values for advanced economies than the other two agencies,and S&P and Fitch both have higher values than Moody’s for the non-advancedeconomies, suggesting different relative specialisms across agencies. Finally, the resultssuggest that Fitch might have relative regional expertise in Latin and Central America,where it is both active (the number of negative events is twice that of Moody’s and thesame as S&P), and its LFR is more favourable than those of Moody’s or S&P.

(iv) Relative Rating Activity in Non-Crisis Periods

We have a total of 714 non-crisis events during periods when a sovereign was rated byall three agencies. These consist of 280 events in 128 clusters of two or three events,and 434 independent events. Leader-follower analysis is undertaken on both the 714

21 The ‘trigger’ event for this global crisis is open to some debate. Moody’s put Thailand on watch negativeon 13 February, 1997. However, only two negative rating events occurred: a downgrade by Moody’s of Turkeyon 13 March, 1997 and a downgrade by Moody’s of Thailand on 8 April, 1997, against a backdrop of 20positive rating events up to 20 July, 1997, some of which were to regionally related emerging markets. Thus,we seek to exclude the large number of positive events prior to 1 August, 1997.22 The crisis continued for Indonesia, which was subsequently placed on watch negative on 13 September,1999 and was given a default rating on 17 April, 2000.23 Rating events prior to this are captured by the 2001 global crisis, just defined.


1324 HILL AND FAFF

Table 4Analysis of Relative Rating Behaviour Across All Countries During Global Crises

Moody’s Standard & Poor’s Fitch

Neg. Neg. Neg.Events LFR Neg/Pos Events LFR Neg/Pos Events LFR Neg/Pos

Panel A: Global Crisis 1997–1999All 45 0.93 2.05 78 1.10 3.55 39 1.07 2.60Advanced 5 1.35 0.42 12 0.87 0.86 11 0.79 1.10Non-Adv. 40 0.89 4.00 66 1.16 8.25 28 1.20 5.60Crisis 26 0.78 n/a 47 1.20 23.50 26 0.96 26.00Non-Crisis 19 1.14 0.86 31 0.96 1.55 13 1.25 0.93

Panel B: Global Crisis 2001All 8 0.94 0.62 36 1.22 1.38 27 0.78 1.50Advanced 1 3.00 1.00 6 2.05 1.20 3 1.33 0.75Non-Adv. 7 0.57 0.58 30 1.15 1.43 24 0.75 1.71Crisis 6 0.48 6.00 17 1.47 8.50 14 1.34 n/aNon-Crisis 2 2.38 0.17 19 1.03 0.79 13 0.57 0.72

Panel C: Latin & Central American Crisis 2001–2003All 11 0.97 1.22 22 0.98 2.75 22 1.08 3.67Advanced 0 n/a n/a 0 n/a n/a 0 n/a n/aNon-Adv. 11 0.97 11.00 22 0.98 2.75 22 1.08 3.67Crisis 7 0.94 n/a 9 0.85 n/a 12 0.66 n/aNon-Crisis 4 1.05 4.00 13 1.05 1.63 10 2.08 1.67

Notes:The first column for each agency shows the number of negative rating events, and the secondcolumn the leader follower ratio (LFR) calculated as set out in Cooper et al. (2001). Leaders have LFRs ofmore than one and followers have LFRs of less than one. The third column shows the ratio of negative topositive rating events during the relevant crisis period. ‘Advanced’ means that a country is classified as anadvanced economy by the IMF (see the Appendix). ‘Crisis’ means that an event occurs to a country goingthrough an identified crisis period, as set out in Table 2. The entry ‘n/a’ is employed where either thenumber of positive events or the number of negative events is zero.

independent rating events and the 280 events in clusters.24 The results are shown inTable 5.

The analysis in Panel A shows again that S&P is more active than the other agencies.Specifically, during periods when the sub-sample of sovereigns was rated by all threeagencies, S&P had 277 re-rating events versus 227 (Fitch) and 210 (Moody’s). Thereare 184 events across the IMF advanced economies, of which 87 are new informationactions and 97 are copycat actions. Of the 60 rating actions by Moody’s across IMFadvanced economies, 73% relate to new information actions, so Moody’s provides justover 50% of all new actions for these countries versus 29% by S&P and 21% by Fitch.These results support those documented earlier, in which Moody’s revealed higherLFRs for IMF advanced economies during periods of contagion relative to S&P andFitch.

24 The number of events does not reconcile with data presented in other tables, since we consider onlythose events for sovereigns and time periods when all three agencies were active.



Table 5Analysis of Relative Rating Behaviour During Non-Crisis Periods

Panel A: New Information vs Copycat EventsAll Countries Events

(n = 47)IMF Advanced

(n = 16)IMF Non-Advanced

(n = 31)

Total New% Copycat% Total New% Copycat% Total New% Copycat%

S&P 277 49.1 50.9 67 37.3 62.7 210 52.9 47.1Moody’s 210 57.6 42.4 60 73.3 26.7 150 51.3 48.7Fitch 227 37.9 62.1 57 31.6 68.4 170 40.0 60.0Total 714 48.0 52.0 184 47.2 52.8 530 48.3 51.7

Panel B: Lead and Lag Events in Clusters% of Lead % of Lead % of Lead

Lead Lag Events Lead Lag Events Lead Lag Events

S&P 50 51 39 7 11 28 43 40 42Moody’s 40 50 31 12 9 48 28 41 27Fitch 37 52 29 6 11 24 31 41 30Total 127 153 25 31 102 122

Notes:This table relates to an analysis of rating behaviour during non-crisis periods. The sample is restricted to714 non-crisis events during periods when a sovereign was rated by all three agencies and we have fulldata relating to actions by all three for 47 sovereigns. As such, the number of events does not reconcilewith the data presented in other tables. Two events by the same agency on the same day count as one newinformation event.

There are 530 events across IMF non-advanced countries, of which 256 are newinformation events and 274 are copycat events. Of the 210 actions by S&P, 53% are newinformation events, which involves 43% of the total number of new information events,versus 30% for Moody’s and 27% for Fitch. Of the countries suffering a crisis, onlyKorea is an IMF advanced economy. As such, this pattern of results supports the crisisanalysis where S&P was found to be dominant in terms of rating actions, the provisionof new information and rating timeliness (S&P events both lead more clusters and leadmore crises).

These results might suggest that S&P has superior information across IMF non-advanced economies and Moody’s across IMF advanced economies. Accordingly, inpart of our analysis that follows, we examine the relative stock-market reactions torating events along these dimensions.

3. INFORMATION CONTENT OF RATINGS EVENTS

(i) Measurement of Rating Events

Since we consider all three types of rating actions – outlooks, credit-watch proceduresand rating changes – it is expedient to create a timeline of sovereign rating actions foreach sovereign. Gande and Parsley (2005) create a comprehensive ratings index forS&P sovereign ratings in which half a point is added to the rating if there is a change


1326 HILL AND FAFF

in the watch status.25 Our approach is similar, and we assume that the strength of ratingaction increases from outlook change through watch change to rating change. Ratinglevels are coded numerically from AAA ( = 21) through to C ( = 1), employing thefiner notch scale, which accounts for positive and negative gradations (e.g., BBB = 13,BBB+ = 14). Specifically, we construct an index of rating actions26 for each sovereignas follows:

Index =∑

Rat + 0.5 × (w +) + 0.25 × (O+) − 0.5 × (w −) − 0.25 × (O−). (1)

Thus, for example, if the sovereign is rated BB by S&P and the next three S&P eventsare positive outlook, watch positive, upgrade to BB+, the index moves from 10 to 10.25to 10.5 to 11 (AAA = 21, . . . , BB = 10, . . . , C = 1). We are interested in changes in theindex caused by rating actions, so it makes little difference whether we combine therating actions of all three agencies into a single timeline of actions, or construct theindex separately for each agency, other than where there are events by more than one agency forthe same sovereign on the same day. We combine all events occurring on one day into one‘amalgamated’ event, for which we calculate changes in the index for that day acrossall agencies. Full details of amalgamated events follow in Section 3 (iii) and Section 3(iv).27

The index also allows us to differentiate between upgrades and downgrades, whichgive rise to different changes in the index. A rating change to a sovereign already oncredit watch or with an informative outlook leads to a smaller change in the indexthan one that does not follow either of these events. Kaminsky and Schmukler (2002)argue that, where sovereign bonds are placed on credit watch prior to a rating change,the rating change will be anticipated and thus the rating event will have a lower newscontent.28

Credit ratings of sovereigns also change when countries are assigned ‘default’ratings, and when a country is re-rated following default. It might be argued thatdefault ratings are different from other credit ratings, since a rating agency has nodiscretion in awarding these ratings. However, Hill et al. (2010) present evidence ofagency discretion in the award of a default rating – Fitch and S&P vary considerablyacross both sovereigns and timing in awarding such ratings, and Moody’s does notemploy a default rating at all. We therefore categorise default ratings as ratingdowngrades and re-ratings following default as rating upgrades. As is apparent fromTable 1, the number of these events is very small. For the purpose of calculating ourindex, a default rating has a value of 0. Our multivariate analysis allows us to test forthe significance of these default events independently.

25 Gande and Parsley (2005) refer to credit-watch procedures as outlooks. When creating our index, wemake a distinction between shorter-term credit-watch reviews and longer-term credit outlooks.26 We are indebted to a referee for making this general suggestion.27 Assuming that a combined index summed the ratings of each agency, the absolute changes to this indexwould be the same as the absolute changes to all of the single rating agency indices, unless actions occurredto the same sovereign on the same day.28 Holthausen and Leftwich (1986) specifically examine the information content of re-ratings that arepreceded by credit-watch additions. While they acknowledge that their results are compromised by smallsample size, they tend to find that ratings that occur as resolutions of the credit-watch process provide lessinformation than re-ratings that are not preceded by a watch procedure, which would tend to support theargument of Kaminsky and Schmukler (2002).



(ii) Measurement of Rating Returns

Boot et al. (2006, p. 101) argue that ‘ratings and credit risk are obviously related’and suggest that the information content of rating agency announcements are bestevaluated by examining their impact on security prices. A sovereign rating can bedowngraded owing to fiscal mismanagement independent of whether the businesscommunity is doing well or poorly.29 However, as explained in the introduction,sovereign issuer ratings are related to corporate ratings via the sovereign ceilingprinciple (albeit that some companies may pierce this ceiling), such that changesin the sovereign rating impact upon the creditworthiness of companies within thecountry.30 Several studies report a relationship between corporate credit/default riskand the pricing of company securities (see Chava and Purnanandam, 2010; and Aretzet al., 2010 – the latter gives a neat summary of this literature), thus providing alink between sovereign issuer ratings and the sovereign stock market. Further reasonsfor examining the stock-price reaction to rating changes are more pragmatic. Bonddata are not available for many sovereigns (particularly less-advanced economies),and infrequent trading is a problem where such data are available. In addition,Holthausen and Leftwich (1986, p. 59) conclude that ‘the signal to noise ratio maybe more favourable for stock than bond data’. As such, following Brooks et al.(2004), we employ stock-market returns to analyse the informativeness of rating agencyannouncements at the sovereign level, which has the significant advantage of greatlyexpanding the feasible sample due to the much greater availability of these datacompared with non-equity data.

Our stock-market analyses are based on a sub-sample of 47 countries for which stockdata are available. We carefully consider an appropriate method for assessing marketimpact. Our preferred measure of abnormal return is the mean-adjusted return, whichwe report alongside raw returns. The mean daily return for each sovereign priorto each event is calculated using 200 daily observations for the period t = −230to t = −30.31 This mean represents the expected daily return, which is subtractedfrom the return for each day in the event windows under consideration to givethe daily abnormal return (AR). Abnormal returns are cumulated over consecutivedays to give cumulative abnormal returns (CARs). Gande and Parsley (2005) sug-gest minimising the event window to reduce spillover effects of rating changes of‘related’ countries, and accordingly select a two-day event window (0, +1), which weadopt.

Following Boehmer et al. (1991), we base our test statistics on the standardizedabnormal return. Daily market return indices for each country and for the world index(denominated in US dollars) are obtained primarily from Datastream (Thomson

29 We are indebted to an anonymous referee for drawing this issue to our attention.30 As per footnote 2, Arteta and Hale (2006) show that country risk premia adversely affect the privatesector’s access to foreign capital markets.31 Standard event study methodology employs daily abnormal returns using the market model. We findthat the market model does not fit our returns well and, further, we question the validity of employing evena simple index adjustment using a world market index, since many emerging markets have a low correlationwith the world market index. In mitigation, the markets with more severe illiquidity and friction problemstend to be those for which no stock index data are available and are thus excluded from the event studyanalysis. Nonetheless, while unreported, for the sake of robustness we also calculate abnormal returns viaboth the market model and an index model, where the market return is the Thomson Datastream Worldindex. The market model is estimated using 200 daily return observations, t = –230 to t = –30. We find thatindex and market model adjusted returns tend to lie somewhere between the raw returns and mean-adjustedreturns, and that these results would not alter any inferences/conclusions drawn in this paper.


1328 HILL AND FAFF

Financial). However, for certain countries, a market return index denominated in USdollars is not available via Datastream, in which case the following alternative sourcesare variously employed: S&P/IFCF, The Bank of New York, FTSE Group and MorganStanley Capital International.

Wansley and Clauretie (1985), Steiner and Heinke (2001), Hull et al. (2004) andPurda (2007) all report significant pre-rating announcement returns before negativerating events. Possible explanations include that they are due to bad news thatprecedes a rating event, or that pre-announcement speculation and/or informationleakage/dissemination occurs. Hull et al. (2004) suggest that the market anticipatesCRA behaviour, and examination of the pre-announcement (−10, −1) impact ofrating actions by CRAs allows comment to be made upon the extent to which CRAactions are anticipated.

Examination of the post-announcement (+2, +11) impact of rating actions by CRAsallows comment to be made upon the efficiency with which any new information isabsorbed by the market. Further, changes in the credit rating alter the risk profileof a sovereign issuer and that of firms located within the domestic market, and itis expected that rating upgrades would be followed by lower returns, and ratingdowngrades by higher returns. Failure to adjust to this change in the risk profilewould show up as positive abnormal returns following a downgrade and negativeabnormal returns following an upgrade. Consistent with this, Brown et al. (1993) findthat positive (negative) events that decrease (increase) the volatility of the returns ofa firm provide negative (positive) CARs following the event. Extant studies into ratingevents do not report significant returns in the post-event window.

Finally, the reason that downgrades are informative whereas upgrades are not haslong been debated. We examine the possibility that, for sovereigns, this is driven byincluding returns on sovereigns during crisis periods.

(iii) Univariate Analysis of Crisis Period Stock-Market Impact

We analyse stock data for 150 negative rating events during the crisis periods discussedabove. A total of 59 out of this group occur on the same day as another event. Tocalculate changes in the rating index for these events, we combine all of the eventsoccurring on one day into one event. A total of 46 of these ‘same-day’ events arepairs in which an agency re-rates the sovereign and places it on watch negative on thesame day. There are also three event ‘triplets’ and one event ‘quadruplet’. Thus, thesample for the analysis is as follows: 91 single-day events + 27 amalgamated events32

= 118 events. We augment this with the five events identified as being in a crisisperiod by Kaminsky and Schmukler (2002; see footnote 13) to bring the total to 123events.

Of the 123 negative events, 97 are in clusters and 26 occur independently. Of theclustered events, 27 cases lead the cluster and 70 are ‘follower’ events. Our first analysisexamines returns on (a) all 123 events and (b) 26 independent events (non-clustered).We then divide the sample by timeliness of the rating into (a) 53 non-follower events(26 independent and 27 leader) and (b) 70 follower events. We undertake furtherunivariate analyses based solely on 53 non-follower events.33

32 There are (23 × 2) + (3 × 3) + (1 × 4) = 59 same-day events; 23 + 3 + 1 = 27 amalgamated events.33 Non-follower ‘leader’ events are clustered events so we cannot rule out contamination in the post-eventwindow (+2, +11). However, since prior studies show that most effects occur in the –10, +1 window, we are



We present the results of our univariate analysis relating to crisis periods inTable 6. The following features are of note in the table. First, a significant negativemean CAR of −7.90% (1% level) is observed in the 10-day pre-event window and of−2.23% (5% level) in the two-day event window. However, the mean cross-sectionalCAR in the post-event window is not statistically different from zero. The finding ofa significant pre-event CAR supports similar results in Wansley and Clauretie (1985),Steiner and Heinke (2001), Hull et al. (2004) and Purda (2007), and it suggests thateither rating actions follow swiftly after bad news, or that they are anticipated and/orthat information is disseminated prior to the official announcement date. Second, rawreturns are more negative than the counterpart mean-adjusted returns, reflecting thefact that average returns in the (−230, −30) period prior to the event windows (−10,+11) are negative. Third, follower events, which lag other events, have a significantnegative mean CAR in the pre-event window. However, some of this return may be dueto the leading event. Only non-follower events have a significant negative mean CAR inthe event period, and at −2.98% this is approximately double the mean event windowCAR of follower events.

Fourth, in Panel B of Table 6, non-follower downgrades are associated with asignificant mean event window CAR of −3.14% (5% level), whereas credit watchesand outlooks have an insignificant mean event window CAR. The mean pre-eventreturns for downgrades are −4.70% (5% level) and can be contrasted with thepre-event mean CAR for watch and outlooks of −11.07% (1% level). Table 2suggested that watch events might be more timely. A possible explanation of thepre-event significant returns is that CRAs are reacting to the stock market ratherthan vice versa. However, the insignificant and usually positive post-event returnssuggest that the causality is from rating actions to the stock market. As such, wesuggest that dissemination/anticipation occurs prior to the official announcementdate but that, since credit-watch procedures and outlooks are more timely, theseinvoke a greater reaction to the initial (leaked) news.34 We can test whether thesepre-event returns can be explained by anticipation/dissemination via our multivariateanalyses. For those events that are anticipated, the pre-event (−10, −1) return willbe negatively related to the event (0, +1) return because where (some of) thereaction to an event has already occurred, the reaction in the event window will besmaller.

Fifth, S&P (non-follower) events invoke a mean CAR across the (−10, +1)window of −11.86% (summing the significant (−10, −1) and (0, +1) windowmean CARs), against −9.61% (−2.16%) for Fitch (Moody’s). Analysis in Section2 suggested that S&P is the dominant rater of the non-advanced countries35 withwhich crisis periods are associated. While this evidence appears to suggest that theactions of S&P have more credibility, the Fitch and Moody’s sample sizes are verysmall.

Sixth, across the full (−10, +1) window, we find that new events, if anything, leadto a lesser reaction than copycat events. As such, this dichotomy gives no plausible

not unduly concerned about this and we undertake further univariate analyses based solely on non-followerevents.34 We do not provide a separate breakdown into ‘watch’ and ‘outlook change’ events since there are onlyseven of the latter.35 We also found that S&P has a greater tendency to presage crises, is more active during the crises in termsof number of rating events, provides more new information (more than the other agencies combined) andits rating events are the first events across 50% of crisis clusters.


1330 HILL AND FAFF

Table 6Crisis Period Market Impact: Negative Rating Events

Mean Adjusted Returns Raw Returns

N Pre-Event Event Post-Event Pre-event Event Post-Event

Panel A: Analysis Across Full SampleAll Events 123 −0.0790 −0.0223 0.0103 −0.0956 −0.0256 −0.0062

(−5.71)† (−2.58)∗ (0.01) (−6.62)† (−2.96)† (−0.94)Non-Clustered Events 26 −0.0413 −0.0091 0.0374 −0.0583 −0.0125 0.0212

(−2.06)∗ (−0.28) (0.94) (−2.57)∗ (−0.66) (0.38)By TimelinessNon-Follower 53 −0.0710 −0.0298 0.0166 −0.0851 −0.0327 0.0029

(−3.89)† (−2.47)∗ (0.20) (−4.39)† (−2.74)† (−0.48)Follower 70 −0.0850 −0.0166 0.0055 −0.1036 −0.0203 −0.0131

(−4.27)† (−1.47) (−0.11) (−5.02)† (−1.75) (−0.80)

Panel B: Analysis of 53 ‘Non-Follower’ EventsBy TypeDowngrade 33 −0.0470 −0.0314 0.0188 −0.0677 −0.0356 −0.0012

(−2.18)∗ (−2.39)∗ (−0.15) (−2.87)† (−2.72)† (−0.82)Watch/Outlook 20 −0.1107 −0.0272 0.0129 −0.1138 −0.0278 0.0098

(−3.44)† (−1.07) (0.62) (−3.42)† (−1.13) (0.41)By AgencyStandard & Poor’s 37 −0.0821 −0.0365 0.0300 −0.0989 −0.0399 0.0138

(−3.59)† (−2.19)∗ (0.48) (−4.21)† (−2.44)∗ (−0.21)Fitch 7 −0.0886 −0.0075 0.0305 −0.1062 −0.0110 0.0130

(−1.86) (−0.65) (0.79) (−2.10)∗ (−0.72) (−0.21)Moody’s 9 −0.0118 −0.0198 −0.0495 −0.0121 −0.0198 −0.0497

(−0.42) (−2.49)∗ (−0.79) (−0.40) (−2.91)† (−0.89)By Information‡

New 34 −0.0578 −0.0296 0.0182 −0.0729 −0.0326 0.0032(−2.53)∗ (−1.71) (0.14) (−2.88)† (−1.90) (−0.42)

Copycat 16 −0.0896 −0.0287 0.0079 −0.1008 −0.0309 −0.0033(−3.50)† (−2.13)∗ (0.06) (−3.98)† (−2.35)∗ (−0.29)

By Index ChangesMultiple Notches 13 −0.0224 −0.0243 0.0658 −0.0425 −0.0283 0.0474

(−0.73) (−1.26) (1.05) (−1.30) (−1.48) (0.71)One or Fewer Notches 40 −0.0869 −0.0316 0.0006 −0.0990 −0.0341 −0.0115

(−3.96)† (−2.14)∗ (−0.30) (−4.28)† (−2.34)∗ (−0.88)

Notes:This table presents event study results for sovereign rating events during crisis periods analysedalong various dimensions. We report the pre-event (−10, −1), the two-day event (0, +1) and the post-event(+2, +11) cumulative abnormal (mean-adjusted) and raw returns. The mean by which returns are adjustedis calculated during the period t = −230 to t = −30. t-statistics are the Boehmer et al. (1991) standardizedcross-sectional t-statistics (in parentheses). † significant at 1% level. ∗ significant at 5% level. ‡ DominicanRepublic data are not available.



insight into differential reaction across rating events. This suggests that investors tendto evaluate rating changes relative to an agency’s own current rating level, rather thanthe rating level of other agencies.

Seventh, there is no evidence that, during crisis periods, mean CARs are higherwhere the number of index notches is greater than 1 (though this part of the analysisis compromised by small sample sizes). This finding tends to suggest that the markethad already discounted shares prior to multiple notch events.

(iv) Univariate Analysis of Non-Crisis Period Stock-Market Impact

We have data for 717 events during non-crisis periods. During these periods, there are483 positive rating events and 234 negative events, and, since there is a pair of same-dayevents in each case, the final samples are 482 and 233, respectively.36

Of 482 positive (233 negative) events, 148 (92) are in clusters and 334 (141) occurindependently. Of the clustered positive (negative) events, 68 (40) lead the cluster and80 (52) are follower events. Our first analysis examines returns on (a) all 482 positiveand 233 negative events and (b) 334 positive and 141 negative independent events(non-clustered). We then divide the sample by timeliness of the rating into (a) 402positive and 181 negative non-follower events (independent and leader) and (b) 80positive and 52 negative follower events.

We present the results for negative (positive) events in non-crisis periods inTables 7 and 8, respectively.37 The features of note in Table 7 are as follows. First, a sig-nificant negative mean CAR of −2.33% (1% level) is observed in the 10-day pre-eventwindow and −0.54% (1% level) in the two-day event window.38 These are only 29%(pre-event window) and 24% (event window) of the magnitude of CARs during crisisperiods but, as both are significantly different from zero, we conclude that the findingof negative rating events being informative is not driven by crisis-period returns. Themean cross-sectional CAR in the post-event window is not statistically different fromzero. Again, the finding of a significant pre-event CAR supports prior studies citedin Section 3(iii). Second, raw returns and mean-adjusted returns are of a similarmagnitude and significance during non-crisis periods, reflecting the fact that averagereturns in the (−230, −30) period prior to the event windows (−10, +11) are aroundzero. Third, only non-follower events are associated with significant negative meanCARs.

Fourth, turning to Panel B, non-follower downgrades are not associated with signif-icant mean CARs in either the event or pre-event windows. However, in untabulatedresults, we do find that S&P (non-follower) downgrades are associated with a pre-event mean CAR of −2.92%, which is significant at the 5% level. Watch events areassociated with significant pre-event (−1.88%) and event window (−1.3%) CARs,whereas outlook events are associated with significant pre-event window CARs of about−4% (1% level). Notably, over the combined (−10, +1) window, CARs on watch

36 The total number of events with stock data, adjusted for amalgamations, is 123 crisis and 715 non-crisiscases, which sum to the 838 events in Table 1.37 In unreported results, we combine the negative crisis events in Table 6 with the negative non-crisis eventsin Table 7. Where appropriate, we comment on these results in our discussion of Table 7. Details of theseresults are available from the authors on request.38 Since most prior studies are based on a world market index adjustment, for comparability, we report thatthe mean CAR employing an index model for the event (pre-event) window is –0.53% (–2.12%), both ofwhich are significant at the 1% level.


1332 HILL AND FAFF

Table 7Non-Crisis Period Market Impact: Negative Rating Events

Mean Adjusted Returns Raw Returns

N Pre-Event Event Post-Event Pre-Event Event Post-Event

Panel A: Analysis Across Full SampleAll Events 233 −0.0233 −0.0054 0.0045 −0.0218 −0.0051 0.0060

(−4.03)† (−2.66)† (1.13) (−4.05)† (−2.68)† (1.03)Non-Clustered Events 141 −0.0234 −0.0084 0.0037 −0.0190 −0.0076 0.0081

(−3.12)† (−2.95)† (0.81) (−3.00)† (−2.88)† (0.93)By TimelinessNon-Follower 181 −0.0250 −0.0061 0.0037 −0.0226 −0.0056 0.0061

(−3.78)† (−2.56)∗ (1.03) (−3.76)† (−2.56)∗ (0.99)Follower 52 −0.0174 −0.0033 0.0070 −0.0189 −0.0036 0.0055

(−1.44) (−0.76) (0.46) (−1.52) (−0.79) (0.31)

Panel B: Analysis of 181 Non-Follower EventsBy TypeDowngrade 67 −0.0125 −0.0026 0.0148 −0.0151 −0.0031 0.0122

(−1.02) (−0.89) (1.92) (−1.43) (−1.03) (1.49)Outlook/Watch 114 −0.0324 −0.0081 −0.0028 −0.0271 −0.0070 0.0026

(−3.83)† (−2.43)∗ (0.17) (−3.54)† (−2.36)∗ (0.34)Credit Outlook/ Watch (N = 114)Watch (and Affirm) 43 −0.0188 −0.0130 0.0054 −0.0217 −0.0136 0.0025

(−2.10)∗ (−2.08)∗ (0.23) (−2.05)∗ (−2.01)∗ (0.27)Outlook 71 −0.0407 −0.0051 −0.0077 −0.0303 −0.0030 0.0026

(−3.20)† (−1.49) (−0.01) (−2.87)† (−1.43) (0.21)By AgencyStandard & Poor’s 92 −0.0326 −0.0049 0.0024 −0.0335 −0.0051 0.0015

(−3.69)† (−1.77) (0.03) (−3.63)† (−1.75) (0.03)Fitch 37 −0.0334 −0.0050 0.0018 −0.0184 −0.0020 0.0168

(−1.15) (−0.66) (1.23) (−1.19) (−0.69) (0.03)Moody’s 52 −0.0057 −0.0089 0.0075 −0.0065 −0.0090 0.0067

(−1.03) (−1.89) (1.32) (−1.04) (−1.89) (1.24)By InformationNew 136 −0.0230 −0.0060 0.0007 −0.0234 −0.0061 0.0077

(−3.29)† (−2.40)∗ (0.92) (−3.10)† (−2.35)† (1.05)Copycat 45 −0.0313 −0.0061 −0.0096 −0.0203 −0.0039 0.0014

(−1.84) (−0.88) (0.47) (−2.13)∗ (−1.04) (0.10)By Index ChangesMultiple Notches 14 −0.0082 0.0052 0.0036 −0.0085 0.0051 0.0033

(−0.30) (0.11) (−0.22) (−0.31) (0.09) (−0.22)One or Fewer Notches 167 −0.0265 −0.0070 0.0038 −0.0238 −0.0065 0.0064

(−3.85)† (−2.62)† (1.09) (−3.84)† (−2.62)† (1.05)

Notes:This table presents event study results for negative sovereign rating events during non-crisis periods,analysed along various dimensions. We report the pre-event (−10, −1), the two-day event (0, +1) and thepost-event (+2, +11) cumulative abnormal (mean-adjusted) and raw returns. The mean by which returnsare adjusted is calculated during the period t = −230 to t = −30. t-statistics are the Boehmer et al. (1991)standardized cross-sectional t-statistics (in parentheses). † significant at 1% level. ∗ significant at 5% level.



Table 8Non-Crisis Period Market Impact: Positive Rating Events

Mean-Adjusted Returns Raw Returns

N Pre-Event Event Post-Event Pre-Event Event Post-Event

Panel A: Analysis Across Full SampleAll Events 482 0.0031 0.0025 −0.0020 0.0143 0.0048 0.0092

(0.65) (2.07)∗ (−0.58) (5.21)† (4.13)† (4.13)†

Non-Clustered Events 334 0.0042 0.0027 −0.0025 0.0149 0.0048 0.0082(1.27) (1.90) (−0.19) (4.80)† (3.57)† (3.55)†

By TimelinessNon-Follower 402 0.0024 0.0031 −0.0015 0.0136 0.0053 0.0097

(0.80) (2.25)∗ (−0.27) (4.89)† (4.14)† (3.98)†

Follower 80 0.0069 −0.0003 −0.0045 0.0179 0.0018 0.0064(−0.24) (0.02) (−0.85) (1.81) (0.85) (1.15)

Panel B: Analysis of 402 Non-Follower EventsBy TypeUpgrade 210 −0.0001 0.0014 −0.0012 0.0166 0.0072 0.0098

(−0.13) (1.32) (−0.24) (2.95)† (2.68)† (2.69)†

Outlook/Watch 192 0.0051 0.0049 −0.0018 0.0166 0.0072 0.0098(1.18) (1.85) (−0.14) (3.93)† (3.16)† (2.96)†

Credit Outlook/ Watch (N = 192)Watch (and Affirm) 71 0.0050 0.0048 0.0044 0.0168 0.0072 0.0161

(0.84) (1.40) (1.19) (2.64)† (2.08)∗ (2.87)†

Outlook 121 0.0051 0.0049 −0.0053 0.0165 0.0072 0.0060(0.88) (1.25) (−1.16) (3.01)† (2.38)∗ (1.51)

By AgencyStandard & Poor’s 181 0.0070 0.0067 −0.0025 0.0170 0.0087 0.0075

(1.64) (3.38)† (−1.30) (4.00)† (4.66)† (1.42)Fitch 108 −0.0051 −0.0017 −0.0044 0.0075 0.0008 0.0081

(−1.23) (−0.59) (−0.40) (1.34) (0.37) (2.03)∗Moody’s 113 0.0022 0.0020 0.0029 0.0140 0.0043 0.0148

(0.24) (0.73) (1.37) (2.67)† (1.78) (3.61)†

By InformationNew 226 0.0084 0.0046 −0.0032 0.0199 0.0069 0.0083

(1.94) (2.79)† (−1.04) (5.09)† (4.23)† (2.03)∗Copycat 176 −0.0054 0.0012 0.0007 0.0056 0.0034 0.0116

(−1.08) (0.18) (0.92) (1.60) (1.43) (3.84)†

By Index ChangesMultiple Notches 31 −0.0004 0.0024 −0.0087 0.0144 0.0054 0.0061

(0.17) (1.17) (−0.07) (1.37) (1.75) (1.19)One or Fewer Notches 371 0.0026 0.0032 −0.0009 0.0136 0.0053 0.0101

(0.78) (2.01)∗ (−0.26) (4.68)† (3.81)† (3.80)†

Notes:This table presents event study results for positive sovereign rating events during non-crisis periods,analysed along various dimensions. We report the pre-event (−10, −1), the two-day event (0, +1) and thepost-event (+2, +11) cumulative abnormal (mean-adjusted) and raw returns. The mean by which returnsare adjusted is calculated during the period t = −230 to t = −30. t-statistics are the Boehmer et al. (1991)standardized cross-sectional t-statistics (in parentheses). † significant at 1% level. ∗ significant at 5% level.


1334 HILL AND FAFF

(outlook) events average about −3% (−4%) against about −1.5% for downgrades.This suggests that credit-watch and credit-outlook events are more timely. The analysisof rating events by index notches tends to support this conclusion, with downgrades ofmultiple notches being associated with mean CARs that are not significantly differentfrom zero.

A combination of 33 crisis and 67 non-crisis period downgrades (untabulated)indicates that downgrades do lead to significant pre-event (−2.39%) and event window(−1.21%) mean CARs, but we can now conclude that this result is driven by crisis-period returns. Kaminsky and Schmukler (2002) also find that outlook changes leadto a significantly larger impact than rating changes, and they suggest that investorsmay anticipate rating changes owing to countries being placed on credit watch priorto a rating change. We investigate this argument shortly.

Fifth, S&P (non-follower) events invoke the only significant mean CAR of −3.26%(1% level) across the (−10, −1) window. Moody’s events provide the greatest eventwindow CAR at −0.89%, but this is only significant at the 10% level. A possibleexplanation is that S&P is more likely to disseminate information prior to a ratingevent than Moody’s. Sixth, again, across the combined (−10, +1) window, we find thatnew events lead to a lesser reaction than copycat events.

We now turn to positive rating events reported in Table 8. We note that meanraw cumulative returns around positive rating events are significantly different fromzero (1% level) in the pre-event, event and post-event windows, but abnormal (mean-adjusted) returns are only significant in the event window (5% level). We find a smallbut significant abnormal reaction to positive rating events (mean CAR of 0.25%).39 Wefind that this result is driven by non-follower events that have a significant reaction of0.31%, whereas follower events are insignificant. In line with prior studies, we do notfind a significant abnormal reaction to rating upgrades, though raw returns do showsuch an effect (Panel B). We do find strong support, however, for S&P rating eventsbeing more informative than the other CRAs, with the mean reaction to S&P eventsbeing 0.67%, significant at the 1% level, against insignificant reactions for Fitch andMoody’s.

We analyse this result further. Of the 181 non-follower S&P events, 86 are ratingchanges, 90 are outlook changes and five are confirmations (this breakdown is nottabulated). The mean event window CARs on S&P rating changes are 0.41% and onoutlook changes are 0.88%, both of which are significant at the 5% level. It wouldseem that S&P provides more timely assessments of positive rating quality than theother agencies via both upgrades and changes in outlook, other than in the case ofMoody’s watch positives, which also provide returns significantly different from zero.The final analysis of positive events is again by the number of index notches: we findno evidence of a greater reaction.

We noted earlier that the extant literature tends to find an asymmetric effect –rating downgrades are informative whereas rating upgrades are not. Focusing onnon-crisis periods, we find that the mean combined pre-event and event window(−10, +1) return in the case of negative rating events is considerably larger inmagnitude than that for positive rating events (−2.9% versus +0.6%), and that

39 Since most prior studies are based on a world market index adjustment, for comparability, we report thatthe mean CAR employing an index model is 0.31%, which is also significant at the 5% level.



the event window (0, +1) effect is twice as large (in absolute terms) for negativerating events (−0.5% versus +0.25%). Thus, we also find evidence of an asymmetriceffect. However, we also find that non-follower positive rating events of S&P producesignificantly positive cumulative abnormal returns. When we examine negative events,we find that outlook and credit-watch events induce a greater market reaction andthat again it is S&P events that are more informative. This is consistent with the earlieranalysis, which suggested that S&P is both more active and more timely in its ratingassessments.

We now investigate Kaminsky and Schmukler’s (2002) argument that investorsmay anticipate rating changes owing to countries being placed on credit watchprior to a rating change, which leads to downgrades (or upgrades) being lessinformative than outlook changes. To this end, we combine data for crisis and non-crisis periods to alleviate problems of small sample sizes, and we examine only single-notch events to avoid complicating the analysis with any multiple notch effects. Inuntabulated analysis, we analyse mean abnormal returns associated with 29 (103) non-forewarned downgrades (upgrades) and 44 (76) forewarned – that is, outlook or watchpreceded – downgrades (upgrades). We consider downgrades first. For non-forewarned downgrades, we find that mean-adjusted returns are significantly differentfrom zero and negative in the (−10, −1) window, and significantly different fromzero and positive in the (+2, +11) window; for forewarned downgrades, significantlydifferent from zero and negative in the (0, +1) window. There is a significant differ-ence in mean-adjusted returns only for the (+2, +11) window, where mean-adjustedreturns are positive for non-forewarned downgrades and negative for forewarneddowngrades.

Now we turn to upgrades. We find that mean-adjusted returns are not signif-icantly different from zero in any window. There is a significant difference inmean-adjusted returns only for the (+2, +11) window, where mean-adjusted returnsare positive for non-forewarned upgrades and negative for forewarned upgrades.Collectively, these results suggest that there is no evidence that non-forewarned(forewarned) rating changes are more (less) informative. As such, our explana-tion that (Fitch and Moody’s) rating changes are untimely is preferred to that ofKaminsky and Schmukler, who suggest that the lower information content of ratingchanges relative to outlook changes is driven by less informative forewarned ratingchanges.

(v) Multivariate Regression Analysis

We undertake a multivariate analysis of the factors that affect the market impact(measured by CAR) of credit-rating events. Our multivariate analysis is based on 482positive rating events (all occurring in non-crisis periods) and 356 negative events(118 in crisis periods and 238 in non-crisis periods). The 482 positive (356 negative)events relate to 260 (180) rating changes and 222 (176) outlook and credit-watchchanges. Our dependent variable is the return in the event window (0, +1). We modelboth mean-adjusted (CAR) and raw returns (R), and equation (2) sets out our initialspecification, which we estimate separately across positive and negative rating events.(The variables Intensity, Crisis and Contagion Dummy are omitted for the positive eventsregression.)


1336 HILL AND FAFF

CARi (Ri ) = α0 + α1Indexi + α2Marketcap + α3Transparency + α4Daysi + α5Notchesi

+ α6Spillover i + α7Intensityi + {Dummy variables : α8Outlooki + α9Follower i

+ α10New Information + α11AntiDirector + α12Invgrade

+ α13Default(Out of Default)40 + α14Advanced(Hi-Rank)

+ [α15Crisisi + α16Contagion Dummyi ]}+ [Rating Agency Effects : α17Fitch + α18Moody]

+ [Anticipation: α19Pre Event Return] + εi (2)

We start by considering three crisis-related variables that are unique to the negativeevents regression, before going on to discuss variables that are employed in bothregressions. To allow for contagion during periods of crisis, Intensity is the net totalnumber of index notches by which all sovereigns were downgraded by all agenciesduring periods of crisis in the 21 trading days prior to the rating event. Crisis takesa value of one for the affected country/region during a crisis period (see Section 2(iv) for details) and zero otherwise. Contagion Dummy takes a value of one for negativeevents not captured by the variable Crisis during global crisis periods (see Section 2 (v)for details) to allow for contagion, and zero otherwise. We expect that the magnitudeof returns would be higher during periods of crisis/contagion, but we are unableto specify ex ante the sign on the variable Intensity that captures the depth of thecrisis, since it might also be argued that, as a crisis deepens, rating changes will beanticipated.

Index is the credit rating of the sovereign immediately prior to the event (codednumerically according to the consolidated rating number shown in equation (1).41

Inclusion of the variable Index follows Kaminsky and Schmukler (2002), where therating value proxies for the ‘vulnerability of the domestic economy’. MarketCap isthe market capitalisation of listed companies in US dollars at the start of the year,sourced from Datastream.42 We also test a logarithmic transformation of Marketcap.Transparency is a sovereign corruption index prepared by Transparency International.We hypothesise that the lower the level of corruption, the greater the degree oftransparency. These last two variables, together with the variable AntiDirector , describedbelow, are designed to capture liquidity and information asymmetry/inefficienciesassociated with sovereign markets, which in turn would be expected to impact uponthe size of the reaction to any news. Following Jorion et al. (2005), the variable Days isdefined as the natural logarithm of the number of days between two successive ratingevents of the same sovereign in the same direction.43 An event that follows other similarannouncements would be expected to be less informative.

We also include a dummy variable, Follower , which specifically accounts for otherevents that precede the rating event under analysis by up to 21 trading days, but notbeyond. Notches is the number of notches by which the rating index changed for aparticular event, which will tend to be ±0.25 for an outlook change, ±0.5 for a watch

40 Default is employed in the negative events regression and Out of Default in the positive events regression.41 As an alternative, we also test the unadjusted rating. The adjustment for watch and outlook proceduresis small and, as such, both measures are very similar and produce similar regression results.42 Where the data were not available from Datastream, we employed statistics supplied by the World Bankand the International Finance Corporation. In a small number of cases, data were missing for some yearsand we therefore estimated a value by assuming a constant growth rate in the capital market across years.43 The first events for a sovereign are set to the maximum value.



change and 0.5 or more or −0.5 or less for a rating change. We might expect exante that the stock-market reaction to an event of several notches would be greaterthan to one of only a single notch or less. However, in our earlier analyses we findevidence to suggest that multiple notch changes may be untimely, whereas credit-outlook and credit-watch procedures, associated with a low value of Notches, are moretimely and informative. Spillover is the net total number of notches by which all tradebloc partners were re-rated in the 21 trading days prior to the rating event across allagencies.44 Spillover examines the potential impact on the domestic stock exchange ofratings changes within other countries in the same trade bloc. As was argued with thevariable Intensity above, for situations in which several rating changes follow each otherin quick succession, it is expected that the first rating action in the series would be themost informative.

Outlook is a dummy variable that takes a value of one if the rating event is achange to the credit-watch or credit-outlook status, and zero for a rating change. Inprevious sections we presented evidence to suggest that rating outlooks and credit-watch procedures are more timely. New information is a dummy variable that indicateswhether a rating event provides new information, as defined in Section 2(iv). Exante theory suggests that new information will lead to a larger reaction. However,our univariate analysis suggests that this is not the case, and that investors seem toevaluate rating changes relative to an agency’s own current rating level rather thanthe rating level of other agencies. AntiDirector relates to ‘Anti-director rights’ – a keymeasure of shareholder rights as set out in Djankov et al. (2008). This is a dummyvariable that takes a value of one where Anti Director rights are high and 0 otherwise.45

Invgrade is a dummy variable that takes a value of one if the rating is at either side ofthe investment grade barrier [(BBB−/BB+ (S&P and Fitch) Baa3/Ba1 (Moody’s)]and zero otherwise. Given the constraints faced by some investors, a potential re-rating that crosses the investment grade barrier is of special significance. Default/Outof default are dummy variables that indicate that a rating action belongs to eachof these sub-categories. Advanced and Hi-Rank are employed to indicate whether asovereign belongs to either of these categories (see the Appendix). Following the samearguments as Kaminsky and Schmukler (2002), reactions may be of lower magnitudefor countries that are highly ranked under either measure.

We compare reactions to the actions of the different agencies via the dummyvariables Moody (which takes a value of one for Moody’s actions and zero otherwise)and Fitch (which takes a value of one for Fitch actions and zero otherwise). Earlier,

44 The trade blocs we consider are as follows. (i) NAFTA, implemented on 1 January, 1994; members arethe United States, Canada and Mexico. (ii) MERCOSUR, founded in 1991; members are Brazil, Argentina,Uruguay, Paraguay and Venezuela, associate members are Bolivia, Chile, Colombia, Ecuador and Peru;Venezuela was an associate member until July 2006. (iii) EU, founder members are France, Germany,the Netherlands, Luxemburg, Italy and Belgium; Denmark, Ireland and the United Kingdom joined in1973, followed by Greece in 1981, Spain and Portugal in 1986, and Austria, Finland and Sweden in 1995;Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta and Poland joined in 2004. (iv)ASEAN, established on 8 August, 1967; five original member countries are Indonesia, Malaysia, Philippines,Singapore, and Thailand; Brunei Darussalam joined on 8 January, 1984, Vietnam on 28 July, 1995, Lao PDRand Myanmar on 23 July, 1997, and Cambodia on 30 April, 1999. Trade bloc data were taken from thewebsites of NAFTA, MERCOSUR, EU and ASEAN.45 To be precise, employing data supplied by Andrei Shleifer, we assigned a value of one to countries withan anti-director rights score of four or greater (in unreported analysis we did a robustness check with ascore of five or greater, and confirm that this dichotomy had less impact). Anti-director rights data were notavailable for three of the 44 countries for which we have stock data, and all of these countries were given avalue of zero for this variable. They are Oman, Saudi Arabia and the Dominican Republic.


1338 HILL AND FAFF

we presented some evidence to suggest that S&P events are more frequent, moretimely and more credible. A key test of our multivariate analysis is whether any‘reputation effect’ exists or whether the reactions to different agencies rating actionscan be explained by (a) rating specifics (e.g., timeliness); (b) sovereign specifics(e.g., information asymmetry) and/or (c) prevailing global conditions (e.g., contagionduring global crises).

Finally, we include the pre-event window (−10, −1) return as an explanatoryvariable to allow us to comment on the extent to which rating agency actions areanticipated prior to the event. As discussed previously, where rating agency actionsare anticipated, some of the reaction to the event will occur in the pre-event windowand large pre-event returns will be followed by smaller event window (0, +1) returns.46

Where rating agency actions are unanticipated, we would expect a significant reactiononly in the event window (our univariate analysis shows that this isn’t the case) andthe coefficient on the pre-event return should not be significant. Where rating agencyactions are caused by other news that also leads to a fall in the stock market in both thepre-event and event window, then there is no reason to suppose that the impact of thisnews should end with the rating action and, thus, post-event returns would be expectedto be significant and in the same direction as the event window return. However, wehave clearly shown in our univariate analyses that post-event abnormal returns are notsignificantly different from zero, and a correlation analysis shows that the post-eventand event window returns for negative events have a negative correlation of −14%,significant at the 1% level.47

For negative events, we re-estimate equation (2), (a) excluding crisis-affectedsovereigns, (b) excluding crisis- and contagion-affected sovereigns and (c) across crisis-and contagion-affected sovereigns only. We also estimate equation (2) separately acrossdifferent rating and sovereign types.

In Table 9 we report results for negative events for parsimonious models in whichthe coefficients on the independent variables are significant at the 10% level orgreater. Given the high stock-market volatility of certain periods, we re-estimate modelswith outliers removed. Outliers are defined as observations for which the studentisedresidual has a value that exceeds ± 3. We report these in Panel A alone. (Other outlier-adjusted results are available from the authors on request.)

Our key findings in relation to negative events are as follows. First, across all negativeevents (Panel A), we only capture between 1.7% and 10.5% of the cross-sectionalvariation in the reactions to sovereign rating events.48 Second, it is apparent thatIntensity is a key determinant of the strength of the reaction to sovereign rating actions.The variable Intensity becomes more negative as more negative rating activity occursduring global crisis periods. This has a significant positive coefficient, indicating thatmean-adjusted returns are more negative the deeper the extent of the global crisis.

46 We are unable to determine whether this anticipation is caused by the dissemination of rating agencyinformation or by other bad news that results in a downgrade being anticipated.47 Across various sub-samples (crisis/non-crisis, advanced/non-advanced; downgrades/outlook andwatch), the correlation between post-event window returns and both pre-event and event window returns iseither significantly negative or not significantly different from zero.48 The R2 value reported by Brooks et al. (2004) for S&P downgrades is 13.8%. The sample employed bythem runs from 1 January, 1973 to 31 July, 2001, and will therefore contain a higher proportion of advancedcountry events. Notably, we can model over 30% of the cross-sectional variation in the reaction to advancedcountry events (see Panel C).



Table 9Multivariate Regression Analysis of Negative Sovereign Rating Events

Panel A: Raw and Adjusted Returns (0, +1): All Negative EventsMean-Adjusted Returns Raw Returns

All Observations Excl. Outliers All Observations Excl. Outliers

Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat

Intercept −0.0010 −0.18 −0.0058 −2.81† 0.0005 0.09 −0.0030 −1.22Intensity 0.0008 1.80 0.0007 2.72† 0.0010 2.06∗ 0.0006 2.38∗Notches 0.0078 1.85 0.0114 2.55∗Default 0.0234 2.58∗ 0.0334 1.64 0.0233 2.64†

Anti Dir. −0.0067 −1.92Pre-Event Ret 0.0684 4.05† 0.0661 4.08†

Obs. Used 356 319 356 318Missing Obs. 0 0 0 0Excl. Outliers. 0 37 0 38Adj R2 0.0166 0.0851 0.0306 0.1045Inclusion of Credit-Rating Agency Dummy VariablesMoody 0.0084 0.98 −0.0024 −0.59 0.0091 1.07 −0.0025 −0.63Fitch 0.0047 0.52 0.0006 0.14 0.0069 0.76 −0.0001 −0.02

Panel B: Adjusted Returns: Crisis and Contagion RegressionsExcl. Crisis and Crisis and

Excl. Crisis Contagion Contagion Only

Coeff. t-stat Coeff. t-stat Coeff. t-stat

Intercept −0.0789 −4.02† −0.0745 −3.33† −0.0090 −1.00Intensity 0.0016 3.23†

Hi Rank −0.0184 −2.84† −0.0211 −2.92†

Market cap. 0.0086 4.12† 0.0083 3.51†

Anti Dir −0.0174 −2.80† −0.0208 −3.08†

Notches 0.0110 1.67Pre-Event Ret −0.0709 −2.35∗ −0.2240 −5.71†

Obs. Used 233 166 190Missing Obs. 0 0 0Adj R2 0.1170 0.2443 0.0094Inclusion of Credit-Rating Agency Dummy VariablesMoody −0.0013 −0.21 0.0085 1.21 0.0150 0.74Fitch −0.0038 −0.57 −0.0080 −1.01 0.0136 0.94

Panel C: Adjusted Returns: (a) Rating Event Type (b) Advanced Country RegressionsDowngrades Outlook/Watch Advanced


Intercept −0.0012 −0.14 −0.0781 −2.21∗ 0.0252 2.53∗Intensity 0.0014 2.54∗New Info. −0.0226 −2.32∗ 0.0201 1.76Anti Dir.Moody 0.0224 2.14∗Market Cap. 0.0053 1.68Crisis −0.0355 −2.47∗ −0.0429 −2.24∗Follower 0.0208 1.64


1340 HILL AND FAFF

Table 9 (Continued)

Panel C: Adjusted Returns: (a) Rating Event Type (b) Advanced Country RegressionsDowngrades Outlook/Watch Advanced


Ctg. Dummy −0.0199 −1.68Notches 0.0378 5.24†

Outlook −0.0214 −2.07∗Fitch 0.0180 1.70Pre-Event Ret −0.1103 −2.31∗ −0.1315 −1.78Obs. Used 176 174 102Missing Obs. 4 2 0Adj R2 0.0628 0.055 0.3028Inclusion of Credit-rating Agency Dummy VariablesMoody see above −0.0069 −0.56 0.0139 1.37Fitch −0.0124 −0.98 0.0052 0.40 see above

Notes:This table reports the outcome of estimating the following cross-sectional regression for negativerating events:

CARi (Ri ) = α0 + α1Indexi + α2Marketcap + α3Transparency + α4Daysi + α5Notchesi + α6Spillover i

+ α7Intensityi + {Dummy variables : α8Outlooki + α9Follower i + α10New Information

+ α11AntiDirector + α12Invgrade + α13Default (Out of Default) + α14Advanced (Hi-Rank)+ [α15Crisisi + α16Contagion Dummyi ]} + [Agency Effects : α17Fitch + α18Moody]+ [Anticipation: α19Pre Event Return] + εi .

The dependent variable is the mean-adjusted (CAR) and raw (R) return in the event window (0, +1). Indexis the credit rating of the sovereign immediately prior to the event. MarketCap is the market capitalisation oflisted companies in US dollars at the start of the year. Transparency is a sovereign corruption index preparedby Transparency International. Days is the natural logarithm of the number of days between two successiverating events of the same sovereign in the same direction. Notches is the number of notches by which therating index changed for a particular event. Spillover is the net total number of notches by which all tradebloc partners were re-rated in the 21 trading days prior to the rating event across all agencies. Intensity isthe net total number of index notches by which all sovereigns were downgraded by all agencies duringperiods of crisis in the 21 trading days prior to the rating event. Outlook is a dummy variable that takes avalue of one if the rating event is a change to the credit-watch or credit-outlook status and zero for a ratingchange. Follower is a dummy variable that specifically accounts for other events that precede the ratingevent under analysis by up to 21 trading days, but not beyond. New Information is a dummy variable thatindicates whether a rating event provides new information. AntiDirector is a dummy variable that takes avalue of one where Anti Director rights are high and zero otherwise. Invgrade is a dummy variable that takesa value of one if the rating is at either side of the investment grade barrier and zero otherwise. Default/Outof default are dummy variables that indicate that a rating action belongs to each of these sub-categories.Advanced and Hi-Rank are dummy variables employed to indicate whether a sovereign belongs to eitherof these categories. Crisis takes a value of one for the affected country/region during a crisis period andzero otherwise. Contagion Dummy takes a value of one for negative events not captured by the variable Crisisduring global crisis periods and zero otherwise. Fitch and Moody are dummy variables that capture thedifference in reactions to the actions of the different agencies. Pre Event Return is the return in the (−10,−1) day window. A full discussion of the variables is given in the text. Panel A reports models for bothraw and mean-adjusted returns, and in Panels B and C, mean-adjusted returns only. Panel A relates to allnegative events, Panel B relates to events organised by time periods associated with different stock-marketconditions and Panel C relates to events organised by type of rating action and type of sovereign rated.Given the high stock-market volatility of certain periods, we estimate models with (a) all observationsand (b) outliers removed. Outliers are defined as observations where the studentised residual has a valuethat exceeds ± 3. To conserve space in Panels B and C, we do not report details of models based onoutliers being removed, but these are available from the authors on request. All reported results relate toparsimonious models where variables are significant at the 10% level or greater. The bottom two rows ofeach panel indicate the results of including the credit-rating agency dummy variables in each equation.∗ significant at the 5% level and † significant at the 1% level.



The fact that rating actions might be anticipated in a global crisis has no bearing onreturns.

Third, there is a positive relationship between the pre-event window and eventwindow returns for the outlier-adjusted regressions in Panel A. Given the contrastwith the results in Panels B and C, where the relationship between pre-event andevent window returns is negative (discussed shortly), we undertake further analysis(the outcome of which is not tabulated), and we discover that these contrasting resultsare driven by downgrades in crisis periods. At first sight, the finding that a large (small)pre-event reaction is followed by a large (small) event window reaction suggests thatother news impacting on the stock market is responsible for both the pre-event andthe event window returns. However, our earlier finding that the post-event returns fordowngrades during crisis periods are not significantly different from zero suggests, infact, that any stock-market reaction is probably caused by a downgrade, and thus thesedowngrades exacerbate the reaction to an already falling stock market. Radelet andSachs (1998) investigate the Asian financial crisis of 1997 and suggest that, despitethe rating agencies’ downgradings being untimely, they nonetheless caused furtherwithdrawals of creditors’ capital.

Fourth, Notches has a significant positive estimated coefficient in our non-outlieradjusted models. This indicates that, all other things being equal, as the number ofnotches becomes larger (i.e., the downgrade becomes more severe), so does the stock-market reaction. Notably, this contrasts our earlier univariate analysis, which showsthat multiple notch downgrades do not lead to a significant reaction. Further analysisreveals that these contrasting findings are driven by a larger reaction in the eventwindow (across both crisis and non-crisis periods) to downgrades and credit watchesrather than to outlooks. (The variable Notches becomes more negative from outlookchanges to credit-watch changes to downgrades.)49 Fifth, across all negative events,the sovereign-specific and rating-specific variables capture the impact of rating actionson the sovereign stock market and no differences in reactions between agencies isapparent beyond this.

Turning to Panel B, we see that our models perform much better outside periodsof crisis and contagion when we can capture up to about 25% of the cross-sectionalvariation in returns. The size of a sovereign’s stock market is positively related tothe reaction to a rating event outside periods of crisis and contagion. However,both the anti-director rights and the UN HDI rank (see the Appendix) variables arenegatively related to the stock-market reaction to rating events. A possible explanationis that negative events are more of a surprise for these countries. Intensity, again, hasa significant and positive estimated coefficient, and the explanation for this resultremains as above. The negative relationship between event window and pre-eventwindow returns outside periods of crisis and contagion suggests that rating events areanticipated. Across the splits in Panel B (untabulated), there is no indication of anyagency reputation effects.

Turning to Panel C of Table 9, different factors are found to impact variations inreactions to downgrades and outlooks. For downgrades, the estimated coefficient onthe variable New Information is significantly negative, indicating that, in the case ofdowngrades, investors do assess rating changes relative to the actions of other agencies.

49 Further analysis leads us to offer a different explanation for the positive relationship between eventwindow returns and the variable Notches in our advanced country regression, as discussed shortly.


1342 HILL AND FAFF

Thus, for example, a BB to BB− downgrade would only have a significant impactif other agencies did not already rate the sovereign BB−. This contrasts with theresult for outlooks/credit watches, where each agency’s action seems to be assessedin isolation. For example, a negative outlook by Moody’s would be neither more norless significant if either Fitch or S&P had already issued a negative outlook for the samesovereign. Timeliness would seem to be more important for outlook/watches than fordowngrades, with Follower ratings giving rise to a less negative reaction (i.e., Followerhas a positive coefficient).

The positive and significant estimated coefficient on the variable Moody’s in thedowngrades regression indicates that the reaction to Moody’s downgrades is lessstrong than to Fitch or S&P downgrades. This difference in reaction is not capturedby other sovereign-specific or rating-action variables and could therefore, be due toa reputation effect. We note that a larger proportion of Moody’s downgrades arepreceded by watch procedures than Fitch or S&P, and in our earlier analysis wegrouped all forewarned downgrades together (outlook and watch preceded). Wetherefore create a variable Watch Forewarned, which takes a value of one for watchforewarned downgrades, and re-estimate our downgrades regression (untabulated).However, Watch Forewarned is not significant and does not alter the finding thatMoody’s downgrades lead to a less negative response. The significant and negativeestimated coefficient on the pre-event return in the watch/outlook regression suggeststhat these events are anticipated.

Stock-market reactions to rating actions in IMF advanced economies (column 3 ofPanel C in Table 9) offer considerably more scope for modelling (as reflected by anadjusted R -squared of 30%) than rating actions in non-advanced sovereigns (with anadjusted R -squared of 0.8%; this estimated model is not tabulated).50 The occurrenceof a crisis period is a significant determinant of the size of the reaction to rating eventsacross advanced countries. Outlook (which takes a value of one for credit watches andoutlook changes) also has a significant role in the advanced economies regression,confirming that they have a significantly more negative reaction than downgrades,commensurate with our earlier finding that these events are more timely and that,outside crisis periods, which largely do not affect advanced economies, they are moreinformative (see Table 7). The number of index notches (Notches) has a significantpositive coefficient – that is, as the number of index notches becomes more negative,so do the associated stock-market returns. On further investigation, we find that thisis explained by very large negative returns on three multiple notch downgrades. Oncethese three observations are removed, the variable Notches is negatively correlated withthe event window return.

We also find that the variable Fitch has a significant and positive estimated coeffi-cient (at the 10% level). Again, this might indicate the presence of a reputation effect– after allowing for sovereign and rating-specific variables, Fitch events across advancedcountries lead to a smaller reaction than to S&P and Moody’s events. Again, thesignificant and negative estimated coefficient on the pre-event return in the advancedcountry regression suggests that these events are anticipated.

Turning to the results of our estimations for positive events, we find that mean-adjusted returns are difficult to model with R -squared values in the region of 0 to 5%,

50 In this model, only the estimated coefficients on the variables Crisis and Contagion Dummy are significant(at the 10% level) and both coefficients are negative.



the exception being positive outlooks for which our model has an R -squared valueof 10%. We do not tabulate the results as our findings can be easily summarised.First, across non-advanced countries, the only significant variables are the dummyvariables Fitch (estimated coefficient of −0.0129, significant at 1% level), Moody’s(estimated coefficient of −0.0070, significant at 10% level) and the pre-event windowreturn (estimated coefficient of 0.0881, significant at 1% level). Thus, on average,the reaction to Fitch and Moody’s rating actions is less than the reaction to S&Pactions, and this difference is not captured by any of our sovereign-specific or rating-action variables. Again, this would therefore tend to indicate a reputation effect beingin operation. The significant pre-event return may suggest that these positive ratingevents exacerbate reactions to pre-existing good news.

Second, across advanced countries, the only significant variables are Spillover , withthe reaction being reduced where this variable has a higher value, commensurate withthere being a lower reaction to anticipated events, and the pre-event window return,which has a negative estimated coefficient. The negative coefficient on the pre-eventwindow return suggests that these events are anticipated.

Third, we had no success modelling upgrades, but positive outlooks are moreconducive to analysis. Again, a high value of the variable Spillover reduces the reaction.In the outlook regression, the variable Notches discriminates between credit-outlookand credit-watch events and, thus, since we find that this variable has a significant andnegative estimated coefficient, this suggests that positive changes in credit outlooks areon average more informative than watch positives. The estimated coefficient on thepre-event return is significant and positive, and the pre-event window, event windowand post-event window returns are all significantly positively correlated. This suggeststhat news that drives stock-market reactions could also be driving positive outlooksand watch procedures, and as such they might not be as timely as negative outlookand watch procedures. Finally, the reaction to Fitch positive outlook and watch ratingactions is, on average, less than those to S&P, but the reaction to Moody’s positiveoutlook and watch procedures is not significantly different from the reaction to S&P.Across positive rating actions, especially those relating to non-advanced countries andto outlook actions, there is some evidence of a reputation effect, with the reaction toS&P actions being stronger.

4. SUMMARY AND CONCLUSIONS

While a considerable literature now exists regarding the market impact of credit-rating changes (at both the individual firm and the sovereign level), few studieshave comprehensively examined the relative rating actions of the three major ratingagencies. Accordingly, we conduct such an analysis using a sample spanning 1990through to June 2006, encompassing sovereign rating events for 101 countries by thethree major raters: Standard & Poor’s, Moody’s and Fitch. We assess the relative levelof sovereign rating activity, type of activity, timeliness of events and the extent to whichrating actions of each agency lead to a new high or low assessment of credit quality.We then go on to examine the information content of rating actions at the sovereignlevel via both univariate and multivariate analyses, and we interpret these results in thecontext of our observations regarding relative rating activity. Owing to both clusteringof events and high stock-market volatility, we find it expedient to examine periods of


1344 HILL AND FAFF

crisis separately in terms of both rating activity and the information content of ratingevents.

Key features of our findings can be summarised as follows. Negative rating eventscluster around identified crisis periods, and watch procedures/outlooks are morelikely to presage these crisis periods. During crisis periods we find that S&P is moreactive, provides more ‘new’ information and is more timely in its rating assessmentsthan their competitors. Outside crisis periods, S&P is still more active, tending to ‘lead’among IMF non-advanced economies. Moody’s appears to take a leading role amongIMF advanced economies.

Turning to the information content of rating events, prior studies find that negativeevents are informative whereas positive events are not. We confirm an asymmetric re-action to positive and negative events, and we show that it is not driven by the inclusionof crisis periods in the negative event sample, since we find that negative rating events(i.e., credit-outlook/credit-watch changes and downgrades) induce negative abnormalreturns in both crisis and non-crisis periods. However, the significant negative reactionto downgrades is not robust to the exclusion of crisis periods, although the reaction todowngrades is twice as large as the reaction to upgrades outside crisis periods (acrossthe (0, +1) window). While there is evidence of an asymmetric reaction to negativeand positive rating events, we do find that positive events lead to significant event-day returns (mostly attributed to S&P credit-outlook announcements). Negative creditoutlook and watch status changes tend to be more informative than downgrades,and we suggest that this is because they are more timely. We are unable to confirmthat positive outlook and watch status changes cause a stock-market reaction, sincewe provide some evidence of stock-market momentum before, during and after suchchanges.

Turning to the relationship between rating activity and information content, ourunivariate analyses suggest that S&P events are more informative than either Moody’sor Fitch, which is as expected from our analysis of relative rating behaviour. Acrossnegative events, our multivariate analysis only confirms a reputation effect in favour ofS&P and Fitch relative to Moody’s in relation to downgrades, and in favour of S&P andMoody’s relative to Fitch in relation to non-advanced country events. In each case, thereaction to Moody’s and Fitch events, respectively, is significantly less (in magnitude)than the reaction to S&P events after controlling for sovereign-specific, rating-specificand macroeconomic effects. While we find that positive rating events are difficult tomodel, there is some evidence of a reputation effect in favour of S&P relative to bothFitch and Moody’s across non-advanced countries.



APPENDIXSample Countries Classified by UN Human Development Index (HDI)

Country Year 1st Rated Country Year 1st Rated Country Year 1st Rated

Panel A: High Rank UN HDI High Rank UN HDI (cont.) Med. Rank UN HDI (cont.)

Argentina† 1986 Portugal∗† 1986 Jordan† 1995Australia∗† 1975 Qatar† 1996 Kazakhstan 1996Bahrain 2000 Romania 1996 Lebanon 1997Barbados 1994 Russia† 1996 Lesotho 2002Belgium∗† 1988 Saudi Arabia† 1999 Moldova 1997Brazil† 1986 Singapore∗† 1989 Mongolia 1999Bulgaria 1996 Slovak Rep.† 1994 Morocco† 1998Canada∗† 1951 Slovenia 1996 Pakistan† 1994Chile† 1992 Spain∗† 1988 Papua N G 1999Costa Rica 1997 Sweden∗† 1977 Paraguay 1995Croatia 1997 Taiwan∗† 1989 Peru† 1997Cyprus∗ 1994 Trinidad & T. 1993 Philippines† 1993Czech Republic 1993 USA∗† 1941 South Africa† 1994Denmark∗† 1981 Uruguay 1993 Sri Lanka† 2005Estonia 1997 Suriname 1999Finland∗† 1977 Panel B: Medium Rank UN Thailand† 1989Greece∗† 1988 HDI Tunisia 1995Hong Kong∗† 1988 Turkey† 1992Hungary† 1992 Turkmenistan 1998Iceland∗† 1989 Azerbaijan 2000 Ukraine 1999Ireland∗† 1987 Belize 1999 Venezuela† 1976Israel∗† 1988 Bolivia 1998 Vietnam 2002Italy∗† 1986 Cameroon 2003Japan∗† 1975 China 1988

Panel C: Low Rank UN HDIKorea (South)∗† 1988 Colombia† 1993Kuwait 1995 Dominican R.† 1997

Malawi 2003Latvia 1997 Ecuador 1997Lithuania 1996 Egypt† 1997

Panel D: Not RankedMalaysia† 1986 El Salvador 1996Malta† 1994 Fiji Islands 1999

Andorra 2003

Mauritius† 1996 Gambia 2002

Aruba 2002

Mexico† 1990 Ghana 2003

Cook Islands 1998

Netherlands∗† 1989 Grenada 2002

Macedonia 2004

New Zealand∗† 1965 Guatemala 2001

Serbia 2004

Norway∗† 1975 India† 1990

Monserrat 2004

Oman† 1996 Indonesia† 1992

Montenegro 2004

Panama 1958 Iran 1999Poland† 1995 Jamaica 1998

Notes:∗Countries classified as ‘advanced economies’ by the IMF.†Countries where stock data are available.

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The Market Impact of Relative Agency Activity in the Sovereign Ratings Market

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