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The Impact of the Greek Sovereign Debt Crisis on European Banks’

Disclosure and its Economic Consequences

Corresponding Author:

Prof. Dr. Peter Fiechter

University of Zurich

Department of Business Administration

Assistant Professor of Financial Accounting

Plattenstrasse 14

CH-8032 Zürich

Tel.: +41 44 634 28 01

Fax: +41 44 634 49 12

email: peter.fiechter@business.uzh.ch

Co-Author:

Dr. Jie Zhou

University of Zurich

Department of Business Administration

Post-Doctoral Researcher

Plattenstrasse 14

CH-8032 Zürich

Tel.: +41 44 634 28 12

Fax: +41 44 634 49 12

email: jie.zhou@business.uzh.ch

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The Impact of the Greek Sovereign Debt Crisis on European Banks’

Disclosure and its Economic Consequences

Abstract

Using a sample of European banks, this paper examines the link between disclosure and

its economic consequences. We exploit an exogenous cost of capital shock created by

the Greek Sovereign Debt Crisis and analyze banks’ disclosure responses to this shock.

First, we find that European banks increase the length of their annual reports from 2009

to 2011, in particular the risk management section. Our cross-sectional results show that

the increase in length of either the annual report or the risk report is positively

associated with the bank-specific cost of capital shock. Second, we find empirical

evidence that the change in risk disclosure mitigates the cost of capital shock; whereas

the change in the length of the annual report is not significantly associated with

subsequent positive market reactions. Finally, our cross-country analysis shows that the

market reaction to the change in disclosure is more pronounced for banks domiciled in a

strong institutional environment.

Keywords: Greek Sovereign Debt Crisis, Disclosure, Cost of Capital,

European Banks, Regulatory Quality

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I. Introduction

The link between corporate disclosure and its economic consequences is a major

issue in accounting research. Most existing literature implicitly assumes that (a)

corporate disclosure is exogenously determined, and (b) that a given level of disclosure

has implications for liquidity, cost of capital, and firm valuation. However, extant

literature typically does not address the dynamic association of corporate disclosure and

its economic consequences. For example, an exogenous shock to the capital market may

lead to disclosure changes that ultimately help mitigating the impact of the shock.

By exploiting the beta shock on firm’s cost of capital created by the Enron scandal

in 2001, Leuz and Schrand (2009) show that these beta shocks are associated with an

increase in the firms’ 10-K disclosures. They also find that firms’ disclosure responses

subsequently reduce firms’ cost of capital. The ongoing Greek Sovereign Debt Crisis

provides a setting to further investigate the dynamic relationship between disclosure and

cost of capital. The setting allows to investigate industry-specific (i.e., European

banking industry), long-term (i.e., from 2009 to 2011), and cross-country (i.e.,

regulatory quality) effects on the dynamic relationship between disclosure and the cost

of capital.

The lack of confidence and the uncertainty about Greece’s financial situation

raised concerns regarding the economic stability of Europe. The European banking

sector is affected either directly through holdings in sovereign bonds or indirectly

through the general market uncertainty that might cause investors to question the

currently provided information of banks about their financial situation. We thus use the

Greek Sovereign Debt Crisis as an exogenous shock to European banks’ cost of capital.

We predict that banks respond to the shock by increasing their disclosure in order to

mitigate negative cost of capital consequences due to lack of transparency. We further

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predict that more strongly affected banks (i.e., more negative shock to the cost of capital)

have more incentives to be transparent than less affected banks.

The second objective of this paper is to examine whether the increase in

disclosure actually helps mitigating the initial cost of capital shock. An increase in

disclosure is expected to reduce the uncertainty about the exposure of European banks

to the Greek Sovereign Debt Crisis, thereby reducing the bank’s cost of capital. Finally,

we predict that the disclosure response is more credible in a strong institutional

environment. Therefore, the positive effect of increased disclosure on the cost of capital

is likely to be more pronounced for banks from countries with high regulatory quality.1

We use a sample of European banks reporting under International Financial

Reporting Standards (IFRS) from 2009 to 2011. To measure the cost of capital shock,

we use the approach suggested by Lockwood and Kadiyala (1988) and used by Leuz

and Schrand (2009) that allows for a quadratic model to estimate the beta during the

event window when the crisis was unfolded. More specifically, we use an event period

from 1 January 2010 to 30 June 2011, as the Greek Sovereign Debt Crisis is not a single

point in time but rather covers several key events.2 For robustness, we also use a shorter

event period from 1 January 2010 to 30 June 2010.

To measure the change in disclosure, we hand-collect the page count of banks’

annual reports. In addition, we collect the page count of the risk reporting, as this

section is likely to be of particular importance during uncertain times. The change in

disclosure is then calculated as the percentage change of page count of either the annual

reports or the risk management sections. We collect various firm-specific characteristics

such as total assets, market-to-book ratio, leverage, and return on assets from Thomson

Reuters to control for factors that might determine the change in disclosure beyond the

1 We use the regulatory quality index by Kaufmann et al. (2009) to proxy for regulatory quality.

2 For an overview about the key Greek Sovereign Debt Crisis events, see Appendix A.

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bank’s response to the cost of capital shock. We also control for different operating

activities by including business models indicator variables. After excluding banks with

missing data and banks with no fiscal-year end as of December 31, we obtain a final

sample of 173 European banks.

The descriptive statistics show that European banks increase the number of pages

of their annual reports from 2009 to 2011 by 10.1%. The increase in page numbers of

the risk management section by 17.2% is even more pronounced. For example, the

increase in risk disclosure of Erste Group Bank AG (Austria) is mainly driven by a new

section that includes net exposures to European sovereign bonds. Similarly, Deutsche

Bank AG (Germany) substantially expands its credit risk section. The anecdotal

evidence and the univariate results indicate that the Greek Crisis is associated with an

increase in the page count of both the annual report and the risk management section.

The descriptive data further reveals a remarkable decrease of the mean market-to-book

ratio by –0.379 as well as a decrease of the mean return on assets by –0.001 from 2009

to 2011.

In our cross-sectional regression estimation, we find a positive relationship

between the cost of capital shock and the changes in disclosure, which is consistent with

predictions. The respond coefficients are both of similar magnitude and of similar

statistical significance throughout all model specifications, irrespective of whether the

percentage change in annual report or the change in the risk management section is the

dependent variable.3 The results do not substantially change when using the shorter

event period from 1 January 2010 to 30 June 2010.

Second, we observe that banks’ disclosure changes are associated with subsequent

positive market reactions. This finding alleviates concerns that banks simply increase

3 To address the potential issue that the impact of the initial shock on the annual report is simply driven

by the increasing length of risk management, further analyses reveal that such an impact remains

when subtracting the page counts of risk management from annual reports.

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their disclosure, but do not provide additional information that is useful to investors.

The coefficient of the percent change of the number of pages of the risk management

section is statistically significant at the 1%-level throughout all regression specifications.

However, the coefficient of the percentage change of the page count of the annual report

is not significant, indicating that investors tend to be more sensitive to banks’ risk report

than to the annual report. We interpret this finding as evidence that the additional

disclosure in the risk management section is more useful to investors to relief their

transparency concerns associated with the Greek Sovereign Crisis.

Third, while the subsequent market reaction to the change in risk management

report is statistically significant in countries with strong institutional environments (i.e.,

above median regulatory quality), the market reaction is not significant for either the

change in annual report or the change in risk report in a weak institutional environment

(i.e., below median regulatory quality). This finding suggests that risk-relevance and

credibility are crucial conditions for disclosure to help mitigating investors’

transparency concerns.

Overall, the evidence in this paper adds to the disclosure literature by

documenting that European banks respond to the cost of capital shocks created by the

Greek Sovereign Debt Crisis. Second, we find that banks respond by increasing the

overall disclosure level in the annual report and, in particular, the risk management

section. We also contribute to the literature by finding that investors react to the risk

report disclosure but not to the general increase in reporting through the annual report.

By suggesting that the capital market reacts differently to the varying contents of

disclosure, this finding might be of interest to regulators and standard-setters. Finally,

the cross-country setting allows us to shed light on the link between institutional

differences and European banks’ financial reporting.

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The study has several caveats. First, our inferences of the study are limited to the

extent that concurrent events have significantly influenced the risk management

reporting during our sample period from 2009 to 2011 (e.g., new risk disclosure

requirements). However, no major IFRS standards became effective during our sample

period. Second, investigating a long sample period from 2009 to 2011 has both

advantages and disadvantages. On the one hand, we can tackle several key events and

combine them into one event window. On the other hand, a long investigation period

increases the likelihood that concurrent market-wide events influence our tests on the

subsequent market reaction to increased disclosure. For robustness, however, we

attempt to address this issue by using a shorter event window, and the inferences remain.

In addition, the fact that the market reaction is significant only for the risk reporting and

in strong institutional environments increases confidence in our inferences. Third, when

using a standard market model (CAPM) instead of the quadratic market model to

measure the beta shock, the results are less significant. If, however, risk parameters of a

stock become affected by the event, the parameters of the standard market model are

intertemporally unstable, resulting in biased residuals (Brenner 1977). We thus believe

that the quadratic model by Lockwood and Kadiyala (1988) better performs in our

specific setting, in particular as the quadratic model allows for recovery during the event

window. Finally, we acknowledge that the European Sovereign Debt Crisis is (a) not

limited to Greece, and (b) not yet over. Therefore, we caution from interpreting our

evidence as being conclusive.

The paper is organized as follows. Section II provides a general overview and

develops hypotheses. Section III contains the research design. Section IV describes the

sample and data. The results are presented in Section V. Section VI concludes.

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II. Background and Hypotheses

2.1 The Impact of the Greek Crisis on Banks’ Disclosure

The relationship between firms’ accounting information disclosure and their cost

of capital is a major issue in accounting research. The theoretical link is as follows.

Releasing more information mitigates information asymmetry and adverse selection

problem between firms and investors or among investors, improves future liquidity of a

firm’s securities (Diamond and Verrecchia 1991; Verrecchia 2001) and, therefore, also

reduces the cost of capital. Easley and O’Hara (2004), however, suggest a direct link

between accounting information and cost of capital. They argue that more information

reduces the uncertainty about the size and the timing of future cash flows, thereby

reducing the cost of capital. However, Hughes et al. (2007) note that this link critically

depends on whether information risk is diversifiable. The pricing effect characterized by

Easley and O’Hara (2004) can be diversified away when the economy is large.

Most existing disclosure literature implicitly assumes that (1) disclosure is

exogenously determined, and (2) that a given level of disclosure has implications for the

firm’s cost of capital. However, it is conceivable that a dynamic relation exists—an

exogenous change in the cost of capital may lead to an adjustment in reporting practice,

which ultimately leads to a reduction in the cost of capital. Although a few studies

attempt to identify firm-specific events (i.e., dividend changes or share repurchases) that

affect disclosure policies (Grullon et al. 2002; Kumar et al. 2008), evidence on changes

in disclosure policies caused by market-wide events is limited.

The study of Leuz and Schrand (2009) exploits the Enron scandal as an

information-related shock to the cost of capital. Assuming that a firm’s disclosure

policy is optimal at a given point in time, an information-related shock to the cost of

capital should trigger a re-evaluation of the firm’s disclosure policy. Leuz and Schrand

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(2009) further argue that the scandal leads investors to revise their belief about the

quality of their information and hence changes firms’ cost of capital. As a consequence,

firms need to reconsider their disclosure policy and change their disclosure level due to

the changed cost of capital.

The Greek Sovereign Debt Crisis can be also viewed as an information-related

shock to European firms’ cost of capital. The lack of confidence and the uncertainty

about Greece’s financial situation raised concerns regarding the economic stability of

Europe. The ongoing debt crisis in Greece is not only an issue of one nation’s default

risk, but also has a domino effect on a global scale (Dissanayake 2012). Therefore, the

Greek crisis has a large impact on the magnitude and nature of firms’ risks, particularly

on the European banking sector that is likely to be most exposed to the crisis.

The European banking sector is affected either directly through holdings in

sovereign bonds or indirectly through the general market uncertainty that might cause

investors to demand more than the currently provided information of banks about their

financial situation (e.g., sovereign bonds exposure by country). In such an environment,

transparent financial reporting is of major importance for European banks. Investors are

likely to lose confidence in the creditworthiness of European countries and thus are

concerned about their banks’ exposures. As a consequence, investors revise their belief

about the precision of the currently provided information, and they seek more financial

information about banks’ financial performance, risk attributes, and exposure to

sovereign debt.

If the costs of capital change because of the crisis, bank managers are expected to

consider whether they should adjust their disclosure. Managers have an informational

advantage against outside investors regarding the bank’s financial situation and risk

exposure (Myers and Majluf 1984). Therefore, managers generally have some degree of

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choice whether and what information to disclose (Lang and Lundholm 1993; Healy et al.

1999). However, if the bank’s cost of capital is affected by an exogenous market shock,

managers have incentives to mitigate the impact of the shock by adjusting their

disclosure (Leuz and Schrand 2009).

Therefore, we first hypothesize that the level of disclosure of European banks

significantly increases after the unfolding of the Greek Sovereign Debt Crisis. We also

make the cross-sectional prediction that the disclosure response is more pronounced for

banks with larger cost of capital shocks (Hypothesis H1).

2.1 The Consequence of Banks’ Disclosure Responses

The premise behind our first hypothesis is that managers tend to reconsider their

disclosure policy to cope with the Greek crisis because they perceive a potential cost of

capital benefit from their expanded disclosure. Prior literature documents consistent

evidence with respect to the cost of capital hypothesis. For instance, Botosan (1997)

finds a negative relationship between cost of capital and the extent of voluntary

disclosure for firms with low analyst following. Piotroski (1999) finds that the effects of

providing additional segment disclosures can induce an increase in the market

capitalization. Botosan and Plumlee (2002) find a negative linkage between cost of

capital and analyst rankings of annual report disclosures. Finally, Graham et al. (2005)

find that firms with high analyst following are likely to have a low cost of capital.

To the extent that investors can use the additional information to reduce their

concerns, the costs of capital decrease. We thus predict (Hypothesis H2a) that the

capital market significantly responds to banks’ disclosure change. That is, the increase

in disclosure can help mitigating the initial cost of capital shock.

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At the country level, we further discuss whether the credibility of banks’

disclosure differs across countries. Enhanced disclosure environments can reduce the

cost of following the firm, enable investors to better assess the prospect of the firm, and

therefore decrease the cost of capital (e.g. Merton 1987; Barry and Brown 1985).

International accounting research finds that both accounting properties and the

implementation of accounting standards vary widely across countries. For example, Hail

and Leuz (2006) provide evidence that countries with better legal institutions and

investor protection enjoy a lower cost of capital. Daske et al. (2008) argue that capital-

market benefits differ across economic settings. They find that the mandatory adoption

of IFRS is associated with greater liquidity only in countries with strong legal

institutions. In addition, Hail and Leuz (2009) suggest that firms that cross-list

experience a reduction in the cost of capital.

It is unlikely that the institutional environment is homogenous across European

banks. Therefore, we expect that the magnitude of the market reaction to banks’

disclosure depends on the institutional environment of the bank. When banks are

domiciled in countries with strong institutional environments, the disclosure response to

the cost of capital shock is more credible, and thus, the market reaction should be more

pronounced compared to disclosure responses from banks in weak institutional

environments.

Therefore, we predict (Hypothesis H2b) that the market reaction is stronger for

banks from countries with a strong institutional environment relative to banks from

countries with a weak institutional environment.

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III. Research Design

To test the first hypothesis, we follow Leuz and Schrand (2009) by using the

following general model specification.

variables controlbeta shockDisclosureChange in 10 (1)

In equation (1), we use two different measures to proxy for the dependent variable

Change in Disclosure.

3.1 Measurement of the Dependent Variable

Consistent with Leuz and Schrand (2009), we first use the percentage change in

the page count of the annual report from 2009 to 2011 (%∆AR1109) to measure the

Change in Disclosure. We download the annual reports from the banks’ websites for the

years 2009 and 2011. The data on the page count of annual reports is hand-collected

from the annual reports.

In addition, we expect that the risk management disclosure is particularly useful

during uncertain times. On the one hand, risk management reporting allows the market

to reassess the risks of a bank’s future economic performance and determine its cost of

capital (Schrand and Elliott 1998; Linsley and Shrives 2006). On the other hand,

managers can use risk information to assess the distribution of future cash flows and

might exercise discretion in disclosing relevant risk information. Therefore, investors

assess the management’s risk reporting strategy as a means of risk handling and

possibly respond to it accordingly. Since the manager also anticipates the outsiders’

reaction strategy, a disclosure game setting emerges (Dobler 2008).

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We use the percentage change in the page count of the risk management report

from 2009 to 2011 (%∆RISK1109) as our second proxy for Change in Disclosure. The

proxy %∆RISK1109 includes both mandatory risk information as required by IFRS 7

and voluntary risk disclosure in the management discussion and analysis (MD&A). The

data on the page count of the risk management section is hand-collected from the annual

reports.

3.2 Measurement of Beta Shock (the cost of capital shock)

The empirical implementation of equation (1) also requires an estimate of the beta

shock. To do so, we need to estimate the systematic risk (beta) for the pre-event period

and the estimated beta of the event period. The beta shock is then calculated as the

difference between the event period beta and the pre-event period beta. Figure 1 of

Appendix B illustrates the pre-event period, the event period, and the post report period.

[Insert Figure 1]

We define the pre-event period starting from June 1, 2009 ending at December 31,

2009. By excluding the time before June 2009, we attempt to avoid inferential

influences on stock returns caused by the sub-prime crisis in 2007/2008. In the pre-

event period, banks are in equilibrium to their disclosure decisions and investors have

rational estimation of their information. Optimal disclosure behavior in turn implies that

banks respond to the Greek Sovereign Debt Crisis by adjusting their disclosure policies

in 2011, heading to a new temporary optimum.

We define the event period starting on January 1, 2010 and ending on June 30,

2011. We choose June 30, 2011 as the ending date to ensure that the event window

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tackles the several key events during the Greek Sovereign Debt Crisis. In addition, at 30

June 2011, a 21% reduction in the economic value of Greek bonds was officially

announced, so that such investments are considered to be impaired. For robustness, in

Section 5.3, we also use a shorter event period from 1 January 2010 to 30 June 2010,

because three major rating agencies had downgraded the long-term credit rating of

Greece before that date.

We use a quadratic model to estimate systematic risk (beta) for the pre-event

period and the event period (e.g., Lockwood and Kadiyala 1988; Cyree and DeGennaro

2002). The model provides period-specific beta estimates.

(2)

with:

Rit = firm i’s common equity daily return,

Rmt = the value-weighted stock market daily return.

Unlike the linear model, the systematic risk (βit) varies as a function of the trading day t.

( )( ) ( ) ( ) (3)

T1 (T2) is the start (end) of the event period. T1 and T2 are specified as the number of

trading days in the event period relative to day t. D1it =1 for T1≤t≤T2, 0 otherwise; and

D2it =1 for t>T2, 0 otherwise.

During the pre-event period, when D1=0 and D2=0, the estimated systematic risk

for this period is constant and equal to (BETA_PRE). When D1=1 and D2=0, the

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estimated systematic risk for this period is determined by the parameter estimates for

, , and . By setting t=0, the beta for the event period (BETA_EVT) is equal

to ( ) . Finally, we calculate the beta shock as the event beta minus

the pre-event beta (SHOCK = BETA_EVT – BETA_PRE). We predict that the

coefficient of the beta shock (SHOCK) in equation (1) is positive.4

3.3 Measurement of the Market Response to Increased Disclosure

We next investigate whether banks’ disclosure changes induce subsequent market

reactions. In other words, whether more disclosure can help mitigating the cost of

capital shocks arising from the Greek Crisis. To measure the market reaction in the

post-report period (POST_RESPONSE), we use the changes in systematic risk (beta)

from the event period to the post-report period. We compute the post-report period beta

(BETA_POST) from 1 January, 2012 to June 30, 2012. Following Leuz and Schrand

(2009), we use a standard market model (CAPM). During the post-report period, annual

reports have been published and investors should have updated their beliefs about the

exposure of European banks to the Greek Crisis. The updated belief is measured as the

difference change between the event period beta and the post report period beta:

POST_RESPONSE = BETA_EVT - BETA_POST. Therefore, higher values of

POST_RESPONSE indicate greater recovery of the beta, mitigating the cost of capital

shock.

3.4 Measurement of the Institutional Environment: Regulatory Quality

4 In Section 5.3, for robustness, we also use the standard market model (CAPM) to calculate the beta

shock.

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We use regulatory quality to distinguish between strong and weak institutional

environments. To proxy for regulatory quality, we use the index variable constructed by

the World Bank (Kaufmann et al. 2009), capturing perceptions of the ability of the

government to formulate and implement sound policies and regulations that permit and

promote private sector development. We define the binary variable RegQual equal to 1

if the regulatory quality index is higher than the sample median; and 0 otherwise. We

then split our sample by RegQual, and we expect to find that the market reaction to

increased bank level transparency is particularly strong in countries where RegQual

equals 1.

3.5 Control Variables

Apart from the cost of capital shock caused by the Greek crisis, a bank’s

disclosure can also be influenced by other factors. We thus include several control

variables in the regression model.

3.5.1 Firm Size

Several studies find a positive association between firm size and disclosure (e.g.,

Ahmed and Courtis 1999; Eng and Mak 2003). One explanation is that the costs of

disclosure for larger firms are lower due to economies of scale (Lang and Lundholm

1993). Diamond and Verrecchia (1991) suggest that larger firms benefit more from

disclosure than smaller firms because of differences in the investor base and their

demand for disclosure. However, for a smaller firm with a relatively low ex ante

disclosure level, the marginal return to an increase in disclosure can be higher than for a

large firm with an already high disclosure level. We thus have no prediction on the sign

of the relationship between bank size (or change in bank size) and the change in

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disclosure. We use the natural logarithm of total assets in millions Euro (LASSETS) as a

proxy for firm size.

3.5.2 Growth Opportunities

The disclosure choice can be influenced by the ratio of market value to book value

of equity (MTB). MTB is a proxy for the investment opportunity set and the associated

financing considerations, which partly determine disclosure costs (Nagar et al. 2003)

and the information asymmetry between management and investors (Verrecchia 1990).

We predict a positive relationship between MTB and disclosure choice.

3.5.3 Firm Performance

The perception that firms’ willingness to disclose information is related to their

performance is widespread, but the direction of the relationship is not clear. One

explanation is that boards of directors and investors hold managers accountable for

current stock performance (Healy and Palepu 2001). For example, CEO turnover could

be related with poor stock performance (Warner et al. 1988 and Weisbach 1988).

Management will tend to use disclosure to reduce the likelihood of undervaluation and

to justify poor earnings performance.

An alternative motive for disclosure is to reduce transaction costs. Firms can

precommit to disclosure prior to observing performance, reducing the cost of private

information acquisition (Diamond 1985). Similarly, King et al. (1990) argue that firms

may issue management earnings forecasts to lower the opportunity for investors to

benefit from informed trading and reduce the incentives for private information

acquisition. In these models, therefore, disclosure is unrelated to firm performance

because firms precommit to a disclosure policy.

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The empirical evidence on the relationship between firm performance and

disclosure is mixed. Some research on management earnings forecasts (Penman 1980;

and Lev and Penman 1990) suggests that firms tend to disclose more frequently when

they are experiencing favorable earnings results. Lang and Lundholm (1993) find a

strong positive relationship between analyst disclosure ratings, the level of firm

performance, and the variability of firm performance. However, research focusing on

later time periods (Skinner 1994) provides evidence that firms are more likely to

disclose bad new than good news.

In sum, the results from theoretical and empirical research suggest that disclosure

could be increasing, constant, or even decreasing in firm performance. We use the

return on assets (ROA) to proxy for firm performance, and we do not make a prediction

on the sign.

3.5.4 Leverage

Jensen and Meckling (1976) argue that highly leveraged firms incur more

monitoring costs and will seek to reduce these costs by disclosing more information.

Frankel et al. (1995) examine the relationship between external financing transactions

and management's tendency to issue qualitative or quantitative forecasts of annual

earnings. They document a positive association between external financing transactions

and disclosure activity, arguing that management issues forecasts to communicate with

investors, and that management views disclosure as valuation-relevant. Therefore, we

believe that leverage could be an important determinant of the disclosure choice.

The empirical evidence concerning the relationship between information

disclosure and firms’ leverage is ambiguous. Taylor et al. (2008) find a positive

relationship, while others (Belkaoui and Karpik 1989; Chow and Wong-Boren 1987)

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find a negative association. In addition, Branco and Rodrigues (2008) find the

relationship to be insignificant. We measure leverage (LEV) by the ratio of total debt to

total assets, and we do not make a prediction on the sign.

IV. Sample Description

We use a sample of European banks applying IFRS and with December fiscal-

year ends. The financial data to compute the stock returns as well as the control

variables are collected from Thomson Reuters. We exclude banks with missing values

for total assets, return on assets, total debt, or market capitalization for either 2009 or

2011. In addition, we download the annual reports from the banks’ websites for the

years 2009 and 2011. The data on page count of annual reports and risk reports are

hand-collected from the annual reports. We exclude banks where the annual report 2009

or 2011 is not available. Our final sample consists of 173 European banks.

[Insert Table 1 here]

Table 1, Panel A, shows summary statistics of the two proxies for Change in

Disclosure. The first proxy for the variable Change in Disclosure is the percentage

change in the page count of the annual reports from 2009 to 2011, denoted in the

analysis as %∆AR1109. The second disclosure proxy is the percentage change in the

page count of the risk management reports from 2009 to 2011, denoted as %∆RISK1109.

We find a substantial increase in disclosure in both the annual reports and the risk

management sections. The average length of annual reports has increased by 10.1%

from 2009 to 2011, and the average length of the risk management section has increased

by 17.2%. As the increase of the length of annual reports could be driven by the

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expansion of risk management reports, we further check the percentage change in the

page count of annual reports excluding the risk management section. We find that the

disclosure of other-than-risk information has increased by 10% (not tabulated). These

univariate findings reveal that the accounting transparency problem arising from the

Greek Sovereign Crisis is associated with an increase in page count of both the annual

reports and the risk management section.

Table 1, Panel B, reports descriptive statistics for the control variables. We

include bank-specific characteristics as our control variables measured at the end of the

fiscal year 2009 and 2011, respectively. We also include control variables measured as a

change (indicated as “∆”) from 2009 to 2011. The analysis shows a remarkable decrease

of the mean market-to-book ratio by –0.380 and also a drop of the mean return on assets

by –0.001. All control variables, except the return on assets, differ significantly (t-test

not tabulated) from 2009 to 2011.

[Insert Table 2 here]

Table 2 provides descriptive statistics for the parameter estimates, systematic risk,

and the beta shocks based for the event period from 1 January 2010 to 30 June 2011.

We compute related variables by using the method from Lockwood and Kadiyala

(1988). Their quadratic specification can capture the direction and the curvature of the

shock in ways that allows for a recovery of the shock during the event period (Leuz and

Schrand 2009). We find that the average event beta (0.504) is higher than the pre-event

beta (0.282), indicating that banks’ stocks tend to be more volatile when the Greek

Sovereign Crisis unfolds. Finally, we compute the beta shock by using the difference

between the pre-event beta and the event beta, which is 0.222.

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V. Empirical Results

5.1 The Impact of the Greek Crisis on Banks’ Disclosure

To analyze the disclosure response of European banks to the Greek Sovereign

Crisis, we implement the following specification of equation (1).

(4)

_

432

121

LEVROAMTB

LASSETPREBETASHOCKDisclosurein Change

Change in Disclosure is the percentage change of the page count of either the annual

report (%∆AR1109) or the risk management (%∆RISK1109). Beside the explanatory

variable of interest (SHOCK) and the control variables (LASSET, MTB, ROA, and LEV),

we include BETA_PRE to control for the possibility that banks with higher pre-event

betas are more responsive (Leuz and Schrand 2009). An extended model also includes

the changes of the control variables from 2009 to 2011. To control for the bank-specific

business model, we use indicator variables for the following bank types: Regional

Banks, Money Center Banks, Investment Services, and S&L Banks.

[Insert Table 3 here]

Models (1) to (3) show a positive and significant relationship between the beta

shock (SHOCK) and the percentage change in the page count of the annual report. In

addition, models (4) to (6) report that the beta shock (SHOCK) is significantly related

with the percentage change in page count of the risk management report. The

coefficients are of similar magnitude and significance across the two dependent

21

variables %∆AR1109 and %∆RISK1109, respectively. The positive association between

the beta shock and the disclosure changes is consistent with H1 that banks change their

disclosure in response to the impact of the Greek crisis. In particular, the disclosure

response is more pronounced for banks that experience a more severe cost of capital

shock.

Furthermore, we address the possibility that the changes in annual reports may be

simply driven by the changes in risk management section. The impact of beta shocks on

annual report disclosure may thus partly reflect the impact of beta shocks on risk

management section. To explore this issue, we examine the relationship between the

percentage changes in page count of annual report excluding the risk management

section (%∆OTHER1109). We find a significant impact of beta shocks

on %∆OTHER1109 (not tabulated) at the 1%-level, consistent with our previous

findings.

Referring to the control variables, the results show a positive and significant

relationship between the return on assets in 2009 (ROA09) and both dependent variables

(%∆AR1109 and %∆RISK1109), suggesting that banks tend to disclosure more when

they are experiencing good performance. In addition, we find a significantly negative

relationship between the total assets 2009 (LASSET09) and the change in annual report.

A possible explanation is that the length of smaller banks’ annual report catches up with

their larger peers from 2009 to 2011. An alternative explanation is that―during

turbulent times―the marginal benefit for increased disclosure is higher for small banks

than for large banks. For the risk management section, we do not find a significant

negative relation between LASSET09 and the change in reporting. However, the

significant negative coefficient of –0.372 for %∆ASSETS1109 indicates that banks

which become smaller increase their risk reporting. A possible explanation for this

22

finding is that large troubled banks, which were forced to reduce their exposure by

reducing their assets, simultaneously increased their risk disclosure. Relatedly, the

significant negative coefficient of the market-to-book ratio 2009 in models (4) and (5)

indicates that undervalued companies increase their risk reporting.

5.2 The Consequences of Banks’ Disclosure Responses

Table 4 shows the results on whether the cost of capital shock is mitigated by the

disclosure response. Note that the dependent variable POST_RESPONSE is defined as

the difference between the event period beta and the post report beta, so that higher

values of POST_RESPONSE indicate greater recovery of the beta, mitigating the cost of

capital shock.

[Insert Table 4 here]

As expected, in Table 4, we find a significantly positive relation between the

percentage change in risk report (%∆RISK1109) and the decline in beta

(POST_RESPONSE) for models (4) to (6). In line with H2a, an increase in risk-related

disclosure provides information that is useful to investors, thus mitigating the cost of

capital shock. However, we do not find that %∆AR1109 is significantly related to

POST_RESPONSE, indicating that investors tend to be more sensitive to banks’ risk

report than to their annual report. We attribute such different reactions to the important

role of risk reporting during uncertain times. In addition, risk reporting is helpful for

investors to assess the distribution of future cash flows of the reporting entity and to

evaluate its future economic performance.

To further investigate the effect of the country-level institutional environment on

the relationship between changes in disclosure and subsequent market reactions, we

23

repeat the analysis by splitting the sample into banks from high versus low regulatory

quality environments. Table 5 reports the results of the analysis.

[Insert Table 5 here]

We find that, in Column (B), the coefficient of BETA_EVT remains significantly

positive for the banks with a high regulatory quality environment, while it is

insignificant for the banks with low regulatory quality environment. This result is in line

with H2b that institutional environments can affect the impact of disclosure on the cost

of capital. Banks in countries with sound ability of the government to implement

policies and regulations display a significant link between bank-specific risk disclosure

and the cost of capital. For the annual report disclosure, we again do not find any

significant relation between the disclosure response and the cost of capital reduction.

5.3 Additional Analyses and Robustness Checks

In this section, we discuss the results of several robustness tests designed to

increase confidence in our findings. First, we derive beta shocks from a short event

window.5 For the short event period, we choose 30 June 2010 as the ending date,

because Greece’s long-term credit ratings had reached junk status around that time.

Second, we use beta shocks derived from a standard market model (CAPM) with a

short event window. For parsimony, we only report the results including the full set of

control variables.6

In Column (A), models (1) and (2) report a positive and significant relationship

between the beta shock (SHOCK_SHORT) and both dependent variables (%∆AR1109

5 For more information, see Appendix A and Figure 2 in Appendix B.

6 For the descriptive statistics of related variables, see Appendix C.

24

and %∆RISK1109), consistent with our results in Table 3. Both the coefficients and

significance levels are comparable to the coefficients from the main analysis (SHOCK).

This result reflects the trade-off between the long and short event window: On the one

hand, as the Greek Crisis is not a single point in time, SHOCK_SHORT calculated from

a shorter event period may not be able to tackle all the shocks created by the crisis. On

the other hand, the beta calculation by using a longer event window is more sensitive to

possible concurrent events.

In Column (B) of Table 6, we find that (SHOCK_CAPM) is positively associated

with %∆AR1109 at the 5%-level. The relationship between %∆RISK1109 and

SHOCK_CAPM is only of weak significance, but has the expected direction.7 This

relationship, however, is significant at the 10%-level when we use the model excluding

the change variables of bank-specific characteristics from 2009 to 2011.

[Insert Table 6 here]

We also test the economic consequences of banks’ disclosure responses by using

(a) the beta shocks from the short event window, and (b) the beta shocks derived from

the standard market model (CAPM). We define POST_RESPONSE_SHORT as

BETA_EVT_SHORT minus BETA_POST, and we define POST_RESPONSE_CAPM as

BETA_EVT_CAPM minus BETA_POST. In model (1) of Table 7, we find robust results

that the market reaction is significantly related to the percentage change in the risk

management section at the 1%-level. Again, we do not find any significant evidence

that the market reaction is related to banks’ annual report disclosure (not tabulated).

7 We also use beta shocks derived from the standard market model (CAPM) with a long event window.

However, we do not find significant results. The results thus indicate that the quadratic beta estimates

better perform for longer event windows, as the quadratic approach allows for potential recovery of

the shock during the event period.

25

Models (2) and (3) of Table 7 show that the reaction to the risk disclosure is more

pronounced for banks from countries with high regulatory quality.

[Insert Table 7 here]

Model (4) in Table 7 also provides supportive evidence that the market response is

positively related with the risk disclosure change when the event beta is derived from a

standard market model (CAPM). However, in model (5) of Table 7, the coefficient of

0.088 for %∆RISK1109 is only marginally significant (t-statistic= 1.64; p-value =

10.6%) for banks domiciled in strong institutional environments. We attribute the low

significance level to the small sample size for this test (N=83), working in favor of

accepting the null hypothesis.

IV. Conclusion

In this paper, we analyze the impact of the cost of capital shocks created by the

Greek Sovereign Crisis on European banks’ disclosure policy. We estimate the cost of

capital shocks by a quadratic market model, and the proxy for disclosure response is

based on the change in page count of either banks’ annual reports or their risk

management section.

Since the unfolding of the Greek Sovereign Crisis at the end of 2009, we find that

European banks have increased the page numbers of their annual reports, particularly

the section containing the risk management. We find that the increased disclosure is

significantly related to the beta shock caused by the Greek Crisis, indicating that banks

are more likely to increase their disclosure when they are experiencing a negative

capital market shock.

26

We further document a significantly positive relationship between the changes in

risk management disclosure and subsequent recovery of banks’ beta. In contrast, we do

not find significant evidence of such relationship for the general increase in the length

of annual reports. We interpret the finding as evidence that the capital market reacts

differently to the varying contents of the disclosure. As the risk management report can

provide useful information to investors, the market is more sensitive to the risk

disclosure than to the general disclosure in the annual reports.

We finally investigate to what extent the effect of risk disclosure on cost of capital

differs across the institutional environment. We use regulatory quality to distinguish

between strong and weak institutional environments. We find that the mitigating cost of

capital effect is substantially stronger for banks from countries with higher regulatory

quality, possibly because of higher credibility of the disclosed risk information in strong

institutional environments.

27

Appendix

Section A describes key event dates related to the Greek Sovereign Crisis

between December, 2009 and June, 2011. We identify the pre-event, event, and post-

report windows as illustrated in Section B. Section C reports beta estimates from both

the short event window and the standard market model (CAPM), which we use in our

robustness checks.

Appendix A. Key Event Dates Related to the Greek Sovereign Crisis

Dec. 8, 2009: Fitch downgrades Greece’s credit rating from A- to BBB+. Bond grades

from the three major agencies eventually reached junk status.

April 27, 2010: Standard & Poor's slashes Greece's long-term credit ratings by three

notches from BBB minus to BB plus, or junk status and warns debt holders that

they only have an average chance of between 30 to 50 percent of getting their

money back in the event of a debt restructuring or default.

May 18, 2010: Greece receives euro14.5 billion in bailout loans, just in time to meet a

crucial debt refinancing deadline.

June 14, 2010: Moody's slashes Greece's government bond ratings by four notches to

Ba1 from A3.

We choose June 30, 2010 as the ending date of our short event period, because three

major rating agencies have downgraded Greece’s long-term credit ratings from BBB- to

BB+ or junk status before June 30, 2010.

Jan. 14, 2011: Fitch cuts Greek debt by one notch, from BBB- to BB+, or junk status.

April 23, 2011: European Commission data shows the Greek budget deficit jumped to

13.6 per cent of gross domestic product in 2009.

May 24, 2011: The Greek government announces that it would sell stakes in state-

controlled companies and form a sovereign wealth fund, to stem criticism that it

has dragged its feet on measures to raise revenue and cut spending.

28

June 17, 2011: Prime Minister George Papandreou replaces finance minister George

Papaconstantinou with his main party rival, Evangelos Venizelos.

June 29, 2011: Parliament passes the 28 billion euro austerity bill in the face of two

days of violent protests during which some 300 protesters and police are injured.

The package contains severe spending cuts and tax increases. The European

Union had set passage of the bill as a precondition for further aid.

July 3, 2011: European finance ministers agree to release a vital euro8.7 billion

instalment of aid money for Greece but postpone a decision on a second

bailout.

July 21, 2011: European leaders propose a new financial assistance package for

Greece, under which private bondholders will be asked to contribute

€40 billion towards the relief of Greece’s debt burden.

More specifically, bondholders, whose debts mature through 2020, will be given four

options. Each of the options is intended to result in a 21% reduction in the economic

value of the bonds. As a result, investor generally considered their investments in Greek

sovereign debt to be impaired at 30 June 2011. Therefore, we choose 30 June 2011 as

the ending date of our main event period.

Sources: news from www.boston.com and alerts from www.pwcinform.com

29

Appendix B: Event Windows

Figure 1: Main Event Window

Figure 2: Short Event Window

01-06-09 31-12-09 30-06-11 31-12-11 30-06-12

Time

Pre-event period Event period Post report period

Market reaction

Disclosure response

Greek Crisis unfolds

01-06-09 31-12-09 30-06-10 31-12-11 30-06-12

Time

Pre-event period Event period Post report period

Market reaction

30-06-11

Disclosure response

Greek Crisis unfolds

30

Appendix C: Descriptive statistics of model parameter estimates, systematic risk, and the beta shock with a short event

window and a standard market model (CAPM)

Variable N Mean Std.Dev Min p25 p50 p75 Max

parameter estimates

173 0.24205 0.21663 -0.13291 0.05834 0.22220 0.38543 1.01827

173 -0.00004 0.00015 -0.00048 -0.00015 -0.00001 0.00005 0.00038

173 0.00461 0.00524 -0.00426 0.00082 0.00328 0.00778 0.02114

estimates of systematic risk

BETA_PRE_SHORT 173 0.24205 0.21663 -0.13291 0.05834 0.22220 0.38543 1.01827

BETA_EVT_SHORT 173 0.38728 0.56338 -0.63488 -0.02705 0.71804 1.49010 1.99711

BETA_PRE_ CAPM 173 0.22395 0.21044 -0.18390 0.04072 0.20503 0.35997 1.03023

BETA_EVT_ CAPM 173 0.49046 0.46265 -0.14507 0.10549 0.37291 0.77089 1.70838

estimates of shock and response

SHOCK_SHORT 173 0.14522 0.43363 -0.74791 -0.14011 0.06490 0.39379 1.51163

SHOCK_ CAPM 173 0.26651 0.32599 -0.32070 0.01555 0.17864 0.47217 1.24073

POST_RESPONSE_SHORT 173 -0.05264 0.34232 -0.97925 -0.23288 -0.041667 0.15623 0.99711

POST_RESPONSE_CAPM 173 0.05054 0.25957 -0.88298 -0.07770 0.04900 0.17863 0.73180

This table reports descriptive statistics for the parameter estimates from the beta modes in equation (2) and (3), measures of systematic risk over the short event period, beta

estimates using a standard market model (CAPM), and beta shocks. The pre-event period is from 30 Jun, 2009 to 31 Dec, 2009. The short event period is for 1 Jan, 2010 to

30 Jun, 2010. The pre-event beta is equal to (BETA_PRE_SHORT). During the short event period, ( )( ) (

) . We compute the event period beta (BETA_EVT_SHORT) at day t=0. SHOCK_SHORT is BETA_EVT_SHORT minus BETA_PRE_SHORT. We compute

BETA_PRE_CAPM and BETA_EVT_ CAPM by using a standard market model (CAPM). SHOCK_CAPM is BETA_EVT_CAPM minus BETA_PRE_CAPM. During the

post-report period, systematic risk (BETA_POST) is estimated using a market model from 1 Jan, 2012 to 30 Jun, 2012. POST_RESPONSE_SHORT is

BETA_EVT_SHORT minus BETA_POST. POST_RESPONSE_CAPM is BETA_EVT_CAPM minus BETA_POST.

31

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35

Table 1: Descriptive statistics for the disclosure proxies and bank characteristics

Variables N Mean SD Min p25 p50 p75 Max

%∆AR1109 173 0.101 0.263 -0.677 -0.022 0.061 0.168 2.147

%∆RISK1109 173 0.172 0.45 -0.842 0.000 0.091 0.250 3.000

LASSET09 173 9.028 2.499 4.046 7.221 9.06 10.641 14.449

LASSET11 173 9.149 2.508 4.127 7.23 9.156 10.67 14.587

ROA09 173 0.005 0.021 -0.141 0.002 0.006 0.011 0.168

ROA11 173 0.004 0.027 -0.143 0.001 0.006 0.011 0.223

MTB09 173 1.107 1.184 0.018 0.585 0.894 1.294 13.176

MTB11 173 0.727 0.77 0.019 0.294 0.583 0.911 8.278

LEV09 173 0.891 0.119 0.057 0.884 0.92 0.939 0.982

LEV11 173 0.894 0.121 0.009 0.883 0.923 0.941 0.998

∆LASSET1109 173 0.121 0.197 -0.457 0.006 0.118 0.222 0.712

∆ROA1109 173 -0.001 0.021 -0.127 -0.005 -0.001 0.003 0.079

∆MTB1109 173 -0.380 0.558 -4.897 -0.514 -0.289 -0.093 0.438

∆LEV1109 173 0.003 0.022 -0.076 -0.007 0.001 0.011 0.085

Regional Banks 173 0.705 0.457 0.000 0.000 1.000 1.000 1.000

Money Center Banks 173 0.087 0.282 0.000 0.000 0.000 0.000 1.000

Investment Services 173 0.052 0.223 0.000 0.000 0.000 0.000 1.000

S&L Banks 173 0.116 0.321 0.000 0.000 0.000 0.000 1.000

Regulatory Quality 173 1.113 0.656 -0.556 0.951 1.255 1.642 1.915

The table reports summary statistics for the disclosure proxies, the control variables, and the country-specific regulatory quality. %∆AR1109 is

the percentage change of the page count of the annual report from 2009 to 2011. %∆RISK1109 is the percentage change of the page count of the

risk report. LASSET09 (LASSET11) is defined as natural logarithm of total assets in million euro per 31 Dec, 2009 (2011). ROA09 (ROA11) is

defined as return on total asset per 31 Dec, 2009 (2011). MTB09 (MTB11) is defined as market to book value ratio per 31 Dec, 2009 (2011). LEV09

(LEV11) is defined as the debt-to-asset ratio per 31 Dec, 2009 (2011). ∆LASSET1109, ∆ROA1109, ∆MTB1109, ∆LEV1109 are the change of each

variable from 2009 to 2011. Regional Banks, Money Center Banks, Investment Services, and S&L Banks are indicator variables controlling for the

bank-specific business model. Regulatory Quality is an index variable constructed by the world bank (Kaufmann et al. 2009).

Panel B: Control Variables

Panel A: Change in Disclosure

36

Table 2: Descriptive statistics on model parameter estimates, systematic risk, and the beta shock

Variable N Mean Std.Dev Min p25 p50 p75 Max

parameter estimates

173 0.28239 0.23529 -0.10313 0.08009 0.25888 0.43423 1.07327

173 -0.00000 0.00001 -0.00003 -0.00001 0.00000 0.00001 0.00004

173 0.00128 0.00163 -0.00123 0.00003 0.00069 0.00236 0.00649

estimates of systematic risk

BETA_PRE 173 0.28239 0.23529 -0.10313 0.08009 0.25888 0.43423 1.07327

BETA_EVT 173 0.50419 0.54877 -0.30925 0.05153 0.29404 0.88479 2.12740

BETA_POST 173 0.43992 0.53519 -0.27908 0.05323 0.24070 0.71424 2.12143

estimates of shock and response

SHOCK 173 0.22181 0.38752 -0.61016 -0.04786 0.11056 0.41314 1.45836

POST_RESPONSE 173 0.06427 0.26807 -0.83981 -0.07468 0.05661 0.17240 1.81215

This table reports descriptive statistics for the parameter estimates from the beta modes in equation (2) and (3), measures of systematic risk over the event period, and the

beta shock. The pre-event period is from 30 Jun, 2009 to 31 Dec, 2009. The long event period is for 1 Jan, 2010 to 30 Jun, 2011. The pre-event beta is equal to

(BETA_PRE). During the long event period, ( )( ) ( ).The event period contains 389 trading days, and it is centred on t=0,

with T1 set at -194 and T2 set at 194. We compute the event period beta (BETA_EVT) at day t=0. SHOCK is BETA_EVT minus BETA_PRE. During the post-report period,

systematic risk (BETA_POST) is estimated using a standard market model from 1 Jan, 2012 to 30 Jun, 2012. POST_RESPONSE is BETA_EVT minus BETA_POST.

37

Table 3: The impact of the Greek crisis on banks’ disclosure

Dependent variable:

Variable (1) (2) (3) (4) (5) (6)

SHOCK 0.195*** 0.188*** 0.223*** 0.244** 0.225** 0.203*

(3.06) (3.45) (3.35) (2.37) (2.12) (1.88)

BETA_PRE 0.054 0.061 0.059 -0.066 -0.043 -0.086

(0.49) (0.65) (0.64) (-0.37) (-0.24) (-0.53)

LASSETS09 -0.033** -0.032** -0.039** -0.025 -0.023 -0.021

(-2.05) (-2.23) (-2.47) (-0.96) (-0.84) (-0.81)

MTB09 0.004 0.003 0.059 -0.054*** -0.044** -0.038

(0.18) (0.22) (1.48) (-2.94) (-2.09) (-0.92)

ROA09 1.652** 1.256* 0.806 1.918* 1.736* 2.027**

(2.42) (1.70) (1.03) (1.93) (1.77) (2.42)

LEV09 0.145 0.026 0.012 0.149 0.101 0.257

(0.79) (0.11) (0.05) (0.64) (0.45) (1.06)

%∆LASSETS1109 -0.034 -0.383**

(-0.35) (-2.17)

%∆MTB1109 0.122 -0.026

(1.56) (-0.28)

%∆ROA1109 -1.358 -0.527

(-0.86) (-0.18)

%∆LEV1109 -1.591 0.778

(-1.37) (0.30)

Constant 0.201 0.069 0.116 0.283* 0.198 0.122

(1.64) (0.54) (0.82) (1.86) (1.45) (0.71)

N 173 173 173 173 173 173

R-squared 0.073 0.147 0.177 0.051 0.078 0.105

Business Model Controls No Yes Yes No Yes Yes

Column (A) Column (B)

%∆RISK1109

The table reports OLS coefficient estimates and, in parentheses, t -statistics based on heteroskedasticity-robust standard errors. In Column

(A), %∆AR1109 is the dependent variable; whereas in Column (B), %∆RISK1109 is the dependent variable. See Table 1 and Table 2 for

the definition of the regression variables. In models (2), (3), (5), and (6), we include business model indicator variables for regional banks,

money center banks, investment services, and S&L banks. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels

(two-tailed), respectively.

%∆AR1109

38

Table 4: The consequences of banks’ disclosure response

Dependent variable: POST_RESPONSE

Variable (1) (2) (3) (4) (5) (6)

%∆AR1109 0.087 0.109 0.079

(0.85) (1.08) (0.81)

%∆RISK1109 0.144*** 0.155*** 0.141***

(2.93) (3.24) (2.73)

LASSETS11 0.015 0.014 0.013 0.015* 0.014 0.013

(1.64) (1.53) (1.38) (1.66) (1.52) (1.37)

MTB11 0.012 0.012 0.007 0.026* 0.025 0.018

(0.76) (0.69) (0.28) (1.73) (1.36) (0.74)

ROA11 -0.563 -0.499 0.973 -0.563 -0.475 0.714

(-0.99) (-0.83) (1.19) (-0.98) (-0.78) (0.87)

LEV11 0.026 -0.038 -0.077 0.014 -0.054 -0.115

(0.17) (-0.22) (-0.39) (0.09) (-0.32) (-0.60)

%∆LASSET1109 -0.047 0.004

(-0.34) (0.03)

%∆MTB1109 -0.019 -0.015

(-0.60) (-0.50)

%∆ROA1109 -3.699** -3.481*

(-2.18) (-1.97)

%∆LEV1109 -1.187 -1.401

(-0.95) (-1.10)

Constant -0.113 -0.110 -0.058 -0.128 -0.126 -0.061

(-0.90) (-0.83) (-0.40) (-1.04) (-0.97) (-0.42)

N 173 173 173 173 173 173

R-squared 0.033 0.049 0.086 0.083 0.103 0.132

Business Model Controls No Yes Yes No Yes Yes

Column (A) Column (B)

The table reports OLS coefficient estimates and, in parentheses, t -statistics based on heteroskedasticity-robust standard errors. The

dependent variable is POST_RESPONSE for all models. In Column (A), %∆AR1109 is the main independent variable; whereas in Column

(B), %∆RISK1109 is the main independent variable. See Table 1 and Table 2 for the definition of the regression variables. In models (2), (3),

(5), and (6), we include business model indicator variables for regional banks, money center banks, investment services, and S&L banks.

***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed), respectively.

39

Table 5: The consequences of banks’ disclosure across institutional environments

Dependent variable: POST_RESPONSE

High RegQual Low RegQual High RegQual Low RegQual

Variable (1) (2) (3) (4)

%∆AR1109 0.183 0.037

(0.85) (0.44)

%∆RISK1109 0.164** 0.072

(2.08) (1.26)

LASSETS11 0.019 0.002 0.015 0.004

(1.39) (0.18) (1.03) (0.28)

MTB11 0.024 0.021 0.032 0.026

(0.58) (0.54) (0.76) (0.64)

ROA11 -1.913 1.899 -1.574 1.623

(-1.17) (1.33) (-0.84) (1.16)

LEV11 -0.010 -0.058 -0.037 -0.090

(-0.05) (-0.16) (-0.17) (-0.26)

%∆LASSET1109 0.098 -0.007 0.145 0.014

(0.31) (-0.05) (0.44) (0.10)

%∆MTB1109 0.017 -0.033 0.015 -0.034

(0.22) (-0.64) (0.21) (-0.67)

%∆ROA1109 -6.364 -4.017* -3.181 -3.956*

(-1.66) (-1.82) (-0.87) (-1.73)

%∆LEV1109 -5.336 -0.333 -5.180 -0.485

(-1.58) (-0.23) (-1.53) (-0.34)

Constant -0.203 -0.020 -0.226 -0.020

(-1.34) (-0.08) (-1.38) (-0.08)

N 83 90 83 90

R-squared 0.125 0.124 0.174 0.134

Business Model Controls Yes Yes Yes Yes

Column (A)

The table reports OLS coefficient estimates and, in parentheses, t -statistics based on heteroskedasticity-robust standard

errors. The dependent variable is POST_RESPONSE for all models. In Column (A), %∆AR1109 is the main independent

variable; whereas in Column (B), %∆RISK1109 is the main independent variable. High (low) RegQual equals 1 if the regulatory

quality index by Kaufmann et al. (2009) is above (below) the sample median, and 0 otherwise. See Table 1 and Table 2 for the

definition of the regression variables. In models (2), (3), (5), and (6), we include business model indicator variables for regional

banks, money center banks, investment services, and S&L banks. ***, **, and * indicate statistical significance at the 1%, 5%,

and 10% levels (two-tailed), respectively.

Column (B)

40

Table 6: The impact of the Greek crisis on banks’ disclosure: Robustness checks

Dependent variable: %∆AR1109 %∆RISK1109 %∆AR1109 %∆RISK1109

Variable (1) (2) (3) (4)

SHOCK_SHORT 0.144** 0.209*

(2.09) (1.84)

BETA_PRE_SHORT -0.044 -0.219

(-0.42) (-1.27)

SHOCK_CAPM 0.215** 0.202

(2.18) (1.35)

BETA_PRE_CAPM 0.039 -0.126

(0.39) (-0.71)

LASSETS09 -0.029* -0.014 -0.033* -0.016

(-1.73) (-0.52) (-1.88) (-0.60)

MTB09 0.060 -0.028 0.053 -0.044

(1.39) (-0.69) (1.30) (-1.04)

ROA09 0.936 1.884*** 0.885 2.043**

(1.49) (2.67) (1.18) (2.50)

LEV09 0.037 0.306 0.023 0.274

(0.14) (1.19) (0.09) (1.11)

%∆LASSET1109 -0.058 -0.420** -0.030 -0.375**

(-0.58) (-2.36) (-0.30) (-2.12)

%∆MTB1109 0.125 0.001 0.113 -0.034

(1.49) (0.01) (1.38) (-0.37)

%∆ROA1109 -0.823 0.387 -1.067 -0.183

(-0.50) (0.14) (-0.67) (-0.06)

%∆LEV1109 -1.150 1.321 -1.286 1.100

(-1.01) (0.51) (-1.10) (0.41)

Constant 0.047 0.072 0.054 0.063

(0.33) (0.45) (0.38) (0.38)

N 173 173 173 173

R-squared 0.153 0.116 0.155 0.102

Business Model Controls Yes Yes Yes Yes

Column (A) Column (B)

The table reports OLS coefficient estimates and, in parentheses, t -statistics based on heteroskedasticity-robust standard

errors. In models (1) and (3), %∆AR1109 is the dependent variable; whereas in models (2) and (4), %∆RISK1109 is the

dependent variable. In column (A), the cost of captal shock is calculated by using a short event window from 1 January 2010

to 30 June 2010 (SHOCK_SHORT ). In column (B), the cost of captal shock is calculated by using a standard market model

(SHOCK_CAPM ). See Table 1, Table 2, and Appendix C for the definition of the regression variables. In all models, we

include business model indicator variables for regional banks, money center banks, investment services, and S&L banks. ***,

**, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed), respectively.

41

Table 7: The consequences of banks’ disclosure response: Robustness checks

Dependent variable:

Full sample High RegQual Low RegQual Full sample High RegQual Low RegQual

Variable (1) (2) (3) (4) (5) (6)

%∆RISK1109 0.152*** 0.151** 0.116 0.107** 0.088 0.071

(2.84) (2.18) (1.40) (2.52) (1.64) (1.05)

LASSETS11 0.007 0.012 0.015 -0.016 -0.003 -0.028*

(0.65) (0.82) (0.79) (-1.63) (-0.25) (-1.78)

MTB11 -0.028 0.006 0.072 0.037 0.033 0.089*

(-0.67) (0.12) (0.98) (1.11) (0.89) (1.77)

ROA11 0.388 0.081 -2.704 0.257 -0.596 0.704

(0.36) (0.05) (-1.44) (0.33) (-0.41) (0.46)

LEV11 -0.448 0.136 -1.408*** -0.149 -0.033 -0.371

(-1.22) (0.58) (-2.65) (-0.61) (-0.20) (-0.88)

%∆LASSET1109 0.179 0.144 0.357 0.025 -0.076 0.205

(1.15) (0.55) (1.63) (0.19) (-0.30) (1.12)

%∆MTB1109 -0.089* 0.004 -0.148* -0.002 0.013 -0.050

(-1.81) (0.05) (-1.93) (-0.05) (0.21) (-0.92)

%∆ROA1109 -7.073*** -10.528*** -5.576** -4.087** -5.425 -5.494**

(-3.68) (-3.35) (-2.31) (-2.45) (-1.52) (-2.26)

%∆LEV1109 -2.818* -5.755 -1.822 -2.438** -2.789 -2.759*

(-1.89) (-1.57) (-1.00) (-2.18) (-1.09) (-1.73)

Constant 0.146 -0.200 0.646** 0.196 0.007 0.392

(0.60) (-1.08) (2.10) (1.16) (0.05) (1.48)

N 173 83 90 173 83 90

R-squared 0.156 0.260 0.213 0.129 0.179 0.202

Business Model Controls Yes Yes Yes Yes Yes Yes

Column (A) Column (B)

POST_RESPONSE _CAPM POST_RESPONSE _SHORT

The table reports OLS coefficient estimates and, in parentheses, t -statistics based on heteroskedasticity-robust standard errors. In Column (A),

POST_RESPONSE_SHORT is the dependent variable; whereas in Column (B), POST_RESPONSE_CAPM is the dependent variable. In all models, %∆AR1109

is the main independent variable. High (low) RegQual equals 1 if the regulatory quality indexby Kaufmann et al. (2009) is above (below) the sample median, and

0 otherwise. See Table 1, Table 2, and Appendix C for the definition of the regression variables. In all models, we include business model indicator variables for

regional banks, money center banks, investment services, and S&L banks. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-

tailed), respectively.