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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