CORPORATE GOVERNANCE AND THE INSOLVENCY RISK OF FINANCIAL INSTITUTIONS

CORPORATE GOVERNANCE AND THE INSOLVENCY RISK OF FINANCIAL INSTITUTIONS Jamshed Iqbal *, Searat Ali ** *University of Vaasa, Department of Accounting and Finance **Griffith University, Department of Accounting, Finance and Economics Preliminary and incomplete Draft: May 08, 2016 Abstract We investigate whether corporate governance is related to insolvency risk of financial institutions. Using a large sample of U.S. financial institutions from 2005 to 2010, we find that corporate governance is positively related with insolvency risk of financial institutions as proxied by Merton s distance to default measure and credit default swap spread. We also find that better corporate governance increases insolvency risk relatively more for larger financial institutions and during the period of global financial crisis. Our findings highlight that too-big-to fail and deposit insurance policies encourage excessive risk taking by financial institutions. JEL classification: G01, G20, G21, G30, G32, G34 Keywords: corporate governance; boards, insolvency risk; bank risk-taking; financial crisis J. Iqbal gratefully acknowledges the financial support of Marcus Wallenbergin liiketaloudellinen tutkimussäätiö for this project. * Address: University of Vaasa, Department of Accounting and Finance, P.O. Box 700, FI-65101 Vaasa, Finland; E-mail address: jiqbal@uva.fi ** Address: Griffith University, Department of Accounting, Finance and Economics, Business 1 (N50) 0.37 Nathan campus, Griffith University, 170 Kessels Road, Nathan QLD 4111 Australia; E-mail address: searat.ali@griffithuni.edu.au

2 1. Introduction This paper empirically examines the relationship between corporate governance and insolvency risk of financial institutions. In theory, corporate governance affects stock market liquidity since corporate governance provides better monitoring of managers that prevents information concealment. Therefore, stronger corporate governance mechanisms enhance transparency and reduce information asymmetry (Chung et al., 2010). As a result, stronger corporate governance increases stock market liquidity and reduces credit risk. Better corporate governance not only affects the performance of the firms (Gompers et al., 2003; Brown and Caylor, 2006; Chhaochharia and Laeven, 2009; Ammann et al., 2011) but also encourages increased risk-taking that results in higher growth of firms (John, Litov, and Young, 2008). However, for financial institutions, the optimal degree of risk taking is different than for non-financial firms because financial institutions have deposit insurance subsidy (e.g., Merton, 1978). So, financial institutions benefit from deposit insurance if they become distressed. This financial safety encourages financial institutions to take excessive risks. Because lager financial institutions are considered too big to fail by regulators so they can benefit relatively more from deposit insurance at a substantial cost to stakeholders (see Acharya, Anginer and Warburton 2014). Because of this, stronger corporate governance may increase more risk taking in larger financial institutions. More recently, in the wake of financial crisis, several studies also shed light on the role of corporate governance towards risk taking and financial performance of financial institutions (Adams, 2012; Fahlenbrach and Stulz, 2011; Peni and Vahamaa, 2012). Specifically, several studies focus on risk taking by financial institutions especially during the financial crisis

3 (Pathan, 2009; Laeven and Levine, 2009; Berger et al., 2013; Iqbal, Strobl and Vahamaa, 2015). In this regard, Pathan (2009) document that strong boards, in banks, were taking excessive risk during the financial crisis. Thus, good corporate governance practices also encourage rather than constraining excessive risk-taking in the financial industry (Iqbal et al., 2015). We argue that this excessive risk taking may lead to the default of financial institution. In this paper, we investigate whether corporate governance mechanisms help predict the chances of default? Or can corporate governance mechanisms affect the risk of default (insolvency risk)? We augment the previous corporate governance literature in following ways: First, based on gaps in prior literature, we provide relationship between corporate governance and insolvency risk for large sample of U.S financial institutions. As far as to our knowledge, this is the first study to show the relevance of corporate governance to financial institution s insolvency risk. This study shows how strong governance can lead to the probability of default of financial institutions which can cause instability in the financial system. Secondly, we show that strong boards tend to have more insolvency risk. This question is still relevant since the literature does not provide a satisfactory answer regarding the role of boards towards controlling the agency relationship 1. Further, most of the previous literature on board effectiveness does not include financial firms in their sample (see Adams et al. 2010). We also confirm the previous literature (Adams and Mehran 2012) that in financial industry restrictions on board size can be counter-productive. Lastly, the present study aims at extending scarce literature on corporate governance in financial industry (Caprio et al. 2007; Fahlenbrach and Stulz 2011; Adams and Mehran 2012, Berger et al. 2013). 1 Adams et al. (2010) provide a survey of literature on the role of board of directors.

4 In summary, we find that insolvency risk of financial institutions, proxied by its marketbased distance to default or credit default swap spread, is positively associated with the overall index of the shareholder-friendliness of its corporate governance. Notably, we also find that insolvency risk of financial institution is positively associated with the shareholder-friendliness of a financial institution s corporate governance especially for large financial institutions. We also find that the positive association between corporate governance and insolvency risk is strong during the period of financial crisis. Our findings regarding the association between corporate governance and insolvency risk are broadly consistent with the prior literature on risktaking of financial institutions (see e.g., Pathan, 2009; Fortin, Goldberg and Roth, 2010; Beltratti and Stulz, 2012). Our findings suggest that good corporate governance may encourage excessive risk-taking in the financial industry. Deposit insurance subsidy could also be a contributing factor towards excessive risk-taking. Since financial institutions are entering into more complex activities and have broadened their scope therefore this effect has amplified in the recent years. Therefore, it has become difficult for regulators to keep pace with the changes. The present paper not only attempts to bridge the gap in literature but also offers several implications for the practitioners and researchers. The findings of present endeavor would be of benefit for corporate executives, regulators, investors and researchers who encourage practices of good corporate governance, add value or study the subject of corporate governance. The results would assist the corporate managers to control risk taking behavior by making reforms in the corporate governance mechanisms. The present study would also help to deeply understand the systems of corporate governance on regulations and financial decision. Financial regulators can benefit from this study in order to enhance the economic growth, reduce bankruptcies and add value to the wealth of stockholders by focusing on the corporate governance areas. The

5 findings would also assist the investors in making investments in corporations considering the practices of corporate governance so that they may evaluate influence on the long-term value of their investments. Lastly, researchers can further contribute to the knowledge by developing the model of corporate governance by working on this research. This study also makes significant contribution in the prior literature of corporate governance. The remainder of the paper is organized as follows. Section 2 reviews the related literature on Insolvency risk and bank risk-taking. It also highlights how risk-taking differs between financial and non-financial firms. Section 3 describes the data and introduces the variables used in the empirical analysis. Section 4 presents the methods and reports our empirical findings on the association between corporate governance mechanisms and the insolvency risk of financial institutions. Finally, the last section concludes with policy implications and limitations. 2. Related literature Writing in progress 3. Data and variables In this study, we investigate the relationship of corporate governance mechanisms and insolvency risk for a sample of 556 publically traded U.S. financial institutions over the 2005-2010 period. To empirically examine the relationship between corporate governance mechanisms and insolvency risk, we collect data on corporate governance mechanisms from the Corporate Governance Quotient database developed by Institutional Shareholder Services (ISS). Insolvency risk data is constructed from accounting and market data collected from BankScope

6 and Bloomberg. Lastly, data on financial statement and balance sheet variables is collected from BankScope of Bureau Van Dijk. Starting from entire population of U.S. financial institutions (commercial banks, investment banks, non-bank lending institutions, and financial services firms) in Corporate Governance Quotient database, we first identify the financial institutitons for which the insolvency risk data (Distance-to-Default and Credit Default Spread) is available. Doing so, we are left with 650 financial institutions. We then eliminate the financial institutions from our sample having insufficient data on financial statement and balance sheet variables taken from BankScope. This leaves us with a final sample of 556 individual financial institutions and an unbalanced panel of 2126 firm-year observations. 3.1. Financial Insolvency Risk The dependent variable in our study is the insolvency risk (Insolvency Risk). Since the seminal work of Beaver (1966), a number of accounting and market-based financial distress models have been developed in the literature. The validity of accounting-based models has been questioned due to the backward-looking nature of the financial statement through which these models are derived (Agarwal and Taffler, 2008). On the other hand, market-based models using the option pricing approach developed by Black and Scholes (1973) and Merton (1974) provide an appealing alternative to the prediction of financial distress conditions of listed firms and have been used in extant empirical studies (e.g., Hillegeist et al., 2004; Bharath and Shumway, 2008; Charitou et al., 2013). Such a methodological approach overcomes the criticisms of accountingbased models through the forward-looking nature of market data. Market data reflect

7 expectations of a firm s future cash flows, and hence should be more appropriate for prediction purposes. Another prevalent feature of such models is their provision of a finer volatility assessment that aids in predicting the risk of financial distress (Beaver et al., 2005).2 Empirical studies such as Gharghori et al. (2006) find the, Merton (1974) market-based model to be superior to their accounting counterparts in predicting default in the Australian context. Similarly, Hillegeist et al. (2004) recommend researchers to use market-based models of default prediction since these models contain more information about default than accounting-based models. We therefore use the market-based Merton (1974) distance to default (DD) in gauging financial distress (see appendix A: general procedure to calculate DD). We also check the robustness of our results by using the market-based credit default swap spread (CDS) to proxy pricing of financial distress. CDS are credit derivatives that allow the transfer of the firm s default risk between two agents for a predetermined time period. In a typical CDS contract, the protection seller offers the protection buyer insurance against the default of an underlying bond issued by a certain company (the reference entity). In the event of default by the reference entity, the seller commits to buy the bond for a price equal to its face value from the protection buyer.3 In exchange for the insurance, the buyer pays a quarterly premium, called the CDS spread, quoted as an annualized percentage of the notional value insured. Therefore, by definition, the CDS spread is the pricing of the financial distress risk (Das et al., 2009). The higher the financial distress risk of the reference entity, the higher is the CDS spread. Tang and Yan (2010) find that the CDS spread captures the major portion of the firm 2 Volatility is a critical factor in predicting default risk since it captures the probability that the value of a firm s assets will decrease to such a point that the firm will be unable to repay its debt obligations. Ceteris paribus, the higher the volatility, the higher is the default risk. Depending on asset volatilities, two firms with identical leverage ratios can have substantially different chances of financial distress. Therefore, measures of volatility should be incorporated in financial distress models. 3 In practice, the terms of the CDS could involve physical delivery of the defaulted bond or cash settlement.

8 level determinants of financial distress. Thus, the CDS spread should serve as an alternative measure of a firm s financial distress conditions. 3.2. Corporate governance measures We use the Corporate Governance Quotient (CGQ) index issued by Institutional Shareholder Services (ISS) to measure the strength of the corporate governance mechanisms of financial institutions. 4 These data are obtained from the RiskMetrics Group. CGQ is based on 67 different firm-specific attributes, which represent both the internal and external governance of the firm. The different corporate governance elements included in CGQ are audit committees, board of directors, charter/bylaws, director education, executive and director compensation, ownership, progressive practices, and state of incorporation. The governance data underlying the CGQ is collected from public filings, company websites, and surveys conducted by the ISS. The values of CGQ may range from 0 to 100, with higher values of the quotient corresponding to stronger, more shareholder-focused corporate governance mechanisms. In addition to the aggregate governance measure CGQ, we also use four sub-indices, called board, compensation and ownership, auditing and takeover that summarize information different attributes related to the various aspects of corporate governance. The takeover subindex, for instance, has a higher score, if there are fewer corporate governance-related barriers to takeovers. These sub-indices may take values from 1 to 5, with higher values of the index representing stronger, more shareholder-friendly mechanisms. 4 The ISS Corporate Governance Quotient been previously used as a proxy for the strength of corporate governance, for instance, in Chhaochharia and Laeven (2009), Ertugrul and Hegde (2009), and Peni et al. (2013).

9 3.3. Control variables In order to investigate the association between corporate governance and insolvency risk, we account for several institution specific control variables that may have effect on insolvency risk. We follow the previous studies on risk taking of financial institutions (e.g., Pathan, 2009; Fortin et al., 2010; Brunnermeier et al., 2012; Berger et al., 2014; Mayordomo et al., 2014; Iqbal et al., 2015) to control for firm size, profitability, growth, and assets as well as income structure. When comparing financial institutions, size is the most important control variable. The size (Size) variable is constructed as the log of a financial institution s total assets. This approach is also consistent with the previous studies. Larger financial institutions may pursue riskier strategies, if they are considered to be too big to fail. Moreover, Brunnermeier et al. (2012) show that lager financial institutions are also systemically important. Secondly, in previous literature capital ratio (or leverage ratio) is used when comparing financial institutions. However, in this study we do not include capital ratio as our both the measures for insolvency risk (DD and CDS) have equity as a main constituent in their calculation. In addition to Size, we account for the institution s financial performance, growth, and asset and income structure. We measure financial performance with Return on assets which is calculated as the ratio of net income to total assets. Growth is measured as the percentage change in the amount of outstanding loans. We control for the institution s business model and asset structure with the ratio of net loans to total assets (Loans to assets) and the ratio of deposits to total assets (Deposits to assets). Finally, we use the ratio of non-interest income to total income (Non-interest income) to control for the level of income diversification and non-

10 traditional banking activities. The data on our control variables are obtained from Bureau van Dijk Bankscope. The definitions of variables are summarized in Table A. (insert Table A about here) 4. Empirical analysis 4.1. Descriptive statistics and correlations Table 1 presents the descriptive statistics for the variables used in the empirical analysis. Descriptive statistics show that our sample of financial institutions is quite heterogeneous in terms of corporate governance strength as CGQ varies from 0.5 (minimum) to 100 (maximum) and average of 50.2. Further, the corporate governance sub-indices, board, compensation, audit, and takeover, also vary from lowest (0) to the highest (5) suggesting that our sample of financial institutions is diverse in terms of very weak and very strong corporate governance mechanisms. In addition to this, our sample is also quite heterogeneous in terms of insolvency risk. It can be noted from Table 1 that DD has a minimum value of -2.2 and a maximum value of 20.7. Moreover, CDS varies from a minimum of -2.4 to a maximum of 7.9 with a mean value of 3.6. Table 1 further depicts that our sample is also quite heterogeneous in terms of control variables. Sample contains small and large U.S. financial institutions. There is considerable variable in size ranging from 12.7 million to 2.26 trillion USD. In brief, our sample of U.S. financial institutions is very heterogeneous.

11 (insert Table 1 about here) Table 2 shows the pairwise correlations among the variables used in the analysis. It can be noted from the table that CGQ and governance sub-indices have a negative (positive) correlation with DD (CDS), 5 suggesting better governed financial institutions have a higher level of insolvency risk. Moreover, as expected, the two insolvency risk variables, DD and CDS, are inversely correlated by construction (r=0.93). As the correlation results do not control the factors that affect financial distress, they should be viewed with caution. 6 The correlations also indicate that larger financial institutions have less insolvency risk. (insert Table 2 about here) 4.2. Univariate tests Work in progress (insert Table 3 about here) 5 There is a significant negative correlation of CG variables with the components of DD i.e. asset volatility and equity volatility, suggesting that better governed firms are more volatile. 6 We also observe a significant difference at the 1% level in the insolvency risk measures between the high CGQ firms and the low CGQ firms (results available on request).

12 4.3. Regression results We examine the association between corporate governance and insolvency risk by estimating several alternative fixed-effects panel regressions of the following form: InsolvencyRisk i,t = a + b 1 Governance i,t-1 + b 2 Size i,t-1 + b 3 Global Financial Crisis i,t-1 + b 4 Return on assets i,t-1 + b 5 Loans to assets i,t-1 + b 6 Loan growth i,t-1 + b 7 Deposits to assets i,t-1 (1) + b 8 Non-interest income i,t-1 + åa k Firm k i + n-1 2010 å k=1 y=2006 w y Year i y +e i,t where the dependent variable Financial InsolvencyRiski,t is one of the two alternative measures of insolvency risk distance to default and credit default swap spread) for financial institution i at time t. First, distance to default measures the difference between the asset value of the financial institution and the face value of its debt, scaled by the standard deviation of the financial institution s asset value (see Campbell, Hilscher and Szilagyi, 2008, p. 2899). Second, the credit default swap spread (CDS) is the pricing of the financial distress risk (Das et al., 2009). CDS are credit derivatives that allow the transfer of the firm s default risk between two agents for a predetermined time period. Governancej,t is either the CGQ which measures the strength of the institution s corporate governance mechanisms or BoardQ which measures the strength of the board of directors. In order to capture the effect of global financial crisis we also estimate the modified versions of Equation (1) where we include interaction variable Governance GFC. Where GFC denotes the crisis year 2008. Further, we also use interaction variable Governance Size in order to examine the big financial institution phenomenon. This signals a financial institution with total assets exceeding 50 billion dollars.

13 As discussed earlier, we use several firm-level control variables in order to control for the effects of observable characteristics of financial institutions that may impact the financial distress. These control variables are defined as follows: Size is measured as the logarithm of total assets, Loans to assets is the ratio of net loans to totals assets, Loan growth is the percentage change in loans from year t 1 to year t, Deposits to assets is the ratio of deposits to total assets, and Non-interest income is the ratio of non-interest income to total income. Finally, the regressions also include firm and year fixed effects, and errors are clustered at the firm level. All independent variables are lagged one year to reduce endogeneity concerns. Table 4 reports the estimates of ten alternative versions of Equation (1) with the Distance to Default (DD) as the dependent variable. Models 1 and 6 include only Size and Return on assets as the control variables for parsimony. Whereas, Model 2 and 7 include full set of control variables and year fixed-effect and Model 3 and 8 include firm fixed-effects along with full set of control variables. Further, Model 4 and 9 include interaction variables CGQ GFC and BoardQ GFC for global financial crisis. Lastly, in Model 5 and 10 we include size interaction variables CGQ Size and BoardQ Size respectively. The adjusted R 2 s of all the models are almost 50 percent. The F-statistics for all the ten alternative regressions are statistically significant at 1 percent level. (insert Table 4 about here) Table 4 depicts that the overall corporate governance index has a negative and statistically significant coefficient in Models 1, 2, 3, 6 and 7 suggesting that more shareholderfriendly corporate governance increases insolvency risk of financial institutions. Moreover, in

14 Models 4 and 9 negative effect is stronger during the period of financial crisis and in Models 5 and 10 negative effect is also stronger for larger financial institutions. This shows higher insolvency risk during financial crisis period and also suggests that larger financial institutions take on more risk as they benefit from a too-big-to-fail status. In summary, Table 4 indicates that financial institutions with stronger, more shareholder-focused corporate governance structures and boards of directors are associated with higher insolvency risk. This finding is broadly consistent with the previous literature on the effects of corporate governance on bank risk-taking (see e.g., Pathan, 2009; Fortin et al., 2010; Iqbal et al., 2015). Economic Significance (insert Table 5 about here) Table 5 presents the regression estimates of Equation (1) with credit default swap spread (CDS) as the dependent variable. Regressions in this table are analogous to Table 4 with estimates of ten alternative versions of Equations (1). The adjusted R 2 s of these regressions vary from 30 percent to 53 percent and the F-statistics are significant at the 1 percent level, indicating a good fit of the estimated models. Here, the adjusted R 2 s of these regressions vary from 45 percent to 52 percent. The F-statistics are significant at the 1 percent level that indicates a good fit of the estimated models. Again, the Governance variable in Models 1-5 is CGQ and in Models 6-10 is BoardQ. Overall, the regression estimates with CDS as dependent variable are analogous to the DD results reported in Table 4. Table 5 shows that stronger corporate governance mechanisms are associated with higher insolvency risk. This effect is even stronger during the period of financial crisis and for larger financial institutions. This is further evidence

15 that insolvency risk of financial institutions is positively associated with shareholder-friendly corporate governance. Bigger financial institutions may be riskier, because they expect to bailout by regulators in case of insolvency since they have too-big-to-fail status. Economic Significance (insert Table 6 about here) Table 6 reports the estimates of six alternative versions of Equation (1) with the Distance to Default (DD) as the dependent variable. However, here Governancej,t represents four subindices e.g. board, compensation, audit and takeover. Model 1 only includes size as a control variable and Model 2 includes only Size and Return on assets as the control variables for parsimony. Whereas, Model 3 and 4 include full set of control variables and year fixed-effect and Model 4 also includes firm fixed-effects along with full set of control variables. Further, Model 5 includes interaction variables Governance Indices GFC for global financial crisis. Lastly, in Model 6 we include size interaction variables Governance Indices Size. The adjusted R 2 s of all the models are almost 50 percent except Model 1 where adjusted R 2 s is 34.6 percent. The F-statistics for all the six alternative regressions are statistically significant at 1 percent level. Table 6 depicts that the overall board index has a negative and statistically significant coefficient in Models 1-3 suggesting that more shareholder-friendly and strong boards increases insolvency risk of financial institutions. Model 5 shows that compensation sub-index has strong negative coefficient suggesting better alignment of interests increases insolvency risk during the

16 period of financial crisis. Lastly, Model 6 shows that lager financial institutions have more insolvency risk. (insert Table 7 about here) Table 7 reports the regression estimates of Equation (1) with credit default swap spread (CDS) as the dependent variable. Regressions in this table are analogous to Table 4 with estimates of six alternative versions of Equations (1). Here, also, Governancej,t represents four sub-indices. The adjusted R 2 s of all the models vary from 29 percent to almost 52 percent. The F-statistics for all the six alternative regressions are statistically significant at 1 percent level. The results are very similar to Table 6 where board index is positive and statistically significant in Models 1-3 showing that more shareholder-friendly and strong boards increases insolvency risk of financial institutions. Model 5 shows that compensation sub-index has strong positive coefficient suggesting better alignment of interests increases insolvency risk during the period of financial crisis. Lastly, Model 6 shows that lager financial institutions have more insolvency risk. Table 6 depicts that the overall board index has a negative and statistically significant coefficient in Models 1-3 suggesting that more shareholder-friendly and strong boards increases insolvency risk of financial institutions. Model 5 shows that compensation sub-index has strong negative coefficient suggesting better alignment of interests increases insolvency risk during the period of financial crisis. Lastly, Model 6 shows that lager financial institutions have more insolvency risk.

17 In summary, from the regression results reported in Table 4, 5, 6 and 7 we find that insolvency risk of financial institutions is positively associated with the shareholder-friendliness of a financial institution s corporate governance especially for large financial institutions and during the period of global financial crisis. Prior literature (e.g., Mehran et al., 2011; Beltratti and Stulz, 2012; de Haan and Vlahu, 2015) highlight that strong, shareholder-friendly governance practices may motivate excessive risk-taking in the financial industry in order to increase shareholders wealth. We empirically provide support to this argument. 5. Conclusions In the aftermath of the financial crisis, investors and regulators are looking global financial markets doubtfully and this is arguable related to the corporate governance failures and unethical behavior of corporate executives (Kirkpatrick 2009). Therefore, in this paper we examine the relationship between corporate governance and insolvency risk of financial institutions. In this paper, we provide evidence that more shareholder-friendly corporate governance is related with increased insolvency risk of the financial institutions. We use market based distance to default and credit default swap spread variables as proxy for insolvency risk. Furthermore, the association between corporate governance and insolvency risk is stronger during the period of financial crisis and for larger financial institutions. We find contradictory results to the previous studies on relationship between corporate governance and insolvency risk of non-financial firms. It is because financial institutions are different and can take more risk than non-financial firms.

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23 Table A. Variable Definitions and Sources. Variable name Definition Data source Insolvency Risk Variables Distance to default Credit Default Swap Spread Annual average of distance-to-default based on stock based on stock price variability credit derivatives that allow the transfer of the firm s default risk between two agents for a predetermined time period Obtained from Risk Management Institute at NUS Obtained from Risk Management Institute at NUS Governance variables Corporate governance Overall corporate governance index Board Corporate governance index based on board characteristics Compensation and Corporate governance index based on ownership compensation and ownership characteristics Auditing Corporate governance index based on auditing characteristics Takeover Corporate governance index based on takeover characteristics ISS ISS ISS ISS ISS Bank control variables Size Logarithm of total assets BankScope Return on assets Ratio of net income to total assets BankScope Growth Percentage change in the amount of outstanding loans BankScope Loans to total assets Ratio of net loans to total assets BankScope Non-interest income Ratio of non-interest income to total income BankScope

24 Table 1. Descriptive statistics. Variable Mean St.dev. Min Max P25 P75 No. of obs Dependent variables: Distance to Default 2,2 2,1-2,2 20,7 0,8 3,1 3151 Credit Default Swap Spread 3,6 1,8-2,4 7,9 2,7 4,7 3148 Corporate governance variables: Corporate Governance Quotient 50,2 27,6 0,5 100,0 27,2 73,4 4448 Board Quotient 2,9 1,4 0,0 5,0 2,0 4,0 4448 Compensation 3,3 1,4 0,0 5,0 2,0 5,0 4448 Audit 3,2 1,5 0,0 5,0 2,0 5,0 4448 Takeover 3,1 1,3 0,0 5,0 2,0 4,0 4448 Control variables: Size 30,4 181,0 0,0127 2260,0 0,7 3,6 2342 Return on assets 0,4 2,5-18,4 44,3 0,1 1,0 2338 Loans to assets 67,1 15,2 0,0 93,5 61,0 76,6 2292 Loan growth 7,3 24,8-84,2 704,5-2,7 12,9 2132 Deposits to assets 18,7 113,0 0,0 1440,0 0,5 2,7 2322 Non-interest income 24,0 38,6-938,4 271,5 13,9 31,5 2331

25 Table 2. Correlations. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (1) Distance to Default 1,00 (2) Credit Default Swap Spread -0,96 1,00 (3) Corporage Governance Quotient -0,07 0,07 1,00 (4) Board Quotient -0,05 0,06 0,83 1,00 (5) Compensation -0,01 0,02 0,42 0,27 1,00 (6) Audit -0,01 0,02 0,34 0,25 0,06 1,00 (7) Takeover 0,10-0,09 0,10 0,04-0,10 0,01 1,00 (8) Size 0,14-0,11-0,02-0,05-0,05 0,13 0,15 1,00 (9) Return on Assets 0,58-0,63-0,06-0,06-0,03-0,04 0,08 0,07 1,00 (10) Loans to assets -0,13 0,14-0,04-0,01 0,10-0,08-0,09-0,34-0,12 1,00 (11) Loan growth 0,15-0,16-0,04-0,03-0,08 0,00 0,07 0,02 0,26-0,05 1,00 (12) Deposits to assets -0,07 0,06 0,02 0,03 0,07-0,12-0,07-0,27-0,19 0,35-0,16 1,00 (13) Non-interest income 0,11-0,11 0,00-0,02-0,06 0,03 0,04 0,20 0,13-0,24 0,04-0,18 1,00

26 Table 3. Univariate tests.

27 Table 4. Corporate governance and Distance to Default (DD). Variable Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8) Model (9) Model (10) Corporate Governance variables: CGQ -0.00423 *** -0.00314 *** -0.00267 * -0.00133 0.0138 (-4.09) (-3.21) (-1.65) (-0.92) (1.63) CGQ GFC -0.00337 * (-1.73) CGQ Size -0.00115 ** (-2.01) BoardQ -0.0897 *** -0.0602 *** -0.0187-0.0224 0.469 *** (-4.30) (-3.01) (-0.60) (-0.76) (2.74) BoardQ GFC -0.0696 * (-1.74) BoardQ Size -0.0360 *** (-3.11) Control variables: Size 0.149 *** 0.126 *** -0.469 ** 0.127 *** 0.187 *** 0.147 *** 0.125 *** -0.462 ** 0.128 *** 0.231 *** (9.35) (7.51) (-2.52) (7.56) (5.43) (9.27) (7.44) (-2.48) (7.57) (6.08) GFC -0.615 *** -0.587 *** (-4.43) (-3.91) Return on assets 0.284 *** 0.442 *** 0.266 *** 0.440 *** 0.442 *** 0.283 *** 0.441 *** 0.268 *** 0.439 *** 0.441 *** (25.87) (23.69) (12.52) (23.57) (23.71) (25.84) (23.55) (12.56) (23.46) (23.63) Loans to assets -0.00775 *** 0.0215 *** -0.00762 *** -0.00815 *** -0.00769 *** 0.0219 *** -0.00760 *** -0.00834 *** (-3.85) (3.98) (-3.78) (-4.03) (-3.82) (4.06) (-3.78) (-4.13) Loan growth -0.00152-0.00172-0.00151-0.00155-0.00147-0.00171-0.00145-0.00151 (-1.40) (-1.65) (-1.39) (-1.43) (-1.36) (-1.63) (-1.33) (-1.39) Deposits to assets 1.103 *** 0.460 1.095 *** 1.053 *** 1.097 *** 0.418 1.085 *** 1.040 *** (4.81) (0.76) (4.78) (4.57) (4.78) (0.69) (4.73) (4.53) Non-interest income 0.000358 0.000498 0.000368 0.000284 0.000328 0.000540 0.000359 0.000235 (0.48) (0.71) (0.50) (0.38) (0.44) (0.77) (0.49) (0.32)

28 Table 4. Continued. Variable Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8) Model (9) Model (10) Constant Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm fixed effects No No Yes No No No No Yes No No Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adjusted R 2 50.3% 53.2% 50.6% 53.2% 53.3% 50.3% 53.2% 50.5% 53.2% 53.4% Observations 2122 1924 1924 1924 1924 2122 1924 1924 1924 1924 The table reports the estimates of ten alternative versions of the following panel regression specification: DD i,t = a + b 1 Governance i,t-1 + b 2 Size i,t-1 + b 3 Global Financial Crisis i,t-1 + b 4 Return on assets i,t-1 + b 5 Loans to assets i,t-1 + b 6 Loan growth i,t-1 + b 7 Deposits to assets i,t-1 + b 8 Non-interest income i,t-1 + åa k Firm k i + n-1 k=1 2010 å y=2006 w y Year i y +e i,t where the dependent variable DD i,t is the Distance to Default measures the difference between the asset value of the financial institution and the face value of its debt, scaled by the standard deviation of the financial institution s asset value. Governance i,t is either CGQ (Corporate Governance Quotient) which measures the strength of the firm s corporate governance mechanisms or BoardQ (Board Quotient) which measures the strength of the board of directors. The control variables are defined as follows: Size is measured as the logarithm of total assets, Global Financial Crisis is the dummy variable for global financial crisis, Return on assets is the ratio of net income to total assets, Loans to assets is the ratio of net loans to totals assets, Loan growth is the k percentage change in loans from year t 1 to year t, Deposits to assets is the ratio of deposits to total assets, and Non-interest income is the ratio of non-interest income to total income. Firm is a i y dummy variable for firm i and Year is a dummy variable for fiscal years. The reported adjusted R 2 s are the overall R 2 s which account for the explanatory power of the firm and year fixedeffects. The t-statistics (reported in parentheses) are based on robust standard errors, which are adjusted for heteroskedasticity and within-firm clustering. ***, **, and * denote significance at the i 0.01, 0.05, and 0.10 levels, respectively.

29 Table 5. Corporate governance and Credit Default Swap Spread (CDS). Variable Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8) Model (9) Model (10) Corporate Governance variables: CGQ 0.00355 *** 0.00271 *** 0.00253 * 0.00109-0.0132 * (3.78) (3.02) (1.69) (0.83) (-1.70) CGQ GFC 0.00301 * (1.68) CGQ Size 0.00108 ** (2.06) BoardQ 0.0766 *** 0.0511 *** 0.0190 0.0136-0.427 *** (4.05) (2.79) (0.65) (0.51) (-2.71) BoardQ GFC 0.0692 * (1.89) BoardQ Size 0.0325 *** (3.05) Control variables: Size -0.103 *** -0.0732 *** 0.734 *** -0.0737 *** -0.130 *** -0.102 *** -0.0722 *** 0.728 *** -0.0747 *** -0.168 *** (-7.14) (-4.74) (4.30) (-4.77) (-4.11) (-7.06) (-4.67) (4.26) (-4.81) (-4.80) GFC 0.295 ** 0.249 * (2.32) (1.81) Return on assets -0.250 *** -0.483 *** -0.351 *** -0.481 *** -0.483 *** -0.250 *** -0.482 *** -0.353 *** -0.481 *** -0.482 *** (-25.09) (-27.87) (-17.77) (-27.74) (-27.89) (-25.07) (-27.74) (-17.79) (-27.66) (-27.82) Loans to assets 0.00846 *** -0.0106 ** 0.00835 *** 0.00885 *** 0.00841 *** -0.0111 ** 0.00832 *** 0.00901 *** (4.57) (-2.11) (4.51) (4.75) (4.54) (-2.20) (4.49) (4.84) Loan growth 0.000861 0.000735 0.000860 0.000883 0.000822 0.000735 0.000801 0.000853 (0.87) (0.77) (0.87) (0.90) (0.84) (0.76) (0.81) (0.87) Deposits to assets -1.172 *** -0.245-1.165 *** -1.126 *** -1.167 *** -0.206-1.155 *** -1.117 *** (-5.55) (-0.43) (-5.52) (-5.31) (-5.53) (-0.36) (-5.47) (-5.29) Non-interest income -0.000655-0.000740-0.000665-0.000586-0.000622-0.000777-0.000649-0.000544 (-0.98) (-1.15) (-0.99) (-0.87) (-0.93) (-1.20) (-0.97) (-0.81)

30 Table 5. Continued. Variable Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7) Model (8) Model (9) Model (10) Constant Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Firm fixed effects No No Yes No No No No Yes No No Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Adjusted R 2 45.2% 51.6% 45.9% 51.6% 51.6% 45.2% 51.5% 45.8% 51.6% 51.7% Observations 2122 1924 1924 1924 1924 2122 1924 1924 1924 1924 The table reports the estimates of six alternative versions of the following panel regression specification: CDS i,t = a + b 1 Governance i,t-1 + b 2 Size i,t-1 + b 3 Global Financial Crisis i,t-1 + b 4 Return on assets i,t-1 + b 5 Loans to assets i,t-1 + b 6 Loan growth i,t-1 + b 7 Deposits to assets i,t-1 + b 8 Non-interest income i,t-1 + åa k Firm k i + n-1 k=1 2010 å y=2006 w y Year i y +e i,t where the dependent variable CDS i,t is the credit default swap spread is the pricing of the financial distress risk (Das et al., 2009). CDS are credit derivatives that allow the transfer of the firm s default risk between two agents for a predetermined time period. Governance i,t is either CGQ (Corporate Governance Quotient) which measures the strength of the firm s corporate governance mechanisms or BoardQ (Board Quotient) which measures the strength of the board of directors. The control variables are defined as follows: Size is measured as the logarithm of total assets, Global Financial Crisis is the dummy variable for global financial crisis, Return on assets is the ratio of net income to total assets, Loans to assets is the ratio of net loans to totals assets, Loan growth is the percentage change in loans from year t 1 to year t, Deposits to assets is the ratio of deposits to total assets, and Non-interest income is the ratio of non-interest income to total income. Firm is a dummy variable for firm i and Year is a dummy variable for fiscal years. The reported adjusted R 2 s are the overall R 2 s which account for the explanatory power of the firm k i y i and year fixed-effects. The t-statistics (reported in parentheses) are based on robust standard errors, which are adjusted for heteroskedasticity and within-firm clustering. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively.

31 Table 6. Corporate governance Sub-indices and Distance to Default (DD). Variable Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Corporate Governance variables: Board -0.128 *** -0.0887 *** -0.0637 *** -0.0120-0.0443 0.388 ** (-5.00) (-3.99) (-2.98) (-0.37) (-1.42) (2.09) compensation -0.0380-0.00603 0.000743-0.0275 0.0871 *** -0.198 (-1.56) (-0.28) (0.04) (-1.00) (2.92) (-1.12) Audit -0.00991-0.00109 0.00721-0.00156-0.0164 0.293 * (-0.46) (-0.06) (0.40) (-0.07) (-0.61) (1.76) Takeover 0.0573 ** 0.0314 0.0325-0.0309 0.0601 ** 0.255 (2.26) (1.42) (1.54) (-0.86) (2.06) (1.37) Board GFC -0.0341 (-0.80) Compensation GFC -0.163 *** (-3.99) Audit GFC 0.0345 (0.98) Takeover GFC -0.0608 (-1.47) Board Size -0.0307 ** (-2.44) Compensation Size 0.0140 (1.16) Audit Size -0.0199 * (-1.73) Takeover Size -0.0154 (-1.21)

32 Table 6. Continued. Variable Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Control variables: Size 0.137 *** 0.144 *** 0.121 *** -0.450 ** 0.126 *** 0.288 *** (7.33) (8.86) (7.10) (-2.39) (7.36) (4.15) GFC -0.0499 (-0.21) Return on assets 0.282 *** 0.440 *** 0.268 *** 0.439 *** 0.440 *** (25.69) (23.49) (12.54) (23.50) (23.53) Loans to assets -0.00757 *** 0.0219 *** -0.00789 *** -0.00821 *** (-3.75) (4.05) (-3.91) (-4.05) Loan growth -0.00154-0.00168-0.00151-0.00151 (-1.41) (-1.60) (-1.40) (-1.40) Deposits to assets 1.106 *** 0.431 1.127 *** 1.066 *** (4.80) (0.71) (4.90) (4.61) Non-interest income 0.000327 0.000520 0.000333 0.000318 (0.44) (0.74) (0.45) (0.43) Constant Yes Yes Yes Yes Yes Yes Firm fixed effects No No No Yes No No Year fixed effects Yes Yes Yes Yes Yes Yes Adjusted R 2 34.6% 50.3% 53.1% 50.5% 53.6% 53.4% Observations 2126 2122 1924 1924 1924 1924 The table reports the estimates of ten alternative versions of the following panel regression specification: DD i,t = a + b 1 Governance i,t-1 + b 2 Size i,t-1 + b 3 Global Financial Crisis i,t-1 + b 4 Return on assets i,t-1 + b 5 Loans to assets i,t-1 + b 6 Loan growth i,t-1 + b 7 Deposits to assets i,t-1 + b 8 Non-interest income i,t-1 + åa k Firm k i + n-1 2010 å k=1 y=2006 w y Year i y +e i,t

33 where the dependent variable DD i,t is the Distance to Default measures the difference between the asset value of the financial institution and the face value of its debt, scaled by the standard deviation of the financial institution s asset value. Governance i,t represents one of the four sub-indices e.g. Board, Compensation, Audit and Takeover. The control variables are defined as follows: Size is measured as the logarithm of total assets, Global Financial Crisis is the dummy variable for global financial crisis, Return on assets is the ratio of net income to total assets, Loans to assets is the ratio of net loans to totals assets, Loan growth is the percentage change in loans from year t 1 to year t, Deposits to assets is the ratio of deposits to total assets, and Non-interest income is the ratio of non-interest income to total income. Firm is a dummy variable for firm i and Year is a dummy variable for fiscal years. The reported adjusted R 2 s are the overall R 2 s k i y i which account for the explanatory power of the firm and year fixed-effects. The t-statistics (reported in parentheses) are based on robust standard errors, which are adjusted for heteroskedasticity and within-firm clustering. ***, **, and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively.