More Related Content Similar to The Role of Credit Rating Agencies in the Financial Market (20) 3. Vu Anh Tran – 500409016
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Introduction
Credit rating has significant impact on financial markets due to the reliance of international
investors, and therefore, greatly influences mechanism and transmitting channels. The issue
of stability versus accuracy has been going on for long (Cantor, 2006), putting credit rating
agencies (CRAs) under constant pressure of maintaining credibility.
By putting focus on different standards, CRAs have different opinions about the credibility of
an organisation. It results in rating conflict, further escalates the market signal noise which is
harmful for the whole market. An experienced investor will not take action until there are at
least some forms of certainty established, thus they tend to cease trading when it occurs.
Nevertheless, maintaining credibility is essential to CRAs. They prioritise the minimisation of
mistakenly interpreted signals risk. Blames have been coming onto the agencies for falling to
predict market crisis, given that credit rating measures the default risk and payback ability.
From CRAs’ perspective, the probability of a bankruptcy happens is lower than the case of
bad investment.
This paper aims to critically analyse the relevance of research literature theory on the
behaviours of top CRAs including S&P, Moody’s, Fitch and local CRAs namely JCR, S&I. The
first research question addresses the widening of credit rating gap and its impact on the
market. The second research question concentrates on analysing the cause and current
situation of lead-lag relationship among the CRAs.
The remainder of this paper is organised as follows. Section 2 briefly introduces the scene of
CRAs and addresses several influential literatures. Section 3 discusses the matter of split
rating and its impact on financial market. Section 4 examines the timing of CRA actions.
Section 5 summarises the findings.
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Literature Review
According to Shin & Moore (2003), split ratings is the scenario when credit rating agencies
(CRAs) have conflict opinions on the credibility of the same firm. As consequence, market
participants are bewildered of the reliability and thus, desperately react in order to avoid
taking on high risk without additional return. Ismail et all (2015) hypothesises that split
rating is the direct consequence of asymmetric information between firms and CRAs, since
the independent analysis lacks access to insider information.
Currently, most firms have their credibility ranked by at least two CRAs. Moody’s, S&P and
Fitch are the three biggest names dominating the international market. While Moody’s and
S&P make up about 80%, Fitch alone secures 15% of the share. Both Vu et al (2015) and
Livingston et al (2010) agree with classifying CRAs into two groups: the bigger CRAs
consisting of Moody’s, S&P, Fitch and the rest are smaller CRAs. Business firms are also
divided into superior rating and inferior rating organisation for the purpose of research.
According to the recent researches, the credit gap between Moody’s, S&P and Fitch range
from one outlook to one rating notch. In order to smoothen credit rating changes, CRAs
utilise credit outlooks and credit watches besides rating upgrade/downgrade
announcements. Their application have been increasing significantly with Moody’s is the
most active one in releasing signals. It supports Hill (2010) finding that watch and outlook
are strong predictors of rating changes. Also, evidences have been found that S&P outlook
has a high prediction rate, while Moody’s and Fitch’s watch data outperform in accuracy.
Split Rating
Information asymmetry is generally believed to be the key reason leading to split rating. The
lack of efficient communication channel in the market eventually draws misinformation
between responsible firms. Ismail et al (2015) reports that rating split can be converged and
diminished by the communication of debt-signal via public announcement. Market
participants have the incentive to reveal insider information about the disputed issues in
order to reduce market noise, as they are perceived as negative signals of future operation.
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Optimal capital structure, therefore, can be adopted and then diminish price discovery
process for that respective bonds.
Among the three dominant CRAs, the rating gap between S&P and Moody’s has a
considerable impact over credit spread sensitivity to credit event, while Fitch’s influence has
remained modest even though its size has been increasing recently. As pointed out by
Alsakka (2010), CRAs might show favour in assigning rating for home country firms, implying
the isolated market effect. Accordingly, oversea firms like S&P and Moody’s might not gain
significant influence as Fitch in Europe and vice versa. Also, S&P and Moody’s compete
directly in the same market, thus their interaction and disagreement raises more concerns.
As studied by Vu et al (2015), S&P rating changes have the strongest impact to the financial
market out of three top CRAs. The rating signal of Moody’s, on the other hand, has
considerable influence on bond market only when it is the upgrade of superior rating firms.
This phenomenon has not been explained yet, because Moody’s tends to rate more
favourably after the IPO. Evidence of Fitch split rating relationship with financial market is
not as strong as those of S&P and Moody’s. However, Fitch and S&P agree with each other
rating most of the time.
Livingston et al (2010) argued that Moody’s credit rating is more conservative than S&P’s.
His finding based on the fact that in most of split rating between the two US CRAs from 1998
to 2008, Moody’s assigns lower rating. The second base for that conclusion is that
statistically, investors prefer Moody’s than S&P. However, ratings assigned during split alone
does not justify the CRA’s opinions and investor’s preference relies on many factors, namely
stability, timeliness and firm’s structure.
First, Moody’s run its IPO in 2000. Since then, it has been the only big publicly traded CRA
and has raised arguments about the independence of rating. Kedia (2014) conducted a
research comparing between Moody’s and S&P rating from 1995 to 2005, excluding the
announcement year 2000. The results clearly revealed that Moody’s was considered as the
most conservative one among the three prior to the IPO. Strong evidence of favourable
credit rating assignments has been found for new corporate bonds and outstanding bonds
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issued after the IPO. Additionally, there has been no changes in the default rate, further
supporting the explanation that lower rating standards originates from the transformation
in ownership structure and trusting investor pressures. Hirth (2014) approves of that
explanation, acknowledging the positive correlation between insider and rating inflation.
Second, Moody’s and S&P have different perspectives on credit rating policy. While S&P
emphasises high short-term accuracy, Moody’s takes a careful approach ensure rating
stability, according to Alsakka et al (2012). Therefore, Moody’s is preferred due to the
incentive of institutional investors to have a stable measurement. Fund managers find it
easier in balancing portfolio with less market noise, especially when they are under
consistent scrutiny of contributors.
Another research conducted by Vu, Alsakka and ap Gwilym (2015) look at this matter more
carefully. They take into account the direction and frequency of rating movements by
running crosschecking tests for the data collected during 21st September 2000 and 31st
December 2012. The article suggests that S&P is likely to downgrade a firm’s rating
following a negative events. In opposition, Moody’s tends to show more favour in credit
rating within acceptable range. Surprisingly, Alsakka (2014) publishes that both the highest
number of negative credit rating signal and by far, the biggest rating decline during the
observed period both belong to Moody’s.
There are several approaches to address the influence of split rating to financial market.
First, it has a substantial role because of the cost difference. The bigger the credit gap, the
riskier the investment. Institutional investors are only allowed to invest in firms of certain
rating level and since they are big market participants, their decision heavily manipulates
smaller players’ action. Falling to achieve the required credit ratings consequently results in
loss of significant potential fund. According to Livingston (2010), shareholders demand
higher yields for split-rated bond to make up for the information opacity of such bonds.
Especially when market downturn occurs, higher yield premiums are normally requested in
order to minimise the loss even though the split-rating declines. Thus, firms pay the cost of
split ratings and its consequent impact. Firms with significant information opacity encounter
more difficult access to the capital market and investors require greater opacity premiums.
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Second, it is essential for policy makers to monitor and regulate CRAs. At request, CRAs
assign sovereign credit rating which represents the country’s credibility and sovereign
default risk. Fama and French (1992) reveal that common risk factors correlate with short-
term changes in sovereign credit spread. Information regarding the level of risk associated
with investing in a particular country and political risks are two key determinants of the
rating measurement. According to Cantor (1996), sovereign yields negatively correlate with
national credibility and credit ratings. As a result, a good sovereign credit rating is critical in
order to access international funding. Nevertheless, Alsakka et al (2010) mentions the lack
of sovereign rating signals or bank rating changes in the pre-crisis period. Ryan (2012)
supports that by blaming the failure banking sector in 2008 on CRAs for incorrectly rating
assignment. Several solutions have been proposed, including performance ranking the CRAs
in term of accuracy; tightening the regulations and internal policies; additional examinations
for risky products; facilitating CRAs’ liability to reliance investors; and increasing
government’s independence from credit ratings.
Third, market participant’s perspective are heavily driven by the credit opinions and
divergence in credit opinion. The signaling benefits of a consistent credit rating is believed
to be able to increase investor’s trust in the business future. It is expected that if the
investors find a firm’s particular rating is informative and credible, more aggressive
investment strategies will be executed. There are clear evidence from Vu, Alsakka and ap
Gwilym (2015) research that signal continuation communicates higher information value
than signal reversal. A reasonable explanation is that when there is positive news on the
superior ratings, it is believed to abstractedly hint at a sustainable trend while the second
type of events is regarded as noisy information which increase investor ambiguity. Inferior
ratings also reflect high default risk and ambiguity, especially when there are further
downgrades resulting in a wider split gap. In addition, Ellsberg paradox (1951) proposes that
people unconsciously take controlled risk over an ambiguous scenario. Thus, market
reaction to negative rating changes has stronger information value. Additionally, expected
announcements have significantly stronger impact than the unexpected. Reasonable
justification is that CRAs’ changes are considered as confirmation of the news, hence sudden
change in rating confuses investors.
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Vu et al (2015) announces that there are distinctions in the way market reacts to rating
announcements for different firms. It is expected that credit upgrade for superior ratings
has more power than credit upgrade for inferior ratings and vice versa. It could be argued by
signal continuation and signal reversal as above, which is also supported by winner-loser
effect of Hsu, Wolf (2001). In financial market, superior firm’s success is regarded as long-
term achievement, indicating a new share price ceiling. On the other hand, the credibility
increase of inferior firms is regarded as a short-term liability avoidance. Additionally, an
increase in credit rating catches investment fund’s interests and consequently, capital/debt
structure is shifted. Former shareholders are forced to rebalance their portfolio to adjust to
that new change (Hendershott, 2015). These explanation works for the opposite situation,
in which credit downgrade of inferior firms convey more information value than that of
superior firms. In addition, Vu et al (2015) reports that rating transition probabilities is
significantly higher for inferior rating, implying that information value of inferior firms leads
to higher probabilities of credit changes. And although market participants are generally
agree with Moody’s and S&P, they hold more pessimistic view about the inferior ratings,
thus require higher information level in order to trigger an action.
Relative Timing of Rating Agencies’ Action
It is critical to crosscheck the rating benchmark of three top CRAs in order to critical analyse
the CRAs reaction. Despite of the split rating, it is believed that there is still some material
heterogeneity among the three biggest CRAs. Cantor (1996) specified several
macroeconomic statistics that guide credit rating levels, namely budget balance/GDP, GDP
per capita, governance indicators and Reserves/GDP. The model established by Cantor
(1996) did successfully predict up to 93% credit rating level of S&P. No evidence has been
found for Moody’s and Fitch but the general benchmarks are expected to be relatively
similar.
Besides following their own rating guide, CRAs are obligated to observe and follow each
other signal movements due to increasing competition. It has been argued by skeptical
theorists that the CRAs’ independence is compromised and they are cooperating in releasing
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rating signals. However, evidence of the independence between the three top CRAs has
been found by Alsakka et al (2010). This is further supported by Hirth (2014)’s evolutionary
game theory, in which the off-equilibrium states is practically possible. And then, the
presence of one honest CRA will eventually turn the whole market back to the win-win
situation. Without incentives, motivations and conditions are not sufficient for such
argument to stand.
Plausible explanations of the lead-lag relationship between CRAs focus on the relationship
between information and rating guidelines. First, in order to release rating outlook, watch or
rating change, certain criteria must be fulfilled. Different CRAs utilise different sets of
guideline, thus, the weight of that information varies. Then, even if all requirements have
been met, CRAs have to act accordingly to their own policies. Second, the information have
to sustain complicated processes and it takes time. It can be due to either the need for
stability or avoiding hasty changes but commonly, it is less expensive to delay the
announcement than overreacting.
It has been suggested by Alsakka et al (2010) that there is a chain reaction among the three
top CRAs due to the notoriety deterioration of sluggish downgrades. In return, Gutler (2007)
states that for any lead and lag relationship during the period from 1997 to 2004, the
subsequent credit rating changes have much bigger impact on the market than when the
first one is released. It is speculated that the effect of information flow on CRAs’ actions
might have a n-shaped relationship. When the information first being translated into credit
signal by the first CRA, its weight in other CRAs’ determining guidelines soars, leading to
more credit signals released in the same direction. At the peak point when several market
participants are taking advantage of it, the information loses its importance to the lagging
CRAs. They will either react to the newest information or adapt to the new equilibrium.
There are two kinds of lead-lag relationship, one among the top CRAs, the other is between
the bigger agencies and the smaller ones. Moody’s and S&P are empirically proven to be the
lead but each takes separate directions. While Moody’s aims to be the first one releasing
upgrade rating announcements, it often lags behind S&P and Fitch in releasing downgrade
signals. Alsakka (2012) found out that Moody’s tend to follow S&P and Fitch with a credit
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signal too if there has been an upgrade previously, implying that Moody’s plays both the
lead and the lag.
Gutler (2007) publishes that compared to positive signal, downgrade changes are more
likely to be followed. This makes S&P, which is the most powerful negative signal leading
CRA, to be the most independence one. Thus the negative rating changes by S&P have the
highest probability to be followed by Fitch and Moody’s. It is led to believe that Fitch’s
announcements affect S&P in return, due to the information advantage of “home region”
knowledge. This is rejected by Gutler et al (2007) research, which tested and found that
credit rating by both Moody’s and S&P are not subject to that familiar preference. This is
empirically supported by ap Gwilym et al (2012) that Fitch’s credit signal follow both S&P
and Moody’s to a greater extent than vice versa. Therefore, there are much higher
probabilities that Fitch’s ratings are influenced by the other two than vice versa.
It is suggested that smaller CRAs often lag behind the bigger ones and tends to follow them
due to their dominant market share. CRAs, namely JCR and R&I, has been found lagging
behind and following the three global CRAs rating changes in considerable proximity,
especially for downgrade news. Nevertheless, some special markets which is largely
separated sometimes lead bigger CRAs also. Because of regional characteristics and local
privilege information access, Moody’s, the most stabilised CRA, lags behind the smaller ones
in smaller extent. As for Fitch and S&P, no evidence of their dependence has been found
yet.
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Conclusion
To concluded, split rating is direct consequence of information asymmetry in this high
frequency trading world, where signalling plays a vital role in shaping the market
mechanism. This effect occurs in all magnitudes, conflict between the dominant firms and
the lead-lag across CRA’s level. One key explanation is the high competitiveness, leading to
the situation in which each firms try to specialising themselves. As a result, the international
market is defragged into segments.
The financial market experience negative impact from the credit gap. Firms are obligated to
pay yield premium for the information opacity as investors lose faith. On a greater scale,
countries are suffered from CRA’s inability to correctly predict financial distress but are still
required to pay for risk valuation. Last, the financial market is currently heavily influenced
by the signalling impact as international investors are paying increasingly effort to predict
future performance based on rating announcement. Additionally, inferior rating firms
attract more attention from market participants due to their high transition probabilities.
Due to the differences in rating guideline and interdependence among CRAs, the relative
timing of rating announcements adjust to the information flow. Among the top three firms,
evidence suggests that S&P is the most independence one, followed by Moody’s, and then
Fitch. Smaller agencies tend to lag behind and follow bigger firms’ action. With the factor of
local/regional knowledge being rejected, the empirical results are consistent with the theory
of information flow.
There are, however, unexplained phenomenon that requires further research. First,
investors seems to prefer Moody’s over S&P, despite of the favourable rating after the IPO
in 2000. The accuracy of Moody’s ratings is under question, accompanying with potential
financial losses to the reliance investors. Second, the matter of Moody’s play both the lead
and the lag in relationship with smaller rating firms needs further research. It is speculated
that in order to maintain stability, Moody’s actively takes into account every possible factors
to avoid rating reversal.
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