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JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 53, No. 2, Apr. 2018, pp. 899–935 COPYRIGHT 2018, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195 doi:10.1017/S0022109017001089

Market Reaction to Bank Liquidity Regulation

Brunella Bruno, Enrico Onali, and Klaus Schaeck*

Abstract We measure market reactions to announcements concerning liquidity regulation, a key in- novation in the Basel framework. Our initial results show that liquidity regulation attracts negative abnormal returns. However, the price responses are less pronounced when coincid- ing announcements concerning capital regulation are backed out, suggesting that markets do not consider liquidity regulation to be binding. Bank- and country-specific characteris- tics also matter. Liquid balance sheets and high charter values increase abnormal returns whereas smaller long-term funding mismatches reduce abnormal returns. Banks located in countries with large government debt and tight interbank conditions or with prior domestic liquidity regulation display lower abnormal returns.

I. Introduction We present the first empirical analysis of how stock markets respond to the

new global standards of bank liquidity regulation, which have been gradually adopted between 2015 and 2018. Unlike capital regulation, which has been a cor- nerstone of the regulatory framework for years, regulating bank liquidity was typi- cally not a major concern before the recent crisis in most countries. The regulatory package (known as Basel III) that the Basel Committee on Banking Supervision (BCBS) developed in response to the recent financial crisis1 constitutes a funda- mental change in that respect. For the first time, the BCBS has introduced global standards for bank liquidity to improve liquidity risk management and banks’ ability to withstand liquidity shocks (Calomiris, Heider, and Hoerova (2015)).

*Bruno, [email protected], Bocconi University Department of Finance; Onali, [email protected], Aston University Business School; Schaeck (corresponding author), [email protected], University of Bristol Department of Accounting and Finance. We are grateful to James Barth (the referee), Elena Carletti, Piotr Danisewicz, Giuliano Iannotta, Paul Malatesta (the editor), Rafael Repullo, Andrea Resti, Kasper Roszbach, Rhiannon Sowerbutts, and Jim Steeley for comments on previous drafts.

1During the crisis, several banks faced substantial liquidity outflows and shortages owing to over- reliance on volatile funding sources, improper asset-liability management, and off-balance-sheet posi- tions that gave rise to liquidity risk (Strahan (2012)). Consequently, banks hoarded liquidity (Acharya and Merrouche (2013)) and reduced liquidity provision to other banks and to the real sector (Cornett, McNutt, Strahan, and Tehranian (2011)).

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Policy makers view these new standards as a major improvement in regulation after the recent crisis, as the following quote illustrates:

[The Basel III framework is] a landmark achievement that will help pro- tect financial stability and promote sustainable economic growth. The . . . global liquidity framework will significantly reduce the probability and severity of banking crises in the future.2

The new rules address different aspects of liquidity risk. The liquidity cover- age ratio (LCR) aims to ensure that banks have high-quality liquid assets (HQLA) available to accommodate short-term cash outflows. The net stable funding ra- tio (NSFR) requires a minimum volume of stable funding sources given a bank’s asset structure to mitigate the risk of funding mismatches over a 1-year horizon. These two ratios aim to achieve different objectives and they are, at least from a theoretical perspective, equally important for financial stability. An empirical question that we seek to answer is whether the market perceives the LCR as more important than the NSFR, or vice versa.

By requiring banks to switch to higher quality and more liquid assets, and toward more stable funding sources, the new standards are likely to affect banks’ operations, in particular, the structure of the balance sheet in terms of maturity structure and asset and funding choices. Therefore, complying with these rules has the potential to affect bank profitability and valuation. Industry representatives have expressed disquiet about the costs arising from the new rules that would re- duce profits (Institute of International Finance (2012)). The final version of Basel III, released after 5 years of intensive discussions and lobbying, entailed several amendments to the original proposal and a weakening of the initial guidelines.

To examine how market participants react to liquidity regulation, we use event-study methodology and exploit 7 official announcements by the BCBS re- garding the introduction of global liquidity standards. We turn to this methodology because the new regulation is gradually implemented over several years from 2015 onward and an examination of the long-term impact of the LCR and the NSFR on bank profitability is not feasible because of the lack of historical information for the two ratios and the impossibility of computing them with the current level of disclosure in bank balance sheets. Therefore, only an event-study approach and the combination of market and accounting data can be used to examine how mar- kets expect bank profits to be affected.

We study European banks because their funding metrics compare unfavor- ably during and after the crisis with international peers, making them less pre- pared than U.S. and Japanese banks to meet the requirements in Basel III (Eu- ropean Banking Authority (2012)). Moreover, the sovereign debt crisis amplified between 2010 and 2012 the funding problems in Europe where banks are sub- ject to different national liquidity regimes, making convergence a desirable, yet challenging, objective to level the playing field. Finally, our cross-country com- parison that focuses on large listed banks that display bank- and country-specific

2Nout Wellink, chairman of the BCBS and president of the Netherlands Bank, “Basel III Rules Text and Results of the Quantitative Impact Study Issued by the Basel Committee,” Bank of International Settlements press release, Dec. 16, 2010. Available at http://www.bis.org/press/p101216.htm.

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heterogeneities allows examining how banks headquartered in similar yet differ- ent banking systems respond to the new rules.

Our empirical strategy proceeds in three steps. First, we examine cumula- tive abnormal returns (CARs) and cumulative market-adjusted returns (CMARs) over 3-day event windows to estimate aggregate and average market reactions to 7 announcements by the BCBS relating to proposals, amendments, and revisions of rules concerning bank liquidity regulation for the period Feb. 2008–Jan. 2013. Second, we disentangle stock price reactions that also refer to confounding events such as announcements concerning capital regulation from those that exclusively address liquidity regulation. To this end, we calculate CARs and CMARs over event dates that relate only to statements regarding liquidity regulation. Third, we analyze whether banks respond heterogeneously. We correlate the cross-sectional variation in CARs and CMARs with bank-specific and country-specific charac- teristics. These tests illustrate how CARs and CMARs increase or decrease as an effect of the preexisting liquidity conditions, funding mismatch, charter values, and country-specific characteristics that affect the 2-way feedback between the sovereign’s creditworthiness and refinancing conditions in the interbank market.

Several arguments suggest that bank stock prices may respond to liquid- ity regulation. Even if the regulation is not implemented yet, bank stock prices can reflect the market perception regarding the possible consequences of liquidity regulation.

On the one hand is the view is that regulation serves the public interest to pro- mote welfare at the expense of the regulated industry (Needham (1983)). Clearly, achieving and maintaining safe and sound banking, and mitigating systemic risk serves the public interest. Indeed, these arguments are put forward by the pol- icy community for regulating bank liquidity. On the other hand is the capture theory. Stigler (1971) and Peltzman (1976) posit that regulation is desired by the regulated industry to reap benefits at the expense of opposing parties. Consistent with this view, lobbying by the banking industry against this regulation resulted in amendments that watered down the initial proposal and led to an eventually less restrictive regime.

Analyzing the effects of changes in regulation using event studies has re- ceived much attention in the economics and banking literature (Schwert (1981), Dann and James (1982), James (1983), Allen and Wilhelm (1988), Slovin, Sushka, and Bendeck (1990), Wagster (1996), Bayazitova and Shivdasani (2012), Norden, Roosenboom, and Wang (2013), and Horváth and Huizinga (2015)). However, event-study methodology is not without challenges. First, the public de- bates about regulation during and after the crisis made investors anticipate tighter regulation, suggesting that markets may have expected regulatory changes before the announcements of the BCBS. We deal with this problem using a 3-day event window centered on the announcement day. Moreover, we test for anticipation ef- fects using placebo events we pretend occurred before the actual events. Second, liquidity regulation is only one component of the regulatory framework. Some announcements relating to liquidity regulation coincide with announcements on capital regulation or other aspects of Basel III. Establishing a causal effect of mar- ket reactions to liquidity regulation requires identifying such confounding news. A key contribution of our study is to disentangle the effects of announcements that

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also contain information concerning other components of Basel III (specifically, capital adequacy regulation) from the effects that are solely attributable to liquid- ity regulation. We refer to these tests as the liquidity-only bucket. These tests shed light on the question of whether market participants view liquidity regulation as binding and help establish their value relevance. Last, share prices may also re- spond to corporate events, such as changes in corporate governance or dividend cuts. We present tests that omit bank-specific events that coincide with the BCBS announcements.

Our initial set of tests points toward negative stock price reactions to the introduction of liquidity regulation. The aggregate effect on shareholders of Eu- ropean banks based on the CARs for all 7 events is equivalent to an average de- crease in market capitalization between 269 and 354 million euros, depending on the proxy for the market portfolio (MSCI Europe and MSCI World, respec- tively). These figures represent a decline in market capitalization of −5.27%, and −6.95%, respectively. However, our inferences are more nuanced when we sepa- rate out the announcements relating to capital regulation and other components of Basel III. Tests based on the liquidity-only bucket highlight that the average stock price reaction is either only marginally significant or insignificant. These analyses suggest that markets do not consider these new rules to be binding. Therefore, their value relevance is more limited than our initial results suggest. Our interpre- tation is plausible because the standards were not implemented yet. Specifically, whereas a smooth and gradual process of implementation was finalized for the LCR, the NSFR becomes a binding requirement in 2018 and observers anticipate further modifications to be made (Santos and Elliott (2012)).

Further tests illustrate that bank-specific variables explain the cross-sectional variation in stock price reactions to test the motivation behind the LCR and the NFSR. Banks with a greater LIQUID ASSETS RATIO (defined as the ra- tio of liquid assets to customer and short-term funding), which serves as a proxy for the LCR, display less negative CARs and CMARs. This is in line with the idea that banks with more liquid balance sheets react better to the new rules. Our proxy for the NSFR, the CORE FUNDING RATIO, enters our tests with a negative sign, suggesting more negative CARs and CMARs for higher levels of CORE FUNDING RATIO. As explained in detail below, this result reflects that the CORE FUNDING RATIO is largely driven by Tier 1 capital, an expensive source of funds.3 Country-specific factors also matter: Banks from countries with more government debt experience more negative CARs and CMARs, and the neg- ative wealth effects are more pronounced if banks are headquartered in countries that already had national provisions for liquidity risk in place before the new rules.

Our research complements the evolving literature on Basel III. These studies address the effect of new regulation on economic growth and the cost of lending (Banerjee and Mio (2018), Duijm and Wierts (2016)). Our work is also related to the broader literature on banking regulation and its impact on banking sector de- velopment, performance and stability (Barth, Caprio, and Levine (2004)), and the effect of regulation compliance on soundness (Demirgüç-Kunt, Detragiache, and

3Including off-balance-sheet items (e.g., guarantees and other contingent liabilities) and excluding liquid assets in the denominator of the CORE FUNDING RATIO does not alter our results.

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Tressel (2008)). Our work differs from these studies by focusing on the reaction by market participants to the introduction of a novel component of the Basel III framework: the regulation of liquidity risk.

We proceed as follows: Section II describes the institutional background, and Section III introduces testable predictions. Section IV describes the data and Section V the methodology. The results are presented in Section VI. Section VII concludes.

II. Institutional Background On Feb. 21, 2008, the BCBS published a document entitled Liquidity Risk:

Management and Supervisory Challenges (Event 1) as an initial response to the 2007–2009 crisis. This document summarizes the findings of a review by the com- mittee on national supervisory regimes and banks’ practices to manage liquidity in times of difficulty. In light of banks’ poor liquidity management and the di- versity of national liquidity regimes, the document illustrates possible actions to strengthen liquidity risk management and coordinate supervision.

On June 17, 2008, the BCBS proposed 17 Principles for Sound Liquidity Risk Management and Supervision (Event 2), a review of a previous document on liquidity management introducing criteria for funding structure and liquidity standards. The Committee issued the final version of the Principles on Sept. 25, 2008 (Event 3) after receiving comments. Next, the BCBS released the Interna- tional Framework for Liquidity Risk Measurement, Standards and Monitoring on Dec. 17, 2009 (Event 4). This framework aims to elevate the resilience of in- ternational banks to liquidity shocks and harmonize international liquidity risk supervision. It also introduces global standards for liquidity risk supervision to achieve two objectives:

i) To strengthen banks’ ability to withstand liquidity shocks over a short-term period, the BCBS developed the LCR. The standard requires banks to hold sufficient HQLA, such as cash or government bonds, to meet a severe cash outflow for 30 days. The standard was introduced in 2015.

ii) To promote long-term resilience and reduce maturity mismatch, the BCBS proposed the NSFR. It incentivizes the use of stable funding sources by lim- iting short-term wholesale funding. Specifically, it requires banks to hold equity and liability financing expected to be reliable sources of funds over a 1-year horizon. The amount of stable funds required is conditional on liq- uidity characteristics of assets on and off the balance-sheet. The standard will be introduced in 2018.

Appendix A presents a detailed description of the two requirements.4 The initial reaction to the announcements about regulating liquidity was negative.5

4Both measures are included in the Dec. 2010 Basel III: International Framework for Liquidity Risk Measurement, Standards and Monitoring (Event 6). From this point, supervisors monitored the ratios. However, there was a great deal of objection by the industry and the BCBS indicated the like- lihood of further refinements in the formulas before the start of the mandatory period. Furthermore, data gathered during the observation period may lead to further changes (Santos and Elliott (2012)).

5See, for example, “Basel Was Faulty,” Financial Times, Dec. 17, 2009.

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Next, the BCBS decided to gradually introduce the standards to avoid detrimental effects on lending.6 Consequently, the initial proposal was amended, and in two cases (Events 5 and 7) these adjustments resulted in a loosening of the liquid- ity requirements relative to previous announcements. We briefly discuss the key features of these events.

A first set of amendments was set out in the Annex to the BCBS press release (https://www.bis.org/press/p100726.htm) on July 26, 2010 (Event 5). The objec- tive of the revisions was “to achieve a calibration and definition that penalises imprudent liquidity profiles, while minimising system level distortions” (p. 5). In particular, the LCR was relaxed by widening the range of qualifying liquid assets, and the NSFR was modified to favor retail over wholesale banking, mainly by loosening requirements for customer deposits and residential mortgages.

In response to these objections and to take into account problems in the Eu- rozone, the BCBS modified the short-term liquidity standard. The Committee an- nounced the introduction of Basel III: The Liquidity Coverage Ratio and Liquidity Risk Monitoring Tools on Jan. 7, 2013 (Event 7). The main changes to the LCR entailed a wider set of HQLA and more lenient assumptions for the calculation of net cash outflows. The document also clarified the possibility for banks to fall be- low the minimum LCR requirement during periods of stress. Moreover, the BCBS decided to delay the full implementation of the standards.7 We summarize the 7 key events examined in this research in Figure 1. Table 1 assesses the impact of each event on the probability of stricter liquidity rules after each event and the cor- responding stock price effect. Appendix B describes the BCBS events in greater detail.

FIGURE 1 Timeline of Events

Figure 1 shows the timeline of events from 2008 to 2013.

Dec. 16, 2010

Release of Basel III: International Framework

for Liquidity Risk Measurement, Standards

and Monitoring

Event 6

July 26, 2010

Release of the July 2010 Annex, containing the

key broad agreements of the governors and heads

of supervision

Event 5

Dec. 17, 2009

Release of International Framework for Liquidity

Risk Measurement, Standards and

Monitoring

Event 4

Sept. 25, 2008

Release of Principles for Sound Liquidity Risk Management and

Supervision

Event 3

June 17, 2008

Release of Principles for Sound Liquidity Risk Management and

Supervision

Event 2

Feb. 21, 2008

Release of Liquidity Risk: Management and Supervisory Challenges

Event 1

Jan. 7, 2013

Release of Basel III: The Liquidity Coverage Ratio

and Liquidity Risk Monitoring Tools

Event 7

Time

6See the press release “Consultative Proposals to Strengthen the Resilience of the Banking Sector Announced by the Basel Committee,” Dec. 17, 2009 downloadable at http://www.bis.org/press/p091 217.htm.

7The LCR was introduced on Jan. 1, 2015 with a minimum requirement of 60%. It will rise in annual steps of 10% to reach 100% on Jan. 1, 2019.

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TABLE 1 Events and Predicted Effects on Probability of Stricter Rules after Each Event

Table 1 presents the event dates and a brief description for each event. The final column gives an overview about whether the event increased or decreased the probability of introducing stricter rules for liquidity regulation.

Probability of Introducing Expected Effect on Event Date Short Description Stricter Rules Bank Stock Prices

Feb. 21, 2008 Release of Liquidity Risk: Increased Negative Management and Supervisory Challenge

June 17, 2008 Release of Principles for Increased Negative Sound Liquidity Risk Management and Supervision

Sept. 25, 2008 Release of Principles for Increased Negative Sound Liquidity Risk Management and Supervision

Dec. 17, 2009 Release of International Increased Negative Framework for Liquidity Risk Measurement, Standards and Monitoring

July 26, 2010 Release of the July 2010 Decreased Positive Annex, containing the key broad agreements of the governors and heads of supervision

Dec. 16, 2010 Release of Basel III: Increased Negative International Framework for Liquidity Risk Measurement, Standards and Monitoring

Jan. 7, 2013 Release of Basel III: The Decreased Positive Liquidity Coverage Ratio and Liquidity Risk Monitoring Tools

III. Testable Predictions We develop predictions for how bank stock prices respond to liquidity

regulation. Next, we discuss how bank- and country-level features amplify or mit- igate the stock price returns.

A. Hypotheses on Market Reactions to Liquidity Regulation Several arguments suggest that liquidity regulation attracts negative stock

price reactions. Introducing quantitative liquidity requirements aims to correct banks’ suboptimal liquidity choices to restore optimal liquidity. A priori, it ap- pears plausible to expect that such regulation interferes with banks’ business op- erations that are geared toward profit maximization, and shareholders are likely to respond negatively.

The negative view on holding liquidity is driven by the fact that liquidity facilitates managerial moral hazard (Jensen (1986)). Moreover, liquidity holdings are considered to be costly for several reasons. Myers and Rajan (1998) highlight the “dark side” of liquidity. By holding more liquid assets, firms are less likely to commit to a specific investment strategy that protects creditors because more liquid firms have a higher value in liquidation. A manager therefore uses the more liquid assets to alter the implicit property rights she has to maximize her own benefit. Indeed, Glaeser and Shleifer (2001) and Ratnovski (2009) propose that quantity regulation is costly: A precise definition of what constitutes a liquid asset

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in the context of bank liquidity regulation is difficult. Although assets that are unconditionally eligible for central bank repo operations may have guaranteed liquidity, they are restricted to a subset of high-quality government bonds. Thus, these assets are expensive to hold. Other assets such as highly rated corporate bonds may be liquid during tranquil times but their liquidity may dry up during market stress. Ex ante, it is therefore difficult to judge which assets constitute liquid assets that serve as a building block for liquidity regulation.

Moreover, mandating banks to hold liquid assets gives rise to opportunity costs because it interferes with asset choices in a similar way as do minimum reserve requirements. Slovin et al. (1990) show that increases in unremunerated reserve requirements reduce shareholder wealth. Such requirements are an excise tax on banking activities that lowers future cash flows. Although liquidity reg- ulation does not compromise banks’ asset choices and profitability to the same extent as do reserve requirements, the direction of the effect is similar. In the ab- sence of such regulations, banks may deploy funds to finance loans that attract higher yields than government bonds. Liquidity regulation, similar to reserve re- quirements, is therefore likely to reduce income and, concomitantly, bank profits.

We also acknowledge an alternative hypothesis that predicts positive share- holder wealth effects. The point of departure for this view is that the new rules reduce systemic risk and limit contagion (Cifuentes, Ferrucci, and Shin (2005)). By forestalling contagious failures and improving soundness, liquidity regulation increases the probability of bank survival and simultaneously increases charter values. Banerjee and Mio (2018) illustrate the contagion-reducing effect of liquid- ity regulation. They show that tighter liquidity regulation reduces interconnected- ness of the banking sector. Finally, share prices may respond positively because of convergence benefits toward a global standard of liquidity regulation. Such con- vergence benefits arise from a common standard of regulating liquidity. This is beneficial because Beck, Todorov, and Wagner (2013) demonstrate in the context of resolving cross-border bank distress that national regulators may have biased incentives to intervene in such institutions.

B. What Explains the Cross-Sectional Variation in Stock Price Returns? Beyond understanding how markets respond to liquidity regulation, we in-

vestigate whether bank- and country-specific characteristics amplify or mitigate shareholder wealth effects. Economic intuition and prior work on liquidity risk suggest that bank-specific characteristics warrant further investigation. This anal- ysis is also relevant from a policy perspective to enable regulators to gauge how banks are likely to respond to the new rules.

We focus on characteristics that capture the motivation behind liquidity reg- ulation: funding mismatches and funding mix. Ideally, any such analysis pays attention to the LCR and the NSFR, and these ratios’ components. However, the data required to compute both ratios are not yet disclosed in the balance sheets. To overcome this challenge, we rely on plausible approximations of the LCR and the NFSR using data from BankScope. Subsequently, we also discuss the role of charter values because holding liquidity increases the likelihood of preserving the bank as a going concern and preserves the value of the charter.

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1. Long-Run Funding Mix: Liability Composition

The mix of funding sources and the extent to which banks are exposed to funding mismatch, reflected in the composition of the liability side of the balance sheet, is likely to increase or decrease stock price reactions. The rules for liquidity regulation tighten the funding conditions by placing restrictions on what consti- tutes long-term stable funding sources. In particular, poorly capitalized banks as well as those relying on wholesale short-term funds may be forced to issue equity and move toward long-term borrowing to comply with the NSFR.

We argue that tightening funding for poorly capitalized banks may attract greater negative stock price reactions as these banks are more likely to suffer from an increase in funding costs. In contrast, institutions whose balance sheet char- acteristics signal that assets are largely supported by long-term stable funds are less pressed to adjust the asset–liability mix relative to banks far off the required standards. We therefore expect the negative shareholder wealth effects to be smaller in magnitude for banks with a relatively limited funding mismatch. This will be reflected in a higher coverage of assets financed by long-term funds (e.g., equity). This hypothesis matches the intuition behind the NSFR.

Hypothesis 1. A higher funding mismatch has a negative impact on the price re- action to bank liquidity regulation.

We test this hypothesis using the CORE FUNDING RATIO (the sum of Tier 1 capital plus hybrid debt and customer deposits divided by total assets) as a proxy for the NSFR. Because this ratio is inversely related to funding mismatch, Hypothesis 1 is consistent with a positive coefficient.

2. Liquidity of Bank Balance Sheets: Asset Composition

Banks whose balance sheet characteristics suggest that the volume of liquid assets can absorb large and sudden cash outflows are in a better position to accom- modate liquidity requirements and are less pressed to adjust the asset and liability mix.8 Thus, we expect the adverse shareholder wealth effects that we anticipate to be less pronounced for banks with more liquid assets. Such a hypothesis directly tests the intuition behind the LCR.

Hypothesis 2. More liquid balance sheets have a positive impact on the share price reaction.

To test this hypothesis, we use the ratio of liquid assets to customer and short-term funding (LIQUID ASSETS RATIO) as a proxy for the LCR.9

8Ideally, an empirical proxy for the NFSR also considers asset quality in terms of risk weights, ratings, and issuers. However, in a cross-country study like ours, such a granular level of data is not available.

9Customer funding consists of current accounts, savings accounts, and time deposits from cus- tomers. Note that meeting the standards of the LCR may also require banks to move toward more high-quality assets. In other words, banks that have ex ante poor asset quality may have to adjust the asset side to comply with the LCR. We therefore examine the effect of a key indicator of asset quality, the ratio of nonperforming loans to total assets, on CARs and CMARs. However, the t-statistic on this variable remains indistinguishable from 0 and our other inferences are unaffected. The results are available from the authors.

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3. Charter Values

Recent work by Ratnovski (2009) establishes a link between banks’ liquidity choices and charter values. The basic premise is that maintaining liquidity insures the charter value. In a 2-bank model, he shows that banks reduce liquidity if char- ter values are low or when banks expect shocks that will reduce charter value. These outcomes are due to interbank strategic complementarities with respect to liquidity choices.10 When banks expect their competitors to be illiquid (e.g., dur- ing crises or when other banks suffer liquidity shocks) and bailouts are likely, a bank will choose high levels of illiquidity. In contrast, when other banks are liquid or when other banks are likely to survive liquidity shocks, banks have incentives to be liquid.

Although the 2-bank model takes a banking-system perspective, Ratnovski’s (2009) work also translates into two bank-level predictions. First, individual banks’ liquidity levels should correlate positively with charter values.11 This pre- diction reflects that banks insure charter values by holding liquidity. Second, be- cause liquidity regulation reduces the probability of bailouts and the associated rents, banks with higher charter values are likely to attract better price reactions than banks with lower charter values.

Hypothesis 3. Higher charter values have positive effects on price reactions to liquidity regulation.

Our empirical strategy relies on two alternative measures of charter values: the market to-book ratio (MARKET TO BOOK RATIO) and the ratio of cus- tomer deposits to total assets (CUS DEP TO TOT ASSETS, a proxy for core deposits). These proxies find motivation in Keeley (1990) and Goyal (2005).

4. Two-Way Feedback Loops between Banking Sector Conditions and Sovereign Debt

We also investigate country-specific factors to understand how they affect the market reaction to announcements related to the new liquidity regulation. The interbank market conditions between core Eurozone members and coun- tries located at the periphery such as Greece, Ireland, Italy, Portugal, and Spain (GIIPS) differ considerably. Figure 2 shows these countries on a map, illustrated in dark gray.

These characteristics may play a role for the way equity markets respond to liquidity regulation. Acharya, Drechsler, and Schnabl (2014) suggest that this divergence of interbank market conditions is largely due to sovereign problems in these peripheral countries, reflecting 2-way feedback effects from sovereign

10Ratnovski (2009) focuses on crises. He shows that banks herd in equilibrium. This results in low levels of liquidity when they expect other banks to display suboptimal levels of liquidity. This behavior is driven by banks’ anticipation of rents that arise from bailouts that distort liquidity choices. The choice trades off the preservation of charter values against bailout rents. The intuition is that liquid banks have a higher probability to survive and realize long-term returns. In contrast, illiquid banks are likely to fail, and this attracts bailout rents.

11In unreported tests, we run auxiliary regressions of the proxies for charter values (MARKET TO BOOK RATIO and CUS DEP TO TOT ASSETS) on the LIQUID ASSETS RATIO. We find positive and significant associations, supporting Ratnovski’s (2009) intuition in his model. The re- sults are available from the authors.

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FIGURE 2 Core and Periphery Countries in Europe

Figure 2 plots a map with all countries in our sample. Greece, Ireland, Italy, Portugal, and Spain are considered periphery countries in the public debate about the Eurozone crisis. These countries experienced sovereign debt problems; they are highlighted in dark gray.

credit risk to the domestic banking industry, and vice versa. Le Leslé (2012) also attributes differences in bank funding conditions between core and peripheral countries to concerns about the fragility of the economies in stressed Eurozone countries. To illustrate, deposit rates in interbank markets at short maturities be- low 1 year for core countries in the Euro area declined in line with the Euribor spread in the aftermath of European Central Bank’s actions in 2011, but deposit rates for banks located at the periphery remained around 150 basis points higher at the end of 2012. Given that tighter liquidity regulation implies higher cost of capital, we expect tighter interbank funding conditions in countries that experi- enced sovereign debt problems to trigger greater negative stock price reactions. Therefore, for countries whose banks are on average net lenders in the interbank market, we anticipate larger stock price reactions to liquidity regulation than for countries whose banks are, on average, net borrowers (and therefore have tighter funding conditions).

Hypothesis 4. Higher average interbank ratios on the country level have positive effects on the price reaction to liquidity regulation.

Related to the previous argument is the link between a country’s fiscal posi- tion, government debt, and domestic interbank market conditions. Budget deficits signal that governments spend more than they earn per fiscal year. Unless such deficits are exclusively financed by minting funds, they result in increases in na- tional debt, which further undermines the sovereign’s creditworthiness, aggra- vating the adverse feedback effects between sovereign risk and the banks’ re- financing conditions. Importantly, the recent bailouts in the form of recapital- izations are closely associated with increases in government debt. For instance,

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Lane (2012) highlights that Ireland and Portugal witnessed increases in debt- to-gross-domestic-product (GDP) ratios during the crisis, and Acharya et al. (2014) show that increases in financial sector distress correlate positively with in- creases in the public-debt-to-GDP ratios. Via the 2-way feedback loop, the higher sovereign credit risk triggers further increases in the refinancing costs of the bank- ing sector. For these reasons, we expect worse price reactions for banks domiciled in countries with high debt-to-GDP ratios, and in particular for those in the pe- riphery countries, as they were most affected by the Eurozone crisis.

Hypothesis 5. Greater government indebtedness has a negative effect on the price reaction to liquidity regulation. Being domiciled in a GIIPS country has a negative impact on the price reaction to liquidity regulation.

Finally, we hypothesize that banks headquartered in the Eurozone may dis- play more negative stock price reactions than banks located elsewhere. Figure 3 highlights in dark gray the countries that are not members of the Eurozone.

This differential effect reflects concerns about contagion in the Eurozone that may ultimately affect the refinancing conditions in the banking sector and liquidity risk. Such concerns were repeatedly raised in the media and find support in academic work. For instance, the Financial Times reports on Feb. 24, 2010 that debt levels in several Eurozone countries raised fears of ratings downgrades for other member countries, and the Wall Street Journal reports on Nov. 26, 2010 on contagion risk arising from Greece and Ireland for Belgium (see D. Oakley, “Sovereigns: Debt Levels Raise Fears of Further Downgrades,” Financial Times, Feb. 24, 2010, and F. Robinson, “Belgian Debt and Contagion,” Wall Street Journal, Nov. 26, 2010). Recent work by Lucas, Schwaab, and Zhang (2014)

FIGURE 3 Non-Eurozone Countries

Figure 3 highlights the countries that are not members of the Eurozone (Sweden, Denmark, Switzerland, and the United Kingdom). These countries are highlighted in dark gray.

Bruno, Onali, and Schaeck 911

investigates default probabilities in 9 Eurozone member countries conditional on default by Greece. They show that Ireland and Portugal would be most affected by a Greek default with conditional probabilities of failure of approximately 30%. Other countries (e.g., Austria, Belgium, Germany, Spain, France, and Italy) display conditional default probabilities below 20%.

Hypothesis 6. Location in a Eurozone country negatively affects the price reac- tion to liquidity regulation.12

We rely on several measures to capture the role of interbank conditions, sovereign debt, the government’s fiscal position, and bank location. We use BankScope to compute the position of a bank in the interbank market by scal- ing money lent to other banks by the money borrowed from other banks. A higher interbank ratio suggests that a bank is a net placer in the interbank market. Next, we calculate the average of this variable, called INTERBANK RATIO, for each country and each year. To gauge how the fiscal position affects stock prices, we re- trieve the debt-to-GDP ratio (DEBT TO GDP RATIO) from World Bank Devel- opment Indicators. The effect of Eurozone membership is captured with a dummy, EUROZONE, that equals 1 if a country uses the euro currency, and 0 otherwise. We use a dummy variable, GIIPS, to identify banks located in any of the 5 GIIPS countries that experienced sovereign debt problems.

5. Preexisting Domestic Liquidity Regulation

Several countries had some form of domestic liquidity regulation in place be- fore the announcements by the Basel Committee. To establish which countries had guidelines for liquidity risk in place, we first screen the survey by the World Bank on Banking Regulation and Supervision, using the waves for 2003, 2007, and 2012 (Cihak, Demirgüç-Kunt, Martinez Peria, and Mohseni-Cheraghlou (2012)). Using several waves ensures obtaining information about guidelines on liquidity risk before the crisis, and we can trace the evolution of these provisions during the sample period. We focus on the section in the database that contains details about guidelines for liquidity risk. We then verify this information by cross-checking the details with information from the Web sites of the countries’ central banks and the Bank for International Settlements (BIS) (see, e.g., de Haan and van den End (2013), Banerjee and Mio (2018)).13 Denmark, Germany, the Netherlands, Switzerland, and the United Kingdom had provisions in place that aimed to bol- ster banks’ liquidity holdings. Figure 4 highlights these countries in dark gray.

The Danish rules stipulate that banks are required to have adequate liquidity, and they are based on ratios in the spirit of the LCR. Similar provisions exist for Germany. Since 2003, the Dutch guidelines also contain quantitative liquidity re- quirements that resemble the new proposals. Switzerland introduced quantitative

12An additional reason for expecting a negative impact of being domiciled in the Eurozone is the fact that large Eurozone banks may be supervised directly by the Single Supervisory Mechanism (SSM) led by the European Central Bank. If the SSM is considered by market participants to be a tougher supervisor than national supervisors, this may also explain why new liquidity regulations have a relatively large negative impact on stock prices of banks from the Eurozone. Section IV.A highlights that many of the banks in our sample are indeed subject to the SSM.

13See also http://www.bundesbank.de/Navigation/EN/Tasks/Banking supervision/Liquidity/ liquidity.html

912 Journal of Financial and Quantitative Analysis

FIGURE 4 Countries in Europe with Preexisting Domestic Liquidity Regulation

Figure 4 shows which countries had preexisting domestic rules for the regulation of bank liquidity in place. Switzerland, the United Kingdom, Germany, and Denmark had such legislation before the announcements of the Basel Committee on Banking Supervision in place. These countries are highlighted in dark gray.

liquidity requirements in 1988, and the United Kingdom had minimum liquidity requirements in place with the objective to avoid maturity mismatches. We expect shareholders of institutions headquartered in these countries to respond more neg- atively to the new rules because they are potentially better able than shareholders elsewhere to gauge the effect of new regulations for bank profitability.

However, the effect of prior legislation may go in the opposite direction. For banks that are already subject to national liquidity requirements, any new liquidity requirement as part of Basel III may not lead to an observable reduction in bank profitability. Hence, it is possible that shareholders of institutions headquartered in these countries will respond less negatively to the new liquidity regulation. We therefore offer two alternative hypotheses on the effect of preexisting domestic liquidity regulation:

Hypothesis 7A. Being headquartered in a country with preexisting domestic reg- ulations on liquidity has a negative impact on the price reaction to liquidity regulation.

Hypothesis 7B. Being headquartered in a country with preexisting domestic reg- ulations on liquidity has a positive impact on the price reaction to liquidity regulation.

Our tests use a dummy, LIQUIDITY REGULATION, that equals 1 when a country has preexisting domestic liquidity regulation in place, and 0 otherwise.14

14In the United Kingdom, the Financial Services Authority imposed capital requirements on in- dividual banks that were bank specific but not publicly announced (Aiyar, Calomiris, Hooley, Ko- rniyenko, and Wieladek (2014)). In unreported tests, we remove all U.K. banks from the sample to

Bruno, Onali, and Schaeck 913

A negative coefficient on this dummy variable indicates that Hypothesis 7A is ap- plicable, whereas a positive coefficient supports Hypothesis 7B. If the two effects offset each other, the coefficient may be insignificant.

IV. Data, Representativeness, and Choice of Event Dates We now discuss our sample. Although our empirical work considers only

listed banks, liquidity regulation extends to all banks, irrespective of their listing. Therefore, we discuss sample representativeness. Subsequently, we elaborate on the choice of our event dates.

A. Data and Representativeness The starting point for the sample selection is the population of commercial

banks and bank holding companies (BHCs) in the European Union and Switzer- land, as reported in BankScope. BankScope is our source for the bank-specific variables used below to establish heterogeneous responses to liquidity regulation. We include Switzerland because of the vast size of its banking system and the linkages of Swiss banks to banks in the European Union.

BankScope contains data for 142 banks and BHCs in Europe listed on a stock exchange. We filter out banks without deposits to ensure that the sample banks engage in financial intermediation. This screen reduces the sample by 14 banks for which BankScope records 0 deposits. We also omit 7 observations for 5 banks because of negative common equity, resulting in a final sample of 128 banks and BHCs. Appendix C presents more details.

For these banks, we retrieve daily closing prices from DataStream from Feb. 21, 2007 to Jan. 7, 2013 for our event study.

Table 2 provides an overview of the sample composition. Panel A shows the number of sample banks and their aggregate market share by country to illus- trate the representativeness, as these 128 banks are only a subset of the European banking system. This discussion is important because liquidity regulation applies to all banks in the European Union and Switzerland but very often only the largest banks are publicly listed. The sample banks account on average for 57% of total banking system assets in the domestic banking systems, 57% of total loans, and 39% of total liabilities. In Sweden, the sample banks represent more than 91% of total banking system assets, more than 90% of total loans, and more than 77% of total liabilities. The relevance of these banks is also reflected in the fact that many are systemically important. Of the 78 sample banks located in the Eurozone, 40 fall under the SSM.

Panel B of Table 2 reports means and standard deviations for the bank- and country-specific variables used to explain the CARs and CMARs below. Apart from the values on the DEBT TO GDP RATIO, the data refer to 2007–2012 to allow for the delay in the release of information via the annual reports. Close scrutiny of the standard deviations highlights considerable within-country hetero- geneities for the bank-specific variables, where the standard deviations range from

avoid any confounding effects from these bank-specific capital requirements that may have coincided with the announcements about liquidity regulation. Our findings remain virtually unchanged; these tests are available from the authors.

914 Journal of Financial and Quantitative Analysis

a low of 0.02 in Malta for the CORE FUNDING RATIO to a high of 1.13 for the Swiss LIQUID ASSETS RATIO. These heterogeneities are most pronounced for MARKET TO BOOK RATIO, whose standard deviations are between 0.29 (Fin- land) and 2.27 (Sweden).

B. Event Dates Our event dates refer exclusively to official announcements by the BCBS

that result in proposed or actual changes in liquidity regulation. This choice is a restrictive, yet plausible, criterion. Any other news and debates regarding the introduction of liquidity regulation are based on and influenced by the debates within the BCBS and its representatives.

The selection of event dates proceeds as follows. First, we use public information from the BIS Web site (https://www.bis.org/) to determine all events and dates leading up to the Basel III framework. We consider all events in the sections of the BIS Web site referred to as: i) the Global Regulatory Framework for Capital and Liquidity, comprising the entire spectrum of measures introduced by the BCBS through Basel II, Basel 2.5, and Basel III accords, and ii) the

TABLE 2 Sample Composition by Country, Representativeness, and Descriptive Statistics

Table 2 reports in Panel A details for the composition of the sample. We present the number of banks by country, and report on whether the banks headquartered in these countries are from the European Union (EU); the Eurozone; Greece, Ireland, Italy, Portugal, and Spain (GIIPS); or were subject to domestic liquidity regulation that precedes the new rules issued by the Basel Committee on Banking Supervision (BCBS). We also present details about the representativeness of the sample for coverage in terms of total banking system assets, loans, and deposits, and we show how many banks fall under the Single Supervisory Mechanism (SSM) led by the European Central Bank. Panel B presents descriptive statistics in terms of means and standard deviations (SD) by country for the bank- and country-specific variables in our regressions. EUROZONE equals 1 if a country uses the euro currency, and 0 otherwise. GIIPS equals 1 if a bank is located in Greece, Ireland, Italy, Portugal, or Spain, and 0 otherwise. INTERBANK_RATIO is calculated as money lent to other banks by the money borrowed from other banks, averaged for each country and each year. DEBT_TO_GDP_RATIO is the ratio of government debt to Gross Domestic Product for a country. LIQUID_ASSETS_RATIO is the ratio of liquid assets to customer and short-term funding. CORE_FUNDING_RATIO is the sum of Tier 1 capital plus hybrid debt and customer deposits divided by total assets. MARKET_TO_BOOK_RATIO is the ratio of market value of equity divided by common equity. CUS_DEP_TO_TOT_ASSETS is the ratio of customer deposits to total assets.

Panel A. Sample Composition for Country-Based Portfolio

Coverage in Coverage in Coverage in No. of Preexisting % of Total % of Total % of Total Banks Domestic Banking Banking Banking under Liquidity System System System the

Banks EU Eurozone GIIPS Regulation Assets Loans Deposits SSM

Austria 6 Yes Yes No No 28.38% 34.97% 22.82% 2 Belgium 2 Yes Yes No No 36.87% 42.45% 9.34% 2 Cyprus 2 Yes Yes No No 42.90% 49.60% 38.74% 2 Denmark 29 Yes No No No 60.89% 56.88% 56.21% N/A Finland 3 Yes Yes No Yes 13.18% 16.16% 8.30% 1 France 7 Yes Yes No No 42.27% 32.72% 15.02% 6 Germany 10 Yes Yes No Yes 54.89% 52.17% 25.75% 5 Greece 7 Yes Yes Yes No 66.22% 69.01% 22.21% 5 Ireland 2 Yes Yes Yes No 50.05% 62.88% 28.26% 2 Italy 14 Yes Yes Yes No 68.70% 75.04% 49.38% 5 Luxembourg 3 Yes Yes No No 9.11% 21.40% 3.78% 0 Malta 2 Yes Yes No No 68.47% 84.90% 40.96% 1 Netherlands 5 Yes Yes No Yes 34.89% 33.94% 11.22% 2 Portugal 4 Yes Yes Yes No 52.23% 55.41% 21.87% 2 Spain 7 Yes Yes Yes No 81.26% 82.58% 22.70% 5 Sweden 4 Yes No No No 91.66% 90.56% 77.73% N/A Switzerland 13 No No No Yes 62.83% 58.84% 45.20% N/A United Kingdom 8 Yes No No Yes 67.85% 61.75% 60.31% N/A Total banks 128 115 74 34 21 57.22% 56.97% 38.59% 40

(continued on next page)

Bruno, Onali, and Schaeck 915

TABLE 2 (continued) Sample Composition by Country, Representativeness, and Descriptive Statistics

Panel B. Means and Standard Deviations of Key Variables by Country and for the Full Sample

Austria Belgium Cyprus Denmark Finland

Mean SD Mean SD Mean SD Mean SD Mean SD

INTERBANK_RATIO 0.184 0.0914 0.2187 0.0893 0.0758 0.0608 0.1385 0.1074 0.1495 0.0785 DEBT_TO_GDP_RATIO 0.0067 0.0005 0.0085 0.0003 0.0111 0.0019 0.0036 0.0008 0.0040 0.0006 LIQUID_ASSETS_RATIO 0.2200 0.1101 0.2943 0.069 0.2009 0.072 0.2691 0.3095 0.3050 0.2445 CORE_FUNDING_RATIO 0.6433 0.0879 0.3223 0.1859 0.7817 0.1179 0.7176 0.1846 0.4032 0.1934 MARKET_TO_BOOK_RATIO 0.9851 0.3721 0.6396 0.927 0.9268 0.6981 0.9518 0.5425 0.9347 0.2916 CUS_DEP_TO_TOT_ASSETS 0.5894 0.109 0.2883 0.1758 0.7163 0.1082 0.6146 0.1643 0.3643 0.1989

France Germany Greece Ireland Italy

Mean SD Mean SD Mean SD Mean SD Mean SD

INTERBANK_RATIO 0.1715 0.1092 0.1390 0.0952 0.1691 0.1426 0.0836 0.0770 0.2062 0.2422 DEBT_TO_GDP_RATIO 0.0079 0.001 0.0047 0.0006 0.0123 0.0008 0.0065 0.0026 0.0109 0.0007 LIQUID_ASSETS_RATIO 0.5838 0.3121 0.4387 0.404 0.1405 0.0645 0.1541 0.0642 0.3291 0.2439 CORE_FUNDING_RATIO 0.3384 0.2306 0.5494 0.2428 0.6374 0.1018 0.5132 0.0536 0.4836 0.2044 MARKET_TO_BOOK_RATIO 1.2679 1.2126 1.0688 0.6248 1.1005 0.4579 0.7969 0.7586 1.1835 1.193 CUS_DEP_TO_TOT_ASSETS 0.3250 0.2294 0.5051 0.2448 0.5759 0.0830 0.4490 0.045 0.4129 0.1823

Luxembourg Malta Netherlands Portugal Spain

Mean SD Mean SD Mean SD Mean SD Mean SD

INTERBANK_RATIO 0.0807 0.0498 0.0365 0.0343 0.0800 0.0508 0.1211 0.0790 0.0700 0.0749 DEBT_TO_GDP_RATIO 0.0013 0.0005 0.0099 0.0038 0.0053 0.0007 0.0082 0.0010 0.0042 0.0009 LIQUID_ASSETS_RATIO 0.8115 1.0784 0.31 0.0499 0.4250 0.8088 0.1864 0.0668 0.1916 0.1213 CORE_FUNDING_RATIO 0.3438 0.2656 0.8076 0.0242 0.4571 0.2523 0.5256 0.0788 0.4573 0.0633 MARKET_TO_BOOK_RATIO 1.3519 2.0375 2.1406 1.1298 0.8612 0.5131 1.1049 0.7964 1.2427 0.5707 CUS_DEP_TO_TOT_ASSETS 0.4200 0.2089 0.8000 0.0312 0.4385 0.2340 0.4707 0.0839 0.4021 0.0565

United Sweden Switzerland Kingdom Total

Mean SD Mean SD Mean SD Mean SD

INTERBANK_RATIO 0.1295 0.0458 0.0902 0.1349 0.0658 0.0409 0.133 0.1306 DEBT_TO_GDP_RATIO 0.0038 0.0001 0.0025 0.0002 0.0069 0.0018 0.0059 0.0032 LIQUID_ASSETS_RATIO 0.4860 0.1802 0.8232 1.1353 0.4972 0.2753 0.3800 0.5111 CORE_FUNDING_RATIO 0.3441 0.0596 0.5338 0.2571 0.4954 0.2384 0.5496 0.2309 MARKET_TO_BOOK_RATIO 2.2387 2.2738 1.6343 1.2738 1.3589 1.0619 1.1859 1.0129 CUS_DEP_TO_TOT_ASSETS 0.3111 0.0574 0.5201 0.2213 0.4507 0.2192 0.4964 0.2064

Basel Committee’s Response to the Financial Crisis, which focuses initiatives undertaken by the BCBS since the 2007–2009 crisis.15

Second, we refine the list of events by considering only those related to Basel III, dropping initiatives related to the Basel II and Basel 2.5 accords. As discussed above, to establish the wealth effects that are causally attributable to liquidity reg- ulation, we need to be aware that Basel III encompasses other types of regulation. We therefore select only events with proposed or actual changes in liquidity reg- ulation, and drop events with capital requirements only. However, some rules that address liquidity regulation were released at the same time as measures on cap- ital requirements. We consider these dates only when the event involves a major change in liquidity regulation. To understand the extent to which market reactions are driven by announcements other than those related to liquidity and disentangle market reactions to liquidity regulation from confounding announcements relat- ing to other aspects of Basel II, we also calculate CARs and CMARs over the

15See http://www.bis.org/bcbs/basel3/compilation.htm and http://www.bis.org/bcbs/fincriscomp .htm, respectively.

916 Journal of Financial and Quantitative Analysis

event dates that entail only initiatives on liquidity (i.e., Events 1, 2, 3, and 7, i.e., the liquidity-only bucket).

Third, we conduct a media search to ascertain that the events we focus on do indeed convey significant information to the market. This media search helps rule out anticipation effects, a key concern for event-study analysis. To this end, we carefully search major international media outlets (Financial Times, Wall Street Journal, Wall Street Journal Europe, International Herald Tribune) via Lexis- Nexis for up to 1 week after each of the 7 event dates. This exercise suggests substantial international media coverage corresponding to all events included in our empirical tests.16 To allay concerns about anticipation effects, we extend this news search to 1 week, that is, 5 trading days, before the event date.

Finally, we record the day of the week on which the BCBS publicly released its statement. For our calculation of abnormal returns across event dates, we verify that each announcement was released before the closing times of European stock exchanges.17 This condition ensures that the new information is available to all stock exchanges.

V. Econometric Methodology Modeling market reactions to the announcements of the BCBS presents

econometric challenges. The literature lacks consensus on the choice of estima- tion method for abnormal returns. Moreover, Brown and Warner (1980) stress that short event-window models based on estimation of the security’s beta (such as the market model or the capital asset pricing model) do not lead to significantly more precise estimates for the abnormal returns. For these reasons, we estimate both abnormal returns and market-adjusted returns for the event window (−1,1), fol- lowed by calculation of the CARs and CMARs for each event (Focarelli, Pozzolo, and Casolaro (2008)).

We estimate the abnormal return (AR) using the market model extended to include day-of-the-week dummies (Kaplanski and Levy (2010)):

(1) ARi ,t = Ri ,t −

( αi +βi Rm,t +

5∑ d=2

λd Dd

) ,

where Dd=1 if d=2 for Tuesdays, d=3 for Wednesdays, d=4 for Thursdays, and d=5 for Fridays, and Dd=0 otherwise.

Consistent with prior work (e.g., Armour, Mayer, and Polo (2017)), we employ an estimation window of 260 trading days (−261,−2) for the market model. Because we consider a long sample period, using different estimation windows for each event instead of only one estimation window for all events allows for potential parameter instability over time. We also adjust for first-order

16We employ a variety of keyword searches to assess the international press coverage of the BCBS’s initiatives included in our analysis. In particular, we use the following keywords: bank liq- uidity, liquidity proposals, Basel Committee, BIS, Bank for International Settlements, liquidity risk, Basel 3, Basel III, bank supervisors, bank supervision, and liquidity management.

17When the hour of the press release is unavailable, we screen the international press to check whether any European bank stock reaction is reported on the date of the event in response to the BCBS’s announcement.

Bruno, Onali, and Schaeck 917

autocorrelation in the error term of the market model regression using the Prais– Winsten method (Allen and Wilhelm (1988)).

We compute the market-adjusted return (MAR) as the difference between the log return of the security (Ri ,t ) and the log return of the proxy for the market portfolio (Rm,t )

(2) MARi ,t = Ri ,t − Rm,t .

The literature highlights that the MAR is free of bias resulting from sig- nificant events in the estimation period, which undermines estimation of the beta (Fuller, Netter, and Stegemoller (2002)). Because our sample period covers part of the 2007–2009 financial crisis and the Eurozone crisis, it is likely that significant events affected estimation of the beta.

We then compute the corresponding CAR and CMAR for the 3-day event window (−1,1). Focusing on short event windows is particularly useful for the purpose of this study because this restrictive criterion reduces the impact of poten- tially confounding events. Their influence typically increases as the event window widens.

CARi ,t =

t2∑ t=t1

ARi ,t ,(3)

CMARi ,t =

t2∑ t=t1

MARi ,t .(4)

For our regressions we rely on 3-day event windows, where t1 is the trading day before the event and t2 is the trading day after the event.

We adopt the MSCI World and the MSCI Europe as proxies for the mar- ket portfolio. These proxies, especially the first one, are less subject to bias than national indices because of potential effects of liquidity regulation on the stock prices of nonfinancial firms of a country.

The first step of our analysis focuses on the marketwide reaction to liquid- ity regulation using equal-weighted and market-weighted portfolios of the bank stocks in our sample. We compute the aggregate effect of the regulation by con- sidering the average CARs and CMARs over all 7 events, followed by tests for the 4 liquidity-only events that back out the effects of other aspects of Basel III.

To correctly gauge the market reaction to stricter liquidity rules even though Events 5 and 7 (see Table 1) are associated with loosening of the initial propos- als, we follow the standard approach in the literature pioneered by Armstrong, Armstrong, Barth, Jagolinzer, and Riedl (2010), and multiply the corresponding CAR or CMAR by−1 for these two events. For example, a positive price reaction for Events 5 and 7 suggests that bank shareholders benefit from looser liquidity requirements, and therefore it would not make sense to sum the raw CARs or CMARs for these events to the CARs and CMARs for the other 5 events. After multiplying the CARs and CMARs for these events by −1, the sum of CARs and CMARs for all events measures the market reaction to stricter liquidity rules appropriately.

Because we have only 7 and 4 events for the total effects and the liquidity- only events, respectively, calculating the significance of the CARs and CMARs

918 Journal of Financial and Quantitative Analysis

based on the assumption of normality may lead to unreliable t-statistics. For this reason, we rely on bootstrap simulations to evaluate the significance of the cumu- lative effect of all 7 events and the effect of the 4 liquidity-only events, similar to Armstrong et al. (2010).

The bootstrap simulations are performed as follows: We exclude days that fall in the 3-day window for the 7 events to consider only nonevent trading days. Next, we randomly choose 7 (or 4, for the liquidity-only results) nonoverlapping placebo events for Feb. 1, 2008 to Feb. 28, 2013. This step is repeated 800 times. Finally, we compute the sum of the CARs and CMARs over all 7 (and 4) events for each of the 800 samples of placebo events. These steps ensure that the simulated data represent the distribution of CARs and CMARs under the null hypothesis because they are estimated for nonevent trading days (for which announcements related to liquidity regulation did not occur). The p-values are computed on the basis of the number of cases for which the CAR or CMAR is larger or smaller than the estimated value based on 2-tail tests.

For the second stage of our analysis, we run regressions of the CARs and CMARs on both a vector of bank-level (Bi,t) and a vector of country-level (Ci,t) characteristics:18

CARi ,t = a+ bCi,t+ cBi,t+ εi ,t ,(5) CMARi ,t = d + eCi,t+ f Bi,t+ ηi ,t .(6)

The vector Bi,t consists of the CORE FUNDING RATIO, LIQUID ASSETS RATIO, CUS DEP TO TOT ASSETS, and MARKET TO BOOK RATIO. The vector Ci,t comprises DEBT TO GDP RATIO; the dummy vari- ables EUROZONE, GIIPS, and LIQUIDITY REGULATION; and the INTER- BANK RATIO.19 We winsorize all bank-level explanatory variables at the 1st and 99th percentiles, as well as the CARs and CMARs (Armour et al. (2017)). Be- cause some of the variables are time invariant, such as GIIPS or EUROZONE, we estimate all the regressions using a random effects model with heteroscedasticity- robust standard errors clustered at the country level.

VI. Results We first provide a visual inspection of the behavior of CARs and CMARs

around the event date, and then present the empirical analysis for the market reactions. Subsequently, we show tests that relate bank-specific and country- specific characteristics to the CARs and CMARs.

18Investigating the cross-sectional determinants of CARs using such a 2-stage approach is common in banking. Focarelli et al. (2008) employ a similar methodology to examine how the share of the arranger of syndicated loans (as well as characteristics related to the syndicate structure, the credit facility, and the borrower) affect the share price reaction of the borrower.

19All coefficients in the tables on the cross-sectional regressions should be read as follows: A negative coefficient signals that an increase in variable Bi,t generates a decrease in the CAR/CMAR (i.e., if the CAR/CMAR is negative, this amplifies the reduction). A positive coefficient signals that an increase in variable Bi,t generates an increase in the CAR/CMAR (i.e., if the CAR/CMAR is negative, this mitigates the reduction and potentially leads to a positive CAR/CMAR).

Bruno, Onali, and Schaeck 919

A. Market Reaction to Bank Liquidity Regulation: Aggregate and Individual Effects Graphs A–D in Figure 5 illustrate the behavior of equal-weighted and

market-weighted CARs and CMARs around a 3-day event window centered on the announcement date (represented with a vertical solid bold line). The solid line represents CARs (Graphs A and B) or CMARs (Graphs C and D) estimated using the MSCI World as a proxy for the market portfolio, and the dashed line repre- sents CARs (Graphs A and B) or CMARs (Graphs C and D) estimated using the MSCI Europe as a proxy for the market portfolio. Graphs A and C use a market- weighted portfolio of banks, and Graphs B and D use an equal-weighted portfolio of the bank stocks in our sample. The four graphs consistently indicate declines in CARs and CMARs around the announcement dates. A slight decline on the day before the announcements suggests there may have been information leakages; this supports our choice to focus on the event window (−1,1).

FIGURE 5 CARs and CMARs around the Event Dates

Graphs A–D of Figure 5 illustrate the behavior of equal-weighted and market-weighted cumulative abnormal returns (CARs) and cumulative market-adjusted returns (CMARs) around a 3-day event window centered on the announcement date (represented with a vertical solid bold line). The solid line represents CARs (Graphs A and B) or CMARs (Graphs C and D) estimated using the MSCI World as a proxy for the market portfolio, and the dashed line represents CARs (Graphs A and B) or CMARs (Graphs C and D) estimated using the MSCI Europe as a proxy for the market portfolio. Graphs A and C use a market-weighted portfolio of banks, and Graphs B and D use an equal-weighted portfolio of bank stocks.

Graph A. Market-Weighted Portfolio CAR(–1, 1) Graph B. Equal-Weighted Portfolio CAR(–1, 1)

Graph C. Market-Weighted Portfolio CMAR(–1, 1)

Market-Weighted World

Market-Weighted Europe

Graph D. Equal-Weighted Portfolio CMAR(–1, 1)

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920 Journal of Financial and Quantitative Analysis

1. Aggregate Effects

Table 3 presents our first set of analyses. We compute the total and average effects for all 7 events. Table 3 reproduces the findings for both equal-weighted and market-weighted portfolios. We also present bootstrapped p-values for the average CAR or CMAR, based on 800 bootstrap simulations for Feb. 1, 2008– Feb. 28, 2013, as discussed above. Importantly, we present the results for the liquidity-only bucket based on randomly selected trading days. The latter setup is the most rigorous way of establishing the causal effects of liquidity regulation on bank stock prices.

Panel A of Table 3 uses the MSCI World as a proxy for the market in- dex, and Panel B uses the MSCI Europe. Panel A points toward negative wealth effects arising from the announcements. The t-statistics for the total effect are

TABLE 3 Market Reaction to Announcements Concerning Bank Liquidity Regulation

Table 3 presents event-study evidence for all 7 Basel Committee on Banking Supervision (BCBS) announcements about the effect of bank liquidity regulation for a sample of banks from the European Union and Switzerland. We present cu- mulative abnormal returns (CARs) (equal-weighted (EW) and market weighted (MW)) and cumulative market-adjusted returns (CMARs) (both EW and MW). Panel A uses the MSCI World as a proxy for the market portfolio, and Panel B uses the MSCI Europe as a proxy for the market portfolio. The CARs and CMARs are estimated according to equations (1)–(4). BS p-value is the p-value for the average CAR and CMAR calculated according to 800 bootstrap simulations for Feb. 1, 2008–Feb. 28, 2013. For each simulation, we estimate the average CAR and CMAR according to equations (1)–(4) for 7 (or 4, for the liquidity-only Events 1, 2, 3, and 7) randomly selected trading days. To consider only nonevent trading days, we exclude days that fall in the 3-day window for the 7 events. We randomly choose 7 nonoverlapping placebo events for Feb. 1, 2008–Feb. 28, 2013. This step is repeated 800 times. We compute the sum of the CARs and CMARs over all 7 events for each of the 800 samples of placebo events. These steps ensure that the simulated data represent the distribution of CARs and CMARs under the null hypothesis, because they have been estimated for nonevent trading days (for which, therefore, announcements related to bank liquidity regulation did not occur). The p-values are computed on the basis of the number of cases for which the CAR or CMAR is larger or smaller than the estimated value (2-tail tests). *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Dependent Variable CAR(−1,1) EW CAR(−1,1) MW CMAR(−1,1) EW CMAR(−1,1) MW

Panel A. Market Portfolio Proxy: MSCI World

Actual Events Total (all events) −0.0695*** −0.1319** −0.0666 −0.1548*** Average (all events) −0.0099*** −0.0188** −0.0095 −0.0221*** BS p-value (all events) 0.0100 0.0175 0.1049 0.0100 Total (liquidity only bucket) −0.0430* −0.0766* −0.0484 −0.0785* Average (liquidity only bucket) −0.0108* −0.0192* −0.0121 −0.0196* BS p-value (liquidity only bucket) 0.0674 0.0674 0.1074 0.0899

Placebo Events (5 trading days earlier) Total (all events) −0.0234 0.0504 −0.0335 0.0463 Average (all events) −0.0033 0.0072 −0.0048 0.0066 BS p-value (all events) 0.3845 0.2372 0.5318 0.2122 Total (liquidity only bucket) −0.0098 0.0551 −0.0198 0.0573 Average (liquidity only bucket) −0.0025 0.0138 −0.0050 0.0143* BS p-value (liquidity only bucket) 0.6192 0.1099 0.6542 0.0999

Panel B. Market Portfolio Proxy: MSCI Europe

Actual Events Total (all events) −0.0527** −0.1003*** −0.0231 −0.1111*** Average (all events) −0.0075** −0.0143*** −0.0033 −0.0159*** BS p-value (all events) 0.0200 0.0100 0.6517 0.0075 Total (liquidity only bucket) −0.0325* −0.0577* −0.0334 −0.0632* Average (liquidity only bucket) −0.0081* −0.0144* −0.0084 −0.0158* BS p-value (liquidity only bucket) 0.0799 0.0799 0.2797 0.0574

Placebo Events (5 trading days earlier) Total (all events) −0.0066 0.0783* −0.0059 0.0739* Average (all events) −0.0009 0.0112* −0.0008 0.0106* BS p-value (all events) 0.6866 0.0974 0.9988 0.0749 Total (liquidity only bucket) −0.0023* 0.0656* −0.0141 0.0630* Average (liquidity only bucket) −0.0006* 0.0164* −0.0035 0.0158* BS p-value (liquidity only bucket) 0.8015 0.0674 0.7191 0.0574

Bruno, Onali, and Schaeck 921

statistically significant in 3 of 4 cases. The average CAR(−1,1) and CMAR(−1,1) ranges between −0.0095 and −0.0221, and the price reaction is stronger for the market-weighted portfolios (with a CAR of−0.0188 and a CMAR−0.0221) than for the equal-weighted portfolios (−0.0099 and −0.0095), suggesting that bigger banks react more negatively to the regulation than smaller banks. Panel B shows that using the MSCI Europe yields very similar inferences. The t-statistics are significant in 3 of 4 cases for the total effect. Likewise, the average CAR and CMAR considering all 7 events are larger in magnitude for the market-weighted portfolio (−0.0143 and −0.0159) than for the equal-weighted portfolio (−0.0075 and −0.0033).

The economic magnitude of these aggregate effects is sizable. Considering the sum of the CAR(−1,1) for all 7 events for the market-weighted portfolios, the overall loss in market capitalization amounts to a staggering 354 million euros in Panel A of Table 3 (equivalent to a reduction in market capitalization for all listed banks in Europe by −6.95%), and 269 million euros in Panel B (a decline in market capitalization by −5.27%).

2. Individual Effects

Clearly, we focus on the aggregate effect of announcements concerning liq- uidity regulation. For completeness, however, we also briefly discuss the market reactions to the 7 individual announcements. These tests are useful to establish whether the market responses are consistent with the hypothesized direction of the effects in terms of tightening or loosening of the initial proposals concerning liquidity regulation. To preserve space, these tests are relegated to Table A.1 in our Internet Appendix (available at www.jfqa.org), which reports the CARs and CMARs.

Three observations stand out. First, several individual announcements at- tract negative market reactions. This is consistent with both a visual inspection of the data and the aggregate effect documented so far. Second, consistent with our expectations, Events 5 and 7 result in positive stock price reactions because of the loosening of the initial proposals (consistent with Table 3, we multiply these CARs and CMARs by−1 in Table A.1 in the Internet Appendix). Third, the largest price reaction is for Event 7, which is the final and definitive announcement for the LCR, which again relaxed previously proposed liquidity rules. The magni- tude of the market reaction suggests that the extent of the relaxation surprised the market more than the previous announcements.

3. Liquidity-Only Bucket

However, these tests still leave open the possibility that confounding an- nouncements from the BCBS on capital regulation or any other component of Basel III drive the results. Concluding that liquidity regulation causally drives re- ductions in market capitalization would be inappropriate. Indeed, our inspection of the tests based on the liquidity-only bucket highlights that the t-statistics are weakly significant or insignificant only when we back out confounding effects. In line with the decline in statistical significance, the economic magnitude of the adverse price movements is less pronounced. The loss related to Events 1, 2, 3,

922 Journal of Financial and Quantitative Analysis

and 7 amounts to 206 million euros in Panel A of Table 3, and 154 million euros in Panel B.

Which factors are behind these weaker inferences? First, the findings for the liquidity-only bucket are likely due to a loss of power in the tests when only 4 events are considered. Second, the events we exclude from the liquidity-only bucket are important for market participants in the sense that these announce- ments received extensive media coverage because they cover not only liquidity regulation but also other characteristics of Basel III.20 The decline both in terms of statistical significance and in terms of economic magnitude is therefore not sur- prising. Liquidity regulation is only one component of the revised Basel frame- work. The findings for the liquidity-only bucket are therefore bound to be weaker. Third, and most important, the revisions and the lobbying by the industry to miti- gate the effects of tight liquidity regulation suggests that markets did not consider the new rules to be binding at the time of the announcement. The successful lob- bying fueled expectations about further modifications (Santos and Elliott (2012)). For instance, the LCR has been amended twice since its initial proposal, to even- tually allow additional, less liquid instruments to be considered as HQLA (Ful- lenkamp and Rochon (2017)). This is consequently reflected in limited effects for bank valuation.

4. Placebo Tests

Table 3 also contains tests based on placebo events that assume the events considering the CARs and CMARs occur 5 trading days before each of the actual events. These tests help demonstrate whether the results are driven by a downward trend in returns of bank stocks relative to the market in the days surrounding the events. Moreover, these analyses rule out anticipation effects that may have occurred before the event windows.

Irrespective of the choice of the proxy for the market portfolio, all placebo events in Panels A and B of Table 3 remain insignificant at the 5% level. These tests suggest the absence of anticipation effects and alleviate the concern that the significance of the results for the actual events is due to short-run trends in the CARs and CMARs.

B. Country- and Bank-Specific Characteristics and Stock Price Reactions Table 4 shows how bank- and country-specific variables correlate with the

stock price reactions. We again present results with both the MSCI World (Panel A), and the MSCI Europe (Panel B) indices as a proxy for the market portfolio.

Importantly, these tests demonstrate that bank-specific variables explain much of the variation in CARs and CMARs. Our analysis that focuses on the intuition behind the LCR using the LIQUID ASSETS RATIO as a proxy for the LCR supports the idea that banks with liquid balance sheets display posi- tive CARs, with t-statistics between 3.494 and 4.268. The proxy for the NFSR,

20A representative news item from that time reads: “Global banking regulators reached a break- through agreement yesterday to tighten capital requirements and impose new worldwide liquidity and leverage standards, but softened some of their proposals and delayed others until at least 2018 (Finan- cial Times (July 26, 2010), Brooke Masters, “Bank Regulators Reach Deal on Liquidity”).”

Bruno, Onali, and Schaeck 923

the CORE FUNDING RATIO, enters the regressions with a negative sign.21 This result empirically refutes Hypothesis 1. The pecking order theory provides a plau- sible explanation for this result. Myers and Majluf (1984) posit that issuing equity is expensive, a prediction that finds ample support in empirical work (e.g., Cornett and Tehranian (1994)). As the CORE FUNDING RATIO is considerably influ- enced by Tier 1 capital, which increases banks’ weighted average cost of capital (Baker and Wurgler (2015)), well-capitalized banks may react negatively to the new standards because they are likely to be more resilient to liquidity shocks.22

TABLE 4 Determinants of CARs and CMARs

Table 4 presents tests that explain the effect of the bank-specific variables CORE_FUNDING_RATIO, LIQUID_ASSETS_ RATIO, MARKET_TO_BOOK_RATIO, CUS_DEP_TO_TOT_ASSETS, and the country-specific variables average INTERBANK_RATIO, DEBT_TO_GDP_RATIO, EUROZONE membership, location in GIIPS, and LIQUIDITY_ REGULATION on the cross-sectional variation of cumulative abnormal returns (CARs) and cumulative market-adjusted returns (CMARs). Panel A uses the MSCI World as a proxy for the market portfolio and Panel B uses the MSCI Europe as a proxy for the market portfolio. The CARs and CMARs are estimated according to equations (1)–(4). We use random effects regressions with robust standard errors clustered at the country level. EUROZONE equals 1 if a country uses the euro currency, and 0 otherwise. GIIPS equals 1 if a bank is located in Greece, Ireland, Italy, Portugal, or Spain, and 0 otherwise. INTERBANK_RATIO is calculated as money lent to other banks by the money borrowed from other banks, averaged for each country and each year. DEBT_TO_GDP_RATIO is the ratio of government debt to Gross Domestic Product for a country. LIQUID_ASSETS_RATIO is the ratio of liquid assets to customer and short-term funding. CORE_FUNDING_RATIO is the sum of Tier 1 capital plus hybrid debt and customer deposits divided by total assets. MARKET_TO_BOOK_RATIO is the ratio of market value of equity divided by common equity. CUS_DEP_TO_TOT_ASSETS is the ratio of customer deposits to total assets. CORE_FUNDING_RATIO_DUMMY is a dummy that equals 1 if the CORE_FUNDING_RATIO is above the 75th percentile, and 0 otherwise. All bank-level variables, CARs, and CMARs are winsorized at the 1st and 99th percentiles. Constant is included but not reported. t -statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Dependent Variable CAR(−1,1) CMAR(−1,1) CAR(−1,1) CMAR(−1,1)

Panel A. Market Portfolio Proxy: MSCI World

CORE_FUNDING_RATIO −0.059*** −0.052** (−3.319) (−2.394)

LIQUID_ASSETS_RATIO 0.012*** 0.011*** 0.015*** 0.014*** (4.268) (3.494) (3.815) (3.694)

CORE_FUNDING_RATIO_DUMMY 0.006 0.006 (0.889) (0.743)

CORE_FUNDING_RATIO × −0.025*** −0.029*** LIQUID_ASSETS_RATIO (−2.703) (−2.589)

CUS_DEP_TO_TOT_ASSETS 0.091*** 0.092*** 0.031*** 0.042*** (3.961) (3.440) (2.592) (2.660)

MARKET_TO_BOOK_RATIO 0.000 0.000 0.001 0.001 (0.192) (0.049) (0.684) (0.417)

INTERBANK_RATIO 0.082*** 0.075** 0.069** 0.063** (2.682) (2.374) (2.318) (1.976)

DEBT_TO_GDP_RATIO −0.009 −0.009 −0.010* −0.008 (−1.549) (−1.376) (−1.691) (−1.439)

EUROZONE −0.000 −0.001 0.003 0.002 (−0.019) (−0.375) (1.367) (0.576)

GIIPS 0.003 0.002 0.001 0.000 (0.536) (0.250) (0.246) (0.017)

LIQUIDITY_REGULATION −0.004* −0.003 −0.005** −0.004* (−1.953) (−1.272) (−2.397) (−1.722)

R 2 0.0321 0.0427 0.0308 0.0437 No. of obs. 754 754 754 754 No. of banks 121 121 121 121

(continued on next page)

21We obtain qualitatively very similar results when we substitute the CORE FUNDING RATIO with the Tier 1 capital ratio. These tests are available from the authors.

22The correlation between the Tier 1 capital ratio and the CORE FUNDING RATIO is 0.59.

924 Journal of Financial and Quantitative Analysis

TABLE 4 (continued) Determinants of CARs and CMARs

Dependent Variable CAR(−1,1) CMAR(−1,1) CAR(−1,1) CMAR(−1,1)

Panel B. Market Portfolio Proxy: MSCI Europe

CORE_FUNDING_RATIO −0.064*** −0.060** (−3.712) (−2.523)

LIQUID_ASSETS_RATIO 0.013*** 0.012*** 0.015*** 0.015*** (4.136) (3.631) (3.555) (3.574)

CORE_FUNDING_RATIO_DUMMY 0.005 0.005 (0.788) (0.536)

CORE_FUNDING_RATIO × −0.024*** −0.025** LIQUID_ASSETS_RATIO (−2.578) (−2.207)

CUS_DEP_TO_TOT_ASSETS 0.092*** 0.102*** 0.028** 0.043*** (4.063) (3.450) (2.393) (2.584)

MARKET_TO_BOOK_RATIO 0.001 −0.001 0.002 0.000 (0.365) (−0.258) (0.876) (0.101)

INTERBANK_RATIO 0.090*** 0.091** 0.076*** 0.078** (3.022) (2.524) (2.614) (2.125)

DEBT_TO_GDP_RATIO −0.010 −0.012 −0.010* −0.012* (−1.580) (−1.627) (−1.694) (−1.694)

EUROZONE −0.001 −0.001 0.002 0.002 (−0.344) (−0.314) (1.101) (0.775)

GIIPS 0.004 0.003 0.002 0.001 (0.700) (0.369) (0.352) (0.089)

LIQUIDITY_REGULATION −0.006** −0.004 −0.007** −0.005** (−2.115) (−1.561) (−2.429) (−1.989)

R 2 0.0308 0.0428 0.0284 0.0419 No. of obs. 754 754 754 754 No. of banks 121 121 121 121

Thus, they may be less willing to bear the additional costs of adjusting their asset/liability composition to comply with the new standards.

Shareholders of banks with more high-quality capital seem to view liquidity regulation as particularly onerous and undesirable. To verify this statement em- pirically, we use an interaction between a CORE FUNDING RATIO DUMMY (which equals 1 if the CORE FUNDING RATIO is above the 75th percentile, 0 otherwise) and our proxy for the LCR, the LIQUID ASSETS RATIO. In line with our interpretation, this interaction term enters negatively and significantly, suggesting that banks with a large stable funding base are less exposed to liquid- ity shocks that may arise from a sudden dry-up of short-term funds. Therefore, those banks are also less pressed to maintain large liquidity holdings to buffer such a shock.

The magnitude of the effect is considerable. The values from the second col- umn in Table 4 for the coefficient on the CORE FUNDING RATIO indicate that moving from the sample’s 25th percentile to the 75th percentile of the distribution of the CORE FUNDING RATIO is associated with a decline in the CAR(−1,1) of 1.98% and 2.15% for the models using the MSCI World and the MSCI Europe, respectively. The effects for the ratio of CUST DEP TO TOT ASSETS suggest increases in the CAR(−1,1) of 2.88% for the MSCI World (2.91% for the MSCI Europe). For the LIQUID ASSETS RATIO, these values are 0.35% for the MSCI World and 0.38% for the MSCI Europe. These results suggest that regulation on the LCR (as proxied by the LIQUID ASSETS RATIO) has had a relatively smaller effect on bank share prices than regulation on the NSFR (as proxied by

Bruno, Onali, and Schaeck 925

the CORE FUNDING RATIO). A plausible interpretation for this result is that the regulation on LCR was severely dampened over time (especially in associa- tion with Event 7).

We find some support for the hypothesis that charter values matter. Although the coefficients on MARKET TO BOOK RATIO remain insignificant, the coef- ficients on CUS DEP TO TOT ASSETS are positive and significant, consistent with Hypothesis 3.

Next, we discuss country-level characteristics. Location at the periphery, ap- proximated by the GIIPS dummy variable, and Eurozone membership do not affect the CARs or CMARs.23 However, preexisting domestic liquidity regula- tion, government indebtedness, and interbank market conditions play a role. The INTERBANK RATIO is positively and significantly related to the CARs and CMARs, suggesting that being domiciled in a country with tighter funding condi- tions in the interbank market decreases the CARs and CMARs. This result is con- sistent with Hypothesis 4. There is also some evidence of negative effects from preexisting domestic liquidity regulation, which supports Hypothesis 7A. The co- efficients indicate that shareholders of banks in countries that already had some form of liquidity regulation in place experience more negative wealth effects. The fact that bank-specific variables are more robustly associated with abnormal re- turns than are country-level variables is not surprising. Bank-level characteristics are more likely to play a greater role for the cross-sectional variation in stock price returns than country-level characteristics.

Liquidity-Only Bucket

Table 5 replicates these regressions but excludes the Events 4, 5, and 6 to focus on the liquidity-only bucket. Our findings are similar.

C. Further Robustness and Falsification Tests A potential concern using event-study methodology is that the news re-

leased by the BCBS coincides with bank-specific events such as earnings an- nouncements, ratings downgrades, and changes in corporate governance struc- tures, which also affect share prices. To rule out such concerns, we screen the international press via LexisNexis and use the following keywords: dividends, earnings, CEO, losses, write-downs, restatement, downgrade, rating, fraud, annual

23It may be argued that banks located in highly indebted countries, the GIIPS, may not be able to use their governments’ debts as collateral to borrow from the European Central Bank at all, or only with large haircuts, to obtain the liquidity they need to satisfy liquidity requirements. To address this, we run our baseline regressions again after replacing the dummy variable GIIPS with GIIPS × GOVERNMENT BONDS, where GOVERNMENT BONDS is the ratio of government bonds to total assets. As explained by Gennaioli, Martin, and Rossi (2014), although BankScope does not provide a breakdown of government bonds by nationality, more than 75% of banks’ bondholdings correspond to domestic bonds. Therefore, the interaction between GIIPS and GOVERNMENT BONDS captures the effect of holding a large portion of assets in bonds issued by GIIPS countries. The coefficients on GIIPS× GOVERNMENT BONDS are negative, large, and significant. Results are available from the authors. In unreported tests, we examine whether Eurozone banks are more strongly affected during the Eurozone crisis to further examine the 2-way feedback loop. We use an interaction term between EUROZONE and a dummy for CRISIS years that equals 1 in the years from 2010 onward. The mag- nitude of the coefficient on the interaction term is larger than for the EUROZONE dummy in the main specification, reinforcing the view that the nexus between banking conditions and sovereign debt amplifies the stock price reactions.

926 Journal of Financial and Quantitative Analysis

report, manipulate, inspection, restructuring, M&A, merger, acquisition, stock split, dilution, fired, restructuring, issue, takeover. We then replicate the regres- sions from Tables 4 and 5 but exclude banks for which our news search sug- gests the presence of confounding events over a 3-day window, centered on the event day. Our findings remain unchanged. These tests are relegated to the Internet Appendix, Tables A.2 and A.3.

Although our main tests already correct for heteroscedasticity, equation (1) may not sufficiently capture the impact of volatility changes on the standard errors. The phenomenon that volatility tends to cluster can undermine the assump- tion of constant variance. If volatility increases around the announcement days we study and we ignore such volatility clustering, we may unintentionally overreject the null hypothesis (Boehmer (1991)). To address this issue, Table A.4 of the Internet Appendix presents tests based on generalized autoregressive conditional

TABLE 5 Determinants of CARs and CMARs for the Liquidity-Only Bucket

Table 5 presents tests to explain the effect of bank-specific variables CORE_FUNDING_RATIO, LIQUID_ASSETS_RATIO, MARKET_TO_BOOK_RATIO, CUS_DEP_TO_TOT_ASSETS, and country-specific variables average INTERBANK_RATIO, DEBT_TO_GDP_RATIO, EUROZONE membership, location in GIIPS, and LIQUIDITY_REGULATION on the cross- sectional variation of cumulative abnormal returns (CARs) and cumulative market-adjusted returns (CMARs). These tests are similar to those reported in Table 4, except that we focus on the liquidity-only bucket. Panel A uses the MSCI World as a proxy for the market portfolio and Panel B uses the MSCI Europe as a proxy for the market portfolio. The CARs and CMARs are estimated according to equations (1)–(4). We use random effects regressions with robust standard er- rors clustered at the country level. EUROZONE equals 1 if a country uses the euro currency, and 0 otherwise. GIIPS equals 1 if a bank is located in Greece, Ireland, Italy, Portugal, or Spain, and 0 otherwise. INTERBANK_RATIO is calcu- lated as money lent to other banks by the money borrowed from other banks, averaged for each country and each year. DEBT_TO_GDP_RATIO is the ratio of government debt to Gross Domestic Product for a country. LIQUID_ASSETS_RATIO is the ratio of liquid assets to customer and short-term funding. CORE_FUNDING_RATIO is the sum of Tier 1 capital plus hybrid debt and customer deposits divided by total assets. MARKET_TO_BOOK_RATIO is the ratio of market value of equity divided by common equity. CUS_DEP_TO_TOT_ASSETS is the ratio of customer deposits to total assets. All bank- level variables, CARs, and CMARs are winsorized at the 1st and 99th percentile. t -statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Dependent Variable CAR(−1,1) CMAR(−1,1)

Panel A. Market Portfolio Proxy: MSCI World

CORE_FUNDING_RATIO −0.080** −0.078** (−2.506) (−2.227)

LIQUID_ASSETS_RATIO 0.010*** 0.008** (3.008) (2.376)

MARKET_TO_BOOK_RATIO −0.001 −0.001 (−0.628) (−0.328)

CUS_DEP_TO_TOT_ASSETS 0.103*** 0.101** (2.632) (2.310)

INTERBANK_RATIO 0.046 0.035 (1.243) (0.966)

DEBT_TO_GDP_RATIO −0.004 −0.001 (−0.418) (−0.099)

EUROZONE −0.006** −0.005* (−2.114) (−1.842)

GIIPS 0.005 0.004 (0.911) (0.636)

LIQUIDITY_REGULATION −0.008*** −0.007*** (−4.038) (−3.175)

Constant −0.018** −0.019** (−2.229) (−2.301)

R 2 0.0250 0.0226 No. of obs. 401 401 No. of banks 117 117

(continued on next page)

Bruno, Onali, and Schaeck 927

TABLE 5 (continued) Determinants of CARs and CMARs for the Liquidity-Only Bucket

Panel B. Market Portfolio Proxy: MSCI Europe

CORE_FUNDING_RATIO −0.090*** −0.086** (−2.886) (−2.236)

LIQUID_ASSETS_RATIO 0.011*** 0.009*** (3.031) (2.578)

MARKET_TO_BOOK_RATIO −0.001 −0.000 (−0.401) (−0.063)

CUS_DEP_TO_TOT_ASSETS 0.110*** 0.110** (2.886) (2.335)

INTERBANK_RATIO 0.056 0.059 (1.475) (1.449)

DEBT_TO_GDP_RATIO −0.007 −0.005 (−0.719) (−0.539)

EUROZONE −0.006** −0.005 (−2.139) (−1.523)

GIIPS 0.007 0.005 (1.035) (0.807)

LIQUIDITY_REGULATION −0.010*** −0.008*** (−4.786) (−3.496)

Constant −0.013 −0.017* (−1.467) (−1.862)

R 2 0.0319 0.0281 No. of obs. 401 401 No. of banks 117 117

heteroscedasticity (GARCH) modeling.24 Our inferences remain unchanged when all 7 events are considered. Likewise, the results for the cross-sectional determi- nants of the CARs and CMARs remain unaltered when we exclude the announce- ments associated with a negative effect on the probability to impose stricter liq- uidity rules (i.e., Events 5 and 7).

Falsification Tests

Before offering some concluding remarks, we embark upon a falsification exercise. This analysis raises the question of whether the effects we capture are limited to bank stocks.

We run the tests from Table 3 but use the national indices (see Appendix D) instead of the portfolios of bank stocks to calculate CARs and CMARs. The idea is to analyze whether other firms, that is, nonfinancial corporations, are affected by the announcements by the BCBS. The results are shown in Table A.5 of the Internet Appendix. Out of 18 tests in Panel A that focus on CARs, only the anal- yses for Belgium, Luxembourg, and the United Kingdom enter weakly signifi- cantly; all other tests remain insignificant. Focusing on the liquidity-only bucket

24We estimate the market model similar to equation (1), but according to an AR(1)–GARCH(1,1) model instead of a Prais–Winsten model. The conditional mean equation is estimated as follows:

Ri ,t = αi +βi Rm,t + ρRi ,t−1+

5∑ d=2

λd Dd + εi ,t .

The conditional variance equation is estimated according to:

hi ,t = γ0+ γ1(εi ,t−1)2 + γ2hi ,t−1.

The daily abnormal returns are the residuals of the conditional mean equation.

928 Journal of Financial and Quantitative Analysis

further strengthens these inferences: We find significant effects only for the United Kingdom. Panel B reports on CMARs. We find only one significant association for the full sample (Luxembourg) and one for the liquidity-only bucket (United Kingdom). Thus, for most of the countries in the sample, we can rule out any simultaneous effects for other listed companies arising from liquidity regulation.

VII. Concluding Remarks In this article, we use event-study methodology to present the first analy-

sis of the effect of bank liquidity regulation, a major innovation of the Basel III framework, on bank stock prices. Central to this landmark event in banking regu- lation are two new ratios that address liquidity risk, the NSFR and the LCR.

The regulatory process leading to the introduction of liquidity regulation con- sists of 7 separate but related announcements by the BCBS. Over approximately 5 years, the BCBS made several amendments and revisions to the initial proposal in response to comments received by the banking industry. We exploit this grad- ual release of new information about details of the new regulation to establish the effects on shareholder wealth in terms of CARs and CMARs for listed banks in Europe.

Although proponents of liquidity regulation place great emphasis on point- ing out the role of global liquidity standards for banking system soundness, the controversial debate and criticism highlighted by the banking industry in the run- up to the implementation of the new rules suggests that regulating liquidity is considered costly.

Our first set of results does indeed suggest that market participants respond negatively to announcements about bank liquidity regulation. This finding is con- sistent with the view that complying with liquidity regulations interferes with banks’ asset and liability choices and ultimately reduces profits. However, further tests that separate out stock price reactions to liquidity regulation from responses to announcements to capital regulation and other aspects of Basel III, which occur at the same time and are contained in the same press release, weaken these infer- ences and the average stock price reaction is at best weakly significant. There may be two reasons for the weaker inferences: First, the long time horizon until the rules are fully enforced may have led markets to believe that the new regu- lation is not (yet) binding, and therefore only of marginal value relevance at the time of the press release. This may be further amplified by the challenges posed by any new regulation: Banks can only gradually build the competencies and pro- cedures needed to comply with the rules and may not yet fully comprehend the ramifications that arise from liquidity regulation. Second, and more important, al- though liquidity regulation is likely to gradually become an important tool of the regulatory framework, capital regulation currently remains the dominant compo- nent of Basel II, both in terms of what is considered by markets to be binding and in terms of value relevance. What our tests for the liquidity-only bucket in- dicate is that liquidity regulation currently has some limited potential to affect bank conduct beyond capital regulation. Therefore, these findings can be viewed as suggestive evidence that regulating bank liquidity is a complement to capital regulation.

Bruno, Onali, and Schaeck 929

In addition, our data support the view on regulation according to which not all regulated firms are equally affected. Bank-specific characteristics such as the liquidity of balance sheets and the exposure to funding mismatch decrease neg- ative stock price reactions. We also find some support for theories about the role of charter values, which posit that holding liquidity insures charter values. Country-specific characteristics also play some role. The 2-way feedback loop be- tween the domestic banking industry and the sovereign’s creditworthiness affects shareholder wealth effects. High government indebtedness and strained fund- ing conditions in the domestic interbank market reinforce each other, and banks from countries with these characteristics display bigger share price reactions than elsewhere in Europe. The CARs are lower in countries where banks are subject to domestic liquidity regulation in place before the announcements by the BCBS.

Clearly, our initial exploration of the market reaction to the introduction of quantity regulation of bank liquidity can be viewed only as a starting point for a more comprehensive research agenda that explores the effects of liquidity regu- lation. Ultimately, more work is required to better understand how the new rules will affect bank conduct, and asset and liability composition. Additional work is also needed to establish how the new rules affect the soundness of individual banks and the financial system on the whole. We leave these questions for future research.

Appendix A. The Liquidity Standards The liquidity coverage ratio (LCR) aims to ensure that a bank has an adequate stock

of unencumbered high-quality liquid assets (HQLA) that consist of cash or assets that can be converted into cash at little or no loss of value in private markets to meet its liquidity needs for a 30-calendar-day liquidity stress scenario:

LCR = Stock of HQLA

Total Net Cash Outflows over the Next 30 Calendar Days ≥ 100%.

As in its final version (Jan. 7, 2013), to qualify as HQLA (the numerator of the ratio), assets should be liquid in markets during a time of stress and, in most cases, be eligible for use in central bank operations. HQLA are composed of Level 1 and Level 2 assets. Level 2 assets are subject to limits and a range of haircuts conditional on their market liquidity.

The denominator of the LCR is the total net cash outflows, that is, total expected cash outflows minus total expected cash inflows. Expected cash outflows (inflows) are calculated by multiplying the outstanding balances of various categories or types of liabilities and off- balance-sheet commitments with the rates at which they are expected to run off or be drawn down.25 Banks are expected to meet this requirement on an ongoing basis. However, during a period of financial stress, banks are allowed to use their stock of HQLA, thereby falling below 100%.26 The standard was introduced on Jan. 1, 2015, but a gradual approach will be followed, as the minimum requirement will be initially set at 60% to rise in equal annual steps to reach 100% on Jan. 1, 2019, as reported in Table A1.

25Total cash inflows are subject to an aggregate cap of 75% of total expected cash outflows, thereby ensuring a minimum level of HQLA holdings at all times.

26Nonetheless, the LCR standard is intended as a minimum level of liquidity for internationally active banks; national authorities may require higher minimum level of liquidity, especially if they deem that the LCR does not adequately reflect the liquidity risks that supervised banks face.

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TABLE A1 Evolution of LCR

Table A1 shows the evolution of LCR from Jan. 1, 2015 to Jan. 1, 2019.

Jan. 1, 2015 Jan. 1, 2016 Jan. 1, 2017 Jan. 1, 2018 Jan. 1, 2019

Minimum Minimum Minimum Minimum Minimum LCR = 60% LCR = 70% LCR = 80% LCR = 90% LCR = 100%

The net stable funding ratio (NSFR) establishes a minimum acceptable amount of stable funding based on the liquidity characteristics of an institution’s assets and activities over a 1-year horizon. The NSFR aims to limit overreliance on wholesale funding during times of buoyant market liquidity and encourage better assessment of liquidity risk across all on- and off-balance-sheet items. In addition, the NSFR approach helps counterbalance incentives for institutions to fund their stock of liquid assets with short-term funds that ma- ture just outside the supervisory-defined horizon for that metric. The standard is expressed as the ratio:

NSFR = Available Amount of Stable Funding Required Amount of Stable Funding

≥ 100%.

As for the numerator, “stable funding” is those types and amounts of equity and liability financing expected to be reliable sources of funds over a 1-year horizon under conditions of extended stress. The amount of such funding required for a specific institution (the denominator of the ratio) is a function of the liquidity characteristics of various types of assets, off-balance-sheet contingent exposures, and/or activities pursued by the institution. Liabilities and assets are weighted according to their stability and liquidity characteristics, respectively. The standard was introduced by Jan. 1, 2018.

Appendix B. Event Description Event 1 (Feb. 21, 2008): The Basel Committee on Banking Supervision (BCBS) releases a

document entitled Liquidity Risk: Management and Supervisory Challenges. It sum- marizes the key findings of a study carried out by the Working Group on Liquidity and aims to review banks’ liquidity risk management strategies as well as liquidity supervision practices in member countries.

Event 2 (June 17, 2008): The BCBS issues for public comment the document on enhanced global Principles for Sound Liquidity Risk Management and Supervision. This guid- ance discusses the key elements of a robust framework for liquidity risk management. Such elements include: board and senior management oversight; the establishment of policies and risk tolerance; the use of liquidity risk management tools such as comprehensive cash flow forecasting, limits, and liquidity scenario stress testing; the development of robust and multifaceted contingency funding plans; and the mainte- nance of a sufficient cushion of high-quality liquid assets (HQLA) to meet contingent liquidity needs.

Event 3 (Sept. 25, 2008): Global bank supervisors endorse strengthened sound practice standards for liquidity risk management and supervision. The final document on Prin- ciples for Sound Liquidity Risk Management and Supervision is released.

Event 4 (Dec. 17, 2009): The BCBS issues for consultation a package of proposals to strengthen global capital and liquidity regulations with the goal of promoting a more resilient banking sector. As far as bank liquidity is concerned, the International Framework for Liquidity Risk Measurement, Standards and Monitoring (consulta- tive document) is released. The document introduces two internationally consistent

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liquidity standards (the liquidity coverage ratio (LCR) and net stable funding ra- tio (NSFR)). It also comprises a set of common metrics that should be considered the minimum types of information supervisors should use in monitoring the liquid- ity risk profiles of supervised entities. The proposed set of monitoring tools refers in particular to: contractual maturity mismatch, concentration of funding, available unencumbered assets, and market-related monitoring tools.

Event 5 (July 26, 2010): The Group of Governors and Heads of Supervision (GHOS), the oversight body of the BCBS, meets to review the BCBS capital and liquidity reform package. Main revisions on the liquidity rules deal with: i) LCR: relaxing the def- inition of qualifying liquid assets (e.g., by including high-quality corporate bonds and covered bonds) and introducing a more favorable treatment of certain liabilities (e.g., a lower run-off rate floor for retail and small and medium enterprises (SMEs) deposits), and ii) NSFR: a more favorable treatment of the retail business (e.g., by increasing the available stable funding factor for retail and SME deposits and low- ering the required stable funding ratio for residential mortgages). However, at this stage the BCBS states that both standards require further observation and a number of adjustments. As for the LCR, examples of measures to be refined include the de- velopment of standards for jurisdictions that do not have sufficient Level 1 assets to meet the standard, the introduction of a percentage factor to measure cash inflows, and a clearer definition of operational activities with financial institution counterpar- ties (e.g., custody, clearing and settlement, cash management activities). The BCBS declares that the NSFR requires an “observation phase” to address any unintended consequences across business models or funding structures before finalizing and in- troducing the revised NSFR as a minimum standard by Jan. 1, 2018.

Event 6 (Dec. 16, 2010): The BCBS issues the Basel III rules text, presenting the de- tails of global regulatory standards on bank capital adequacy and liquidity agreed by the GHOS and endorsed by the G20, an international forum including the govern- ments and central bank governors of 19 countries plus the European Union, at their November summit in Seoul. The BCBS also publishes the results of its comprehen- sive quantitative impact study. In particular, the final version of the document Basel III: International Framework for Liquidity Risk Measurement, Standards and Moni- toring is released. The document incorporates and refines amendments announced in broad terms in July 2010. No substantial changes have been made to the NSFR.

Event 7 (Jan. 7, 2013): The BCBS issues the full text of the document Basel III: The Liq- uidity Coverage Ratio and Liquidity Risk Monitoring Tools following endorsement on Jan. 6, 2013 by the GHOS. The revisions to the LCR developed and agreed by the BCBS over the past 2 years include an expansion in the range of assets eligible as HQLA and some refinements to the assumed inflow and outflow rates to better reflect actual experience in times of stress.

The main measures are summarized as follows:

i) The expansion of the list of HQLA by the introduction of Level 2B assets (subject to higher haircuts and a limit of 15% of total HQLA), including corporate debt securities rated A+ to BBB− and certain unencumbered equities (both subject to a 50% haircut), certain residential mortgage-backed securities rated AA or higher (with a 25% haircut);

ii) More favorable treatment of: insured deposits, by a lower outflow on certain types of fully insured retail deposits (from 5% to 3%); fully insured nonoperational deposits from nonfinancial corporates, sovereigns, central banks, and public sector entities (from 40% to 20%); and “nonoperational” deposits provided by nonfinancial corpo- rates, sovereigns, central banks, and public sector entities (from 75% to 40%);

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iii) More favorable treatment of committed liquidity facilities to nonfinancial corpo- rates with the reduction of the drawdown rate on the unused portion of committed liquidity facilities to nonfinancial corporates, sovereigns central banks, and public sector entities (from 100% to 30%). Similarly, better treatment has been applied to interbank credit and liquidity facilities (distinguished from interfinancial credit facilities) to reduce the outflow rate on the former from 100% to 40%;

iv) Better treatment of central bank operations by reducing the outflow rate on maturing secured funding transactions with central banks from 25% to 0%; trade finance, including guidance to indicate that a low outflow rate (0%–5%) is expected to apply;

v) New and standardized treatment for derivatives positions, comprising additional derivative risks included in the LCR with a 100% outflow (relates to collateral substitution and excess collateral that the bank is contractually obligated to re- turn/provide if required by a counterparty); a standardized approach for liquidity risk related to market value changes in derivatives positions; and net outflow of 0% for derivatives (and commitments) that are contractually secured/collateralized by HQLA.

In addition, the BCBS has agreed a revised timetable to phase in the standard and to give effect to the BCBS intention for the stock of liquid assets to be used.

Appendix C. Banks without Deposits or with Negative Equity for 1 or More Years Ageas SA/NV Alpha Bank AE Azimut Holding SpA Bankia SA Brewin Dolphin Holdings Plc Eurobank Ergasias SA, Exor Spa Groupe Bruxelles Lambert Institut Régional de Dévelopement de la Région Nord

Pas-de-Calais-I.R.D. Nord Pas-de-Calais Marfin Investment Group National Bank of Greece SA Paragon Group of Companies Plc Pargesa Holding SA Piraeus Bank SA Robeco NV, Sampo Plc SOFIBUS Patrimoine Swiss Life Holding Tekfen Holding AS Cofitem–Cofimur

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Appendix D. National Market Indices All indices are price indices.

Italy: FTSE MIB INDEX Germany: CDAX GENERAL ‘KURS’ Greece: DJGL GREECE DJTM GREECE Portugal: PORTUGAL-DS Market Spain: IBEX 35 Ireland: IRELAND SE OVERALL (ISEQ) United Kingdom: FTSE 250 Switzerland: FTSE SWITZERLAND France: FRANCE CAC 40 Sweden: OMX STOCKHOLM 30 (OMXS30) Belgium: BEL ALL SHARE Austria: DJGL AUSTRIA DJTM AUSTRIA Cyprus: CYPRUS GENERAL Denmark: OMX COPENHAGEN (OMXC20) Finland: OMX HELSINKI (OMXH) Luxembourg: LUXEMBOURG SE GENERAL Malta: MALTA SE MSE Netherlands: AEX ALL SHARE

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