The US sovereign rating downgrade and its impact on U.S. bank stocks

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The US Sovereign Rating Downgrade and its

impact on U.S. bank stocks

Abstract

This study investigates the effects impact of sovereign rating downgrades on U.S. banks stocks. The impact difference on small and large banks is also investigated. Consistent with the literature, I find that the sovereign rating downgrade will lead to an abnormal return of the bank stocks. This suggests that there is an interconnectedness of the banks and sovereign risk. With respect to the difference in impact of the sovereign rating on large and small banks, the results are more ambiguous.

Keywords: Sovereign rating changes, event study, banks

Name: Balázs Lam

Student number: 6156339 Date: July 18, 2014

Bachelor: Economics and Business, specialization: Economics and Finance Supervisor: Diana Hidalgo Saa

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1. Introduction

US Treasury bonds have been known to be one of the safest in the world. On the 5th

of August 2011 Standard and Poor’s has downgraded the US sovereign rating from a AAA to AA+ rating for the first time in 70 years. This being an exceptional event provides for a unique opportunity to examine the effect of the downgrade on the US stock markets.

The impact of the sovereign ratings is first of all important because it helps to improve the understanding of the price discovery process. Additionally, as the investment portfolios have been internationalized, it is of increasing importance to have more accurate information on country risk. As sovereign ratings are major country risk indicators, it is of importance for investment managers to know what the impact of sovereign risk is on their portfolios. Finally, from an international policy perspective it is of importance to know whether the rating changes actually provide the markets with new information.

The literature has extensively described the effects of credit ratings on bond and respective stocks. A downgrade is usually followed by a negative impact on the bond a respectively stock prices. The impact of sovereign ratings on national stock markets has also been researched extensively. The sovereign rating downgrade usually leads to a negative impact on the stock markets.

In this research I will focus on the impact of the sovereign ratings on the banks in comparison to the rest of the market. There are indicators that the sovereign ratings have an especially high impact on the banks due to the interconnectedness of country risk and the banks. The impact on large banks should be especially large due to their systemic importance.

I find that the sovereign rating downgrade has had a significant impact on the stocks of the banks. The impact difference between the large and small banks, however, was ambiguous.

The remainder of this paper is structured as follows. The next section will summarize the available literature. Section 3 will describe the transmission channels. Section 4 gives a description of the data used for this research. Section 6 will outline the research method used. Section 6 presents the theoretical tests. Section 7 will

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present the results of the empirical analysis, while the final section concludes the paper.

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2. Literature Review

Credit rating agencies base their ratings on qualitative and quantitative data with which they measure a country’s willingness and ability to pay its debts. The “willingness to pay” means that a country will not pay its debts due to political and social costs even if it is able to do so. The qualitative factors used by the rating agencies include e.g. institutional strength, fiscal and monetary flexibility, political stability, and economic vitality (IMF 2010). These factors are extended by quantitative factors such as the level of debt and official international reserves, the debt composition (moreover the currency composition and maturity profile), and the extent of the debt burden (IMF 2010).

The rating agencies play a great role in the financial markets. Central banks often use credit ratings within their regulatory regimes. They use the ratings in a somewhat mechanical way for their rules in accepting certain securities as a collateral in liquidity provisions (IMF 2010). The influence is great on certain investors which use the information provided by rating agencies as parameters within their own risk analysis and in some cases to comply with national compliance standards (SEC 2003). This suggests that the rating changes have an effect on the stock prices of companies as well.

Hooper et al. (2007) state that the impact of sovereign ratings on financial markets is important on three levels. Firstly, they state that it improves the understanding of the price discovery process, especially with respect to the type of information that is incorporated in asset prices by the financial markets. Furthermore, knowing the impact on financial market has an important implication for financial practice. As investment portfolios have been internationalised, people responsible for managing (international) investment portfolios have become in need for greater and more accurate information regarding country risk. As the sovereign ratings are a major country risk indicator, it is of a special importance to know how country risk re-assessment can impact their portfolios. Finally, knowing the impact of sovereign rating changes is of importance from an international policy perspective. If rating changes produce a significant market response, i.e. they actually provide financial markets with new information, this means that rating agencies can lead to intensify or prolong or soothe financial crises.

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There is evidence that bond-rating downgrades of individual companies by Moody’s and Standard and Poor’s have a negative effect on the abnormal return of the companies’ common stocks beginning with the press release of the agency (Holthausen & Leftwich 1985). This suggests that rating agencies provide new information to the capital market. They, however, found little evidence on rating changes upon rating upgrades; this study examined only equity market reactions, most likely because reliable daily bond data was not available (Goh and Ederington 1993).

This effect on stock prices has been further examined. Goh and Ederington (1993) found that a distinction has to be made within the causes of the downgrades. They considered two types of downgrades. One was to due an increase in leverage whereas the other was due to deterioration in the firm’s financial prospects. They found that the former had positive implications for the shareholders, whereas the latter negative. The first are seen as a response to the past known leverage increases, whereas the latter reflect the credit rating agencies’ expectations of the future earnings of the firm. Only the rating downgrade due to deterioration in the firm’s prospect led to a negative market response, while the other provided for no market reaction. Thus Goh & Ederington (1993) state that the rating changes must not be treated as homogeneous and thus its cause must be considered.

Others have consistently found that bond-rating downgrades have had a negative impact on both the bond and stock prices (see Hsueh and Liu 1992, Barron et al. 1997). Hsueh and Liu (1992) find that the effect of bond rating change announcement differs per firm and time, while depending on the quantity of information at the time of the announcement. They find that after rating downgrades and upgrades, there’s a significant abnormal stock price movement. These effects are more pronounced in uncertain market conditions. This indicates that the announcements of the rating changes are less anticipated, thus containing more information if the market is in need of information. The effects are also greater when there’s little information know about the firm.

The studies have also been replicated outside the US. Barron et al. (1997) find that the rating agencies provide information to the capital market within the UK. Rating downgrades and the positive announcement lead to significant excess stock returns. The first finding was consistent with US research, the latter not. They

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differentiated within ratings for short-term and long-term debt and found that rating changes for short-term have no significant impact (Barron et al. 1997).

The impact of the rating downgrades has also been examined for sovereign ratings. Brooks et al. (2004) have found, consistent with research on corporate bond rating changes that the downgrade of a sovereign rating has a negative impact on the domestic stock market return. A different market reaction has been observed for the four major rating agencies. Furthermore they found that multiple rating downgrades don’t show a different effect.

Hooper et al. (2007) have also found a significant impact of sovereign rating changes on national stock markets. They have found that rating upgrades significantly increased USD denominated stock market returns and decreased volatility, whereas the opposite reaction has been observed for rating downgrades. The market responses in terms of both return and volatility have been found to be more noticeable in cases of downgrades, foreign currency debt, emerging market debt, and during crisis periods. Also, they find that the national financial markets are efficient regarding the rating changes. Furthermore, due to information leakage there’s market impact associated prior to the rating changes.

Thus, previous research has shown that the rating changes do have an impact on the financial markets. Interesting is that only the rating downgrades have an effect on the markets. This would mean that only the downgrades provide the markets with new information. Previous research has not really provided us with answers regarding having no impact of positive rating changes.

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3. Transmission Channels

There are indicators that sovereign risk has an especially high impact on banks. The deterioration of sovereign credit ratings implies a decline in creditworthiness, which can drive up the funding costs of banks. Additionally, the banks cannot isolate themselves from the sovereign risk by changing their operations due to the great role of the government bond within the financial system (Davies & Ng 2011).

There are four transmission channels through which sovereign credit risk can affect the banking sector. These are change in fiscal conditions, direct losses on sovereign bonds holdings, use of sovereign bonds as collateral and guarantees by the governments to banks implicitly or explicitly (see Davis & Ng 2011 and Correa et al. 2012).

Firstly, the fiscal conditions that the government imposes on the nation can directly impact domestic economic activity, thereby affecting the demand for financial services. A sovereign rating downgrade generally increases the cost of borrowing, leading to an increase in the need for raising taxes, an increase of the borrowing rate or a decrease of the public expenditures. This is thus likely to lead to a decrease in economic activity and will most likely reduce the profitability of banking activities (Correa et al. 2012).

Secondly, banks often have sizeable positions in OTC derivatives positions. Increases in sovereign risk can press down the mark to market value of these positions (Davis & Ng 2011). Additionally, banks usually hold a significant amount of the domestic government in their portfolios, leading to a large exposure to government bonds. A sovereign rating downgrade will have a direct impact on the banks’ portfolios, especially as banks typically have a strong home bias in their sovereign portfolios (Davies & Ng 2011). One of the reasons for holding a large amount is that government bonds have traditionally been regarded in the context of bank capital regulation as safe assets. Furthermore, government debt is considered a very liquid asset not only because it is used for central bank operations, but also for it is widely accepted as collateral in secured borrowing markets (Correa et al. 2012). Ergo, increases in sovereign risk can cause losses on the bank’s government bond holding, thus weakening their balance sheets.

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Thirdly, as banks often use sovereign bonds as collateral in secured borrowing, a fall in the market prices of sovereign bonds following a downgrade can reduce the value of the collaterals and possibly trigger margin calls from counterparties. Sovereign debt accounts for the majority of the collateral in the private repo markets and its participants are sensitive to changes in its riskiness. A large enough downgrade can lead to exclusion of the specific sovereign bond from being used a eligible collateral. Furthermore, haircuts applied to sovereign securities may materially be increased by counterparties (Davies & Ng 2011).

Fourth, governments tend to be willing to prevent certain banks, in particular banks that are of systemic importance, or in other words “too big to fail”, from failure due to its impact on the consumers and due to its large cost of bankruptcy in terms of output. It can also lead to a confidence crisis that may spill over to other banks and financial institutions, thus leading to a chain of failures and financial crises (Mishkin et al. 2006). The government’s ability to support failing banks is attached to its own creditworthiness (Correa et al. 2012). A sovereign rating downgrade can reduce funding benefits that the banks receive due to implicit or explicit guarantees. The government is less in the position to support a bank during bankruptcy, thereby increasing the risk of bank failure (Davies & Ng 2011).

As the banks can be affected through four transmission channels, which tend to be strong, this could mean that the banks are especially sensitive to sovereign rating risk. The sovereign rating changes are an expression of this risk, thus the rating changes can have a high impact on banks.

Thus, the literature suggests that a sovereign rating downgrade has a negative impact on the national stock markets. It also suggests that this impact should especially be large for banks (Davies & Ng 2011, Correa et al. 2012). The uniqueness of the US sovereign downgrade of August 5, 2011 provides for an excellent opportunity to test the hypothesis presented in the literature. Therefore this paper will try to answer the following question: What is the effect of the U.S. sovereign rating downgrade on the stock returns of banks versus non-banks in the U.S. This results into the following hypothesis:

Hypothesis 1: The U.S. sovereign rating downgrade resulted into larger negative abnormal stock returns of bank stocks than of non-bank stocks.

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Due to the “Too big to fail” theory, a further distinction can be made within the banks. The failure of a large bank can cause immediate failures of its counterparties in other banks and the financial system. It can also lead to a confidence crisis that may spill over to other banks and financial institutions, thus leading to a chain of failures and financial crises (Mishkin et al. 2006). The government’s creditworthiness is attached to its ability to support failing banks (Correa et al. 2012). A sovereign rating downgrade can reduce funding benefits that the banks receive due to implicit or explicit guarantees. As a government is less in the position to support a bank during bankruptcy, there will be an increase in the risk of bankruptcy failure (Davies & Ng 2011). Larger banks have a greater systemic impact, which suggests that the large bank will more likely to be saved during crises. Thus the creditworthiness of the sovereign will have a greater impact banks with a large market capitalization than on banks with low market capitalization. The following hypothesis can be inferred from the previous:

Hypothesis 2: The U.S. sovereign rating downgrade will result into a larger abnormal return for the high cap stocks than the low cap stocks.

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4. Data

The data was collected from the Wharton Research Data Services. The securities of the banks are selected from the population of all securities for which daily return data are available on the files of the Center for Research in Security Prices at the University of Chicago (CRSP). This database contains daily historical stock data from NYSE, Amex, NASDAQ, and NYSE Arca stock exchanges. This includes the daily stock returns of all stocks listed on a stock exchange within the United States.

Furthermore, the banks have been selected according to the bank dataset of the New York Fed1

. This dataset includes all publicly traded commercial banks and bank holding companies from January 1, 1990 to September 30, 2012. Savings and loans companies, thrifts and foreign companies are not included in the dataset. The total number of banks in the dataset is 1,174. The total number of banks has been narrowed to the ones listed on stock exchanges in the year 2011. The total number of banks with available security data was 382. There were fewer companies available with data available on the market capitalization in 2011. The total number of banks used for the portfolio test was 3722

.

The selection of the banks could have an impact on the results. Due to the financial crisis many of the banks have gone bankrupt in the years before 2011. This could imply that the banks that depended greatly on sovereign risk had gone bankrupt in the years before, thus they are excluded in the sample.

1_{http://www.newyorkfed.org/research/banking_research/datasets.html}

2_{The calculations were done by using the Eventus Software. The portfolio test was}

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5. Research Method

To detect the abnormal returns resulting from the downgrade I will make use of an event study methodology as proposed by Brown and Warner (1985), the precise version used in this event study is based on the one summarized by MacKinley (1997). By examining the responses of the stock prices around the announcement of an event, an event study tries to measure the economic effects of an event, such as rating downgrades. An event study is useful to test for the existence of an information effect, in this case the impact of the sovereign rating on certain stock prices. Second, it helps to identify factors that explain changes in firm value on the event date.

An event study specifically measures the impact of a specific event on the value of a firm using financial market date. Given rationality in the marketplace, such a study is useful as the effects of an event are reflected in the stock prices instantly. Therefore, the economic impact of an event can be measured using stock prices that are observed in a short period of time. Other measures, however, that are directly related to the productivity of firms may require many months or years of observation (MacKinlay 1997). More specifically, I want to assess whether sovereign rating changes empirical confirmation of the impact of sovereign ratings on the banks as suggested in the literature. To examine this effect a multi-level analysis will be given on the stock returns of banks.

MacKinlay (1997) gives an overview of the application of the event study method. According to MacKinlay (1997), the first step of conducting an event study is the definition of the of the event of interest and the identification of the event window, which is the period in which the security prices of the companies of interest in this this event are examined. The event window is customarily defined larger than the specific period of interest, allowing to examine the period around the event. The period of interest is usually seen defined in terms of multiple days, encompassing at least the announcement day and the day after this. The price effects on after the day of the announcement will thereby be captured. The pre-event period can also be of interest, the market having acquired information prior to the event date can be examined. The various event periods will be defined below.

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The second step after the event definition is the selection criteria for the inclusion of firms in the study must be determined (MacKinlay 1997). See for more information on the selection criteria Chapter 3 regarding the data.

Furthermore, to examine the impact of the event, a measure of the abnormal return is needed (MacKinley 1997). The abnormal return is defined as the actual ex-post return of the security within the event window minus the normal return of the firm within the same period. The normal return is the expected return without the occurrence of the event. The most common used models for normal returns are the constant return model and the market model (MacKinley 1997). The constant return model assumes a constant mean return of the security, whereas the market model assumes a linear relationship between the market return and the security return.

The estimation window must next be defined (MacKinley 1997), depending on the choice of the normal performance model used. The estimation window is usually prior to the event window. In order not to influence to estimation parameters of the normal performance model, in principle, the estimation period is selected in such a way that it does not contain the event period. The parameter estimates will be used to calculate the abnormal return. The estimation window of this thesis will be defined below.

The normal return can be calculated in various ways. There are two categories of models to calculate the normal return, first the statistical models and second the economic ones (MacKinley 1997). The statistical models are based on statistical assumptions and do not depend on economic arguments. Economic models on the other hand, are based on assumptions regarding investor behavior. The advantage of economic models is to calculate a more precise normal return (MacKinley 1997).

The statistical models assume that the returns of the assets are jointly multivariate normal and independently and identically distributed trough time (MacKinley 1997). This assumption is sufficient for a correct specification of the market model. As the assumption is reasonable and inferences using the normal return models tend to be robust to deviations from the assumption, this strong assumption doesn’t lead in principle to problems. The two statistical models that will be used in this thesis will be the market model and the market adjusted returns model.

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The returns of the market model are related to the rate of return of the market portfolio. The market model has a linear specification due to the assumed joint normality of asset returns. The variance is reduced by removal of the portion of return that can be attributed to the variation of the market return. This increases the ability to detect any effects of the event.

The economic models can provide more constrained normal return models due to restrictions. The two common economic models are the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT) (MacKinley 1997). The CAPM is an equilibrium theory in which the expected return of an asset is determine by its covariance with the market portfolio. This thesis will use the extended model as proposed by Fama and French (1993). This method is an expansion of the Capita Asset Pricing Model (CAPM). It adds market capitalization and book-to-market ratio to the CAPM. This model tries to add other factors that might be able to explain abnormal returns. By applying these models I can measure the impact of the downgrade of the sovereign rating on the stocks of the banks. A negative abnormal return would suggest that there is correlation between the rating and the announcement of the downgrade.

After the specification of the models of the normal return, the abnormal returns observations must be aggregated in order to be able to draw overall conclusions from the event. The aggregation happens through time and securities.

Furthermore, a portfolio test will be applied to whether there’s an overreaction and an availability bias in the portfolio (De Bondt & Thaler 1985). This could mean that the holders of the portfolios would overreact to the downgrade of the stocks. Individual tend to overweigh recent information and under weigh base rate data. They seem to make predictions with respect to the matching rule, i.e. the predicted value is selected in such a way that the current distribution of outcome will match the future outcomes. This violates the basis statistical principle stating that the extremeness of predictions must be moderated by considerations of predictability.

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6. Empirical Definitions

6.1 Event date:

The event day is the day Standard and Poor’s announced the downgrade of the US sovereign rating from AAA to AA+. This was on the 5th

of August 2011. Thus:

T0 = 5 th

of August 2011

6.2 Event and estimation window

Various event windows have been chosen for this research. The research will apply the same event window as Brooks et al. (2004). There’s a 10-day pre-event period t=[-10;-1]. The event date is t=[0;1] and there’s a 9-day post event period t=[2;10]. In addition three other event windows have been selected: t=[0,0], 5;5] and t=[-15;15]. The calculations below will be applied to multiple event windows, in which the cumulative average abnormal return (CAAR) will be calculated. The analysis and the discussion will focus on the main event periods. Based on the literature I expect a small negative result within the pre-event period and larger negative results in the event period and the post-event period.

The estimation windows are used to calculate the OLS-estimators as defined below. A 200-day estimation window is used ending 50 days before the event date.

6.3 Empirical Specifications

Three models for estimating the stock prices will be used, these are respectively the market model, market adjusted returns model and the Fama-French three-factor model.

A.1 Market model:

𝑅!"= 𝛼!+ 𝛽!𝑅!" + 𝜀!" (1) 𝛼_! = Risk-free interest rate;

𝑅_!" = Rate of return of the common stock of the jth

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𝑅_!" = Rate of return of the market index on day t based on the CRSP Value Weighted Index

𝛽! = !"# (!_{!"# (!}!,!!)

!)

𝜀_!" = Random variable with expected value of zero and is assumed to be uncorrelated with 𝑅!".

A.2 Market Adjusted Returns Model:

The market adjusted return model shows the stock prices on the Jth

day, for

calculating the abnormal return, the market return will be subtracted.

A.3 Fama-French three-factor model (Fama and French 1993):

𝑅_!"= 𝛼_!+ 𝛽_!𝑅_!" + 𝑠_!𝑆𝑀𝐵_!+ ℎ_!𝐻𝑀𝐿_!+ 𝜀_!" (2) 𝑆𝑀𝐵! = Average return on small market-capitalization portfolios minus the

average return on three large market-capitalization portfolios;

𝐻𝑀𝐿_! = Average return on two high book-to-market equity portfolios minus the average return on two low book-to-market equity portfolios;

𝑠_! = Measures the sensitivity of 𝑅!" to the difference between small and large capitalization stock returns;

ℎ_! = Measures the sensitivity of 𝑅!"to the difference between value and growth stock returns;

𝜀_!" = Random variable with expected value of zero and is assumed to be uncorrelated with 𝑅!", uncorrelated with 𝑅!" for 𝑘 ≠ 𝑗, not

autocorrelated, and homoskedastic.

Abnormal returns definition

B.1 Market Model abnormal return

𝐴_!" = 𝑅_!"− (𝛼_! + 𝛽_!𝑅_!") (3) Where 𝛼 and 𝛽 are the ordinary least squares estimates of 𝛼 and 𝛽 .

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B.2 Market Adjusted abnormal return

𝑅_!"= 𝑅_!"− 𝐴_!" (4)

𝑅_!" = Rate of return of common stock of the jth

firm on day t.

𝑅!" = Rate of return of the market index on day t.

𝐴!" = Abnormal return jth firm’s stock on day t.

B.3 Fama-French abnormal return

𝐴_!" = 𝑅_!"− (𝛼_! + 𝛽_!𝑅_!" + 𝑠_!𝑆𝑀𝐵_!+ ℎ_!𝐻𝑀𝐿_!) (5) Where 𝛼!, 𝛽!, 𝑠! and ℎ! are the ordinary least squares estimates of 𝛼!, 𝛽!, 𝑠! and ℎ!.

Average abnormal returns calculation

C.1 Average abnormal return (AAR)

𝐴𝐴𝑅_! = !!!!!!

! (6)

Where t is expressed in terms of trading days.

C.2 Cumulative average abnormal return

𝐶𝐴𝐴𝑅_!!,!! = _!! ! 𝐴_!" !!!! !

!!! (7)

Between interval T1 and T2.

Test statistics definition

D. Generalized Sign Test

A generalized sign test is applied at various significance levels to analyze the significance of the results. The generalized sign test will examine if the number of stocks with a positive number of stock with a positive cumulative abnormal return will exceed the number of stocks expected without abnormal performance. The

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number expected is based on the fraction of positive abnormal returns in the estimation period (Cowan 1992).

𝑝 = ! ! ! !"" ! !!! !!!!!""!𝑆!" (8) Where 𝑆!"= 1 ₀if 𝐴!" > 0 otherwise (9)

The normal approximation is used by the test statistic to the binomial distribution with parameter p. The number of stocks in the event window with a positive cumulative abnormal return is defined as w. The generalized sign test statistic is (Cowan 1992):

𝑍! = ! _{!! !!!}!!!! (10)

6.4 Portfolio test

I found data on 372 of the 382 companies to have market capitalization data. I divided the 372 companies into 6 portfolios of 62 ranked on their market capitalization. The abnormal returns of the top and bottom portfolio were calculated based on the Fama-French three-factor model. Further more, a portfolio test was applied (De Bondt and Thaler 1985). The main event periods were used: i) pre-event period (t=[-10;-1]); ii) event period (t=[0,1]) and post-event period (t=[1;10]). The following calculations were applied for the portfolio test (De Bondt and Thaler 1985):

1. Computation of the cumulative abnormal return for each firm in portfolio 1 and 6 in all three main periods;

2. Computation of the average CAR;

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𝑠_!! ₌ _𝐶𝐴𝑅 !,!,!− 𝐴𝐶𝐴𝑅!,! !+ 𝐶𝐴𝑅!,!,!− 𝐴𝐶𝐴𝑅!,! ! ! !!! ! !!! 2 𝑁 − 1 W = Portfolio 6 L = portfolio 1

4. The significance of the test is calculated in the following way: 𝑡_! = 𝐴𝐶𝐴𝑅_!,! − 𝐴𝐶𝐴𝑅_!,! 2𝑆_!! _𝑁

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7. Results and Analysis

The results will be discussed based on three non-overlapping periods: (a) the 10-day pre-event period t=[-10;-1], (b) the event date is t=[0;1] and (c) the 9-day post event period t=[2;10]. The full results are presented in the appendix. The mean of the cumulative abnormal returns of the bank stocks is used to test the hypothesis.

Table 2 – Market Model, Value Weighted Index

Days (t) Mean Cumulative Abnormal _Return Generalized Sign _Z

[-10 , -1] 4.82% 8.391*** [0, 1] -1.33% -2.977** [2 , 10] -3.16% -8.200*** [0 , 0 ] -0.73% -2.158* [-5 , 5] -1.25% -1.031 [-15 , +15] -0.42% 0.300

$,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail test.

The market model shows a positive mean cumulative abnormal return of 4.82% in the pre-event period at a 0.001 significance level. After the announcement, during the event date, there is, as expected, a negative abnormal return of -1.33% with a significance level of 0.01. The post event period shows a negative result of -3.16% with a 0.001 significance level.

Table 3 – Market Adjusted Returns, Value Weighted Index Days (t) Mean Cumulative Abnormal

Return Generalized Sign Z [-10 , -1] 6.42% 14.873*** [0, 1] -0.17% -1.019 [2 , 10] -3.66% -8.607*** [0 , 0 ] -0.68% -1.327$ [-5 , 5] -0.07% 1.544$ [-15 , +15] 0.35% 2.774**

The results with the market adjusted returns model are similar. There is a larger positive return with respect to the pre-event period of 6.42% significant at 0.001. The event period shows a slight negative return of -0.17%, this result is

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however not significant. The post-event period shows a negative mean cumulative abnormal return of -3.66% with a significance level of 0.001.

Table 4 – Fama-French Time-Series Model, Value Weighted Index Days (t) Mean Cumulative Abnormal

Return Generalized Sign Z [-10 , -1] 1.96% 3.569*** [0, 1] -1.08% -2.472** [2 , 10] -3.04% -7.490*** [0 , 0 ] -0.08% 3.364*** [-5 , 5] -0.66% 0.087 [-15 , +15] -0.12% 0.599

As expected the Fama-French model shows slightly less extreme results. The pre-event period has a positive mean cumulative abnormal return of 1.96% significant at 0.001. The even period is also negative with -1.08%. This is significant at 0.01. The post-event period shows a negative return of -3.04% with a significance level of 0.001.

These results confirm the hypothesis that the downgrade resulted in a larger abnormal return of the banks stocks in comparison with the other stocks. This empirically confirms the literature that suggests the interconnectedness of the banks and sovereign risk, of which the credit rating is a measurement. As expected the Fama-Frech three factor model shows results that are less strong than the other model. This is in line with the implications of the model that the model adds other factors that explain for abnormal returns. The results are overall on the other hand, not extremely strong. A -3.04% percent decrease as seen in the post-even period of the Fama-French model might not be strong enough to be used as predictive for the future. To increase the predictive power, however, future research should conduct a similar research in which the effect is analyzed for multiple countries and multiple downgrades.

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Market Capitalization Portfolio, Fama-French

Period CAAR P1 CAAR P6 S2

t

[-10,-1] -0.70% -0.29% 85.65 0.05

[0,1] -2.10%*** -4.70%** 50.30088903 1.76*

[2,10] -5.62%*** -1.65%* 122.531162 -1.5407$

$,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail test. P1 = portfolio 1; P6 = portfolio 6

The portfolio test utilized the Fama-French three factor model, as it is able to explain for more parts of the abnormal return. Portfolio 1 (see appendix 2), which contains stocks of the 62 largest banks with respect to market capitalization, has a pre-event negative cumulative average abnormal return of -0.70%, this results, however not significant. The event period resulted in a negative CAAR of -2.10% significant at a level of 0.001. And the post-event period resulted into a negative CAAR of -5.62% with a significance level of 0.001.

Portfolio 6 contains the stocks with the lowest market capitalizations (see appendix 1). The pre-event period resulted into a negative CAAR of -0.29%, this was however not significant. The event-period resulted into a negative CAAR of -4.70% with a significance level of 0.01. The post-even period resulted into a negative CAAR of -1.65% significant at a level of 0.05.

Comparing the portfolios, it shows that during the event-period there was a larger negative CAAR in portfolio 6. In the event period there’s a larger CAAR in portfolio 1. This means that indeed in the post-even period there’s would be a confirmation of hypothesis 2, meaning that the banks with a larger market capitalization would receive higher negative abnormal returns. This is however not the case for the event period.

With respect to the overreaction, the portfolio test resulted into no significant overreaction of the portfolios in the pre-event period. In the event period there’s an overreaction significant at a level of 0.05 and in the post-event period there’s an overreaction significant at 0.10. This means that the overreactions are not very significant, especially in the post-event period. This could still imply that there’s a confirmation of the second hypothesis. However, more research needs to be done on this topic.

Concluding, there’s a confirmation of the first hypothesis, however with respect to the second hypothesis the results are ambiguous. Overall, the impact of the

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economic crisis that was prevailing during the period before 2011 could also have influenced the data. A great deal of banks went bankrupt and no data was available on these banks, we were however left with 372 banks, which should be enough to provide statistical relevant data.

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8. Conclusion

In this paper, I examined the effect of the US sovereign rating downgrade by Standard and Poor’s on the stock prices of US banks. Two hypotheses were posited: (1) the sovereign rating downgrade resulted into larger negative abnormal stock returns of bank stocks than of non-bank stocks and (2) the downgrade will result into a larger abnormal return for the high cap stocks than the low cap stock.

Consistent with the literature, I find that the sovereign rating downgrade lead to an abnormal return of the bank stocks. This suggests that there is an interconnectedness of the banks and sovereign risk. With respect to the difference in impact of the sovereign rating on large and small banks, the results are more ambiguous. Future is needed on the effect of sovereign rating changes on bank stocks. Combining multiple rating changes in multiple countries could increase the predictive power of the model.

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Appendix 1 – Abnormal returns

Table 1 – Fama-French Time-Series Model, Value Weighted Index

[-10 , -1] 1.96% 3.569*** [0, 1] -1.08% -2.472** [2 , 10] -3.04% -7.490*** [0 , 0 ] -0.08% 3.364*** [-5 , 5] -0.66% 0.087 [-15 , +15] -0.12% 0.599

$,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail tes.

Table 2 – Market Adjusted Returns, Value Weighted Index

[-10 , -1] 6.42% 14.873*** [0, 1] -0.17% -1.019 [2 , 10] -3.66% -8.607*** [0 , 0 ] -0.68% -1.327$ [-5 , 5] -0.07% 1.544$ [-15 , +15] 0.35% 2.774**

Table 3 – Market Model, Value Weighted Index

[-10 , -1] 4.82% 8.391*** [0, 1] -1.33% -2.977** [2 , 10] -3.16% -8.200*** [0 , 0 ] -0.73% -2.158* [-5 , 5] -1.25% -1.031 [-15 , +15] -0.42% 0.300

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Appendix 2 – Portfolio Results

Portfolio 1 – Fama-French, Value Weighted Index

[-10 , -1] -0.70% -0.924 [0, 1] -2.10% -4.734*** [2 , 10] -5.62% -5.496*** [0 , 0 ] -0.62% -1.432$ [-5 , 5] -3.21% -2.194* [-15 , +15] -3.08% -2.448**

Portfolio 6 – Fama-French, Value Weighted Index Days (t) Mean Cumulative Abnormal

Return Generalized Sign Z [-10 , -1] -0.29% -0.512 [0, 1] -4.70% -2.803** [2 , 10] -1.65% -1.785* [0 , 0 ] -1.89% -0.003 [-5 , 5] -4.98% -1.530$ [-15 , +15] -6.23% -1.785*

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Portfolio 1 – Market Model, Value Weighted Index

[-10 , -1] 3.99% 4.780*** [0, 1] -2.29% -4.875*** [2 , 10] -4.89% -4.875*** [0 , 0 ] -1.13% -4.875*** [-5 , 5] -4.01% -2.334** [-15 , +15] -1.88% -1.318$

Portfolio 6 – Market Model, Value Weighted Index

[-10 , -1] 0.26% -0.151 [0, 1] -4.79% -2.699** [2 , 10] -1.79% -1.680* [0 , 0 ] -2.09% -1.425$ [-5 , 5] -5.14% -1.680* [-15 , +15] -6.52% -2.189*

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Portfolio 1 – Market Adjusted, Value Weighted Index Days (t) Mean Cumulative Abnormal

Return Generalized Sign Z [-10 , -1] 0.54% 2.674** [0, 1] -4.16% -7.009*** [2 , 10] -5.28% -4.461*** [0 , 0 ] -1.32% -4.461*** [-5 , 5] -7.00% -5.990*** [-15 , +15] -6.73% -3.442***

Portfolio 6 – Market Adjusted, Value Weighted Index

[-10 , -1] 9.75% 6.582*** [0, 1] 1.03% 2.765** [2 , 10] -2.31% -2.070* [0 , 0 ] -1.65% -0.543 [-5 , 5] 2.60% 2.765** [-15 , +15] 3.37% 2.001*

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Appendix 3 – Portfolio Test

Market Capitalization Portfolio, Fama-French

Period CAAR P1 CAAR P6 S2

t

[-10,-1] -0.70% -0.29% 85.65 0.05

[0,1] -2.10% -4.70% 50.30088903 1.76

[2,10] -5.62% -1.65% 122.531162 -1.5407

$,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail test. P1 = portfolio 1; P6 = portfolio 6

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Portfolio 1 Company Name Market Capa CAR b [-10, -1] CARb [0,1] CARb [2,10]

Wells Fargo & Co 145037.5867 -1.63 -0.26 -0.28

JPMorgan Chase & Co 125441.9758 -2.2 -0.81 -1.77

Bank of America Corp 58579.8153 -3.68 -16.79 7.05

American Express Co 54905.88 -1.26 -0.39 2

U.S. Bancorp 51660.6851 -0.07 -1.27 -6.21

Goldman Sachs Group Inc (The) 43900.8712 1.05 0.46 -5.65

Metlife Inc. 32987.0993 1.82 -0.03 -6.5

PNC Financial Services Group Inc. 30392.09 0.66 1.22 -10.16

Morgan Stanley 29155.2982 -9.21 -2.73 -6.95

Bank of New York Mellon Corp (The) 24084.6093 2.03 -4.79 -9.39

Franklin Resources Inc 20820.1585 3.97 1.42 2.35

State Street Corp 19648.0614 1.45 0.98 -10.13

Capital One Financial Corp. 19451.1586 -7.12 -3.85 6.62

BB&T Corp 17547.0893 -2.7 -1.25 -12.15

Schwab (Charles) Corp 14313.3066 5.35 -2.89 -11.62

Fifth Third Bancorp 11699.9069 2.65 -1.09 -8.78

M&T Bank Corp 9594.6402 -0.09 -3.4 -4.87

Northern Trust Corp 9558.4169 -1.76 -0.1 -8.31

SunTrust Banks Inc. 9504.3159 -4.18 -4.58 -11.29

KeyCorp 7328.6315 2.96 -2.83 -8.91

CIT Group Inc 6997.0142 0.25 -7.23 -2.48

Regions Financial Corp 5412.9131 -0.08 -5.5 -14.27

New York Community Bancorp Inc. 5409.9577 -9.03 -4.3 4.61

Comerica Inc 5091.2172 -3.32 -4.02 -16.11

Huntington Bancshares Inc 4745.5889 2.42 1.91 -8.73

People's United Financial Inc 4480.538 -5.3 -4.71 -2.13

First Republic Bank 3960.0769 -2.84 -4.16 -5.28

BOK Financial Corp 3743.6443 -1.31 -3.44 -2.21

Commerce Bancshares Inc 3394.9672 -2.19 -1.25 -2.4

Hudson City Bancorp Inc 3297.3188 -0.98 -6.02 -14.6

Cullen/Frost Bankers Inc 3241.4782 -0.88 -2.99 -2.53

First Niagara Financial Group Inc 3015.9865 -3.04 -2.6 -2.04

Zions Bancorporation 2997.7178 -9.17 0 -15.36

East West Bancorp Inc. 2949.228 0.46 -2.04 -7.23

Signature Bank 2770.4582 1.91 -1.03 -7.82

Hancock Holding Co 2708.0189 12.77 0.1 -4.98

City National Corp 2319.4058 0.26 -0.47 -13.31

Valley National Bancorp 2105.0524 -1.13 -2.14 -5.5

SVB Financial Group 2074.8965 1.99 -4.97 -15.85

First Horizon National Corp 2059.744 -8.22 -6.49 -13.1

Bank of Hawaii Corp 2044.182 1.02 -0.87 -5.89

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Associated Banc-Corp 1950.1926 4.33 -4.13 -10.47 Prosperity Bancshares Inc 1892.8185 -4.48 -1.48 -6.28 First Citizens BancShares Inc 1798.7936 -4.91 -3.51 -4.76

Webster Financial Corp 1778.3139 -2.24 -5.74 -3.12

Stifel Financial Corp. 1665.0296 -1.84 -4.68 -8.89

TCF Financial Corp 1654.5437 -3.82 -4.15 -7.86

FirstMerit Corp 1652.9676 -9.7 -0.63 -8.73

Trustmark Corp 1558.0092 -1.81 2.23 1.19

UMB Financial Corp 1505.8685 1.07 -1 -1.78

Investors Bancorp Inc 1495.4442 5.09 0.69 4.76

IBERIABANK Corp 1448.1382 -6.25 -1.6 -4.71

F.N.B. Corp 1438.8695 2.78 -0.68 -3.25

Popular Inc 1426.008 1.66 -5.03 1

United Bankshares Inc 1419.5215 0.22 -1.9 -5.66

Umpqua Holdings Corp 1389.7244 7.28 4.36 -9.82

Susquehanna Bancshares Inc 1314.5455 -1.38 -0.12 -3.26 National Penn Bancshares Inc 1281.8925 7.97 1.4 -5.79

Westamerica Bancorporation 1235.785 0.24 -2.67 -6.16

International Bancshares Corp 1233.5421 7.02 5.16 -1.12

Cathay General Bancorp 1174.2893 -6.81 -3.88 7.36

a _{In millions USD} b_{In %}

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Portfolio 6 Company Name Market Capa CARb [-10, -1] CARb [0,1] CARb [2,10]

Southern First Bankshares Inc 24.832 5.64 -14.14 -6.52 First West Virginia Bancorp Inc. 24.7619 -10.16 0.23 -7.16

Summit State Bank 24.674 -5.09 1.43 1.88

Commercefirst Bancorp Inc 24.1283 0.95 5.64 1.5

United Bancshares Inc/OH 23.674 0.22 -1.9 -5.66

Anchor Bancorp/WA 23.46 -4.58 2.48 -10.41

First Bancshares Inc (The)/MS 23.3629 -3.12 -4.67 -8.04

Valley Financial Corp 23.0839 -5.5 2.7 -14.27

Pathfinder Bancorp Inc 22.275 4.03 -3.9 -2.17

Glen Burnie Bancorp 22.1544 -1.53 -7.9 1.14

BancTrust Financial Group Inc 22.0373 4.43 2.12 -3.18 Southwest Georgia Financial Corp 21.5306 14.71 2.43 -7.88

Jefferson Bancshares Inc 21.4974 0.27 -0.25 -4.19

Ohio Legacy Corp 20.5036 5.54 4.08 -14.19

First Community Corp 20.4765 -6.07 -11.3 -5.22

Summit Financial Group Inc 20.2703 24.2 10.11 4.6

Southern Community Financial Corp 20.0241 -0.1 2.46 -19.86

Magyar Bancorp Inc 19.6923 -9.28 8.25 5.01

First United Corp 19.5383 -10.16 -7.89 -11.54

M B T Financial Corp 19.367 -0.48 -2.75 -6.47

WVS Financial Corp. 19.1394 14.36 2.71 -8.22

Colony Bankcorp Inc 18.9034 1.86 0.13 2.81

VSB Bancorp Inc/NY 18.7352 -0.29 -8.62 -7.47

Jacksonville Bancorp Inc/FL 18.5535 -7.06 5.64 -3.99

MacKinac Financial Corp 18.5364 2.83 -3.85 4.2

Old Second Bancorp Inc 18.2455 45.12 6.92 13.96

Alliance Bankshares Corp 17.7828 9.73 -6.21 0.59

Mayflower Bancorp Inc 17.5753 3.2 -5.97 -18.98

Britton & Koontz Capital Corp 16.035 -8.7 -3.19 -7.05 Royal Bancshares of Pennsylvania Inc 15.8691 5.61 -1.17 -0.38 Guaranty Federal Bancshares Inc 15.4356 1.8 -4.44 -1.12

Annapolis Bancorp Inc 15.3962 1.59 -3.34 7.38

Sussex Bancorp 14.5714 -3.7 -17.22 19.09

Citizens First Corp 13.7633 0.93 -1.23 -3.84

New Century Bancorp Inc. 13.72 -8.22 -0.45 -28.21

Rurban Financial Corp 12.7871 28.9 -6.46 -3.19

Eastern Virginia Bankshares Inc 12.1103 -10.63 -1.97 -12.23

Ameriana Bancorp 11.8364 -0.78 -2.93 -14.77

Plumas Bancorp 11.3669 -12.95 3.03 -4.91

Independent Bank Corp 11.2944 2.64 -0.63 -4.9

OptimumBank Holdings Inc 11.2055 -4.39 -22.31 -16.11

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Camco Financial Corp 9.0075 10.34 -6.15 -9.59

Community West Bancshares 8.9245 -17.09 -2.35 -3.3

Carolina Bank Holdings Inc 8.2982 -6.86 -5.41 7.14

PremierWest Bancorp 8.02 -11.05 7.04 -5.51

Southcoast Financial Corp 7.5345 -20.28 -1.85 -5.37

First Financial Service Corp 7.266 -3.21 -19.46 -7.63 First Capital Bancorp Inc/VA 7.2195 -3.75 -12.25 -5.83

Carrollton Bancorp 7.2128 5.05 -16.66 8.06

Village Bank and Trust Financial Corp 5.3038 -21.35 -14.15 4.51 Southern Connecticut Bancorp Inc 5.1532 -5.19 -16.21 -9.4 Princeton National Bancorp Inc 5.0449 -6.1 -10.04 0.91 Oak Ridge Financial Services Inc 4.4658 -3.4 -5.89 1.34

First Security Group Inc 3.9574 -7.76 -52.41 89.32

Northern States Financial Corp 3.8502 -0.11 -3.74 3.42

First Mariner Bancorp 3.0176 -27.2 -1.36 -10.59

Monarch Community Bancorp Inc 2.2334 32.09 -4.43 4.99

Mercantile Bancorp Inc/IL 2.012 -4.55 -1.61 -13.58

Bank of the Carolinas 1.0909 -28.61 -16.47 -19.92

Central Virginia Bankshares Inc 0.9979 2.27 -13.23 2.88 Provident Community Bancshares Inc 0.2149 2.09 8.1 8.8 a _{In millions USD}