The impact of Basel III liquidity announcements on stock returns

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The Impact of Basel III Liquidity Announcements on

Stock Returns

MSc Business Economics Thesis

Author: Nikki Paes

Student Number: 6152937 Specialization: Finance

Supervisor: Dr. T. Yorulmazer

Institution: University of Amsterdam, Amsterdam Business School Date of Submission: 07-07-2015

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Statement of Originality

This document is written by Student Nikki Paes, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

This study investigates the stock market reaction to press releases by the Basel Committee and the Federal Reserve, concerning the regulation of liquidity for banks in the United States. These press releases include the proposals and revisions of the Basel III liquidity requirements, which aim to improve the stability and safety of the banking sector. An event study is used to investigate the abnormal stock returns around four press releases. Univariate and multivariate regression analyses are conducted to measure the effect on abnormal returns conditional on the size and liquidity position of commercial banks. The results imply that the market expects the liquidity requirements to reduce the profitability of banks, leading to lower stock returns. This effect is stronger for banks with low liquidity compared to banks with high liquidity. The size of banks displays minimal significant differences in market reaction to the press releases.

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List of Abbreviations

AR Abnormal Returns

ASF Available Stable Funding

BCBS Basel Committee on Banking Supervision BHAR Buy and Hold Abnormal Return

BHR Buy and Hold Return

BIS Bank for International Settlements CAPM Capital Asset Pricing Model CAR Cumulative Abnormal Return CCM CRSP/Compustat Merged

CRSP Center for Research in Security Prices EMH Efficient Market Hypothesis

FDIC Federal Deposit Insurance Corporation Fed Federal Reserve

FFIEC Federal Financial Institutions Examination Council GDP Gross Domestic Product

GHOS Group of Governors and Heads of Supervision HQLA High Quality Liquid Assets

ISIN International Securities Identification Number LCR Liquidity Coverage Ratio

MMF Monet Market Funds MTB Market-to-Book ratio NSFR Net Stable Funding Ratio

OECD Organization for Economic Co-operation and Development OLS Ordinary least squares

PR Predicted Return

QIS Quantitative impact study ROE Return on Equity

RSF Required Stable Funding

SIC Standard Industrial Classification UBPR Uniform Bank Performance Report

US United States

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1. Introduction

The recent financial crisis in 2008 revealed the weaknesses in the global regulatory framework of the banking sector. One of the main causes of the crisis was the built up of excessive on- and off-balance sheet leverage, accompanied with a lowered quality of bank’s capital bases and the holding of insufficient liquidity buffers (Bank for International Settlements (BIS), 2010). The regulatory system allowed and sometimes even encouraged the built up of the excessive leverage and maturity transformation by banks both in the United States and Europe. Due to the protection of banks from liquidity shocks through deposit insurance and the availability of liquidity from central banks, the lenders of last resort, banks are incentivized to engage in excessive forbearance and suboptimal lending (Kahn, 2005). This resulted in systemic trading losses and credit losses during the crisis, which could not be absorbed by the banking system.

Maturity transformation is an important function of the financial system. Banks transform debt with very short maturities, such as deposits from savers and money market loans, into credits with very long maturities, such as mortgages. The risk arising from maturity transformation plays a major role in bank failure (Brunnermeier and Oehmke, 2013). As banks are often exposed to investor and deposit runs, they should be able to quickly trade their assets in the market to prevent them from credit losses. The overreliance on the wholesale markets makes the banks vulnerable, which was the case with Northern Rock and Landsbanki in 2008. The liquidity risk, stemming from the possibility of bank runs, was historically managed by holding required reserves on deposits followed by deposit insurance and the lender of last resort. During the crisis it became clear that there was need for an improvement in the ability of the banking sector to absorb shocks arising from financial and economic stress. Regulators started to discuss new methods to reduce liquidity risk and increase the stability of the banking sector, leading to a reinforcement of the regulatory framework.

The Basel Committee on Banking Supervision (BCBS)1 responded with the introduction of the capital and liquidity requirements of Basel III in 2010. The focus of the new bank regulations in Basel III was to raise the quality and transparency of the capital base, improve

1_{The Basel Committee on Banking Supervision is a group of international banking supervisory authorities that} was established by the central bank governors of the Group of Ten countries in 1974. They seek to improve regulation, supervision and practices of banks and financial stability worldwide. The BCBS’s Secretariat is located at the Bank for International Settlements (BIS) in Basel, Switzerland.

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governance and increase the liquidity risk management (Blundell-Wignall and Atkinson, 2010). This study focuses on the liquidity framework of the Basel III accord. The Basel Committee wanted to improve the ability of banks to roll over their short-term financing and thereby raise the resilience against liquidity shocks. The BCBS introduced the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR). These ratios require banks to hold more liquid assets or longer-term funding, improving the ability of banks to withstand liquidity shocks.

These new introduced requirements generated concerns among the shareholders of the banks. The new requirements could generate costs and thereby reduce the profitability of banks (Saunders and Cornett, 2008). This might change the beliefs of the shareholders and consequently affect stock returns negatively. It took five years of negotiating to design the regulatory liquidity requirements such that they would have the intended effects on the stability of banks. During the five year period after the financial crisis of 2008, the BCBS and the Federal Reserve (Fed) revealed information about the liquidity standards in press releases. Four press releases are considered to be important releases leading to the introduction of the liquidity requirements. The focus of this research paper is on US commercial banks, to exclude country specific variations in the implementation of the requirements globally. This research will analyze the market reaction to the proposals and revisions of the liquidity standards, by investigating the stock returns of US banks around the time of the announcements on the liquidity standards. The main research question is:

What is the impact of announcements about the Basel III liquidity requirements on stock returns of US banks, conditional on different bank characteristics?

Hypothesized are decreasing stock returns around the 1st and the 2nd press release by the BCBS, which announced the proposed and final liquidity requirements. The stock returns are expected to increase around the 3rd press release by the BCBS, which announced a relaxation of the liquidity requirements. The 4th press release by the Fed, which announced a further tightening of the requirements, was expected to have a negative effect on stock returns. Hypothesized is that these effects on stock returns will be stronger for large banks with low liquidity, as the liquidity requirements are expected to impose a heavier burden on these banks.

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Because the LCR and the NSFR are not being implemented completely before 2018, this research uses a combination of market data and accounting data to measure the impact of the announcements on stock returns. To analyze the market reaction to the liquidity related press releases by the BCBS and the Fed, an event study is used. Abnormal Returns (AR) are calculated on the days surrounding the announcements and the Cumulative Abnormal Returns (CAR) is calculated within different short-term event windows. Accordingly, the CARs are regressed on different bank characteristics. The size of banks, measured by the total amount of assets, and the liquidity level of banks, measured by a liquidity ratio, are key bank characteristics that are expected to affect the market reaction. Three additional robustness tests provide evidence for the effect of these bank characteristics on abnormal returns. The first robustness test uses a longer term event window to capture possible information leakage and delay in market reaction. The second robustness test uses quarterly data on the liquidity position of the banks for the year 2013, derived from Uniform Bank Performance Reports (UBPR) of the commercial banks in the sample. The final robustness test uses the buy and hold abnormal return method, which captures the effect of compounding within the event window.

Most of the existing literature focuses on the macroeconomic impacts of Basel III. Slovik and Cournède (2011) investigated the effect of Basel III on GDP growth and Angelini et al. (2014) on the long-term economic impact. Other literature investigated the wealth effect of earlier Basel agreements (Eyssel & Arshadi, 1990). The unintended effects of the Basel III requirements are the reason to research the market responses, to provide new insight into the ongoing policy debate about regulating liquidity. By focusing explicitly on liquidity regulation and by including more recent announcements, this study aims to create a better understanding of the recent developments in liquidity regulation and its consequences. Additionally, by investigating the impact of the announcements within different groups of banks with high and low liquidity and small-, medium- and large-sized banks, this study contributes to the existing literature.

Section 2 describes the institutional background. Section 3 discusses the related literature. Section 4 introduces the hypotheses and section 5 presents the research methodology. Section 6 describes the data and descriptive statistics. Section 7 contains the empirical results of this study and section 8 additional robustness checks. Section 9 provides the conclusions.

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2. Institutional Background

In 1988 the first Basel Accord was introduced. Central bankers set minimum capital requirements to ensure that banks can absorb sufficient losses through its shareholders’ equity rather than through customer deposits or other funding sources. Basel I and Basel II mainly focus on bank loss reserves, but the recent financial crisis revealed that these requirements were not sufficient to ensure banking stability. Many banks experienced difficulties, despite having adequate capital levels, due to shortcomings in their liquidity base (BIS, 2013). It became clear that there was need for a more robust liquidity base to improve liquidity management and banks’ ability to withstand liquidity shocks. As a result Basel III constitutes a change by introducing global standards for bank liquidity to address the previously neglected role of liquidity risk (Brunnermeier, 2009). In a response to the financial crisis the Group of Governors and Heads of Supervision (GHOS), the oversight body of the BCBS, met in 2008 to propose new international capital and liquidity requirements, to protect the economy from financial crises in the future. The information of the negotiations is made publically through press releases. An overview of the press releases is provided in Table 1.2

After intensive discussions during and after the crisis, the BCBS released a proposal for the framework for liquidity risk measurement, standards and monitoring on December 17, 2009. The framework aims to elevate the resilience of banks to adverse liquidity shocks, by introducing two liquidity ratios (BIS, 2009). The LCR had to strengthen the banks’ ability to withstand liquidity shocks over a short term period (30 day period), and is introduced in 2015. The assets are weighted according to their liquidity and the liabilities are weighted according to their run off rates.3 The NSFR promotes longer term resilience (one-year period) and will be introduced in 2018.

After intensive discussions, on the 16th of December 2010 the BCBS publicly revealed the final version of the Basel III liquidity framework (BIS, 2010). This announcement was considered by the BCBS as an important step in protecting financial stability and promoting economic growth. It contains the final minimum LCR and NSFR that regulated banks had to hold, and additional certainty about both the detailed regulations and the implementation timeline (BIS Press Releases, 2010). A few changes have been made to the ratios announced

2

The content of the press releases is included in Appendix 3. 3_{Additional information about the LCR is included in Appendix 2.}

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in December 2009, mainly about the qualification of the High Quality Liquid Assets (HQLA) in the LCR.

The banking industry was very critical about the new requirements. Banks complained that the Basel III liquidity standard would force them to cut back lending to customers and businesses. To ensure that the LCR could be introduced without disruption to the orderly strengthening of banking systems or the ongoing financing of economic activity, they designed a more gradual approach (BIS, 2013). The BCBS renegotiated the Basel Committee’s amendments to the LCR as a minimum standard, and announced the revised version on January 6, 2013. The GHOS agreed to phase-in the LCR, requiring the minimum to begin at 60% in 2015, rising in equal steps of 10 percentage points per year to reach 100% at the 1st of January 2019. Additionally, banks were allowed to fall below the LCR requirement during periods of stress, using a buffer for liquidity.

On the 24th of October 2013, more than eight months after the BCBS announced the new phase-in LCR, the Federal Reserve proposed a rule to strengthen the liquidity position of US financial institutions further (Board of Governors of the Federal Reserve System, 2013). This rule is generally consistent with the BCBS’s LCR standard, but is more stringent in several areas. The Fed introduced a full-LCR, which applies to banking organizations with total consolidated assets of more than $250 billion or more than $10 billion in on-balance sheet foreign exposure. They also introduced a modified-LCR which applies to banking organizations with total consolidated assets between $50 and $250 billion. Banking organizations with assets less than $50 billion are not subject to the LCR by the Fed and remain subject to the framework by the BCBS. The proposed transition period of this LCR is shorter than that included in the Basel agreement. The LCR by the Fed requires 80% compliance starting at the 1st of January 2015, 90% compliance starting at the 1st of January 2016 and 100% compliance starting at the 1st of January 2017. Furthermore, the range of assets that will qualify as HQLA and the assumed rate of outflows of certain kinds of funding are different compared to the BCBS’s LCR. The modified LCR requires holding enough HQLA to cover 21 days of net cash outflow, instead of 30 days. Cash outflows are generally 70% of the 30-day period of the full-LCR and do not include the requirement to calculate the peak cumulative outflows. These differences with the BCBS proposal were unexpected and intensely discussed during the US proposals comment period closing January 31, 2014.

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7 Table 1. Overview of the liquidity related press releases No. Ann. Date Short Description

1 17-Dec-09 Announcement by the BCBS: Release of the proposal for an International Framework (Thursday) for Liquidity Risk Management Standards and Monitoring.

2 16-Dec-10 Announcement by the BCBS: Release of the final International Framework for (Thursday) Liquidity Risk Management Standards and Monitoring.

3 06-Jan-13 Announcement by the BCBS: Release of the revision of The Liquidity Coverage Ratio (Sunday) and Liquidity Risk Monitoring Tools

4 24-Oct-13 Announcement by the Fed: Release of a rule to strengthen the liquidity positions of

(Thursday) large financial institutions.

Note: Announcement 3 falls on a Sunday, therefore the next first trading day is used as the event date.

3. Literature Review

In order to develop empirical predictions for the market reaction to changes in liquidity regulation and the background of liquidity regulation, this section includes the related literature.

3.1. Reserve Requirements

Historically the US imposed reserve requirements at the national level in 1863 with the passage of the National Bank Act, enabling national banks to issue national bank notes and requiring them to hold a 25% reserve against such notes and deposits. In the late 19th and 20th centuries, bank runs and panics demonstrated that these reserve requirements where not substantial to convert the deposits for the entire banking system, because the reserves could not meet a customer’s demand for cash and at the same time satisfy reserve requirements (Calomiris and Gorton, 1991). In 1913 the Federal Reserve System was created to improve the stability of the financial system. After the introduction of the system, reserve requirements were still imposed to insure against unexpected withdrawals. The reserve requirement was a minimum value of the ratio of required reserves to a category of deposits held at depository institutions. But with the introduction of the Fed, institutions that failed the requirements, could borrow from a Federal Reserve Bank or from an institution holding excess reserves (Bouwman, 2013). They act as lenders of last resort in periods of extreme stress.

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Since membership of the Fed was optional for state-chartered banks, in 1970 some banks began to leave the System due to rising interest rates. These rising interest rates increased the costs that banks incurred to satisfy the reserve requirements at the Fed, because the Fed did not pay interest on reserves. This changed on October 3, 2008, when the Emergency Economic Stabilization Act of 2008 authorized the Fed to start paying interest on excess reserve balances and required reserves (Board of Governors of the Federal Reserve System, 2008). This authorization resulted in large increases in reserves (Figure 1). Banks did not want to lend anymore since earning a risk-free return on their funds at the Fed was more lucrative (Bouwman, 2013). This money was mainly coming from money market funds (MMF), due to a run on MMFs that developed in September 2008. This run followed the failure of Lehman Brothers on September 14, 2008, as the asset value of the Reserve Primary Fund that held Lehman debt securities, market below $1 per share (Schmidt et al., 2012). Figure 1: Total Reserves and Required Reserves in $ Billion (January 1960 to April 2013)

Source: Bouwman (2013), using data from Aggregate Reserves of Depository Institutions and the Monetary Base, Not Seasonally Adjusted.

The intentions of the Fed for paying interest on the reserves was to improve the lending programs while maintaining the federal funds rate close to the target established by the Federal Open Market Committee. The large increase in excess reserves implies that the incentive to lend to firms and households was reduced. Consequently, due to these lending and money policies by the Fed the reserve requirements became ineffective (Keister et al., 2009).

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9 3.2. The Liquidity Ratios

During the financial crisis in 2008, regulators became concerned with the liquidity position of financial institutions, leading to a strengthening of the liquidity framework by introducing the LCR and the NSFR. According to Accenture (2013), the regulators seek to achieve four goals with the introduction of the ratios. The first goal is to promote the short-term resilience of bank’s liquidity risk profile and secondly, to improve the banking sector’s ability to absorb shocks that arise from financial and economic stress. Thirdly, they should provide a sustainable maturity structure for assets and liabilities and finally, they should incentivize banks to fund their activities with more stable sources of funding.

The focus of the LCR is on short-term resilience of the liquidity risk profile of a bank. The ratio requires sufficient HQLA to survive a 30-day stress scenario, without attracting new funds. HQLAs are weighted according to their level of liquidity, and consist of cash or assets that can be quickly converted into cash. The denominator of the LCR is the net cash outflows, which have weights according to their withdrawal risk.4

𝐿𝐶𝑅 = Stock of HQLA

Total net cash outflows of the next 30 days≥ 100%

The HQLAs are expensive and have relatively low returns due to their lower risk profile. This coincides with Merton’s theoretical analysis (1973), in which there is a positive relation between expected excess returns and risk. Less risk is associated with a greater probability of low returns and more risk is associated with a greater probability of high returns. This risk return tradeoff is formulated in the Intertemporal Capital Asset Pricing Model (ICAPM) by Merton, which implies that the expected returns on a portfolio should be equal to the risk- free rate plus a risk premium. The HQLAs are less risky assets because they can be easily and immediately converted into cash at little or no loss of value. As HQLAs tend to generate lower returns due to their lower risk profile, they are very expensive assets to put on a bank’s balance sheet. Therefore the market expectation is that the holding of expensive HQLAs will have a negative effect on the profitability of the banks (KPMG, 2012).

The focus of the NSFR is to ensure that bank activities are funded with more stable sources of funding on a structural basis. The long-term period of one year limits the reliance on short

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term liabilities. Therefore the NSFR complements the LCR by promoting more stable, longer-term funding instead of the short longer-term funding mismatches in the LCR. The NSFR is designed to limit the risk of short term funding against long term obligations, the maturity transformation risk. The NSFR is defined as the amount of available stable funding (ASF) relative to the amount of required stable funding (RSF), which should be equal to at least 100% on an ongoing basis.

𝑁𝑆𝐹𝑅 = Available amount of Stable Funding (ASF)

Required amount of Stable Funding (RSF)≥ 100%

ASF consists of the portion of capital and liabilities expected to be reliable over one year. The RSF is a function of the liquidity characteristics and residual maturities of the various assets held by that institution, and those of its off-balance sheet exposures (BIS, 2013).

3.3. Market Reaction

The introduction of the liquidity framework was closely followed by the banking industry and became subject to an ongoing debate about liquidity regulation. During the five years after the proposed liquidity framework, several revisions of the original framework were announced. This research measures the market reaction to the proposals and revisions of the liquidity standards, to derive a better understanding of the debate and the decisions.

The aim of regulating liquidity is to stabilize banks and improve the safety of investing in banks. The decision of investors to buy banks equity depends on their expectations of the banks safeness and future returns, therefore the announcements on the new regulation are expected to affect the banks stock returns. The strengthening of the resilience of banks due to the liquidity regulation may eventually improve economic growth. If investors believe that the regulators will achieve the stabilization and improved safety of the banking sector, it could positively affect equity values (Blundell-Wignall and Atkinson, 2010). On the other hand, this improved safety and stability of the banking sector comes at a cost. Banks will need to hold more high quality liquid assets and shortening the maturity of some lending on the asset side or they have to hold more retail deposits and more longer-term wholesale funding on the liabilities side. And as many banks will be attempting to take the same actions at the same time, additional costs may arise (KPMG, 2012). Investors might expect a decrease in profitability as a result of the increasing costs by holding more expensive high quality liquid assets (Bordeleau and Graham, 2010). The new regulations can drive stock returns in both

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directions depending on investor beliefs about future stock returns. The market reaction to the revision of the liquidity requirements depends on whether the investors believe the revisions to be a relaxation or a tightening of the original requirements. According to Schwert (1981) investors seek regulation that increases security prices, and at the same time they avoid regulation that decreases security prices. This can transfer wealth from one bank to another, depending on different bank characteristics. In the worst case it can lead to investors’ overall reluctance to invest in bank equity.

3.4. Bank Characteristics

Banks with different characteristics are likely to be differently affected by the announcements about the liquidity framework. The liquidity of the banks’ balance sheet may affect the market reaction to announcements about the new liquidity ratios. According to Saunders and Cornett (2008), banks that hold more liquid assets in the form of loans or securitized assets are less exposed to liquidity shocks. By holding more liquid assets and assets of higher quality, the availability of collateral for the bank will be improved and it will signal that it is solvent. This contributes to accessing external funding from money markets and capital markets, especially when the conditions for liquidity tighten (Ratnovski, 2013). To attract external financing it is not only important to hold more liquid assets, a liquidity buffer, but also to increase the transparency of the bank and enhance its ability to communicate solvency information. These characteristics affect the investor’s beliefs about the stability of the bank.

The size of banks is also considered to be an important factor in the reaction by the market to the liquidity announcements. According to Berger and Bouwman (2009), large banks can create liquidity more easily than smaller banks, as they have easier access to the lender of last resort and are the first to benefit from the safety net. The tendency of large banks to rely on government intervention in case of shortages in combination with the ‘too big to fail’ status, results in the holding of lower liquidity levels for large banks (Vodová, 2011). The aim of the liquidity requirements is to ensure that banks, in normal times, hold sufficient liquid assets and have a sound funding structure, such that central banks are only asked to perform as lenders of last resort in extreme stress levels.

Moreover, size is an important determinant in funding sources. Large banks more typically rely on short-term wholesale funding, making them less financially stable. Cornett et al. (2011) show that the withdrawal of funds from wholesale deposits and loss of other sources of

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short-term financing are causing more liquidity risk than the loss of demand deposits. Consequently, large banks encounter more liquidity risk due to their unstable sources of funding. The introduction of the liquidity framework by the BCBS aims to encourage banks to use more stable funding sources and more sustainable maturity structures of assets and liabilities to reduce liquidity risk. Larger banks are better able to raise funds in money markets and capital markets, enabling them to diversify their funding sources and investors to reduce the effects of funding shocks (Kashyap, 1999). As larger banks have access to a wider variety of borrowers and a broader deposit base, they trade assets and liabilities more quickly and cheaply and thereby create liquidity more easily than smaller banks (Demsetz, 1995). Consequently, larger and more diversified banks have less need to build up liquidity buffers. These papers suggests that large banks are more likely to have lower liquidity levels due to government intervention, funding sources and access to more markets, which indicate a stronger effect of the liquidity requirements for larger banks. According to the most recent BCBS Quantitative Impact Study (QIS) for European banks, published in September 2012, there was an aggregate shortfall of Euro 1.8tn for the banks that would have failed to meet the 100% LCR target as at December 2011. The majority of the larger banks (group 1) were below the liquidity requirement standard, whereas the majority of the smaller banks (Group 2) exceeded it. This QIS indicates that larger banks are therefore more likely to be hit by the liquidity standards, and will benefit more when the criteria for HQLA are eased or by phasing in the period (BIS, 2012).

3.5. Efficient Markets

To investigate the impact of the announcements on stock returns, the efficient market hypothesis (EMH) by Fama (1965) is considered. The EMH focuses on efficient markets where prices instantly reflect all relevant information available in the market. As the existing stock prices always reflect all relevant available information, one cannot identify under or overvalued stock before the market does. If prices only reflect publicly available information, the market is called semi-strong efficient. This thesis follows Damodaran (2013) and assumes that the regulated banks stock market is semi-strong efficient. The information made public with the press releases on the Basel III liquidity requirements is expected to be significant and therefore reflected in the stock prices. Sheila Bair, chairman of the US Federal Deposit Insurance Corporation in the period from 2006-2011, gives the impression that the outcomes of the meetings were not determined prior to each meeting (Bair, 2012). This reduces the

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possibility of relevant information leakage prior to the negotiations and reduces the anticipation effects.

3.6. Shareholder Wealth Effects of Basel Agreements

This study focuses on how shareholder wealth is affected by the introduction of the new Basel III liquidity requirements. Earlier papers have researched the implications of regulatory changes by the Basel agreements on shareholder wealth. The paper by Eyssel and Arshadi, (1990) investigates the wealth effects of Basel I. They examined the wealth effects of announcements by the BCBS to impose a pre-determined minimum level of risk-adjusted capital across international boundaries. They calculated the abnormal returns around four regulatory announcements concerning the proposed capital requirements, agreements on a revision of the requirements and the final version of the requirements. These announcements led to an overall decrease in the equity values of large publicly traded banks, with a less negative reaction to the announcements about the revised requirements. They also found that banks with capital levels that were deficient relative to the mandated levels faced the largest value losses. The findings by Eyssel and Arshadi are in line with a paper by Cooper, Kolari and Wagster (1991), who estimated the market reaction to numerous announcements about Basel I on the international capital requirements in different countries (USA, Canada, UK and Japan). The most important conclusion from their study was that Canadian, US and British banks would be adversely affected by the new capital regulation. They found significant declines in equity returns for these banks in response to news announcements, with US bank stocks exhibiting the largest negative reaction. The paper by Lu, Shen and So (1999) examines the market reaction to announcements about Basel I especially for small commercial banks in the US. They applied an event study around an announcement by the Fed on the 24th of January 1986, describing the proposed risk-adjusted capital requirements. Their empirical results show that the imposition of the risk-adjusted capital requirements had a non-positive price effect on stock returns of big commercial banks and a positive effect on stock returns of small banks. These findings supported the notion that the Accord created wealth effects in the banking sector.

The introduction of the Basel III capital and liquidity requirements is subject to a paper by Härle et al. (2010). They explain that due to the introduction of these requirements, the banks faced a significant challenge particularly to achieve technical compliance with the new rules and ratios, and to reorient the institution for success. They expect that the new liquidity

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requirements affect the profitability of European banks negatively and significantly reduced their return on equity on a long-term basis. They estimated a gap of $800 billion in short-term liquidity and a gap of $3.2 trillion in long-term funding. These gaps were expected to have a substantial impact on profitability when closing and therefore the Basel III would reduce the return on equity for the bank by about 3 percentage points in the US.

3.7. Macro-economic effects of Basel III

Much research has been done on the macroeconomic effects of the Basel I, II and III agreements. Slovik and Cournède (2011) at the Organisation for Economic Co-operation and Development (OECD) estimated the impact of the announced Basel III capital requirements on GDP growth. They found that economic output is mainly affected by an increase in bank lending spreads, as banks increase the banks funding costs for their customers due to higher capital requirements. These results are in line with a study by Santos and Elliot (2012), which used a similar methodology and showed that financial reform will likely result in a modest increase in bank lending rates in the United States, Europe, and Japan in the long term. The lenders operating costs increase as a result of the higher safety margins in terms of capital and liquidity. They found significantly smaller lending rate increases than Slovik and Cournède. The increasing costs affect bank customers, employees, and investors. However, according to Santos and Elliot banks appear to have the ability to adapt to the regulatory changes without actions that would harm the wider economy.

The macro-economic costs of increasing capital and liquidity requirements are also measured by Roger and Vlček (2011). They find that the medium-term macroeconomic costs of raising capital and liquidity requirements are likely to be quite moderate. They also find that the implementation period and the adjustment strategy used by banks are important determinants for the impact of the regulatory changes. Where Roger and Vlček focus on the medium-term economic impact, the paper by Angelini et al. (2014) focuses on the long-term economic impact of the Basel III reforms. They use macroeconomic and econometric models, including the model by Roger and Vlček, to measure the impact. They find that the economic costs of the new regulatory standards for bank capital and liquidity are considerably below existing estimates of the benefits that the reform should have by reducing the probability of banking crises. They also find that reforms decreases output volatility modestly and that the inclusion of countercyclical capital buffers can substantially amplify the decreasing effect on output volatility. Angelini et al. (2014) and the Basel Committee (BIS, 2010) both find the

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introduction of the NSFR to result in a 0.08% decline in the level of economic output, relative to the baseline (medians across available model).

4. Hypotheses

Investors are expected to react to the press releases of the Basel III liquidity requirements, stemming from the previous related literature. The first announcement on December 17, 2009 is expected to negatively affect stock returns, due to the expected decline in a bank’s profitability as a result of the tighter requirements. Banks that have a lower liquidity ratio at the time of the announcement are expected to be more negatively affected by the announcements than banks that have a higher liquidity ratio. Additionally, larger banks are expected to be more negatively affected than smaller banks, as they have less need to hold sufficient liquidity buffers due to easier access to the lender of last resort, protection by the safety net and access to more markets (Berger and Bouwman, 2009). Therefore, meeting the requirements will be especially expensive for large banks with low liquidity, expecting a larger decrease in abnormal returns for these banks.

Hypothesis 1: The announcement on December 17, 2009 about the Basel III liquidity

framework has a more negative effect on stock returns for large banks with low liquidity.

The second announcement on December 16, 2010 revealed the final framework for liquidity management with the minimum liquidity ratios to hold. This announcement is also expected to negatively affect the stock returns. As these liquidity requirements are more binding for larger banks and banks that have lower liquidity, they are expected to be more negatively affected.

Hypothesis 2: The announcement on December 16, 2010 about the Basel III liquidity

requirements has a more negative effect on stock returns for large banks with low liquidity.

The third announcement on January 6, 2013 about the revision of the liquidity ratio is expected to positively affect stock returns, as regulated banks encounter less pressure to hold more liquid assets or longer-term funding. The revised LCR allows banks to use a broader range of liquid assets to meet the liquidity buffer, which is expected to be especially

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beneficial for large banks. Furthermore, banks with low liquidity will experience less difficulty in meeting the requirements of the revised LCR standards.

Hypothesis 3: The announcement on January 6, 2013 about the Basel III liquidity

requirements has a more positive effect on stock returns for large banks with low liquidity.

The fourth announcement on October 24, 2013 by the Fed proposed a more stringent LCR standard and is therefore expected to have a negative effect on stock returns. The transition period became shorter and the range of assets that are qualified as HQLA and the assumed rate of outflows of certain kinds of funding became more tighten. The implementation period and the adjustment strategy used by banks are important determinants for the impact of the regulatory changes (Roger and Vlček, 2011). As a result, the stock returns of banks in the US are expected to decline due to the tightening, especially for large banks with low liquidity.

Hypothesis 4: The announcement on October 24, 2013 about the Federal Reserve liquidity

requirements has a more negative effect on stock returns for large banks with low liquidity.

5. Methodology

This study uses two methods to test the hypotheses. First an event study is used to measure the effect of the announcements on the stock returns, which is in line with the study by Eyssel and Arshadi (1990). They used an event study to measure the wealth effect of the first Basel announcement. The second method is a regression analysis to examine the effects of different bank characteristics on abnormal stock returns. Both methods are further explained in the following sections.

5.1. Event Study

Event studies examine the return behavior for a sample of firms around events of interest (Kothari and Warner, 2006). An event study assesses the impact of an event on the value of companies, by calculating abnormal returns around the event dates. By calculating these abnormal returns, an event study can reveal important information about how markets react to a given event. To conduct the event study, first the events of interest are identified, secondly the abnormal returns are estimated and finally the results are calculated and analyzed.

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5.1.1. Identify the events of interest

The first step is to identify the events of interest. In this research the four announcements about liquidity regulation are considered the events of interest and are defined in Table 1.

The four announcements are official press releases by the BCBS and the Fed that proposed or revised the liquidity requirements since the 2008 financial crisis. By using the website of the BIS, the most important announcements concerning the liquidity regulation were selected. The announcement by the Fed was selected from the website of the Board of Governors of the Federal Reserve System. The selection of these events was done by dropping the press releases about the Basel III capital requirements, and by keeping the press releases that involved the proposal or revisions of liquidity regulation. In order to apply a valid event study, the identified abnormal returns should be truly associated with the event of interest. According to Siegel and McWilliams (1997), the inference of significance of an event study relies on three assumptions. First the markets have to be efficient, second the event has to be unanticipated and third, there should not be confounding events within the event window. The first assumption is covered by the Efficient Market Hypothesis. This study follows Damodaran (2013) and assumes that the market is semi-strong efficient. The second assumption is partially covered by the note from Sheila Bair (2012), indicating that there are no anticipation effects as the outcomes of the negotiations are not known before. It is important to note that the events of interest in this study are interrelated and subject to an ongoing public debate. Therefore it is impossible to completely rule out the anticipation effect. However, the selected press releases all involve a major development in liquidity regulation, assuming that these changes could not be fully anticipated and consequently drive the stock prices.

The third assumption about confounding events is most important. If there are other relevant events that contain new and price relevant information during the event window, it is difficult to investigate the impact of the event of interest on abnormal returns. These confounding events around the announcement dates that affect stock returns should therefore be identified. Confounding events in this study can include developments in the banking sector or bank specific-confounding events as dividend declaration, earnings warnings or share repurchases. These events could create a bias in the sample.

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To identify the confounding events, international media sources are searched for news related to the US banking sector. First the list of failed banks provided by the Federal Deposit Insurance Corporation (FDIC) was searched, to find the failing banks and acquiring banks around the announcements. Table 2 contains an overview of failing banks in the eleven days around each of the four announcement dates. These are all non-systemically important banks and are therefore not expected to significantly affect the stock returns of all banks in the sample. The acquiring institutions are removed from the sample when testing the related announcements. Additional online media is searched for relevant events in the banking sector around the announcement days. The press releases by the Fed and the BIS from five days before to five days after the announcement days did not contain news that was expected to significantly affect the stock returns of all US banks. Searching the Financial Times’ articles and blogs neither did provide news about significant events in the eleven days around the announcement days. For banks it is challenging to isolate the effect of the events of interest for larger event windows, as there are likely to be bank-specific confounding events almost every trading day. The effects of bank-specific confounding events are minimized due to the large sample of 380 banks.

Table 2: An overview of failed banks at the time of the announcements

Date Bank Name City Acquiring Institution

Related Announcement

18-Dec-09 RockBridge Commercial Bank Atlanta FDIC (no acquiring bank) Ann. 1 18-Dec-09 Citizens State Bank New Baltimore FDIC (no acquiring bank) Ann. 1 18-Dec-09 Imperial Capital Bank La Jolla City National Bank (LA) Ann. 1 18-Dec-09 First Federal Bank of California Santa Monica OneWest Bank, FSB Ann. 1 18-Dec-09 Peoples First Community Bank Panama City Hancock Bank Ann. 1 11-Jan-13 Westside Community Bank University Place Sunwest Bank Ann. 3

30-Oct-13 Bank of Jackson County Graceville

First Federal Bank of

Florida Ann. 4

5.1.2. Estimate Abnormal Returns

To estimate the abnormal returns on the days on which new information is revealed, this research analyzes the impact of the announcements on the market. To estimate the abnormal returns, a benchmark model is used to predict the “normal returns”. There are different models used in practice to calculate normal returns, most of which are based on the CAPM. According to Binder (1998) the normal returns can be measured by using a mean-adjusted returns model, the market-adjusted returns model, deviations from the market model, the CAPM by Black (1972) or lastly by deviations from a multifactor model. This study uses the

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market model to measure the normal predicted returns, as the market model is the most conventional approach and coincides with the available information in this study. The market model defines the predicted returns by the following formula:

PR̂ = α_i̇,τ ̂ + β_i̇ ̂ r_i̇ _m,τ

The predicted returns (PR̂ ) are calculated by using Ordinary Least Squares (OLS), where i̇,τ α̂ and βi̇ ̂ are the estimated regression coefficients. The return on the market portfolio (Ri̇ m,τ) is given by the Center for Research in Security Prices (CRSP) equal- or value-weighted indices on day τ.

To estimate the abnormal returns, the predicted returns calculated by the market model are subtracted from the daily returns. This results in the following mathematical formula for abnormal returns:

AR̂ = Ri̇,τ i,τ− PR̂ i̇,τ

The abnormal return of bank i at time τ (AR̂ ) is the daily return of bank i at time τ (R_i̇,τ _i,τ) minus the predicted return (PR̂ ) as the benchmark. _i̇,τ

The regression coefficients are calculated over the estimation period by using OLS. According to Peterson (1989), studies that are conducted with daily data, usually have an estimation window of 100 to 300 trading days. In this study the estimation period covers the period of 250 trading days ending 6 days before the event date. In Figure 2, τ = 𝑇0+ 1 to τ = 𝑇1 constitutes the estimation window. The event dates are defined as τ = 0, and the interval τ = 𝑇₁+ 1 to τ = 𝑇₂ represents the event window. The estimation period and the event window do not overlap, such that the regression coefficients of the benchmark model are not affected by the event (MacKinlay, 1997).

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The length of the event windows depends on the ability to date precisely the announcement day. In this study the exact announcement days are known, therefore the short event windows [-2,+2], [-1,+1] and [0] will be used. Using daily return data and short-term event windows increases the significance tests in event studies, as the possibility that confounding events take place in the event window decreases (MacKinley, 1997).

5.1.3. Calculate and analyze abnormal returns

To test and analyze the effect of the announcements on stock returns, the abnormal returns will be aggregated within the three event windows (τ₁,τ₂). The cumulative abnormal returns (CAR) method aggregates the abnormal return for bank i over the event windows (τ1,τ2):

CARi(τ1, τ2) = ∑ AR̂i,τ τ2

τ=τ1

The event window is from τ1 to τ2 with T1 < τ1 ≤ τ2 ≤ T2. The announcement on January 6, 2013 falls on a Sunday, therefore the next first trading day (Monday, the 7th of January) is used as the event day.

To test that the CARs are significantly different from zero, the following t-test is used: t − test = 1

√N∗

CAR_i SD(AR̂ )i,τ

Where N is the number of days within the event window and SD(AR̂ ) is the standard _i,τ deviation of the abnormal returns for the event windows.

Hypotheses 1, 2 and 4 expect a significant negative reaction of the market to the announcements of the liquidity regulation, resulting in lower stock returns than the returns predicted by the market model. Hypothesis 3 expects a positive significant reaction, resulting in higher stock returns than the predicted returns.

5.2. Regression Analysis

Regression analyses are used in order to determine the effects of the Basel III announcements on stock returns conditional on different banks characteristics. Three regression analyses are conducted: an univariate regression analysis on liquidity, an univariate regression analysis on

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size and a multivariate regression analysis on liquidity and size. The regressions are tested using Stata.

5.2.1. Liquidity

First the CARs within each event window of the announcements are regressed on a dummy for banks with a high liquidity ratio, enabling to compare the CARs of banks according to their liquidity position. The following model is used:

CAR_i(τ₁, τ₂) = α_i+ β₁𝐻𝑖𝑔ℎ_i+ β₂𝑅𝑂𝐸_i+ β₃𝑀𝑇𝐵_i+ ε_i

The dependent variable in the model is the CARi(τ1, τ2) for the different event windows. The dummy variable 𝐻𝑖𝑔ℎ_i is equal to one if the banks have a high liquidity position and the α_i captures the banks with a low liquidity position. The liquidity position of the banks is determined by using the liquidity ratio, measured by dividing Liquid Assets by Deposits and Short-Term Funding. Banks that had a liquidity ratio above the mean liquidity ratio in the year that the announcement took place, are classified as “High-Liquidity” and below and equal to the mean as “Low-Liquidity”. The regression will also include control variables for Return on Equity (𝑅𝑂𝐸_i) and Market to Book Ratio (𝑀𝑇𝐵_i) (Fama and French, 1992). The ROE variable is used to control for the effect of a bank’s profitability on CARs, which is especially important to the banks shareholders. The control variable ROE is measured by dividing Net Income by Total Shareholders’ Equity. The MTB ratio controls for the effect of growth on CARs. The MTB ratio of equity is calculated by the log of the ratio of the Market Capitalization (Prices multiplied with Shares Outstanding) divided by the Book Value of Equity for the fiscal quarter ending (Total Shareholders’ Equity).

5.2.2. Size

To compare the abnormal returns of small, medium and large banks and find which size group experiences stronger effects on the returns after the announcements, the CARs are regressed on dummies for size. The size of the banks is defined by the total amount of assets of each bank. The following model is used:

CARi(τ1, τ2) = αi+ β1𝑆𝑚𝑎𝑙𝑙i+ β2𝐿𝑎𝑟𝑔𝑒i + β3𝑅𝑂𝐸i+ β4𝑀𝑇𝐵i+ εi

The independent variables are dummies for size. The dummy variable 𝑆𝑚𝑎𝑙𝑙_𝑖 is equal to one if the bank is small, 𝐿𝑎𝑟𝑔𝑒𝑖 is equal to one if the bank is large and the αi captures the medium

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sized banks. Banks that had assets of more than $250 billion at the time of the announcement are classified as “Large”, banks with assets between $50 billion and $250 billion are classified as “Medium” banks, and banks with assets less than $50 billion are classified as “Small”. The regression will also include the control variables for Return on Equity (𝑅𝑂𝐸i) and Market to Book Ratio (𝑀𝑇𝐵i).

The univariate regressions for liquidity and size are run separately for each announcement date, and are run for all announcement dates together to test the overall reaction to the liquidity requirements.

5.2.3. Multivariate Analysis

After the univariate regressions on liquidity and size, a multivariate regression analysis is conducted on multiple independent variables. The following formula will be used for the multivariate regression:

CARi(τ1, τ2) = αi+ β1𝐿𝑎𝑟𝑔𝑒𝑖+ β2𝑆𝑚𝑎𝑙𝑙i+ β3𝐻𝑖𝑔ℎi

+ β4𝐿𝑎𝑟𝑔𝑒𝑖∗ 𝐻𝑖𝑔ℎ𝑖 + β5𝑆𝑚𝑎𝑙𝑙𝑖∗ 𝐻𝑖𝑔ℎ𝑖 + β6 𝑅𝑂𝐸i+ β7𝑀𝑇𝐵i+ εi

The dependent variable in the model is the CAR_i(τ₁, τ₂) is regressed on the dummies for size and liquidity. It includes the dummies 𝐿𝑎𝑟𝑔𝑒_𝑖 and 𝑆𝑚𝑎𝑙𝑙_𝑖 for the size effect and the 𝐻𝑖𝑔ℎ_i for the effect of liquidity, the α_i captures the medium banks with low liquidity. To compare the regressions for the two dummy variables for size and liquidity, interaction terms between the dummy variables are included. These interaction terms identify how the relationship between the dummy for size and the dummy for liquidity affects the CARs. They control for the misallocated significance of the dummy variables 𝐿𝑎𝑟𝑔𝑒_𝑖, 𝑆𝑚𝑎𝑙𝑙_𝑖 and 𝐻𝑖𝑔ℎ_i by isolating their interrelatedness from the main effects of these dummies. This enables to compare all groups, divided by size and by liquidity, in one multivariate model. The model also includes control variables for Return on Equity (𝑅𝑂𝐸i) and Market to Book Ratio (𝑀𝑇𝐵i).

6. Data and Descriptive Statistics

This study uses data from different datasets provided by Wharton Research Data Services (WRDS). First the CRSP/Compustat Merged (CCM) database is used to gather accounting data. Secondly, Bankscope is used for yearly data on the liquidity ratio of the banks. And

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CRSP is used for data on stock returns. The sample consists of publicly trading US banks that are subject to bank specific regulation. These are commercial banks with Standard Industrial Classification (SIC) code 6020, that operate under Federal or State charter (National Commercial Banks: 6021, and State Commercial Banks: 6022).

6.1. CRSP/Compustat Merged (CCM) Data

The CRSP/Compustat Merged database is used for quarterly accounting data of the banks that are subject to regulation. Data is retrieved for the period January 2009 to December 2013. In order to investigate the effects of size on abnormal returns, the total amount of assets is used to group banks by large-, medium- and small-sized banks. Excluded are the commercial banks with missing values on the variables of interest and banks with less than $500 million in assets, as they are not subject to the liquidity requirements.

CRSP/Compustat Merged (CCM) Quarterly update: Fundamentals Quarterly

Date range: Jan 2009 –Dec 2013

Primary Identifier: GVKEY

SIC Code 6020-6022

Observations: 1.607

Variables: Historical CSRP Permno linking - Permno, Cusip – Cusip,SIC code – SIC, Liabilities Total – LTQ, , Assets Total – ATQ, Net Income (Loss) - NIQ, Stockholders Equity Total – SEQQ

6.2. Bankscope Data

Bankscope (Bureau van Dijk) is used to gather yearly data on the liquidity of US banks included in the sample for the years 2009 to 2013. In order to analyze the effects of the announcements for high liquidity banks and low liquidity banks, the liquidity ratio is used as a proxy for the liquidity of the banks. The liquidity ratio is measured by dividing Liquid Assets by Deposits and Short-Term Funding and is averaged over each year per bank. This ratio is a deposit run off ratio, which shows what percentage of customer and short-term funds could be met if they were withdrawn suddenly. The higher this percentage the more liquid the bank is. The data from Bankscope is merged with the CCM file by matching the International Securities Identification Number (ISIN) per bank from Bankscope with the 9 digit Cusip in CCM, within each year. A large part of the sample is matched manually on Company Name and Year, as the ISINs are incomplete in Bankscope. Excluded are banks with missing values of the liquidity ratio or banks that could not be matched to the CCM data.

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CRSP is used to gather information about stock returns of banks in the US. The Stock Holding Period Return is used, because it incorporates dividends and provides the total return of holding a stock. The Value Weighted Market Return is used as the benchmark model. The CRSP Stock Return for each stock and date are gathered for the same period as the Value Weighted Market Return, from 1 Dec 2008 until 31 Dec 2013. Only one market observation per calendar date is kept in the Value Weighted Market Return file. The CRSP daily stock returns are merged into the data file by Permno and dates. These dates are an expansion around the announcement days in the CCM, for 250 days prior to the announcement day up to 6 days after the announcement day. Excluded are banks with missing values on the variables of interest within the event windows.

CRSP Quarterly update: Daily Stock File

Date range: 01 Dec 2008 –30 Jan 2014 Primary Identifier: Permno

SIC Code 6020-6022

Observations: 605.337

Variables: Cusip – cusip, SIC code – SIC, Price – prc, Number of Shares Outstanding - Shrout, Value Weighted Return Includes Distributions – Vwretd, Stock Holding Period Return – ret

6.4. Summary Statistics

The final sample consists of 380 different commercial banks subject to bank regulation. The descriptive statistics for the total sample can be found in Table 3, which includes the Mean, Standard Deviation (SD), Minima, Maxima and the Median (p50) for all variables used in the regressions. The variables are winsorized at 1%, to exclude extreme outliers and enable a

BankScope (Bureau van Dijk) Yearly update: Financials

Date range: 2009-2013

Primary Identifier: BankScope Index Number

SIC Code 6020-6022

Observations: 42.329

Variables: Company Name – NAME, Country ISO Code – Ctrycode, ISIN Number – isin, Liquid Assets / Dep & ST Funding – Data4035

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more robust computation. The liquidity ratio is summarized over the years 2009 to 2013 and for each year separately.

Table 3: Descriptive Statistics

Variables N Mean SD Min Max p50

Total Assets 13,420 49,627 269,447 503 2.68E+06 2120.507

Daily Returns 13,420 0.003 0.0365 -0.447 1.597 0.00107 Abnormal Returns 13,420 0.00038 0.036 -0.446 1.593 -0.00096 Liquidity - 2009-2013 13,420 7.951 8.798 0.99 60.01 5.32 Liquidity – 2009 3718 7.96497 8.03571 0.99 60.01 5.52 Liquidity – 2010 3454 8.50026 9.04566 0.99 60.01 6.2 Liquidity – 2013 6248 7.63998 9.07547 0.99 60.01 4.84 Return on Equity 13,420 0.00753 0.0248 -0.0531 0.0308 0.01572 Market-to-Book ratio 13,354 6.679 0.557 5.634 7.373 6.82334

Note: Total Assets is in $ millions. Liquidity is the liquidity ratio of liquid assets divided by deposits and short-term funding for each year in which an announcement took place (in %).

From this table we see that the median (p50) of total assets is around twenty times smaller than the mean of total assets. This can be explained by the small amount of very large banks (with more than $250 billion in assets) in the sample compared to small banks (with less than $50 billion in assets). The median of the abnormal returns is negative and smaller than the mean abnormal returns. This indicates that abnormal returns are skewed to the right, so more often abnormal returns were negative. The Return on Equity within this sample is very low overall. The median of ROE 1,57% indicates that the banks in the sample are not completely financially stable and healthy.

The sample statistics provide the number of banks in the sample divided by size and by liquidity in Table 4. First, banks are grouped by size based on the total amount of assets. The total number of banks and the grouping by size changes per announcement, due to changes in the amount of assets per quarter, existence of the banks or missing values at the time of the announcement. Second, banks are grouped according to their liquidity ratio. Banks with liquidity ratios above the average liquidity (from Table 3), in the year that the announcement took place, have high liquidity and below and equal to the average have low liquidity.

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Table 4: Sample statistics per announcement

Ann. 1 Ann. 2 Ann. 3 Ann. 4

Large 11 11 11 10 Medium 15 15 10 10 Small 312 288 262 265 High-liquidity 115 116 82 84 Low-liquidity 223 198 201 201 Total 338 314 283 285

Note: The number of total banks differs per announcement, as banks varied in existence or were excluded from the sample due to missing values.

To test the overall reaction to the different announcements, a sample of 240 commercial banks is used. These banks had all data available at the time of each announcement. Table 5 shows the sample grouped by size and liquidity for each announcement.

Table 5: Sample statistics of all announcements

Ann. 1 Ann. 2 Ann. 3 Ann. 4

Large 6 7 9 9 Medium 14 13 10 10 Small 220 220 221 221 High-liquidity 70 83 70 70 Low-liquidity 170 157 170 170 Total 240 240 240 240

7. Empirical Results

In this section the main results of this research are discussed. First the results of the event study are discussed and accordingly the results of the regression analyses.

7.1. Event study

To test how the market reacts to the announcements about the liquidity requirements in the US, an event study is applied to US banks. The t-test method was used as defined in methodology section 5.1.3. to test whether the abnormal returns for each stock are significantly different from zero. This test was performed on the average abnormal return for each bank within each of the three event windows, over all announcement days. The results of these t-tests are represented in the histograms in Figure 3. All three histograms show a high density to zero.

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Figure 3. These three histograms represent the significance of the average abnormal returns within the three event windows: 1. [-2,+2], 2. [-1,+1], and 3. [0]. The CARs are divided by the square root of the number of days in the event window to calculate these t-tests.

To test the significance of the cumulative abnormal returns around all four announcements and for all banks treated as a group, the CARs for all announcement days are regressed on a constant. This test is preferred to the previous t-test, as it allows for robust standard errors. The results are represented in Table 6.

Table 6: Overall market Reaction to all announcements

CAR CAR CAR

[-2,+2] [-1,+1] [0]

Overall Reaction 0.00151 0.00476*** 0.00162** (0.00149) (0.00138) (0.00078)

Observations 976 976 976

R-squared 0.000 0.000 0.000

This table shows the significance of the cumulative abnormal returns across all banks over all announcements. The significance is tested by running a regression of the CARs on a constant, allowing for robust standard errors. Each column represents one of the three event windows: 1. [-2,+2], 2. [-1,+1], and 3. [0]. The overall market reaction is the reaction to all announcements. The significance at the 1%, 5% and 10% level is denoted respectively by ***, ** and *. The robust standard errors are in parentheses.

From this table it follows that the overall reaction by the market to the liquidity regulation proposals was slightly positive. The event windows [-1,+1] and [0] show a significant small increase in abnormal returns, meaning an increase of 0,48% and 0,16% in the returns on the banks stock during the particular event windows respectively. The R-squares show very low values, indicating that the coefficients do not have high explanatory power.

Applying an event study around the individual announcement dates is more useful, as it separates the expected positive effects for announcement 3. Table 7 shows the results of regressions of the CARs on a constant, allowing for robust standard errors.

The impact of Basel III liquidity announcements on stock returns