• No results found

Does stock market react to the release of 2014 EU-wide bank stress testing results?

N/A
N/A
Protected

Academic year: 2021

Share "Does stock market react to the release of 2014 EU-wide bank stress testing results?"

Copied!
44
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

University of Amsterdam

Faculty of Economics and Business

Master’s Thesis

Does Stock Market React to The Release of 2014

EU-Wide Bank Stress Testing Results?

Jie Zhu (10857869)

MSc Business Economics, Finance track

Supervisor: Dr J.E. Ligterink

Submission Date: 14 December 2015

(2)

Statement of Originality

This document is written by Student Jie Zhu who declares to take full

responsibility for the contents of this document.

I declare that the text and the work presented in this document is

original and that no sources other than those mentioned in the text and

its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the

supervision of completion of the work, not for the contents.

(3)

Abstract

We investigate whether the release of 2014 EU-wide stress test result, which was carried out by European Banking Authority (EBA) and European Central Bank (ECB) etc. in European Union (EU), produced information for the firm investors. Using standard event study techniques, we find that firm investors do react to this release of 2014 EU-wide stress test results, which can be found evidence in the stock market that both bank dependent and bank independent firms have higher return than the market does, suggesting these firms perform better than the market. The bank dependent firms, who have no public debt market access but solely depend on their banks for debt, have lower positive abnormal returns than bank independent firms, which suggest that their investors react heavily than those in bank independent firms. We also find that bank dependent firms whose main bank received a fail score react more heavily than those receives a pass score from the stress test. Our findings suggest firms from countries whose bank failed the stress test are more likely affected by the failure outcome of their banks than firms from country whose bank passed the stress test.

(4)

Contents

Part I: Introduction  ...  5  

Part II: An overview of 2014 European Bank Stress Test  ...  9  

Part III: Literature Review  ...  12  

Part IV: Research Approach  ...  15  

1)   Identify the event of interest  ...  16  

2)   Determine the selection criteria for sample of interest  ...  16  

3)   Estimate the normal and abnormal returns  ...  16  

4)   Estimate the procedure  ...  17  

5)   Test and analyze the results  ...  18  

Part V: Data and Results  ...  22  

Part VI: Conclusion  ...  29  

References  ...  31  

Annex 1  ...  33    

(5)

Part I: Introduction

 

Bank Financial Stability Stress Testing (hereafter referred to as “stress test”) is used to measure a given bank’s ability to deal with an economic crisis. In general, stress tests can be carried out either by the bank itself as part of internal risk management or by supervisory authorities such as Federal Reserve in US, European Banking Authority (EBA) and European Central Bank (ECB) etc. in European Union (EU). The stress tests were mainly focusing on solvency risk for individual banks and incorporating liquidity risk (Gauthier, 2014). Later on, these tests also focus on liquidity risk, credit risk, and market risk, to the bank’s financial health in crisis situations. Therefore, a passing grade would mean that the bank has enough capital to withstand the crisis situations. While if a bank failed in the stress test, the bank will be forced to raise its capital stock and reform to improve financial health by the supervisory authorities.

Several researchers such as Allen & Carletti (2008) have shown that banks play an important role in the economy. They indicated that banks are important for corporate finance and are always critical to the financial system. Therefore, the examination on financial health of banks should also be of interest. We conjecture that companies that have difficulties to finance their operations may rely heavily on bank loans. And as a result of the release of stress test results, investors on firms that are bank dependent may react more heavily, leading in the change of return on stocks of those companies. This thesis is aiming to measure the response of stock markets to the outcome of these stress tests of the EBA of 2014. Or more precisely, this thesis is to investigate the stock market reaction of investors to the 2014 EU-wide bank stress test results.

The 2014 EU-wide stress test is to assess the resilience of EU banks to adverse economic developments, in order to help supervisors assess individual banks and understand remaining vulnerabilities, complete the repair of the EU banking sector and increase confidence. The 2014 test is coordinated by the EBA across the EU and

(6)

is carried out in cooperation with the European Systemic Risk Board (ESRB), the European Commission (EC), the ECB and all relevant authorities. The EU-wide stress test includes 123 banking groups across the EU (and including Norway) with a total of EUR 28,000 billion of assets, which is highly representative as they account more than 70% of total EU banking assets. The EBA published the results of the stress test of 123 banks on 26th October 2014. As suggested by the results, there were 24 participating banks fell below the defined thresholds and are expected to raise an aggregate maximum capital of EUR 24.6 billion1. This can be seen as a strong signal

indicating that the EBA and ECB are taking actions to improve the financial health of European banks.

This study is focused on the reaction of investors who invested in one or more companies listed in the STOXX Europe 600 index2 excluding 44 banks and 12 firms without stock price information. And excluding 48 firms who are not from these 22 EU countries, the sample consists of 496 non-banking firms. Since banks that failed in the test will be required to raise capital, they may mainly do this by reducing their loans to companies. On the other side, investors may also lose their confidence on those bank dependent companies3 and switch to other companies, which may result

in negative or non-positive changes in companies’ stock return. We will exploit the following variations to test our conjecture on the causal link from bank-health to borrower-performance.

Firstly, we exploit the variation that generated between bank dependent firms and bank independent firms. We assume default risk, stock market liquidity, and other factors are fixed and match along for firm size and growth opportunities when                                                                                                                

1 Results of 2014 EU-wide Stress Test (Aggregate Results), page 7-8, European Banking Authority, 26th October 2014.

2 STOXX 600 Index represents large, mid and small capitalization companies across 18 European countries: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and the United Kingdom. Free float market capitalization of 8,314,080 million EUR on 30th April 2015.

3 Bank dependent companies refer to those firms have no public debt market access but solely depend on their banks for debt.  

(7)

comparing the market performance of bank dependent firms with a matched sample of bank independent firms. We expect that bank dependent firms react more heavily than bank independent firms.

Secondly, we exploit the variation generated by banks’ score in the stress test among all firms. Here banks are divided into two groups, pass group and fail group. We expect that firms react more heavily if their main banks receive a fail score in the stress test.

At last, we investigate the variation generated between bank dependent firms and bank independent firms within the same banks’ score group, that is, banks with a pass score or banks with a fail score. We expect that bank dependent firms react more heavily than bank independent firms within the same test score group.

There are a lot studies on bank stress test, as well as on firm investors’ reaction. For example, Haldane (2009) showed the reasons why banks failed the stress test. Haldane pointed out agenda and other elements to address some of the failures exposed by the crisis, which involve a greater degree of engagement both between risk managers and senior management within firms, and between financial firms and the authorities, etc (Haldane, 2009). Peristiani, Morgan, & Savino (2010) investigate whether the stress test produced information demanded by the market and found the stress test helped quell the financial panic by producing vital information about banks as well as bank opacity and the value of government monitoring of banks. But there are only a few studies about the relationship between the bank stress test and firm investors’ reactions. This thesis is about to investigate the causal role of bank stress test outcome on the firm investors’ decisions by analyzing the stock market reaction by both bank dependent firms and bank independent firms.

The results show the same way as we expected. When calculating the cumulative abnormal return of firms, we find that bank independent firms have an over two times

(8)

higher positive cumulative abnormal return than those bank dependent firms (17,62 percent of bank independent firms comparing to 5,27 percent of bank dependent firms), which suggests that bank independent firms have a better performance than bank dependent firms as an reaction to the release of 2014 EU-wide bank stress test. And it shows the same trend when looking at the test score. Firms from country group with pass score banks have an almost two times positive cumulative abnormal return (14.66 percent) of that (8,23 percent) for firms from country group with fail score in the bank stress test. Moreover, within group of firms that are from countries with banks succeed in the stress test, we also observe the same result that bank independent firms have a higher cumulative abnormal return of 10,75 percent than 3,91 percent for bank dependent firms, suggesting that bank independent firms perform better than bank dependent firms in this group. Within group of firms from countries with fail test score banks, the situation is also similar. Bank independent firms still earn a higher cumulative abnormal return than those bank dependent firms (6,87 percent comparing to 1.36 percent). And the CAR of bank dependent firms within fail-test-score country group is the lowest among all CARs. The detailed results and analysis will be discussed in part 5.

As we mentioned in above text, there are studies on bank stress test. But they mainly focus on the reason why banks failed in the test (Haldane, 2009), the bank opacity (Petrella & Resti, 2013), and the value of government monitoring in banks (Peristiani et al, 2010), etc. Peristiani et al. (2010) examine abnormal bank stock returns around the SCAP (Supervisory Capital Assessment Program) in 2009 and indicate that investors may be more interested in the market if banks pass the stress test while they would worry about their investment when banks fail the stress test. Although Peristiani et al. (2010) investigated the information that bank stress test provided for the market, they did not give the information on how is the investors reaction in the stock market related to this information generated by the bank stress test. Within this thesis, we emphasize the study on the relationship between the investors’ reaction and the release of 2014 EU-wide bank stress test result through the performance on

(9)

investors’ invest in the stock market.

The rest of the thesis is organized as follows. Part 2 is an overview of 2014 European bank stress test. Then comes Part 3 of literature review, including related studies. In part 4 we introduce research approach, theories and hypotheses. In part 5 we describe data and present the empirical results. Part 6 concludes the paper.

Part II: An overview of 2014 European Bank Stress Test

 

The 2014 European bank stress test is not the first time of bank stress tests. It is actually the fourth time. The EU-wide stress test started from 20094. But the first result was not made public, which made the test go virtually unnoticed. The second round in 2010 and the third round in 2011 were still in the process of restoring confidence. As to 2014 EU-wide stress test, more information is available on EBA website and the result was also released to the public. So we will only take 2014 EU-wide stress test into study in this thesis.

The 2014 EU-wide stress test is not only to assess the resilience of financial institutions to adverse market developments, but also to contribute to the overall assessment of systemic risk in the European Union financial system. Different from previous stress tests, the EBA will also disclose a fully loaded Capital Requirements Regulation (CRR)/ Capital Requirements Directive IV (CRD4) Common Equity Tier 1 (CET 1) capital ratio for each bank, which is also used to assess the impact of the stress test. The applied minimum hurdle rates across all participating banks were set at 8% CET 1 ratio for the baseline scenario and 5.5% CET 1 ratio for the adverse scenario. All the data were disclosed in the templates to help market participants better understand the data that banks will be disclosing. When selecting the sample, banks from 22 countries, of which should cover at least 50% of the national banking sector, directly or via subsidiaries of parent companies included in the sample as                                                                                                                

(10)

expressed in terms of total consolidated assets as of end of 2013. For each tested bank, the transitional CET 1 ratio was shown for the starting point and the value for year 2016 under both the baseline and the adverse scenarios5.

The EU-wide stress test is conducted in a bottom-up way, by using consistent methodologies, scenarios and key assumptions. Based on the assumption of a static balance sheet, a zero growth assumption was applied for both the baseline as well as the adverse scenario that was designed by the ESRB, which reflects the systemic risks that are now assessed as representing the most pertinent threats to the stability to the EU banking sector. Those assets and liabilities that mature within the time horizon of the exercise assumed to be replaced with similar financial instruments in terms of type, credit quality and maturity as at the start of the exercise. Besides, no capital measures taken after the reference date 31st December 2013 were to be considered6.

Since the EBA has the responsibility to ensure the function, stability and integrity of financial markets in the EU, the EBA is therefore entitled powers to initiate and coordinate the EU-wide stress tests in cooperation with the ESRB, ECB, and the EC. In the 2014 stress test, the EBA was mainly in charge of the development of a common methodology and templates, assisting quality assurance process by providing sets of statistical benchmarks to all competent authorities as a tool to assess the banks’ results. Besides, the EBA also acted as the central data hub for all 123 EU banks. Disclosed data on bank basis included banks’ composition of capital, risk weighted assets (RWA), profit and loss (P&L), exposures to sovereigns, credit risk and securitization. The Competent Authorities (CAs) at national level, including the ECB, ensure the quality of the stress test results in cooperation with the EBA by offering necessary supervisory reaction measures, for example, conveying instructions on completing the exercise to banks and receiving information directly from them.

                                                                                                               

5 Results of 2014 EU-wide Stress Test (Aggregate Results), page 40, European Banking Authority, 26th October 2014.  

(11)

According to the outcomes of the stress test, the 261 billion euros capital depletion mostly caused by 492 billion euros of credit losses, which is only marginally offset by continued but diminished earnings. And in the adverse scenario, the weighted average CET1 ratio falls by 260 basic points (bps) from 11.1% to 8.5%. There were 24 tested banks falling below the capital threshold in the adverse scenario, leading to a shortfall of 24.2 billion euros.7 As the first stress test launched in 2011, the EU-banks had already made efforts to improve their capital position afterwards. Therefore, the starting point for the 2014 EU-wide stress test has actually been strengthened than previous exercises (Figure 1).

In the exercise, banks were required to stress test the common set of risks such as credit risk, market risk, sovereign risk, securitization risk, cost of funding and interest income and assess the impact of the macroeconomic scenario on these risk types during the EU-wide stress test. However, the exercise focused on credit and market risk. Based on the methodology applied in the EU-wide stress test, banks were required to calculate stressed risk exposure amounts based on the scenario as well as defined floors, hence the risk exposure amount cannot fall below the starting level no matter how much exposure defaults in a scenario. Credit risk losses form the majority of the stress impact on capital, covering more than 20,000 billion euros of exposure8.

According to the design of the adverse scenario, there were an upward shift in short-term and long-term interest rates and government bond spreads included in the EU-wide stress test. The methodology specifies the way of funding and also limited the possibility for banks to lend funds, resulting an increased funding cost. At the mean time, the net interest income across the sample decreases in both adverse and baseline scenario comparing with the 2013 level. Turning to the market risk, there were two core elements, one is the impact on net trading income, and the other one is

                                                                                                               

7 Results of 2014 EU-wide Stress Test (Aggregate Results), page 18.   8 Results of 2014 EU-wide Stress Test (Aggregate Results), page 29.

(12)

the realization of the market shocks over the three years of the stress test9.

Part III: Literature Review

 

This thesis is related to several strands in the literatures.

First, we need to overview the literature reviews on the bank stress test to acknowledge some information. In recent years, bank stress tests have become an indispensable part of the toolkit used by central banks and other regulators to conduce macro-prudential regulation and supervision (Greenlaw & Shin, 2012). It is perhaps the most basic of risk-based questions to see the resilience of an exposure to deteriorating conditions, be it a single position or loan or a whole portfolio. The bank stress test is designed to reduce investor uncertainty by implementing a strict, forward-looking test or potential losses at major banking corporations (Quijano, 2014). If the stress test indeed provides new information to the market it should be reflected in update default probabilities for the participating banks. Former Federal Reserve Chairman Ben Bernanke stated that “(bank stress tests) provided anxious investors with something they carved: credible information about prospective losses at banks”.

Peristiani et al. (2010) examine abnormal bank stock returns around the SCAP (Supervisory Capital Assessment Program) in 2009; their results suggest that announcement of the stress test, though not its results produced positive abnormal stock returns. The  results are informative to stockholders.  If the bank passed the bank stress test, the bank’s default probability would adjust downward which could attract the investors’ attention. On the contrary, if the bank failed the bank stress test, its default’s probability would be upward which would lead many investors to worry about. Bessembinder et al. (2009) measured abnormal bond performance around the release of the SCAP results, where the return of a bond is defined as the daily percentage change in its price. It showed that the banks passed the stress test exhibited positive abnormal bond returns.

                                                                                                               

(13)

It is also a concern when market matters to firms. Baker, Stein, & Wurgler (2002) studied when corporate investments would be sensitive to non-fundamental movements in stock prices. According to them, a firm with no debt and a stockpile of cash can insulate its investment decisions from irrational gyrations in its stock price. Equity financing offers a big advantage that the investor takes all of the risk when comparing with bank financing10. Baker et al. (2002) found that stock prices have a stronger impact on the investment of firms that are “equity dependent”, which need external equity to finance their marginal investments. And they also found strong support that firms who rank in the top quintile of the Kaplan-Zingales index11 have investment that is almost three times as sensitive to stock prices as firms in the bottom quintile.    

In order to research the stock market reacts to the 2014 bank stress test. We also need to know whether the bank’s characteristics, risks and other information that could be reflected by the bank stock prices. Brewer et al. (2003) examined that shareholders of banks respond to the financial conditions of individual banks, and that the market was able to differentiate between banks in its response to the failures. Berger et al. (2000) focused on assessments of bank holding companies in the U.S, and compared the value of information from government supervisors, bond markets, and equity markets. Their findings suggest that equity market investors tend to be concerned with future changes in the performance of bank holding companies. While Franke and Krahnen (2005) discussed the securitization of loans by banks in Europe and the U.S, they analyzed the influence of banks’ securitization announcements on their stock returns.

Masahiro Inoguchi (2013) investigates the role of stock and interbank markets in measuring bank performance in Korea, Malaysia and Thailand. The paper employs                                                                                                                

10http://www.investopedia.com/financial-edge/1112/small-business-financing-debt-or-equity.aspx

11 The KZ-Index (Kaplan-Zingales Index) is a relative measurement of reliance on external financing. Companies with a higher KZ-Index scores are more likely to experience difficulties when financial conditions tighten since they may have difficulty financing their ongoing operations. Available at

(14)

panel regression techniques and examines whether interbank transactions and stock prices of domestic commercial banks respond to bank risk and performance in those Asian countries. In the case of stock market, the regression shows that banks risk have influence on each bank’s stock price.  Ogawa (2015) points out that how firms respond to the deterioration of bank health during the financially turbulent periods in the late 2000s in investment decisions and demand for liquidity. It shed light on the cash flow sensitivity of investment and cash holdings using panel data for Asian firms in economies at different stage of financial development. It proves the cash flow sensitivity of investment and cash holdings rises as bank health deteriorates. Moreover, the impact of non-performing loans on the cash flow sensitivity is more prevalent across firms in economies with a higher level of financial intermediary development. When bank health is impaired, bank-dependent firms increase their reliance on internal funds and raise their propensity to save out of cash flow to materialize profitable investment opportunities in the future.

Besides, there are also literatures on bank opaqueness and its relationship with stress test. Petrella & Resti (2013) examined the 2011 European stress test to assess whether and how the test affected bank stock prices. They found that the stress test results were considered relevant by investors and the market was not able to anticipate the stress test results, which were consistent with the idea of bank opaqueness prior to the disclosure of the stress test results. Goldstein & Sapra (2013) argued that there were potential endogenous costs associated with disclosure of stress test results, for example, disclosure might reduce traders’ incentives to gather information by themselves hence reduce market discipline as this may hamper the ability of supervisors to learn from market data for their regulatory actions. However, Goldstein et al. (2013) believe the disclosure of stress test results is beneficial as it promotes financial stability. Petrella et al. (2013) also stated that a stress test exercise leading to worse-than-expected results might provide benefits for financial stability because transparency can reduce the cost of uncertainty, the disclosure may be socially optimal and helpful in improving the future bank management, etc.

(15)

Previous studies show that stress test results enhance market information, indicating that banks are not fully transparent because otherwise the market will stay the same since there should be no new information from the stress test results. In this way, findings in this thesis may offer information on whether or not bank opaqueness still exists in European Union banks. Besides, this thesis is also aiming to contribute to previous studies on economic consequences of supervisory disclosures. For example, Petrella et al. (2013) focus on the stock market reaction of banks while in this thesis, a different approach will be used that overall market reaction other than only banks. The interest will be in the market reaction of investors in bank dependent firms, referring to those firms primarily rely on bank loans.

Part IV: Research Approach

 

The main theory behind the event study of this thesis is the Efficient Market Hypothesis (EMH), developed by Professor Eugen Fama from the University of Chicago. According to EMH, if the market is efficient then all new and price relevant information is immediately included in the stock price. Hence, given that no other events happened on a certain day and assuming the market is efficient, then the change in stock price can be seen as reaction to a certain event and can be explained as the price effect of that event (Fama, 1970). When applying to this thesis, changes in stock price of can be seen as reaction to the release of bank stress test results if other factors remain unchanged.

To examine the effect of the publish at 2014 EU-wide stress test results for banks in Europe on firms’ stock price, we will apply event study methodology in this thesis. According to MacKinlay (1997), an event study is structured as following:

1) Identify the event of interest

2) Determine the selection criteria for sample of interest 3) Estimate the normal and abnormal returns

(16)

4) Estimate the procedure 5) Test and analyze the results

1) Identify the event of interest

The initial task of conducting an event study is to define the event of interest and identify the period over which the event takes place, the so-called event window (MacKinlay, 1997). Here the event is a single event that the announcement of the 2014 EU-wide banks stress test results by the EBA. The event window should include the day of the announcement, which is 26th October 2014. Because the periods before and after the event may also be of interest, it is normal to define the event window to be larger than the specific day of interest for the examination of the periods surrounding the event. In our study, we expand the one to ten days prior to and ten days after the announcement day, which is a 20-trading-day window ranging from 9th

October to 7th November 2014. The prolonged event window therefore could capture the price change trends due to the announcement that occur in the stock market.

2) Determine the selection criteria for sample of interest

After identifying the event, it comes to determine the selection criteria for the sample firms we will use in our study. Because the 2014 stress test covered 22 countries within EU, we will involve 496 firms listed in the STOXX Europe 600 index, excluding 44 banks, 22 no-stock price data firms and 48 firms that are not listed in 22 EU countries.

3) Estimate the normal and abnormal returns

The normal return is defined as the expected return without conditioning on the event occurring. And the abnormal return is the difference between actual ex-post returns of the security over the event window and the normal return of the firm over the event window (MacKinlay, 1997). The abnormal return (𝐴𝑅!,!) for firm i and event date t is expressed as

(17)

𝐴𝑅

!,!

= 𝑅

!,!

− 𝐸(𝑅

!,!

|𝑋

!

) (1)

Where 𝑅!,! is for actual return and 𝐸(𝑅!,!|𝑋!) is for the normal returns for firm i for time period t, respectively. Here 𝑋! means the conditioning information for the

normal return model.

There are different models for estimating the normal return. We choose the market model because the market model is the most commonly used model in prior event studies (Bowman (1983), MacKinlay (1997)). Besides, by assuming a stable linear relation between the market return and the security return, the market model also simplifies the estimation procedure. According to MacKinlay (1997), for any security i the market model is

𝑅

!,!

= 𝛼

!

+ 𝛽

!

𝑅

!,!

+ 𝜀

!,!

(2)

𝐸 𝜀

!,!

= 0      𝑣𝑎𝑟 𝜀

!,!

= 𝜎

!!!

Where R!,! is the period t returns on security i and R!,! is the period t returns on the

market portfolio;ε!,! is the zero mean disturbance term; α!, β!, and σ!!! are the

parameters of the market model. For the market portfolio, we choose S&P index for the stock index. To estimate the market model, we will run a separate regression for each firm by using the data within the estimation window to collect the estimated αs and βs.

4) Estimate the procedure

Since we have chosen the market model, then we need to estimate the parameters of the model by using a subset of the data, the so-called estimation window. Normally the period of estimation window is prior to the event window’s start. In general the event period it generally is not included in the estimation period. Because including the event window in the estimation of the normal model parameters may lead to the event returns having a large influence on the normal return measure since in this situation both the normal returns and abnormal returns would capture the event

(18)

impact (MacKinlay, 1997). So here we choose the 120 trading days prior to the event to avoid this influence for the estimation of the parameters, which is the period of 28th April 2014 to 10th October 2014. The timing sequence of estimation and event window is illustrated with a time line in Figure 2.

Since the market model is a statistical model, we apply the empirically reasonable assumption that asset returns are jointly multivariate normal and independently and identically distributed through time. In this situation, the market model tends to be robust to deviations from the assumption. Additionally, it is also more convenient to modify the framework so that the analysis of the abnormal return is autocorrelation and heteroscedasticity consistent by using a generalized method-of-moments approach (MacKinlay, 1997). The Ordinary Least Squares (OLS) is used here to estimate the market model parameters because the OLS is efficient as well as a consistent estimation procedure under general conditions.

With the parameter estimates for the model, then we could continue with the calculation of the abnormal returns. Considering there was no price shown on several days for some firms during the estimation window, we may expect to see zero daily returns for these firms. Therefore, the parameters for them may also show similar trend in our study (Damodaran, 1999).

5) Test and analyze the results

The abnormal return observations should be aggregated to draw overall inferences for the event of interest. In general, the aggregation is along two dimensions, through time and across firms (securities). We will aggregate the abnormal returns based on our main hypotheses. The sample aggregated abnormal returns over event period t for each firm (security) I is calculated as follows

𝐶𝐴𝑅(!!,!!) = !!!!!!𝐴𝑅! (3)

𝑣𝑎𝑟 𝐶𝐴𝑅 !!,!! =!!!∗ 𝜎!! 𝑡 !, 𝑡! !

(19)

For the variance estimators, we assume that the event windows of the N firms do not overlap is used to set the covariance terms to zero. Hence we could draw inferences about the cumulative abnormal returns to test the null hypothesis that the abnormal returns are zero for all following three main hypotheses that will be tested in this thesis by using

𝐶𝐴𝑅 𝑡!, 𝑡! ~𝑁[0, 𝑣𝑎𝑟 𝐶𝐴𝑅 𝑡!, 𝑡! ] (5) 𝜃 = !"!(!!,!!)

!"#(!"# !!,!! )!/!~𝑁(0,1) (6)

Here, the variance of CAR is come from (4), and CAR from (5). This distributional result is asymptotic with respect to the number of firms N and the length of the estimation window, 120 trading days in our sample (MacKinlay, 1997).

The first hypothesis in this thesis consists with our first conjecture that is performed between two groups of companies under the null hypothesis: investors in bank dependent firms do not behavior different to those in bank independent firms to the release of stress test results.

Hypothesis 1: Investors in bank dependent firms react more heavily than those in bank independent firms in the event of bank stress test results release.

To test this hypothesis, we firstly need to determine whether a firm is bank dependent or not. According to Rajan and Zingales (1998), a firm’s dependence on external finance is defined as the ratio of difference between capital expenditures and cash flow from operations divided by capital expenditures. Because this way measures the amount of desired investment that a firm cannot finance through its internal sources, which is the cash flow generated by the same business (Rajan&Zingales, 1998). In line with Rajan andZingales (1998), we calculate this capital ratio for each firm in the sample over the last 10 years, 2004-2013. Here the cash flow is defined as the sum of cash flow from operations plus decreases in inventories and in receivables, and increases in payables. To aggregate the ratios across companies and over time, we

(20)

sum the numerator and denominator over time for each firm before dividing. For example, we sum the firm’s external finance over 2004 to 2013 and then divide by the sum of firm’s capital expenditures over the last ten years to get the firm’s dependence on external finance, or to say bank-dependence, in the 2000s. In this way, temporal fluctuations will be smoothed and the effects of outliers could be reduced.

For each firm, if its internal cash flow cannot cover capital expenditures, then it will get a positive ratio, meaning this firm is external finance needed, that is bank dependent. We create a dummy variable named bank dependent in our dataset and define bank dependent firms with a value of 1. Hence, those bank independent firms, whose capital ratio are 0 or negative, will be entitled with a value of 0.

When a bank fails the bank stress test, it is considered to be undercapitalized representing bad news to the market (Quijano, 2014). There is a high probability of default.   Chava & Purnanandam (2011) provides causal evidence that banks’ health affects borrowers’ performance when separating the effect of demand on credit from the supply of credit using the exogenous shocks to U.S. banks during Russian crisis of fall 1998. So the investors in the bank dependent firms react more heavily than those in the independent firms.

Hypothesis 2: Investors in firms whose main bank receives a fail score react more heavily than those receive a pass score during the stress test.

This hypothesis will be tested against the null hypothesis that there will be no difference for reaction to the release of bank stress test results between firms from countries with banks passing the stress test and those from countries with banks failed in the stress test. To test this hypothesis, we need to define country group by stress test score of banks. For instance, there are 9 banks from Italy failed in the test so Italy is defined as a fail-score-country and all firms from Italy will get a value of 0 for its test score. By contrast, Spain earns a value of 1 for its test score because there is no

(21)

bank failed in the bank stress test12.

To make the tests be more precise, we will compare bank dependent firms with bank independent firms within same stress test score country group. Under the null hypothesis that there is no difference between bank dependent firms and bank independent firms, we will test the following hypothesis. The investors consider how much they will earn and whether they will earn. They invest their money on the main bank to earn money. If the main banks fail the test, the results will show that the bank has a high probability of default, which will affect the investor’s profit. The investors have risk in the investing. However, if the bank passes the test, the results will be good news to the investors. Investors emphasize more on the losing money. Therefore, investors in firms whose main bank receives a fail score react more heavily than those receive a pass score during the stress test.

Hypothesis 3: Investors in bank dependent firms whose main bank receives a fail (pass) score react more heavily than bank independent firms within the same test-score country group.

This can be done by firstly dividing firms into country groups, one group with countries that banks failed in the test, defined with a value of 0, and the other group with countries that banks passed in the test, defined with a value of 1. Then within each group we compare bank dependent firms with bank independent firms. When a bank passes the bank stress test, it offers a clean experiment for the information content of the results; for the banks, the results also present purely good news, which can lower their probability of default. Thus the investors will comfort with this results. However, if the bank failed the test, the results will increase the investors’ anxious. Combining all of these effects, the investors in bank dependent firms whose main bank receives a fail score react more heavily than those in bank independent firms                                                                                                                

(22)

within the same test-score country group.

Part V: Data and Results

Data can be found mainly through two databases, one is DATASTREAM that is an add-in installed in Excel, and the other one is COMPUSTAT and CRSP within the Wharton Research Data Services (WRDS) system. Daily stock return data for STOXX Europe 600 index firms is available through the DATASTREAM, which will be calculated as stock price in time t1minus price in time t0 and then divided by the

price in time t0. We choose the data range starting from 25th April 2014 to 7th

November 2014 in order to cover both the estimation window and event window. Then we use International Securities Identification Number (ISIN), which uniquely identifies a specific non-US/-Canadian securities issue13, as company codes of the firms in our list to download their fundamental information from COMPUSTAT Global Fundamentals Annual dataset.

Besides, because we will use market model in this thesis, we also need market return information. We use S&P 500 Index as a proxy to study firms’ market behavior because these indexes are created according to clear, unbiased and systematic processes, they are assumed to be accurate, timeliness and consistent. This data can be retrieved from CRSP Index of S&P 500 Indexes dataset. We choose the same data range as we do when collecting stock price information from DATASTREAM. Summary of the dataset on a country basis is shown in Table 1 in annex. As from the table, we observe UK has the largest number of firms listed in the STOXX Europe 600, following by France and Germany. When taking a look at the number of banks failed in the stress test in each country, we notice Cyprus ranks the first place with a fail percentage of 100%, following by Greece and Italy with a fail percentage of 75% and 60%, respectively.

                                                                                                               

13   International  Securities  Identification  Number  (ISIN),  Investopedia,  available  at   http://www.investopedia.com/terms/i/isin.asp  

(23)

As we mentioned in the introduction, we get the results as we expect. The abnormal returns and cumulative abnormal returns for event window from 10th October 2014 to 7th November 2014 with 20 trading days are shown in Table 2and Table 3 (see annex). Plots of the cumulative abnormal returns are also included, with the CAR’s from the market model in Figure 3 and Figure 4.In addition, we also plot the average cumulative abnormal returns for different groups firms, which are shown in Figure 5 and Figure 6. Because the release date is a non-trading day, there is no stock price on the announcement day. Hence, we set the daily return on the event day as 0. Those day one returns could capture the effects that the announcement of the stress test made on event day zero exerted on the stock market after its close.

The left part of the Table 2 shows abnormal returns and cumulative abnormal returns for bank dependent firms and bank independent firms, which is related to our Hypothesis 1. We could see from the table that bank independent firms earn a higher average abnormal return of 0.84 percent (three times higher than 0.25 percent of bank dependent firms) and cumulative abnormal returns of 17.62 percent (higher than 5.27 percent of bank dependent firms). This result suggests that all firms react positively in the event of the release of 2014 EU-wide bank stress test results, while bank independent firms may perform even better than bank dependent firms. The right part of Table 2 shows abnormal returns and cumulative abnormal returns for all firms in both country group that one with all banks pass the 2014 EU-wide bank stress test and the other one with one or more banks fail the stress test. We observe that firms in pass-test-score country group have a higher average abnormal return of 0.70 percent than 0.39 percent for firms in fail-test-score country group and a higher cumulative abnormal return of 14.66 percent than 8.23 percent for firms in the other country group. This result is related to Hypothesis 2, suggesting that those firms from pass-test-score country group may have a better performance as response to the release of the 2014 EU-wide stress test results than firms from fail-test-score country group.

(24)

Table 3 is related to Hypothesis 3 and shows us the abnormal returns and cumulative abnormal returns for both bank dependent and bank independent firms within different country groups. The left part of Table 3 describes the pass-test-score country group. We can see that bank independent firms on average earn both higher abnormal return (0.51percent higher than 0.19 percent) and cumulative abnormal return (10.75 percent higher than 3.91 percent) than bank dependent firms. These results suggest that bank dependent firms may not perform as good as bank independent firms to the release of bank stress test results within pass-test-score country group. Moreover, when looking at the right part of Table 3 within fail-test-score country group, we see that bank independent firms also earn both higher average abnormal returns (0.33 percent than 0.06 percent) and cumulative abnormal returns (6.87 percent than 1.36 percent) than bank dependent firms. These results give us the similar suggestion that we may expect bank independent firms would also perform better than bank dependent firms under condition that their banks got fail score in the stress test.

The CAR plots show a trend that the market gradually learns about the release of bank stress test. From Figure 3 to Figure 6, we observe an upward trend for all the groups in both cumulative abnormal returns and average cumulative abnormal returns. These tell us that the release of bank stress test result is seen as a good signal to the market for all investors. In Figure 6, from day 7 the abnormal return for bank dependent firms are slightly higher than bank independent firms within the country group whose banks passed the stress test, but from the overall trend’s view, bank independent firms still have higher abnormal returns. In the days after the release the CAR is relatively stable or slightly decreased, since we have expected event day one would capture the effects that generated after the close of the last trading day in the stock market.

As suggested by formula (6), we test the CAR to see whether there is a normal distribution. Firstly we calculate the average CAR (𝐶𝐴𝑅(𝑡!, 𝑡!)) for each country group as numerator. Then we get the denominator, standard deviation 𝑣𝑎𝑟  (𝐶𝐴𝑅 𝑡!, 𝑡! )!/! for each group. Now we could compute 𝜃 for all eight groups.

(25)

All numbers can be found in Table 4. We plot these 𝜃s in a histogram shown in Figure 7. It demonstrates a bell shape curve in the graph, which is also known as normal distribution. We may say that our results are verified.

However, we would like to strengthen the test by run a regression of CARs with respect to the dummy variable bank-dependence (noted as BD in the following text), fail-score (noted as F in the following text), and the multiply product of BD and F. To make the regression more meaningful, we redefine the value of fail-score as 1 while pass-score as 0. The result of the regression can be found in Table 5 in the Annex. We firstly run the CARs on BD, the result of which is shown in the first column in the table. The constant intercept value 0.047 indicates that if firms are bank independent, the cumulative abnormal returns of firms would be 4.7%, which means firms react positively to the release of 2014 EU-wide bank stress test results. The coefficient of BD is -0.0039, indicating that if the firm is bank dependent then the cumulative abnormal return of this firm would decrease by 0.39% when all other things remain unchanged. The second column shows the regression of CARs on F. Similarly, we see a negative coefficient of F, meaning if the firm’s main bank failed the stress test the cumulative abnormal return for this firm may decrease by 0.3% while other things are the same. In the third column, we run the regression of CARs on BD, F, and the third dummy variable BD*F that is the product of BD and F. This dummy variable is used to describe the interaction of two attribute dummy variables. This regression of CARs on these dummy variables yields the following model:

𝐶𝐴𝑅! = 0.047 + 0.0005 ∗ 𝐵𝐷 − 0.0001 ∗ 𝐹 − 0.0136 ∗ 𝐵𝐷 ∗ 𝐹 + 𝜖! (7)

The constant intercept value almost remains the same 0.047 as we got in the previous two regressions while the coefficient of BD becomes positive as 0.0005. It indicates that other things being equal, if the firm is bank dependent, the CAR of this firm would be 0.05% higher. If the firm’s main bank received a fail score in the stress test, we see a 0.01% lower effect on the CAR of this firm if other things are fixed, which is

(26)

smaller than the effect we got from the second regression. We may say these changes are due to the third dummy variable, BD*F. We see a negative value -0.0136 of coefficient for BD*F, which means if the firm is bank dependent and its main bank failed in the stress test, then its CAR would be 1.36% lower, holding other things equal. 𝜖! means for control variables that we are not considering here, such as the firm size, the age of the firm, other financing way of the firm, etc. Due to the lack of enough data on these factors, we cannot be able to figure out the effects they may bring so we simply assume they are fixed and will not affect other attribute variables in the regression model. Although the results are not statistically significant, neither the f-test is statistically for all regressions, we see a bigger R-squared value (0.25%) in the third regression than in other two regressions (0.06% and 0.04%, respectively), which tells us that the third regression explains more percentage of our sample comparing to other two regressions. Based on our regressions, we have similar findings that bank dependence may not affect a lot but when thinking of with fail-score of firm’s main bank in the stress test, there may be negative effects on the firm performance and investors’ mind.

In addition, we also look into several measures that may be related to above analysis, such as Tobin’s Q, leverage ratio, and CapEx, etc. Tobin’s Q is calculated as the total market value of a firm divided by the replacement value of the firm’s assets (Tobin & Brainard, 1977). The ratio does not only reflect the shareholders’ earning, but may also reflect the indicators of the enterprise’s comprehensive operating performance. According to Tobin, the two values should be about to equal so the ratio should be about 1. A higher than 1 Tobin’s Q may imply that the firm’s market value is overvalued, while a lower ratio than 1 indicates the firm’s market value is undervalued. We compare the average Tobin’s Q of each group of firms by using data from year 2013 and 2014. The results from year 2013 are more or less the same direction as those from year 2014, which can be found in the first two columns of Table 6. We observe that the average Tobin’s Q value of bank independent firms is lower than bank dependent firms in both years, while firms with their main bank

(27)

passed the stress test have higher average ratio than firms with their main bank failed the test. It is known that a Tobin’s Q above 1 means that the firm is worth more than the cost of its assets, which suggests the company is overvalued. When the Tobin’s Q ratio is between 0 and 1, it costs more to replace a firm’s assets than the firm is worth. Investors may find profit from trading overvalued stocks if they could sell at higher price than they buy them. When combine the two effects, we see similar results. Within both bank dependent firms group and bank independent firms group, firms with its main bank passed the stress test have higher ratio than those whose main bank failed the test. And Tobin’s Q in year 2014 are also seen to be higher than they were in year 2013 for almost all groups, respectively. These again show that firms whose main bank passed the test are more likely to be overvalued firms, which is consistent with our findings that firms whose main bank passed the stress test are more preferable by the investors, hence they could perform better than firms whose main bank failed the stress test in the stock market in the event of 2014 EU-wide bank stress test results’ release.

The next two columns of Table 6 show us the average leverage ratio of each category firms. As we all know that leverage ratio14 measures how leveraged a company is,

and a company’s degree of leverage (that is, its debt load) is often a measure of risk. In general, a high leverage ratio indicates that a company may carry a big burden in the sense that principal and interest payments take a significant amount of the company’s cash flows. Thus we see higher ratios for group of firms whose main bank failed the stress test in both years. However, the high leverage ratio may also suggest that the firm is taking advantages of financial leverage, although with higher risk, investors may expect higher return from their investments on these firms. Therefore, it’s without surprise to see that bank independent firms to have a higher average ratio than bank dependent firms. Besides, it is of interest to see that the leverage ratio for all groups of firms in year 2014 become lower than they were in year 2013, which we                                                                                                                

14   Leverage ratio, InvestingAnswers, available at

(28)

may explain as firms become more aware of leverage risk and take measures to reduce their burden on firm’s cash flows.

Moreover, when we move to the last two columns of Table 6, we have average capital expenditure of firms. They demonstrate higher average CapEx for bank dependent firms, firms whose main bank passed the stress test than for bank independent firms, firms whose main bank failed the test. In accounting, CapEx is added to an asset account, which increases the asset’s basis. A high CapEx may suggest the firm spends more on acquiring or upgrading physical assets, which relates closely to the industry of firms located in. Some industries have high level of capital expenditures such as oil exploration and production, telecom, manufacturing and utilities, etc15. We do not divide our sample by industries but from the figures we computed in the table, we may infer that those bank dependent firms or firms whose main bank passed the stress test might be more likely come from the high level capital expenditures industries. As the return on investment in these areas is very high in general, although at the same time there exist high barriers (like high physical assets investment) for entering these industries, investors may take the chance to entry. This may explain why these higher CapEx firms have more positive performance than firms with lower CapEx.

Therefore, we finish our test here for above analysis. We may conclude that firms react to the release of bank stress test results; bank independent firms react more positively to the event than bank dependent firms. We may also conclude that firms from country group whose bank passed the stress test also perform better than firms from countries whose bank failed the stress test. In addition, we may conclude even within same test score country group, bank independent firms also have a better performance in the stock market than bank dependent firms.

                                                                                                               

15 Capital Expenditure (CAPEX), Investopedia, available at http://www.investopedia.com/terms/c/capitalexpenditure.asp

(29)

Part VI: Conclusion

 

A successful bank stress test program, particularly in a crisis, has at least two components: one is a credible assessment of the capital strength of the tested institutions, to size the capital “hole” that needs to be filled, the other one is a credible way of filling that hole. The bank stress test is to examine whether the bank has the ability to bear the extreme change of the market. If the bank passes the test, this proves the bank has the ability to manage in the future. If the bank fails the test, the bank may not be able to bear the extreme change of market and the needs to be changed. As a consequence, the outcome of the stress test has influence on the stock market. As we all know, the event of release results of 2014 EU-wide bank stress test exerted effects on the EU stock market. In general, we observe an upward effect for all 496 firms listed in STOXX Europe 600 Index during the event time. All investors react to this event. Moreover, bank dependent firms are more likely to react heavily than bank independent firms because they in general perform less good than bank independent firms as from our findings. Those firms from countries with their banks fail the stress test are more likely to react heavily than those from countries with their banks pass the stress test. As suggested by Peristiani et al. (2010), a fail score in the bank stress test maybe a signal of investment being more risky for investors. Besides, within the same test-score country group, bank dependent firms are also seen a more heavy reaction to the stress test outcome than bank independent firms.

As we mentioned in above text, the bank stress test is used to examine whether the bank has the ability to bear the assumed extreme change of the market. The results of the bank stress test may show the possibility of bank default. If the bank passed the test, investors see a downward of default possibility and have more confident with their investments. On the contrary, if the bank failed the test, investors may become worrying about their investments as the riskiness for their investment becomes higher due to the possibility of bank default increases (Peristiani et al., 2010). Therefore, investors may see the release of bank stress test results as an indirect signal of the

(30)

riskiness of their investment. The bank dependent firms rely so much on its main bank that it reacts more heavily than the bank independent firms do.

The bank dependent firms open account in the bank they dependent, develop the normal business, financing and so on. In other words, without the assistance of the bank they rely, they cannot survive. On the contrast, the banks independent firms can manage successfully without the same bank and they can prevent the harm occurred in the bank’s crisis. Therefore, once the outcome bank’s stress test publishes, if the bank fails the test, it proves the bank is not able to prevent the risk and it needs the money to fill the ‘hole’. So the bank is also obligated to raise new capital before the end of six months, either in the private market or by a forced capital injection from the Treasury. Thus, not finding an effect on the bond returns pertaining to these banks would not necessarily imply that the stress test did not produce new information (Quijano, 2014).

However, due to the limitation on data, there are no firms from Cyprus, Slovenia listed in STOXX Europe 600 index, as well as we exclude 48 firms that are not from 22 EU countries whose banks were tested in the 2014 EU-wide stress test. There are factors not considered in this paper. For example, as Rajan and Zingales (1998) mentioned, “common wisdom” may be a concern because firms are more dependent on external financing early in their life than later. Buca & Vermeulen (2012) showed that region is a matter that the effects of firm size are only present in the South of Europe, mainly Spain, Portugal and Italy. In addition, we also did not study whether or not age of each firm has any effect on the analysis, whether or not there is other financing form for firms that affect firm’s demand for bank loans, etc. It would be of interest for further studies to include above mentioned factors or more factors in the study of relationship between investors reaction to the event of release of bank stress test results.

(31)

References

Allen, F., & Carletti, E. (2008). The roles of banks in financial systems. Oxford

Handbook of Banking.

Bessembinder, H., Kahle, K. M., Maxwell, W. F., & Xu, D. (2009). Measuring abnormal bond performance. Review of Financial Studies, 22(10), 4219-4258.

Berger, A. N., Davies, S. M., & Flannery, M. J. (1998). Comparing market and supervisory assessments of bank performance: who knows what when? FEDS Paper, (98-32).

Bowman, R. G. (1983). Understanding and conducting event studies. Journal of

Business Finance & Accounting, 10(4), 561-584.

Brewer, E., Genay, H., Hunter, W.C., Kaufman, G.G., 2003. Does the Japanese stock market price bank-risk? Evidence from financial firm failures. Journal of

Money, Credit, and Banking. 35(4), 507–543.

Chava, S., & Purnanandam, A. (2011). The effect of banking crisis on bank dependent borrowers. Journal of Financial Economics, 99(1), 116-135.

Damodaran, A. (1999). Estimating risk parameters.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work*. The journal of Finance, 25(2), 383-417.

Franke, G., & Krahnen, J. P. (2007). Default risk sharing between banks and markets: the contribution of collateralized debt obligations. In The Risks of Financial

Institutions (pp. 603-634). University of Chicago Press.

Gauthier, C., Souissi, M., & Liu, X. (2014). Introducing Funding Liquidity Risk in a Macro Stress-Testing Framework. International Journal of Central Banking, 10(4), 105-142.

Goldstein, I., & Sapra, H. (2013). Should banks' stress test results be disclosed? An analysis of the costs and benefits. Foundations and Trends in Finance, forthcoming.

Greenlaw, D., Kashyap, A. K., Schoenholtz, K. L., & Shin, H. S. (2012). Stressed out: Macroprudential principles for stress testing. Chicago Booth Research

(32)

Paper, (12-08).

Haldane, A. (2009). Why banks failed the stress test. BIS Review, 18, 2009. MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of

economic literature, 13-39.

Quijano, M. (2014). Information asymmetry in US banks and the 2009 bank stress test. Economics Letters, 123(2), 203-205.

Ogawa, K. (2015). Firm investment, liquidity and bank health: A panel study of Asian firms in the 2000s. Journal of Asian Economics, 38, 44-54.

Peristiani, S., Morgan, D. P., & Savino, V. (2010). The information value of the stress test and bank opacity.FRB of New York Staff Report, (460).

Petrella, G., & Resti, A. (2013). Supervisors as information producers: do stress tests reduce bank opaqueness?. Journal of Banking & Finance, 37(12), 5406-5420.

Tobin, J., & Brainard, W. C. (1977). Asset markets and the cost of capital.

Economic Progress, Private Values and Public Policy: Essays in Honor of William Fellner, 235-62.

(33)

Annex 1

Figure 1 Changes in Core Tier 1 ratio from 2011 to 2013 for major EU banks

(34)

Annex 2

(35)

Annex 3

Table 1 Summary of the dataset

Country Number of banks in this country

Number of banks fail in the stress test in this country

Number of firms listed in STOXX Europe 600 Number of bank dependent firms listed in STOXX Europe 600 Austria 6 1 5 2 Belgium 5 2 13 3 Cyprus 3 3 0 0 Denmark 4 0 16 4 Finland 1 0 16 1 France 11 1 78 15 Germany 24 1 60 13 Greece 4 3 3 1 Hungary 1 0 0 0 Ireland 3 1 9 1 Italy 15 9 18 5 Latvia 1 0 0 0 Luxembourg 2 0 5 1 Malta 1 0 0 0 Netherlands 6 0 29 5 Norway 1 0 9 1 Poland 6 0 0 0 Portugal 3 1 3 1 Slovenia 3 2 0 0 Spain 15 0 23 7 Sweden 4 0 36 8 United Kingdom 4 0 173 53 Total 123 24 496 121

(36)

Annex 4

Table 2 Abnormal returns and Cumulative abnormal return for different groups

Event day Bank dependent firms (bd1) Bank independent firms (bd0) Pass-test-score country group (ts1) Fail-test-score country group (ts0)

AR CAR AR CAR AR CAR AR CAR

-10 0,92 0,92 2,32 2,32 1,90 1,90 1,34 1,34 -9 0,43 1,35 2,51 4,83 2,20 4,10 0,74 2,08 -8 -2,33 -0,98 -7,49 -2,66 -5,71 -1,61 -4,10 -2,02 -7 -0,04 -1,02 0,00 -2,66 0,36 -1,25 -0,40 -2,42 -6 2,33 1,31 7,80 5,14 5,42 4,17 4,71 2,29 -5 -0,84 0,47 -3,57 1,57 -2,18 1,98 -2,23 0,06 -4 1,47 1,94 4,63 6,20 3,76 5,75 2,34 2,40 -3 1,84 3,78 5,35 11,55 4,55 10,30 2,64 5,03 -2 -0,20 3,58 -0,58 10,97 -0,86 9,44 0,08 5,11 -1 -0,55 3,03 -3,17 7,80 -2,39 7,05 -1,33 3,78 0 0,00 3,03 0,00 7,80 0,00 7,05 0,00 3,78 1 -0,74 2,30 -1,43 6,37 -0,84 6,21 -1,32 2,47 2 0,59 2,89 2,69 9,06 1,87 8,08 1,41 3,87 3 0,40 3,28 2,59 11,65 2,07 10,15 0,92 4,79 4 0,24 3,52 0,74 12,39 0,53 10,68 0,44 5,23 5 1,44 4,96 3,77 16,16 2,79 13,47 2,42 7,65 6 -0,26 4,70 -1,31 14,85 -1,14 12,33 -0,43 7,22 7 -0,20 4,50 -1,76 13,10 -0,99 11,34 -0,97 6,25 8 1,35 5,85 3,78 16,87 2,97 14,31 2,16 8,41 9 -0,12 5,72 1,28 18,15 0,56 14,87 0,59 9,00 10 -0,45 5,27 -0,53 17,62 -0,21 14,66 -0,77 8,23 Mean 0.25 0.84 0.70 0.39

(37)

Annex 5

Table 3 Abnormal returns and Cumulative abnormal return for different groups (cont.) Bank dependent firms within pass-test-score country group (tsbd11) Bank independent firms within pass-test-score country group (tsbd10) Bank dependent firms within fail-test-score country group (tsbd01) Bank independent firms within fail-test-score country group (tsbd00)

AR CAR AR CAR AR CAR AR CAR

-10 0,58 0,58 1,33 1,33 0,34 0,34 1,00 1,00 -9 0,42 0,99 1,78 3,11 0,01 0,35 0,72 1,72 -8 -1,40 -0,40 -4,32 -1,21 -0,93 -0,58 -3,17 -1,45 -7 -0,01 -0,41 0,37 -0,84 -0,03 -0,61 -0,37 -1,82 -6 1,41 1,01 4,00 3,16 0,92 0,31 3,80 1,98 -5 -0,48 0,53 -1,71 1,45 -0,37 -0,06 -1,87 0,11 -4 0,95 1,48 2,81 4,27 0,53 0,47 1,81 1,93 -3 1,32 2,79 3,24 7,51 0,52 0,99 2,11 4,04 -2 -0,28 2,51 -0,58 6,93 0,08 1,07 0,00 4,04 -1 -0,38 2,13 -2,01 4,92 -0,17 0,91 -1,16 2,88 0 0,00 2,13 0,00 4,92 0,00 0,91 0,00 2,88 1 -0,26 1,87 -0,59 4,34 -0,48 0,43 -0,84 2,04 2 0,30 2,18 1,57 5,90 0,28 0,71 1,12 3,16 3 0,45 2,63 1,61 7,51 -0,06 0,65 0,98 4,13 4 0,10 2,73 0,43 7,95 0,14 0,79 0,30 4,44 5 0,87 3,60 1,92 9,87 0,57 1,36 1,86 6,29 6 -0,26 3,34 -0,87 9,00 0,00 1,36 -0,43 5,86 7 -0,04 3,29 -0,94 8,05 -0,15 1,21 -0,82 5,04 8 0,86 4,16 2,10 10,16 0,48 1,69 1,67 6,72 9 0,03 4,19 0,53 10,68 -0,16 1,53 0,75 7,47 10 -0,28 3,91 0,07 10,75 -0,17 1,36 -0,60 6,87 Mean 0.19 0.51 0.06 0.33

(38)

Annex 6

Figure 3 Plot of Cumulative Abnormal Return

Note: bd1 represents for bank dependent firms, and bd 0 is for bank independent firms, and ts1 is for firms in country group whose banks pass the stress test and ts0 are for firms in country groups whose banks fail the stress test. The figures cover from event day -10 to day 10. The abnormal return is calculated using the market model as the normal return measure.

(39)

Annex 7

Figure 4 Plot of Cumulative Abnormal Return (cont.)

Note: tsbd11 is for bank dependent firms within country group with a passing score, and tsbd10 is for bank independent firms within country group with a passing test score, and tsbd01 is for bank dependent firms within country group with a failing score, and tsbd00 are for bank independent firms in country group with a failing test score from event day -10 to day 10. The abnormal return is calculated using the market model as the normal return measure.

Referenties

GERELATEERDE DOCUMENTEN

Weer andere aanpakken gaan uit van de loodrechte projectie van de ene vector op de ander (uitgaande van vectoren die vanuit hetzelfde punt beginnen), maar moeten dan vreemde

For future studies, it would be interesting to assess how these ongoing trends towards digitalization influence the need for (and effects of) supplier satisfaction and

The number shown in parentheses is the Monte Carlo constructed standard error of the variable on the return of the real stock market after 1000 repetitions (the number of

Cumulative abnormal returns show a very small significant reversal (significant at the 10 per cent level) for the AMS Total Share sample of 0.6 per cent for the post event

In contrast to much of the earlier work on the relationship between economic development and environmental degradation, their findings suggested that at high

It is women who suffer the “serious moral disapprobation” (Broadhurst and Mason, 2013, p. 295) that comes with a failed attempt a motherhood, which is perhaps why, in the context

This paper focuses on the influences of locative media on creating, consuming and sharing social information. Social information can be defined as the information that is created

CPI: Consumer price index; CRC: Colorectal cancer; CRC-SPIN: Colorectal Cancer Simulated Population model for Incidence and Natural history; CT: Computed tomography;