The Relation of Financial Markets and Bank Financial Strength Ratings
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Abstract
The research outlined in this paper considers data from Nationally Recognized Statistical
Rating Organizations relative to the performance of banking institutions. More specifically, the
study considers whether bank financial strength ratings of global financial institutions contain new
information for the financial markets. If the financial markets are efficient and credit rating
agencies utilize only publicly available information, security prices should change prior to
financial strength rating changes. Prior research has considered the relationship of credit rating
agency data and their impact on the credit default swap spreads of sovereigns (Ismailescu and
Kazemi, 2010), corporate credit default swap spreads (Nordon and Weber, 2004) and/or both type
of entities (Hull, Predescu and White, 2004; Finnerty, Miller and Chen, 2013). The finding that
negative rating changes are more anticipated than positive rating events by the credit default swap
market is consistent with prior research (Hull, Predescu and White, 2004; Nordon and Weber,
2004), but contradicts more recent research (Finnerty, Miller and Chen, 2013). This research
makes a meaningful contribution in that it considers bank financial strength ratings, which are
different from credit ratings utilized in previous research. The results of this research are important
for investors who consider factors that affect credit default swap spreads.
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1.0 Introduction and Background
This study focuses on Bank Financial Strength Ratings (BFSRs), a specialized type of
rating provided by two nationally recognized statistical rating organizations (NRSROs). Moody’s
Investors Service (Moody’s) first created BFSRs in 1995 when it issued BFSRs on 288 U.S. Banks.
In contrast, the Kroll Bond Rating Agency (KBRA) issues BFSRs on nearly all commercial banks
in the United States and many of the largest global financial institutions.
Moody’s initially issued BFSRs to provide investors with an opinion on each bank’s safety
and soundness. It issued BFSRs in response to investor requests, which asked for an assessment
of bank credit profiles without consideration of additional support sources such as current
shareholders, bank holding companies or other affiliate financial institutions. It also provided
detailed metrics concerning a bank’s asset quality, liquidity and capital levels. These items, while
not unique to banks, provide much greater importance for banks than for non-bank companies.
Thus, when compared to general credit ratings, BFSRs more closely reflect each individual bank’s
financial fundamentals without regard to external support. In contrast, a credit rating represents
an evaluation of a given entity’s overall credit risk, including external support factors.
BFSRs are qualitative, non-numerical measures, which evaluate an institution’s probability
of requiring external assistance to avoid a default on one or more of its obligations. Such assistance
would include additional owner investment, help from a bank regulatory group or help from other
official institutions. BFSRs focus on key factors such as a bank’s recent financial performance,
its financial resources and the environment in which it operates. Bank specific items include
franchise value, asset diversification and financial resources. Examples of environmental and
systematic factors that BFSRS consider include: 1) performance of the local/national economy, 2)
strength of the surrounding financial system, 3) strength and complexity of the legal environment,
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and 4) quality of the bank’s regulatory supervision. BFSRs do not consider external credit risk
and credit support factors. Examples of items not considered include sovereign risk and currency
transfer or conversion risk. Sovereign risk includes the risk that a national government will default
on its debt obligations while transfer risk includes the uncertainty that the local currency cannot
be converted into a foreign currency.
In addition to the overall differences and similarities between BFSRs and general credit
ratings, BFSRs are tailored to evaluate commercial bank businesses and organizational structures.
The assets that comprise banks and financial performance metrics used to analyze banks are very
different from those for non-banks. As a result, BFSRs focus on the components of bank safety
and soundness. Specific examples of the components of BFSRs include bank asset quality, loan
portfolio diversification, capitalization, depositor base, and profitability, among other variables.
As is detailed in this research, these factors mirror the bank evaluation categories used by U.S.
bank regulators.
This paper is organized in six parts. The first part consists of this brief introduction. The
second part provides a review of related literature while the third part contains hypothesis
development. The forth part describes the data and methodologies used to analyze the data and
the fifth part includes the empirical results. Lastly, the sixth part offers a summary.
2.0 Related Literature
Two seminal research pieces which considered the relationship between security prices and
credit rating announcements include Hull, Predescu and White (2004) and Norden and Weber
(2004). Broadly, the research found that credit rating agency announcements typically influence
credit markets and CDS spreads. The studies considered whether the financial markets adjust prior
to or after credit rating agency announcements. Hull Predescu and White (2004) considered the
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impact of credit rating announcements on both the bond and CDS markets. Norden and Weber
(2004) considered the response of the stock and CDS markets to rating announcements. The
fundamental question addressed by the research is whether there is informational significance to
rating events. Do the financial markets adjust to rating announcements or other information
released to the market prior to the rating announcement? Stated differently, do credit rating agency
data convey new and additional information to the financial markets? These questions are
important when considering the information asymmetry of financial markets. Does a similar
relationship hold for BFSRs and do those relationships hold in different periods studied? That is
the focus of this research
A number of prior studies have focused on the effect of rating agency information on stock
prices. Holthhausen and Leftwich (1986) found that rating downgrades by Moody’s and S&P are
associated with abnormal stock returns in the three-day window beginning with the rating agency
announcement. Holthhausen and Leftwich (1986) also found no evidence that significant
abnormal performance surrounds rating upgrades. Consistent with the Holthhausen and Leftwich
(1986) research, Dichev and Piotroski (2001) found that rating downgrades result in negative
abnormal returns in the first year following downgrades, but no abnormal returns following rating
upgrades. Norden and Weber (2004) found that both the CDS and stock markets anticipate rating
downgrades. Norden and Weber (2004) also found that downgrades by S&P and Moody’s have
the largest impact on the stock markets. Although the CDS market was young at the time of the
Norden and Weber (2004) research, the finding was that CDS market might lead the stock market
with respect to reaction to credit rating events.
Fundamentally, the theoretical determinants of CDS spreads include the underlying firm’s
leverage, the volatility of the underlying firm’s assets and the risk free rate. Counterparty risk also
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can affect CDS spreads. Based on structural models, defaults occur when the value of a firm’s
assets is less than the value of the firm’s liabilities. Financial theory refers to this concept as
contingent claims analysis which Merton (1974) first modeled. Ericsson, Jacobs and Oviedo
(2009) considered the theoretical CDS spread determinants relative to the actual CDS market
determinants. The research determined that all three theoretical determinants were statistically
and economically significant. In total, the three theoretical determinants explained approximately
60% of spread levels and 23% of spread levels changes. As a result, Ericsson, Jacobs and Oviedo
(2009) concluded that financial theory is successful in explaining the CDS spread levels. Jakovlev
(2007) found that firm leverage, firm asset volatility and the risk free rate explain CDS spreads in
the European credit derivatives market. However, the research also found that non-theoretical
factors, such as bond credit ratings spread differences (Ex - AAA vs BB spreads), add explanatory
power to CDS spread regression models.
In addition to theoretical factors, market factors also affect CDS spreads. Prior research
has considered the market liquidity impacts on both the CDS market and underlying debt markets
(Bongaerts, De Jong and Driessen, 2011, Chen, Fabozzi, Sverdlove, 2010, Sambalaibat, 2014,
Tang and Yan, 2007). Chen, Fabozzi, Sverdlove (2010) studied the liquidity risk effect on single
name corporate CDS contracts and concluded that the bid ask spread used to measure liquidity was
high (10% in 2003) relative to the liquidity risk found in the equity markets. Tang and Yan (2007)
found that liquidity level and risk are significant factors for the pricing of CDS contracts. The
research found that the average CDS liquidity premium was roughly equal to that of U.S. Treasury
bonds and the non-default components of corporate bonds. Bongaerts, De Jong and Driessen,
(2011) found that, given certain market conditions, illiquid assets can have lower returns. The
overall evidence was that liquidity affects CDS pricing leading to the conclusion that CDS spreads
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do not represent pure measures of credit risk. Due to European CDS trading bans in 2010 and
2011, Sambalaibat (2014) was able to test how naked CDS trading affected the liquidity of the
underlying bond market. The main research finding was that the bond and CDS markets are
complimentary markets that each help the other find reduced liquidity costs over the long term. In
the short run, however, a short-term trading ban in one market caused investors to trade more
frequently and/or more heavily in another market. This means that over short time periods the
markets can be competitors for liquidity.
Research, which considers additional factors that impact CDS spreads or underlying bond
spreads, covers disparate topics. Acharya and Johnson (2007) considered insider trading in the
CDS market. The research provided evidence that market information flows from the CDS market
to the equity markets. The research did not find evidence, however, that insider trading adversely
impacted CDS liquidity or pricing. Collin-Dufresene, Goldstein and Martin (2001) researched
credit spreads on individual bonds. The research found that default risk explained approximately
25% of credit spread variation. Systemic factors were much more important in explaining credit
spread variation than firm specific factors. Callen, Livnat and Segal (2009) considered the impact
of firm earnings on CDS spreads. The research found a statistically and economically significant
relation between a firm’s earnings and CDS spread levels. The negative correlation found between
a firm’s earnings and its CDS spreads levels indicates that market participants use accounting
information as an input when considering a firm’s default risk. To consider the performance of
bond spreads relative to CDS spreads, Zhu (2006) tested the theoretical spread levels in each
market. The research found that in the long run, the two markets run in equilibrium consistent
with financial theory. However, over the short run the two markets deviate with the evidence
showing that the CDS moves ahead of the underlying bond market. Since NRSROs consider bank
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earnings as a metric for its BFSRs, the Callen, Livnat and Segal (2009) has importance for both
essays of this research.
A portion of the early CDS literature considers the relationship of the CDS market
performance to credit rating announcements, fixed income markets and equity markets (Hull,
Predescu and White, 2004; Norden and Weber, 2004). Both works of research proved to be
seminal and were consistent in the conclusion that the CDS market recognizes a decline in credit
quality prior to credit rating announcements. In other words, the CDS market anticipates negative
credit events by the CDS market through a change in CDS spreads. Imbierowicz and Wahrenburg
(2009) also considered the effect of credit rating changes on the stock and CDS markets, but also
considered the reason for the rating change. While surprise downgrades are always negative for
bondholders they are not necessarily always negative for company stockholders. The example
cited by the research was a rating downgrade due to intentional capital structure change that could
positively affect a firm’s stock price.
When a firm experiences a credit event, two potential explanations exist: 1) positive
correlations imply market contagion effects and 2) negative correlation imply firm competition
effects (Jorion and Zhang, 2007). The research found that when credit events cluster, as they did
during the financial crisis, “credit contagion” has occurred. This indicates that financial
institutions share credit risk where market or systemic risk affects multiple institutions
simultaneously. A competing and counteracting force on CDS spreads is negative correlation due
the demise or success of a rival. If a rival firm experiences bankruptcy, a competing firm can
benefit. This leads to negative correlation in the CDS spreads of the competing institutions. The
previous research is germane to this research since it considers BFSR changes during and after the
credit crisis.
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Recent research that considered the impact of credit rating announcements on CDS spreads
was Finnerty, Miller and Chen (2013). The research accessed a larger data set than previous CDS
research (Hull, Predescu and White, 2004; Ismailescu and Kazemi, 2010; Norden and Weber,
2004). It found that positive rating events influence corporate CDS spreads. The research also
built on numerous prior studies that had discovered negative company impacts from negative
rating agency announcements (Bannier and Hirsch, 2010; Behr and Güttler, 2008; Cantor, 2004;
Dichev and Piotroski, 2001; Gande and Parsley, 2005, Hull, Predescu and White, 2004; Jorion and
Zhang, 2010; Norden and Weber, 2004; Steiner and Heinke, 2001); Finnerty, Miller and Chen
(2013) also contradicted prior research (Hull, Predescu and White, 2004; Norden and Weber,
2004) with the conclusion that rating upgrade announcements have a statistically significant
impact on CDS spreads. Research that focused on the sovereign CDS market (Ismailescu and
Kazemi, 2010) had similar conclusions as corporate CDS research that the market anticipates
negative rating events, but was more explicit in that positive rating events affect the CDS markets.
The overall conclusion of Finnerty et al (2013) was that positive rating events contain more useful
information for the CDS market than negative rating events. Does a similar relationship exist for
BFSRs (vs general credit ratings used by Finnerty et al, 2013)? That is one of the foci of this
research.
Additional research which considers the relationship between credit rating agencies and
CDS spreads includes Galil and Soffer (2011), which used a unique data testing methodology.
Instead of controlling for multiple rating actions, the research determined that negative news and
negative rating announcements cluster. As a result, using only a portion of the clustered data
reduces research contribution. The research also concluded that the typical methodology of using
uncontaminated samples leads to an underestimation of market response. Flannery, Houston and
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Partnoy (2010) used the CDS spreads of 15 large financial institutions that were prominent during
the credit crisis. The research found that CDS spreads incorporate new information at
approximately the same rate as equity prices but much more quickly than credit rating agencies.
Flannery, Houston and Partnoy (2010) has implications for this research as it concluded that CDS
spreads are leading indicators for rating changes.
Kallestrup, Lando and Murgoci, (2013) found that the CDS market priced financial
linkages across borders and into bank and sovereign CDS spreads. They measured linkages across
borders by the size and riskiness of bank exposures in each country to the domestic government
bonds and residents of each foreign country. The research provides evidence of the contagion
effect of banking systems across different countries and economies. Kallestrup et al (2013) is
important for this study since it consider financial institutions from 17 countries during a period,
which includes the financial crisis.
Since the beginning of the credit crisis, U.S. federal bank regulators placed over 500 banks
into receivership (bankruptcy). As a result, bank default risk was a prevalent discussion topic
during the financial crisis. Research that considered bank default risk, as measured by CDS
spreads includes Norden and Weber (2012). The research considers whether market participants
could utilize CDS spread information to ascertain the default risk of large financial institutions. In
effect, the research utilized CDS spreads as proxies for credit ratings. Grunert, Norden and Weber
(2005) also considered the role of factors that affect internal credit ratings. When combined,
financial and non-financial factors lead to a more accurate assessment of credit risk than the use
of either factor alone. The implications of internal credit ratings have importance, as bank asset
quality are one of the main factors that determine the capital adequacy of each financial institution.
Chiaramonte and Casu (2013) consider the explanatory factors of CDS spreads prior to the
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financial crisis (1/1/2005– 6/30/2007), during the financial crisis (7/1/2007-3/31/2009) and after
the financial crisis (4/1/2009-6/30/2011) using bank specific financial ratios. In both the pre-crisis
and crisis periods, CDS spreads reflected the risk captured by balance sheet ratios. The results
were more evident during the crisis period. Liquidity was a significant explanatory variable only
during the crisis while leverage was an insignificant explanator during all periods. In contrast, this
study considers whether BFSRs explain the default risk of global financial institutions.
Prior research also indicates that CDS spreads of financial firms perform differently than
non-financial firms (Burghof, Schneider, and Wengner, 2012). Alexander and Kaeck, (2008)
found a similar relationship for CDS spread indexes. The explanation for both the Burghof,
Schnedier and Wengner (2012) research and the Alexander and Kaeck (2008) research is that the
more levered the firm, the higher the probability of default. Data indicate that large financial
institutions have had higher debt levels and more volatile leverage ratios than non-financial firms.
Between 2000 and 2009, the mean non-financial U.S. firm leverage was relatively stable between
2.3 and 2.4 times leverage (Kalemli-Ozcan, Sorensen and Yesiltas, 2012). During the same period,
the median U.S. commercial bank leverage ratio was also relatively stable between 10.0 and 10.5
times leverage (Kalemli-Ozcan et al, 2012). Also during the same period, U.S. large commercial
and investment banks (money center institutions) had median pro-cyclical leverage ratios between
14.0 and 20.0 (Kalemli-Ozcan et al, 2012). This data indicate that commercial banks had leverage
ratios 4.2 to 4.6 times higher than the average non-financial firm from 2000-2009. Similarly,
money center institutions maintained leverage ratios which were more volatile and which averaged
5.8 to 8.7 times more volatile than the average non-financial firm. Given the significance of the
leverage differences between financial and non-financial firms, a possible research consideration
might be whether there is a difference in the performance of CDS spreads of non-financial firms
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when compared to financial institutions. The increased leverage cited above is important to this
study since it pertains only to financial institutions.
3.0 Hypothesis Development
Research published prior to 2013 indicate that negative rating changes are anticipated by
the financial markets (Hull, Predescu and White, 2004; Ismailescu and Kazemi, 2010; Norden and
Weber, 2004). They also conclude that positive rating events are not as anticipated as negative
rating events. This indicates that during the period of the studies, there was at least some measure
of inefficiency in the financial markets. More recent literature, however, indicates that the CDS
market anticipates both favorable and unfavorable credit rating events (Finnerty, Miller, Chen,
2013). The more recent research indicates that the CDS market is more efficient than what the
previous research indicated. All of the studies, however, attempt to address whether credit ratings
contain new information. What the referenced research does not address is whether BFSRs contain
new information for the financial markets.
NRSROs provide BFSRs on banking institutions only. NRSROs provide BFSRs based, at
least in part, on publicly available financial information of banking institutions. Unlike general
credit ratings, which are quantitative and qualitative forward looking assessments, BFSRs assess
the current financial strength and soundness of each financial institution. Stated differently,
BFSRs reflect each institution’s current available mix of financial data. Since BFSRs are based
on publicly available information, the financial markets will have “priced in” the information
contained in BFSRs when they are made public. If the financial markets are efficient, CDS spreads
and stock prices will change prior to BFSR changes.
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Therefore:
Hypothesis 1: the financial markets anticipate both positive and negative BFSR
changes.
Financial market participants set CDS spread and stock price levels. Anytime a trade of
securities occurs in the financial markets, willing buyers and sellers must exist and must agree on
a price. Financial asset prices change during each trading day based on the mix of information in
the market about macroeconomic factors, such as a country’s level of economic activity. Financial
asset prices also change based on company specific factors, such as earnings releases and/or
growth in revenues. While financial asset prices change continuously throughout a given trading
day, BFSRs change less frequently. In efficient financial markets, security prices react quickly in
response to new information and in anticipation of expected events. As a result, security prices
should react in anticipation of rating agency announcements. By the time credit rating agencies
announce new ratings, investors in the financial markets will make decisions that lead to security
price changes in advance of rating agency announcements. As a result, I expect BFSR changes to
lag credit spread and stock price changes. If this is the case, CDS spreads and stock prices should
change prior to BFSR changes.
Therefore:
Hypothesis 2: Neither positive nor negative BFSR changes affect CDS spreads or
stock returns.
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4.0 Data Sources and Methodology
I utilize two primary sources to obtain the data necessary to study BFSRs, CDS spreads
and stock prices of global financial institutions: 1) a Kroll Bond Ratings Association subscription
and 2) a Bloomberg data license.
4.1 Kroll Bond Rating Association (KBRA)
For KBRA BFSR data, I accessed a one-time subscription to KBRA’s international bank
database. KBRA provided ratings data on 97 global financial institutions (“the KBRA BFSR
Financial Institutions”) with a total of 2,430 BFSRs. This means that, on average, each institution
in the KBRA BFSR Financial Institutions database has 25 historical KBRA BFSRs. The first
KBRA rating date is 7/31/2000 while the most recent rating date is 1/11/2013. The data set
contains 489 BFSR changes. As I compare BFSR changes to changes in CDS spreads and stock
prices, I filtered the 97 KBRA BFSR financial institutions list by comparing it to the list of
financial institutions with available Bloomberg market data. Institutions with both BFSR and
Bloomberg financial market data comprise the final list of intuitions that I use in my analysis. The
result is that I utilize KBRA BFSRs on 76 financial institutions to conduct my research analysis.
4.2 Bloomberg Data
I utilize a Bloomberg data license to obtain three different types of data: a) Moody’s BFSR
data and Moody’s Credit Rating data; b) CDS spread market data and Stock price market data, and
c) Financial Market Indices on financial institutions. Bloomberg provides access to financial
market and other financial data via a Bloomberg terminal subscription. Details on the three
different Bloomberg data types that I obtained are as follows:
I downloaded Moody’s BFSR data and Moody’s credit ratings data using a Bloomberg
terminal subscription. The Moody’s BFSR data contain 606 ratings, which means that on average,
each financial institution has an average of approximately 8.5 Moody’s BFSRs. Chronologically,
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the first Moody’s BFSR date is 8/2/1995 while the most recent BFSR date is 3/4/2014. Similarly,
the Moody’s Credit rating data contains 731 ratings on 68 institutions, which means that each
institution has an average of approximately 10.75 Moody’s credit ratings. Chronologically, the
first Moody’s credit rating date is 1/17/1983 while the most recent Moody’s credit rating date is
8/8/2016. Thus, the Moody’s credit rating dataset contains more ratings than the Moody’s BFSR
dataset. The Moody’s credit rating data also covers a much wider period than the Moody’s BFSR
data.
I utilize Moody’s Credit ratings data for comparison to the Moody’s BFSR data. I detail
the univariate analysis in Chapter 7. For the avoidance of doubt, I do not compare Moody’s credit
ratings to changes in CDS spreads and/or to changes in stock prices. I provide summary statistics
on the 68 Moody’s Credit Rating institutions by country, geographic region, and currency. As I
analyze BFSR changes relative to changes in CDS spreads and stock prices, I compared the
institution list for which I could obtain Moody’s BFSRs to the Bloomberg financial institutions
list. The result is that I utilize Moody’s BFSRs on 71 financial institutions. I provide summary
statistics on the 71 Moody’s BFSRs institutions by country, geographic region, and currency.
I obtained CDS spreads and stock price data on 86 financial institutions (“the Bloomberg
Financial Institutions”) using a Bloomberg terminal subscription. The Bloomberg CDS market
data includes, among other things, the date, the ask CDS spread, the mid CDS spread and the bid
CDS spread, the reference entity name and the CDS maturity. Similarly, stock price data includes
the date, opening price, closing price, mid-price, bid price and offer price for each institution. For
each CDS trading day, Bloomberg provides a contributed quote containing CDS data. For each
day on which the equity markets have trading activity, Bloomberg provides market data.
Contributed CDS data represent dealer quotes based on CDS contract inventory levels and/or
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market observations. CDS dealers are CDS market participants that maintain CDS inventories and
regularly trade in the CDS market. Contributed CDS data are based on dealer discretion and do
not necessarily represent actual trade data. In contrast, the Bloomberg equity market data is
comprised of actual market activity. Bloomberg provides historical CDS spread data in one
currency for each institution.
I utilized Bloomberg to download data on two financial market indices. One index pertains
to the CDS market and the other index pertains to the stock market. For comparison to financial
institution CDS spread data, I utilized the Market iTraxx European Senior Financial CDS index
(“the CDS Index”) provided by Thomson Reuters. This is the only financials CDS index that is
available for the time period studied. The CDS index is comprised of 25 financial entities, which
references CDS spreads on the senior debt of European financial institutions only. For comparison
to financial institution stock price data, I utilize the S&P 500 financials index (“the Stock Index”).
The Stock Index measures the performance of financial institutions in the S&P 500 Index.
Standard & Poor’s provides the index, which allows investors to obtain long or short equity
exposure on large financial institutions.
Details on the data that I have obtained on the CDS Index and the Stock Index are as
follows. I downloaded 2,270 spreads on the CDS Index for the period 6/12004 through
12/31/2012. The CDS Index achieved its tightest CDS spread level of 6.95 bps on 3/1/2007 and
its widest CDS spread level of 355.31 bps on 11/25/2011. I discuss the CDS Index and how I
utilize it in the below section on research methodology. I also downloaded 3,010 spreads on the
Stock Index for the period 1/16/2001 through 12/31/2012. The Stock Index achieved its highest
price of 509.55 on 2/20/2007 and its lowest price of 81.74 on 3/6/2009. I discuss the Stock Index
and how I utilize it in the below section on research methodology.
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The final Bloomberg CDS data set includes 156,083 downloaded and interpolated CDS
spreads for 86 financial institutions. Chronologically, the first CDS spread date in the data set is
1/1/2004 while the last CDS spread date is 1/25/2013. Thus, the Bloomberg CDS data set covers
nine years from 2004 through 2012, inclusive. Similarly, the final Bloomberg Stock Price data set
includes 170,011 stock prices on 69 financial institutions. Chronologically, the first stock price
date in the data set is 1/18/2001 while the last Stock Price date is 1/14/2013. Thus, the Bloomberg
CDS data set covers 11 years from 2001 through 2012, inclusive.
I utilized the methodology detailed above to compare financial institutions with Moody’s
BFSRs relative to CDS market data, KBRA BFSR data relative to Stock market data, and Moody’s
BFSR data relative to stock market data. The result is that I compare BFSR changes to financial
market changes as detailed in Table 1 below:
Table 1: List of Financial Institutions
The below table contains the final sample of institutions for which I was able to obtain both BFSR
data and market data.
Table 1: List of Financial Institution
KBRA BFSR Data Moody's BFSR Data
Bloomberg CDS Spread Data 76 71
Bloomberg Stock Price Data 62 57
In summary, I compared KBRA and Moody’s BFSRs to Bloomberg CDS spreads on 76
and 71 financial institutions, respectively. I also compared KBRA and Moody’s BFSRs to
Bloomberg Stock Prices on 62 and 57 financial institutions, respectively.
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4.3 Summary and Descriptive Statistics
I provide summary statistics on the KBRA BFSRs, Moody’s BFSRs and Moody’s credit
ratings in Table 2 below:
Table 2: Summary Statistics on Retail and Commercial Banks
The below table contains summary statistics on banks which I obtained KBRA BFSRs, Moody’s
BFSRs and Moody’s credit ratings. Panel A contains summary statistics by geographic region;
Panel B contains summary statistics by currency; Panel C contains summary statistics by country.
Panel A: KBRA Summary Statistics by Geographic Region
Number Region KBRA
BFSRs
Moody’s
BFSR
Moody’s Credit
Ratings
1 Americas 7 1 1
2 Europe 41 44 43
3 Middle East 8 8 8
4 Pacific 20 18 16
Total 76 71 68
Panel B: KBRA Summary Statistics by Currency
Number Currency KBRA BFSRs Moody’s
BFSR
Moody’s Credit
Ratings
1 EUR 42 45 44
2 USD 34 26 24
Total 76 71 68
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Panel C: KBRA Summary Statistics by Country
Number Country Region KBRA BFSRs Moody’s
BFSR
Moody’s
Credit Ratings
1 Australia Pacific 5 6 6
2 Bahrain Middle East 1 1 1
3 Belgium Europe 2 2 2
4 China Pacific 2 2 1
5 Denmark Europe 1 1 1
6 France Europe 4 4 4
7 Germany Europe 9 9 9
8 Greece Europe 1 1 1
9 India Pacific 4 2 2
10 Ireland Europe 2 2 2
11 Italy Europe 7 7 7
12 Japan Pacific 3 2 2
13 Korea Pacific 4 4 3
14 Netherlands Europe 3 3 2
15 Norway Europe 1 1 1
16 Portugal Europe 2 2 2
17 Qatar Middle East 1 1 1
18 Russia Pacific 1 1 1
19 Saudi Arabia Middle East 2 2 2
20 Spain Europe 2 4 4
21 Sweden Pacific 4 4 4
22 Switzerland Europe 1 1 1
23 UAE Middle East 4 4 4
24 United Kingdom Europe 3 4 4
25 United States Americas 7 1 1
Total 76 71 68
Table 2, Panel A provides a breakdown of the KBRA BFSRs, Moody’s BFSRs and
Moody’s credit ratings by geographic region. I assigned each institution to each geographic region
based on its global headquarters location. I classify each institution’s geographic location into one
of four regions: the Americas, Europe, Middle East and Pacific. Europe has the largest financial
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institution count of any region with 41 with KBRA BFSRs and 44 Moody’s BFSRs and 43
Moody’s credit ratings, respectively. This represents approximately 53.9% of the KBRA BFSRs,
62.0% of the Moody’s BFSRs and 63.2% of the Moody’s credit ratings, respectively. The
Americas has the smallest financial institution count of any region with only seven KBRA BFSRs,
one Moody’s BFSR, and one Moody’s credit rating, respectively. This represents approximately
9.2% of the KBRA BFSRs, 1.4% of the Moody’s BFSRs and 1.5% of the Moody’s credit ratings,
respectively.
Table 2, Panel B lists the primary CDS currency for each of the KBRA and Moody’s
Financial Institutions. Panel B pertains to financial institutions with KBRA ratings that have their
Bloomberg data quoted in either the Euros or U.S. Dollar currency. As panel B indicates, 42 of
the 76 KBRA BFSRs have CDS spread data quoted in Euros and 34 of the institutions have CDS
spread data primarily quoted in U.S. dollars (USD). This means that on a percentage basis, 55.3%
of institutions with KBRA BFSRs have CDS contracts quoted in Euros and 44.7% have CDS
contracts quoted in USD. Similarly, Panel B indicates that 45 of the 71 Moody’s BFSR financial
Institutions have CDS spread data primarily quoted in Euros and 26 of the institutions have CDS
spread data primarily quoted in USD. On a percentage basis, this means that 63.4% of institutions
with the Moody’s BFSRs have CDS contracts quoted in Euros and 36.6% have CDS contracts
quoted in USD. Panel B also indicates that 44 of the 68 Moody’s credit rating financial Institutions
have CDS spread data primarily quoted in Euros and 24 of the institutions have CDS spread data
primarily quoted in USD. On a percentage basis, this means that 64.7% of institutions with the
Moody’s credit ratings have CDS contracts quoted in Euros and 35.3% have CDS contracts quoted
in USD.
21
Table 2, Panel C provides a breakdown of the KBRA BFSRs, Moody’s BFSRs and
Moody’s credit ratings by country. I assigned each institution to each country based on its global
headquarters location. Germany has the largest financial institution count of any country with nine
with KBRA BFSRs and nine Moody’s BFSRs and nine Moody’s credit ratings, respectively. This
represents approximately 11.8% of the KBRA BFSRs, 12.7% of the Moody’s BFSRs and 13.2%
of the Moody’s credit ratings, respectively. Several countries have the smallest financial
institution count of any country with one KBRA BFSR and one Moody’s BFSR and one Moody’s
credit rating, respectively.
I also provide descriptive statistics on the 86 banks for which I downloaded CDS spreads
and the 70 banks for which I downloaded stock prices. Descriptive statistics on the CDS spread
data appear below in Table 3, Panels A-C.
Table 3: CDS Descriptive Statistics
Table 3 provides the number of observations, mean CDS spread, CDS spread standard deviation,
the minimum CDS spread and the maximum CDS spread. The Table contains three panels of
descriptive statistics: Panel A by geographic region, Panel B by currency, and Panel C by country.
Panel A: Descriptive Statistics by Geographic Region
Region
No of
Observations
CDS Spread
Mean
CDS Spread
Std. Dev
Min
Spread
Max
Spread
Americas 17,153 110 97 2 712
Europe 86,974 164 176 2 3183
Middle East 8,674 265 79 65 975
Pacific 43,282 120 110 2 2186
Total 156,083
22
Panel B: Descriptive Statistics by Currency
Currency
No of
Observations
CDS
Spread
Mean
CDS
Spread
Std. Dev
Min
Spread
Max
Spread
EUR 88,203 163 175 2 3183
USD 67,880 136 104 2 2186
Total 156,083
Panel C: Descriptive Statistics by Country
Country
No of
Observations
CDS Spread
Mean
CDS Spread
Std. Dev
Min
Spread
Max
Spread
Australia 13,313 87 81 2 850
Bahrain 1,106 443 97 250 728
Belgium 2,341 319 177 58 957
China 4,050 87 76 7 414
Denmark 2,293 84 88 5 337
France 8,899 99 91 2 555
Germany 18,325 160 145 6 1110
Greece 658 1391 465 692 2304
India 6,878 184 132 28 1647
Ireland 3,552 326 390 3 3183
Italy 15,127 162 173 6 1328
Japan 6,651 67 51 6 238
Korea 9,266 130 118 3 785
Netherlands 5,361 79 60 3 320
Norway 1,211 102 40 38 213
Portugal 4,692 278 390 8 1739
Qatar 1,128 191 48 112 385
Russia 1,895 343 279 50 2186
Saudi Arabia 2,298 193 63 86 488
Spain 6,995 207 158 8 985
Sweden 5,827 99 55 8 362
Switzerland 2,346 81 74 5 362
UAE 4,142 277 89 65 975
United Kingdom 10,576 100 88 2 419
United States 17,153 110 97 2 712
Total 156,083
23
Four geographic regions comprise Panel A: Americas, Europe, Middle East and the Pacific.
I assigned an institution to a given geographic region based on the location of the institution’s
global headquarters. Europe has the largest number of the CDS spread observations with 86,974.
The Middle East had the smallest number of the CDS spread observations with 8,674. In addition,
the Americas had the smallest CDS mean spread with an average of 110 bps while the Middle East
had the largest CDS mean spread with an average of 265 bps.
Two currencies comprise Panel B: Euros and U.S. Dollars. I assigned each institution to a
currency based on the primary currency contract type listed on Bloomberg. Of the 156,083 CDS
spread observations, 82,203 (53.5%) observations are in Euros and 67,880 (43.5%) observations
are in U.S. Dollars.
Twenty-five countries comprise Panel C. I assigned an institution to a given country based
on the location of the institution’s global headquarters. Germany was the country with the largest
number of observations with 18,325 followed by the United States with 17,153. Japan’s banks
had the smallest CDS spread mean with an average of 67 bps and Greece has the largest CDS
spread mean with an average of 1,391 bps.
Table 4: Equity Descriptive Statistics
Table 4 provides the number of observations, mean equity price, equity price standard deviation,
the minimum equity price and the maximum equity price. The table contains two panels of
descriptive statistics: Panel A by geographic region and Panel B by currency.
Panel A: Descriptive Statistics by Geographic Region
Region
No of
Observations
Equity Price
Mean
Equity Price
Std. Dev Min Price Max Price
Americas 22,293 77 73 3.17 564.60
Europe 90,774 229 495 0.03 6,095.00
Middle East 11,197 14 5 0.40 58.73
Pacific 45,747 2950 3498 93.75 63,777.00
Total 170,011
24
Panel B: Descriptive Statistics by Country
Country
No of
Observations
Equity Price
Mean
Equity Price
Std. Dev Min Price Max Price
Australia 14,533 34 10 11.89 104.33
Bahrain 1,082 1 1 0.40 2.79
Belgium 2,825 48 27 5.50 106.00
China 5,174 9 2 1.65 21.51
Denmark 2,775 136 47 31.05 252.86
France 8,517 25 10 0.80 91.52
Germany 10,433 96 105 0.00 564.60
Greece 2,719 1117 719 68.74 2,735.28
India 10,971 138 91 13.93 575.05
Ireland 4,540 703 1341 0.07 6,095.00
Italy 17,079 16 14 0.23 119.86
Japan 5,382 612 341 58.80 1,930.00
Korea 5,475 23637 10106 4,315.00 63,777.00
Norway 2,775 59 18 15.83 90.58
Portugal 5,641 2 1 0.03 6.13
Russia 1,431 5 2 1.05 11.52
Saudi Arabia 1,660 30 10 0.00 58.73
Spain 11,184 11 5 1.34 43.54
Sweden 11,124 73 27 15.26 233.47
Switzerland 2,781 33 18 9.66 71.12
UAE 8,455 13 4 1.33 42.78
United Kingdom 11,162 1036 1059 102.50 6,024.96
United States 22,293 77 73 3.17 564.60
Total 170,011 Four geographic regions comprise Panel A: Americas, Europe, Middle East and the Pacific.
I assigned an institution to a given geographic region based on the location of the institution’s
global headquarters. Europe has the largest number of the stock price observations with 90,774.
The Middle East had the smallest number of the stock price spread observations with 11,197. In
addition, the Pacific had the highest mean stock price with an average of 229 while the Middle
East had the lowest mean stock price with an average of 14.
Twenty-three countries comprise Panel B. I assigned an institution to a given country
based on the location of the institution’s global headquarters. The United States was the country
with the largest number of observations with 22,293 followed by the Italy with 17,079. Financial
25
institutions from Korea had the highest equity mean price while financial institutions from Bahrain
had the lowest equity mean price.
4.4 Research Methodology
This study employs a standard event methodology. Prior research also used a standard
event methodology when considering the reaction of the financial markets to credit rating change
(Finnerty, Miller and Chen, 2013; Hull, Predescu and White, 2004; Ismailescu and Kazemi, 2010;
Longstaff, Mithal and Neis, 2005; Norden and Weber, 2004; Zhang, Zhou, Zhu, 2009; Zhu, 2006).
I first explain the methodology utilized for CDS spreads and then I explain the methodology
utilized for stock prices.
4.4.1 BFSR Change and CDS Spread Methodology
I explain the use of a standard event methodology with respect to how CDS spreads of
financial institutions react to changes in BFSRs during the period 2004-2012. I seek to measure
the CDS market response over different time windows where the BFSR change represents time
zero and the window beginning and end are the number of days distance from the BFSR event. I
thus define a BFSR rating change date event as time zero and then consider CDS spread changes
prior to and/or after the rating event. For example, the event window [-30, -1] considers CDS
spreads during the time window beginning 30 days prior to the BFSR change and ending one day
prior to the BFSR change. This measurement occurs repeatedly for each BFSR rating change type
(upgrade vs. downgrade) and for each event time interval during the 2006-2012 period. I define
the “CDS spread change” for each institution with a BFSR change as a raw CDS spread change,
in basis points (bps) over a given event window. For example, on August 8, 2010, KBRA upgraded
JP Morgan’s BFSR from C+ to B-. Thirty days prior to the rating change, JP Morgan had a CDS
spread of 135 bps and one day prior to the rating change, it had a CDS spread of 108 bps. In this
26
example, the bank’s raw CDS spread change equals the CDS spread on the window end date of
108 bps minus the CDS spread on the window beginning date of 135 basis points. Thus, the raw
CDS spread change equals a negative 27 basis points. The negative indicates that the spread
tightened or contracted during the [-30, -1] window period.
To control for changes in market conditions during an event window, I compare the raw
CDS spread change to the CDS spread change of a basket of other financial institutions. I employ
two different methods for comparison purposes. First, I utilize a bank CDS index, which measures
the market movement of a basket of financial institutions. For this research, I accessed the iTraxx
European Senior Financial CDS index (“the iTraxx index”) via a Bloomberg terminal subscription.
Second, to conduct a robustness check and for comparison purposes, I also constructed a bank
index of equally weighted global financial institutions from the CDS spreads of the CDS spread
data set. I utilize this “homemade” CDS index for similarly rated institutions that had CDS spread
data during the applicable event window. If a given institution did not have sufficient data during
the event window, I exclude it from the homemade bank index. Both the iTraxx index and the
homemade CDS index serve the same function: to control for changes in CDS market conditions
during the period of the event windows. Thus, whether I utilize the iTraxx index or the homemade
index for a given scenario, the index represents the change in the market spread for a basket of
similarly rated financial institutions during the event window. I use the index to control JP
Morgan’s CDS response for changes in global CDS market conditions.
Continuing the example from the previous paragraph, if the Index (either the iTraxx or
homemade index) spread tightened or contracted by 20 basis points during the [-30,-1] window,
the adjusted JP Morgan spread equals the previously calculated individual bank’s raw spread
change of -27 basis points less the -20 basis point change in the index. Thus, the adjusted spread
27
change equals -7 basis points. Thus, the adjusted CDS spread is defined as the difference between
the individual banking institution’s CDS spread and the spread of an index (either a homemade
index or market index).
The above example represents the abnormal return (AR) corresponding to one rating event.
Thus, the AR equals the CDS spread change of a given institution greater than or less than the
average CDS spread change a CDS Index over the same time period. To capture the return for all
the applicable rating events, I track and then sum the abnormal returns for the event window into
a value called a cumulative abnormal return (CAR). As the name indicates, CAR is the sum of all
the abnormal returns.
The goal of the above defined standard event methodology is to determine whether BFSR
changes lead to statistically significant changes in CDS spreads of financial institutions. When a
given BFSR of a financial institution changes, it allows for the computation of an AR and a CAR.
As both an AR and CAR are basis point computations, they reflect spread changes. Thus, neither
AR nor CAR are truly “returns” but are more accurately defined as excess spreads. According to
financial theory, CDS spreads are the additional yield for bearing credit risk. In practice, CDS
spreads are the additional yield for bearing credit risk as well as other risks such as liquidity risk
and market risk (Bongaerts, De Jong and Driessen, 2011). If the excess spread calculated as AR
and CAR consistently occurs in anticipation or response to a BFSR change, it has meaning for this
study. Depending on when the CAR occurs relative to the BFSR rating, it may mean that the rating
change provides information to the market. A rating change can represent a fundamental change
in the condition or performance of a given institution, but it can also represent a change in a given
NRSRO’s opinion of the financial strength of the institution. Thus, the methodology I employ
involves trying to determine the impact of BFSR changes on CDS markets.
28
4.4.2 BFSR and Stock Price Change Methodology
I explain the use of a standard event methodology with respect to how stock prices of
financial institutions react to changes in BFSRs during the period 2001-2012. I seek to measure
the stock market response over different time windows where the BFSR change represents time 0
and the window beginning and end are the number of days distance from the BFSR event. I thus
define a BFSR rating change date event as time 0 and then consider stock returns prior to and/or
after the rating event. For example, the event window [-30, -1] considers stock returns during the
time window beginning 30 days prior to the BFSR change and ending 1 day prior to the BFSR
change. I explain the use of a standard event methodology with respect to how stocks financial
institutions react to changes in their BFSRs during the period 2001-2012. This measurement
occurs repeatedly for each BFSR rating change type (upgrade vs. downgrade) and for each event
time interval during the 2001-2012 period. I define the abnormal return for each institution with
a BFSR change as a raw return during a given event window. For example, on July 9, 2010 KBRA
upgraded JP Morgan’s BFSR from C+ to B-. Thirty days prior to the rating change, JP Morgan
had a stock price of $37.14 and one day prior to the rating change, it had a stock price of $38.165.
In this example, the bank’s raw return equals the stock price on the window end date of $38.165
minus the stock price on the window beginning date of $37.14. The difference is of $1.025 is
divided by the $37.14 beginning price. Thus, the raw stock price return equals +2.76%.
To control for changes in market conditions during event windows, I utilize a stock market
index. The index provides a measure of the equity price levels for a basket of financial institutions.
I accessed the S&P 500 financials index (“the stock index”) via Bloomberg terminal subscription.
The purpose the index is that it controls for changes in market conditions during the period of the
event windows. The stock price index change represents the return percentage difference between
29
stock prices of institutions in the index at the window start and window end. Thus, it represents
the percentage change in the equity price levels for a basket of financial institutions during the
event window.
Continuing the example from the previous paragraph, the stock index changed from a value
of 188.35 to a value of 195.51 during the event window. This means that the index increased by
3.80% during the event window. Thus, the adjusted JP Morgan return equals the previously
calculated bank’s raw return of 2.76% less the 3.80% increase in the stock index. Thus, the
adjusted stock price return equals -1.04% This means that after controlling for a changes in market
conditions during the [-30,-1] window, the adjusted JP Morgan’s return is -1.04%. I refer to this
adjusted stock return as an abnormal return. Formulaically, I can define an abnormal return (AR)
for a given bank with a given rating event as:
ARb,t = (Pb,t - Pb,t-1)/ Pb,t-1 - (It - It-1) / It-1 (1)
Where,
ARb,t equals the abnormal return for bank b at time t
Pb,t equals the observed stock price for bank b at time t
Pb,t equals the observed stock price for bank b at time t-1
I t equals the observed stock price of the index at time t
I t-1 equals the observed stock price of the index at time t-1
Equation (1) represents the abnormal return corresponding to one rating event. AR equals
the stock price change of a given institution greater than or less than the average stock price change
for the Index. To capture the return for all the applicable rating events, I track and then sum the
abnormal returns for the event window into a value called a “cumulative abnormal return” (CAR).
30
As the name indicates, CAR is the sum of all abnormal returns. Formulaically, I can define the
CAR for a given window as follows:
CARb,t = ∑𝒕𝒔=𝟎 ARb,t (2)
Where,
CARb,t equals the cumulative abnormal return for each bank b at time t
∑ts=0 equals the sum of an institution’s AR from time 0 to time t
ARb,t equals the abnormal return for bank b at time t.
The goal of the above defined standard event methodology is to determine whether BFSR
changes lead to statistically significant changes in stock prices of financial institutions. When a
given BFSR of a financial institution changes, it allows for the computation of AR and CAR. If
the excess spread calculated as AR and CAR consistently occurs in anticipation or response to a
BFSR change, it has meaning for this study. Depending on when the CAR occurs relative to the
BFSR rating, it may mean that the rating change provides information to the stock market.
4.5 Survivorship Bias
Survivorship bias is the error in judgement resulting from focusing on items that made it
past some selection process. By focusing on the items that survive selection, it means that
researchers overlook or exclude the items that failed to survive selection process. Thus, it involves
the failure to consider equally all items in a given population. As a result, survivorship bias can
lead to inference errors and it ignores failures and can lead to overly optimistic assessments.
In finance research, survivorship bias is relatively common. This is especially true in time
series analysis. Whether considering the composition of a Stock Index or performance of a sample
of mutual funds, survivorship bias exists. According to Brown, Goetzmann, Ibbotson and Ross
31
(1992), survivorship bias is endemic in finance performance, especially those based on time series.
Brown et al (1992) state that superior analysis, not superior data can help avoid survivorship bias.
In this study, I exclude the credit spread data of banks that failed or merged from study.
For example, due to the financial crisis and other factors, Fortis Bank Nederland combined with
BNP Paribas in 2009. As a result, I exclude Fortis Bank credit spreads from study. Excluding
failed or merged banks from the study is not intentional. However, if an institution does not have
sufficient data during the period of study it must be excluded by necessity. This is significant, as
I have excluded the credit spreads of the weakest and most poorly performing institutions – those
that failed or no longer exist. Thus, the sample of banking institutions that I considers contain
survivorship bias. This may cause my analysis to have less credit spread volatility than the credit
spreads of the entire population of banks (both failed and surviving).
5.0 Empirical Results
5.1 CDS Spread Changes given BFSR Changes
Table 5, Panel A shows the CDS spread % change to BFSR downgrades and upgrades for
both investment grade rated and below investment grade financial institutions. The table includes
both KBRA and Moody’s BFSR changes and includes results for CDS contracts in both U.S. dollar
and Euro denominations. As Panel A indicates, four windows show statistical significance, three
of which are BFSR downgrade windows. Only one of the BFSR upgrade windows showed
statistical significance. First, in the [-60,-31] window, adjusted CDS spreads widened 3.4%, which
is significant at a p-level of 5%. Second, in the [-30,-1] window, adjusted CDS spreads widened
3.2%, which is significant at a p-level of 5%. When a downgrade occurs, credit spreads should
widen (ceteris paribus) making both of these results expected. Third, during the 29 days following
32
the 339 BFSR downgrades as indicated by the [1,30] window, adjusted CDS spreads tightened by
an average of 2.2%, which is significant at a p-level of 5%. I do not anticipate a credit spread
decrease (tightening) after a downgrade. However, a possible explanation of the spread decrease
is that the CDS spreads had widened excessively prior to the downgrade and thus the CDS market
made a correction after the downgrade. Lastly, during the in the day prior to and day after 344
downgrades indicated in the [-1,1] window, adjusted CDS spreads widened by an average of 0.9%,
which is significant at a p-level of 5%. This indicates that BFSR downgrades provided the market
new information. CDS spreads widened in reaction to the news. This indicates a market
announcement effect where the CDS market reaction is significantly larger than zero.
Table 5, Panel A: CDS spread changes from June 2004 through December 2012 resulting from
both KBRA and Moody’s BFSR changes, including investment grade and below investment grade
rated institutions using the Market iTraxx European Senior Financial CDS index.
Downgrade Upgrade
Window N Spread ∆ % T-STAT Window N Spread ∆ % T-STAT
[1, 30] 339 -2.2% -2.155** [1, 30] 150 -1.8% -0.91
[1, 10] 341 -1.0% -1.437 [1, 10] 154 -1.1% -0.954
[-1,1] 344 0.9% 2.094** [1, -1] 153 -0.3% -0.501
[-30,-1] 341 3.2% 2.562** [-30,-1] 153 4.3% 1.646
[-60, -31] 339 3.4% 2.167** [-60,-31] 156 -1.0% -0.449
[-90, -61] 339 0.7% 0.451 [-90,-61] 155 -2.0% -1.01
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
For comparison proposes, I provide results using a homemade CDS index. I construct
Table 5, Panel B using a CDS basket of similarly rated financial institutions using spread
information from the dataset. As Panel B indicates, four windows show statistical significance:
three of the BFSR downgrade windows and one of the BFSR upgrade windows. First, during the
period preceding the downgrades indicated by the [-60,-1] and [-30,-1] windows, adjusted CDS
spreads widened 7.5%, and 5.0% which are significant at a p-level of 1%. During the [-1,1]
33
window, spreads widened by 1.0% which is significant at a p-level of 5%. Lastly, the window
corresponding to the [1,30] upgrade window had a spread tightening of 1.84%, which is significant
at a p-level of 10%. The results indicate spreads widening prior to and immediately after BFSR
downgrades and tightening after BFSR upgrades, which is expected. The results are consistent
with the results using a CDS spread index, which I provide in Panel A.
Table 5, Panel B: CDS spread changes from June 2004 through December 2012 resulting from
both KBRA and Moody’s BFSR changes, including investment grade and below investment grade
rated institutions based using a self-constructed CDS Index.
Downgrade Upgrade
Window N Spread ∆ % T-STAT Window N Spread ∆
%
T-STAT
[1, 30] 339 -1.8% -1.459 [1, 30] 150 -4.2% -1.836*
[1, 10] 341 -0.8% -0.975 [1, 10] 154 -1.6% -1.213
[-1,1] 344 1.0% 1.986** [1, -1] 153 -0.5% -0.958
[-30,-1] 341 5.0% 3.421*** [-30,-1] 153 4.3% 1.583
[-60, -31] 339 7.5% 4.033*** [-60,-31] 156 3.0% 1.141
[-90, -61] 339 1.0% 0.564 [-90,-61] 155 1.1% 0.438
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
Table 5, Panel C shows the CDS market response to BFSR downgrades and upgrades for
investment grade rated institutions only. The table includes KBRA and Moody’s BFSR changes
and includes results for both U.S. dollar and Euro denominated CDS contracts. The table applies
to BFSR changes that occur between June 2004 and the end of December 2012. As Panel C
indicates, four downgrade windows show statistical significance, all due to BFSR downgrades.
First, during the 29 day period indicated by the [-60,-31] window preceding 264 downgrades,
adjusted CDS spreads widened by an average of 4.0% which is significant at a p-level of 5%.
Second, during the 29-day period indicated by the [-30,-1] window preceding 267 downgrades,
adjusted CDS spreads widened by an average of 3.1% which is significant at a p-level of 10%. I
expect both of these results, as downgrades are typically associated with spread widening. Third,
34
during the 29 days following the 269 BFSR downgrades as indicated by the [1,30] window,
adjusted CDS spreads declined by an average of 3.2% which is significant at a p-level of 5%. A
possible explanation of the CDS spread decline is that the CDS spreads of institutions with BFSR
downgrades had widened excessively prior to the downgrade. The CDS market then corrected
itself for widening too much prior to the downgrades. Lastly, during the 2 day period in the day
prior to and day after 271 downgrades indicated in the [-1,1] window, adjusted CDS spreads
widened by an average of 1.0%, which is significant at a p-level of 10%. This indicates that BFSR
downgrades provided the market new information. CDS spreads widened providing a market
announcement effect where the CDS market reaction is significantly larger than zero.
Table 5, Panel C: CDS spread changes from June 2004 through December 2012 resulting from
both KBRA and Moody’s, utilizing investment grade rated institutions only and the Market iTraxx
European Senior Financial CDS index.
Downgrade Upgrade
Window N Spread ∆ % T-STAT Window N Spread ∆ % T-STAT
[1, 30] 269 -3.2% -2.414** [1, 30] 73 -1.1% -0.449
[1, 10] 269 -1.2% -1.22 [1, 10] 75 -1.0% -0.843
[-1,1] 271 1.0% 1.714* [1, -1] 74 -0.6% -0.608
[-30,-1] 267 3.1% 1.823* [-30,-1] 75 2.5% 0.891
[-60, -31] 264 4.0% 2.183** [-60,-31] 76 -0.1% -0.042
[-90, -61] 264 2.5% 1.184 [-90,-61] 75 -0.8% -0.308
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
For comparison proposes, I also provide results using a homemade CDS index. I construct
Table 5, Panel D using the CDS basket of similarly rated financial institutions from the dataset.
Institutions included in the index did not have a BFSR change during the window period. As Panel
D indicates, three windows indicate statistical significance, all of which occurred because of BFSR
downgrades. First, during the period preceding the downgrades indicated by the [-60,-1] and [-30,-
1] windows, adjusted CDS spreads widened by 9.3%, and 5.6% which are significant at a p-level
35
of 1%. Second, during the 29 days following the 269 BFSR downgrades as indicated by the [1,30]
window, adjusted CDS spreads declined by an average of 3.2% which is significant at a p-level of
5%. A possible explanation of the CDS spread decline is that the CDS spreads of institutions with
BFSR downgrades had widened excessively prior to the downgrade. The results using a
homemade CDS index are consistent with those provided by the Market iTraxx European Senior
Financial CDS index provided in Panel C.
Table 5, Panel D: CDS spread changes from June 2004 through December 2012 resulting from
both KBRA and Moody’s, utilizing investment grade rated institutions only using a self-
constructed CDS Index.
Downgrade Upgrade
Window N Spread ∆ % T-STAT Window N Spread ∆ % T-STAT
[1, 30] 269 -3.0% -1.874* [1, 30] 73 -3.0% -1.015
[1, 10] 269 -1.1% -0.961 [1, 10] 75 -0.1% -0.046
[-1,1] 271 0.9% 1.455 [1, -1] 74 -0.2% -0.194
[-30,-1] 267 5.6% 2.844*** [-30,-1] 75 1.8% 0.698
[-60, -31] 264 9.3% 4.244*** [-60,-31] 76 4.4% 1.498
[-90, -61] 264 3.6% 1.57 [-90,-61] 75 2.0% 0.668
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
Table 5, Panel E indicates the CDS market response to BFSR downgrades and upgrades
for below investment grade rated institutions only. The table includes rating changes by both
KBRA and Moody’s and also includes results for institutions with contracts denominated in both
U.S. dollar and Euro currencies. The table applies to BFSR changes that occur between June 2004
and the end of 2012. As Panel E indicates, one downgrade window indicates statistical
significance. During the 29-day period indicated by the [-30,-1] window preceding 74 BFSR
downgrades, adjusted CDS spreads widened by an average of 3.4%, which is significant at a p-
level of 5%. I expect spread widening prior to a BFSR downgrade.
36
Table 5, Panel E: CDS spread changes from June 2004 through December 2012 from both KBRA
and Moody’s, utilizing below investment grade rated institutions only and the Market iTraxx
European Senior Financial CDS index.
Downgrade Upgrade
Window N Spread ∆ % T-STAT Window N Spread ∆ % T-STAT
[1, 30] 70 -0.3% -0.159 [1, 30] 77 -2.3% -0.793
[1, 10] 72 -0.6% -0.843 [1, 10] 79 -1.3% -0.663
[-1,1] 73 0.7% 1.243 [1, -1] 79 -0.1% -0.086
[-30,-1] 74 3.4% 2.013** [-30,-1] 78 5.6% 1.386
[-60, -31] 75 2.1% 0.763 [-60,-31] 80 -1.6% -0.558
[-90, -61] 75 -2.3% -1.054 [-90,-61] 80 -2.9% -1.006
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
For comparison proposes, I also provide results using a homemade CDS index. I construct
Table 5, Panel F using the CDS basket of similarly rated financial institutions. Institutions
included in the index did not have a BFSR change during the window period. The [-30,-1]
downgrade window preceding 74 BFSR downgrades, adjusted CDS spreads widened by an
average of 3.9%, which is significant at a p-level of 10%. I expect spread widening prior to a
BFSR downgrade.
Table 5, Panel F: CDS spread changes from June 2004 through December 2012 from both KBRA
and Moody’s, utilizing below investment grade rated institutions only and a self-constructed CDS
Index.
Downgrade Upgrade
Window N Spread ∆ % T-STAT Window N Spread ∆ % T-STAT
[1, 30] 70 0.6% 0.299 [1, 30] 77 -5.1% -1.526
[1, 10] 72 -0.2% -0.237 [1, 10] 79 -2.7% -1.3
[-1,1] 73 1.0% 1.445 [1, -1] 79 -0.8% -1.135
[-30,-1] 74 3.9% 1.918* [-30,-1] 78 6.3% 1.42
[-60, -31] 75 4.3% 1.253 [-60,-31] 80 1.2% 0.303
[-90, -61] 75 -3.4% -1.297 [-90,-61] 80 0.5% 0.119
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
37
In summary, all six panels (Panels A-F) of Table 5 above indicate significance during the
[-30,- 1] window prior to BFSR downgrades. Moreover, ten windows prior to rating downgrades
had CDS spread changes that were statistically significant while no window prior to BFSR
upgrades had CDS spread changes that were statistically significant. This indicates that BFSR
downgrades have a greater impact on the CDS market, and are more anticipated by the CDS
market. Moreover, the results are consistent whether I use a CDS market index (iTraxx) or a
homemade CDS index.
5.2 Stock Price Changes given BFSR Changes
Table 6, Panel A below shows the stock price response to BFSR downgrades and upgrades
for both investment grade rated and below investment grade institutions. The table includes rating
changes by both KBRA and Moody’s and applies to BFSR changes that occur between June 2001
and December 2012. I utilized the S&P 500 financials index to create the results.
Table 6, Panel A: BFSR changes for all periods from both KBRA and Moody’s, including
investment grade and below investment grade rated institutions.
Downgrade Upgrade
Window N Price ∆ % T-STAT Window N Price ∆ % T-STAT
[1, 30] 291 1.0% 1.702* [1, 30] 128 0.0% -0.182
[1, 10] 287 0.0% -0.175 [1, 10] 133 0.0% 0.152
[-1,1] 290 0.0% -1.465 [1, -1] 135 0.0% 1.319
[-30,-1] 296 -0.1% -3.833*** [-30,-1] 137 0.1% 1.906*
[-60, -31] 282 -0.1% -3.013*** [-60,-31] 144 -0.1% -2.041**
[-90, -61] 281 0.0% -1.511 [-90,-61] 146 0.0% 0.062
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
Panel A indicates that two periods preceding BFSR rating downgrades are associated with
statistically significant stock returns. During the periods corresponding to the [-60,-31], and [-30,-
1] windows, adjusted stock price returns change by -0.1%. The [-60, -31] and [-30,-1] windows
indicate significance at a 1% level. Only one window after BFSR downgrades indicates an
38
adjusted stock return that is statistically significant. The [1, 30] window adjusted stock price
returns were 1.0%, which is significant at a 10% level. The BFSR downgrade results indicate that
financial institutions adjusted stock price returns move in anticipation of BFSR downgrades. Once
BFSR downgrades have occurred, however, information related to BFSR downgrades have been
factored into stock prices. While the [1, 30] window indicates significance at the 10% level, it is
much less significant that the 1% significance indicated by the [-60,-31], and [-30,-1] windows.
Panel A also shows two event windows preceding BFSR upgrades showed stock returns
that are statistically significant. During the time periods corresponding to the [-60,-31], and [-30,-
1] windows, adjusted stock price returns were -0.1% and 0.1%, respectively. The windows
indicate significance at a 5% level and 10% for the [-60,-31], and [-30,-1] window respectively.
Overall, the results indicate that financial institution adjusted stock prices move in anticipation of
BFSR upgrades. Thus, the results of Panel A indicate that both BFSR downgrades and upgrades
are anticipated. It also means that BFSR upgrades do not provide new information to the stock
market about financial institutions.
Table 6, Panel B: BFSR changes for the period June 2001 to December 2012 s from both KBRA
and Moody’s for investment grade rated institutions only.
Downgrade Upgrade
Window N Price ∆ % T-STAT Window N Price ∆ % T-STAT
[1, 30] 232 0.9% 1.417 [1, 30] 76 0.0% -0.295
[1, 10] 231 0.0% -0.428 [1, 10] 75 0.0% 0.157
[-1,1] 231 0.0% -1.802* [1, -1] 78 0.0% 0.684
[-30,-1] 236 -0.1% -2.94*** [-30,-1] 79 0.0% 0.895
[-60, -31] 224 -0.1% -2.471** [-60,-31] 85 0.0% -0.888
[-90, -61] 223 -0.1% -2.198** [-90,-61] 83 0.0% 0.708
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
Table 6, Panel B shows the Stock price response to BFSR downgrades and upgrades for
investment grade rated institutions only. The table includes rating changes by both KBRA and
39
Moody’s. The table applies to BFSR changes that occur between June 2001 and December 2012.
While the Panel B results are similar to Panel A results, differences exist too. Panel B indicates
that all three of the periods prior to BFSR rating downgrades are associated with adjusted stock
price changes that are statistically significant. During the 29 day periods corresponding to the [-
90,-61], [-60,-31], and [-30,-1] windows, stock returns were -0.1% for all three windows. The [-
90,-61] and [-60,-31], windows indicate significance at a 5% level and the [-30,-1] windows
indicates significance at the 1% level. In contrast, only one window pertaining to a period after
BFSR downgrades shows statistical significance: the period corresponding to the [-1, 1] window
indicates an adjusted stock return, which is statistically significant at a 10% level. Regarding
BFSR upgrades, none of the event windows indicates statistical significance. Overall, the Panel
B results indicate that adjusted stock returns of investment grade financial institutions move in
anticipation of BFSR downgrades. BFSRs downgrades of investment grade institutions are more
anticipated than BFSR upgrades
Table 6, Panel C shows the stock market response to BFSR downgrades and upgrades for
below investment grade rated institutions only. The table includes rating changes by both KBRA
and Moody’s. The table applies to BFSR changes that occur between June 2001 and the December
2012. As Panel C indicates, two downgrade windows and two upgrade windows indicate statistical
significance. During the 29 day period indicated by the [-30,- 1] window preceding BFSR
downgrades of below investment grade institutions, adjusted stock returns were -0.2%. The results
are statistically significant at a 1% level. During the 29 day period indicated by the [-60,-31]
window preceding BFSR downgrades of below investment grade institutions, adjusted stock
returns were -0.1%. The results are statistically significant at a 10% level. The two upgrade
windows [-60,-31] and [-30,-1] are also statistically significant at a 10% level.
40
Table 6, Panel C: BFSR changes for all periods from both KBRA and Moody’s, including below
investment grade ratings only.
Downgrade Upgrade
Window N Price ∆ % T-STAT Window N Price ∆ % T-STAT
[1, 30] 59 1.6% 0.938 [1, 30] 52 0.0% 0.004
[1, 10] 56 0.0% 0.453 [1, 10] 58 0.0% 0.078
[-1,1] 59 0.0% 0.304 [1, -1] 57 0.0% 1.16
[-30,-1] 60 -0.2% -3.55*** [-30,-1] 58 0.1% 1.847*
[-60, -31] 58 -0.1% -1.739* [-60,-31] 59 -0.1% -1.895*
[-90, -61] 58 0.1% 0.89 [-90,-61] 63 0.0% -0.543
*indicates significance at the p-level of .10
**indicates significance at the p-level of .05
***indicates significance at the p-level of .01
Thus, all of Table 6, Panels A through C indicate significance at a p level of 1% during the
29-day period [-30,-1] prior to BFSR downgrades. This provides evidence that BFSR downgrades
of financial institutions are anticipated by the stock market. While two of the [-30,-1] event
windows prior to BFSR upgrades indicate significance, they are significant at a p level of 10%.
I find mixed support for hypothesis 1 that the financial markets anticipate both positive and
negative BFSR changes. This addresses the concept of whether the CDS and stock markets
assimilate new data related to BFSR changes prior to a BFSR change. Table 5 Panels A through
F show that in each [-30,-1] downgrade window, CDS spreads widened prior to BFSR changes.
Table 6 Panels A through C and table show that in each [-30,-1] and [-60,-31] downgrade window,
stock prices decline prior to BFSR changes. This provides evidence that the financial markets
anticipate BFSR downgrades. In contrast, however, none of the six Panels of Table 5 indicate
CDS spread tightening in the [-30,-1] window prior to BFSR upgrades that are statistically
significant. This provides evidence that the CDS markets did not anticipate BFSR upgrades. It
also provides evidence that the CDS markets anticipates BFSR downgrades more than it
anticipates BFSR upgrades.
41
The data provides support for hypothesis 2 that neither BFSR upgrades nor BFSR
downgrades affect CDS spreads or stock prices. The period of interest when considering whether
BFSRs affect CDS spreads or stock prices is the [-1,1] window. As I detail in Equation 1 of
Chapter 5, I assume that a given BFSR change occurs at time t and that time t is synonymous with
time 0. The window [-1,1] represents a time period surrounding time 0 as t-1 and t+1. Thus, the
window [-1,1] measures the CDS spread or stock price change over a three-day period beginning
one day prior to a BFSR change and ending 1 day after a BFSR change.
Table 5, Panels A-F above pertain to CDS spreads and contain a total of twelve
[-1,1] windows, six for downgrades and six for upgrades. Only three of the twelve windows have
adjusted CDS spreads which are significantly different from zero during the [-1,1] period. The
average adjusted CDS spread change for the 344 downgrade observations of Panel A is associated
with a 0.9% increase in CDS spreads. Similarly, the average adjusted CDS spread change for the
153 upgrade observations is associated with a 0.3% decrease in CDS spreads. In addition, Table
6, Panels A-C above pertain to stock prices and contain a total of six [-1,1] windows, three for
downgrades and three for upgrades. Only one of the six [-1,1] windows has a stock price change
which is significantly different from zero. This occurs in the Panel B where the average stock
price decrease for 231 downgrades is 0.0%, which is significant at a 10% level.
6.0 Summary
This study focuses on BFSRs, a specialized type of credit rating provided by NRSROs.
BFSRs reflect a given bank’s financial performance and fundamentals. In this research, I address
whether BFSR changes can explain changes in financial asset prices. As BFSRs are tailored to the
structure of financial institutions, the results should offer valuable insight into banking institutions.
42
This study extends the existing body of research on financial institutions by considering the impact
of BFSR changes on CDS spreads and stock prices. That makes this research unique. In contrast,
prior research (Finnerty, Miller Chen, 2013; Hull, Predescu, and White, 2004; Ismailescu, and
Kazemi, 2010; Norden and Weber, 2004) consider only the impact of general credit ratings on
financial asset prices.
In this paper, I found evidence that the financial markets anticipate BFSR downgrades.
This is the case for both the CDS and equity markets. The finding that negative rating changes are
more anticipated than positive rating events by the credit default swap market is consistent with
prior research (Hull, Predescu and White, 2004; Nordon and Weber, 2004). However, the topic
had never been previously considered using BFSR data. I also found evidence that neither BFSR
upgrades nor BFSR downgrades impact security prices. The results indicate that BFSRS upgrades
and downgrades affect neither CDS spreads nor stock prices.
The primary financial theories addressed by the research include capital markets efficiency
and asymmetry of information. In an efficient capital market, security prices fully and
instantaneously reflect all relevant information. This leads to security prices that are accurate
signals for proper capital allocation. If credit rating agency and bank regulatory data add to the
mix of information possessed by the capital markets, security prices should immediately reflect
the new and unique information. Moreover, how quickly the financial markets incorporate new
information into security prices is a matter of degree of market efficiency. Seminal research by
Fama (1970, 1976) considered the topic of efficient capital markets. How CDS market participants
adjust CDS spreads in response to or in anticipation of rating agency announcements addresses the
topic of market efficiency (Norden, 2011; Berger and Davies, 1998). In an efficient financial
market, bank security prices would quickly reflect changes in bank ratings.
43
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