THE IMPACT OF LIQUIDITY ON UK BANKS PROFITABILITY: A CASE STUDY OF THE 5 LARGEST UK BANKS.

THE IMPACT OF LIQUIDITY ON UK BANKS PROFITABILITY:
A CASE STUDY OF THE 5 LARGEST UK BANKS.
A THESIS SUBMITTED TO THE BUSINESS SCHOOL,
UNIVERSITY OF WOLVERHAMPTON.
In partial fulfilment for the award of
MASTERS OF SCIENCE (MSc) IN INTERNATIONAL BANKING AND FINANCE
BY
OSAKUADE IFEOLUWA AYOMIDE
SEPTEMBER 2018

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DECLARATION
I declare that this Dissertation/Research Project, in its entirety, is my own work.
It has not previously been presented in whole or part, for any other award. Neither
has it been published in whole or in part elsewhere and presented here without the
proper use of references. Neither has it been commissioned in part or whole to be
written by another party or individual on my behalf.
Signed: OSAKUADE IFEOLUWA AYOMIDE

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ABSTRACT
The global financial crisis of 2007/2008 revealed the importance of liquidity to banks and other
financial institutions. This study investigated the impact of liquidity on UK banks’ profitability.
Five largest UK banks were evaluated (HSBC, Barclays, Lloyds, RBS and Standard Chartered
Bank) over a period of 13 years spanning from 2004 to 2017. This study measured liquidity
using a ratio of liquid assets to total assets, also with an additional liquidity measure of
Liquidity Coverage Ratio (LCR) which accounted for the impact of the new regulatory measure
after the financial crisis, while Return on Asset (ROA) was used to measure profitability.
Furthermore, this research also evaluated if there are other determinants of banks profitability
by controlling for bank size, capital, non-performing loan and macroeconomic variable (Gross
Domestic Product).
This study was classified into two periods; firstly, the full period (2004-2017) and the sub-
period (2009-2017). These classifications were made to determine if there are changes in the
variables after the crisis. This study carried out a time series analysis, correlation analysis and
also employed a fixed effect regression method to estimate its model.
The result of this study highlights that there is no significant relationship between liquidity and
banks profitability; although the outcome stated that there are other determinants of bank
profitability, such as Capital, bank size and Non-performing loan.

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DEDICATION
I hereby dedicate this dissertation to my beloved parents Apostle T.M & Mrs Osakuade, for
their immense support both morally and financially since I commenced this study.
Thank you so much, I love you.

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ACKNOWLEGEMENT
My sincere appreciation to God Almighty for His abundant grace and for granting me the
privilege to finish this research work on a good note. I also want to specially thank my
supervisor, DR. Anna Korzhenitskaya for her immense guidance and contribution towards the
successful completion of this research. Thank you for your constructive criticism and hours of
rigorous scrutiny of this study which has really contributed positively, not just to this study but
has also helped me in developing interest for further research.
Finally, I also appreciate other lecturers of University of Wolverhampton who contributed to
the knowledge acquired over this period. Thank you all

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TABLE OF CONTENT
DECLARATION
ABSTRACT
DEDICATION
ACKNOWLEGDEMENT
1 CHAPTER ONE: INTRODUCTION
1.1 Background of the study......................................................................................................9
1.2 Research Aims/Objectives ................................................................................................10
1.3 Rationale and contribution ................................................................................................10
2 CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction………………………………………………………………………………12
2.2 Bank Regulation …………………………………………………………………………12
2.3 Empirical Studies………………………………………………………………………...15
2.3.1 Liquidity Risk.................................................................................................................16
2.3.2 Liquidity and Profitability……………………………………………………………...16
2.4 Capital and Profitability…………………………………………………………….….20
2.5 Non-performing Loans and Profitability……………………………………………….22
2.6 Bank Size and Profitability…………………………………………………………….23
2.7 Macroeconomic Conditions……………………………………………………………24
2.8 Chapter Summary………………………………………………………………………25
3 CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Introduction........................................................................................................................26
3.2 Research Approach............................................................................................................26
3.3 Sample Selection................................................................................................................27
3.4 Variable Selection..............................................................................................................27
3.4.1 Dependent Variable.........................................................................................................27
3.4.2 Independent Variable......................................................................................................28
3.5 Model Specification & Method of Analysis………………………………………...…...30
3.6 Data Collection……………………………………………………………………….….30
3.6.1 Data Estimation………………………………………………………………………...31
4 CHAPTER FOUR: ANALYSIS AND DISCUSSION
4.1 Introduction........................................................................................................................33
4.2 Time Series Analysis......................................................................................................... 33
4.2.1 Liquidity.……………………………………………………………………………….33
4.2.2 Profitability.…………………………………………………………………………….35
4.2.3 Non-performing Loan………………………………………………………………….36
4.2.4 Capital………………………………………………………………………………….37
4.3 Descriptive statistics…………………………………………………………………....37
4.4 Correlation Analysis……………………………………………………………………41
4.5 Regression Analysis……………………………………………………………………42

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5 CHAPTER FIVE: SUMMARY, CONCLUSIONS
5.1 Introduction........................................................................................................................48
5.2 Summary............................................................................................................................48
5.3 Limitations, areas for further studies and Recommendation………………………...…...49
5.4 Conclusion..........................................................................................................................49
REFERENCES.........................................................................................................................51
APPENDIX..............................................................................................................................56

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LIST OF ABBREVIATIONS
LCR - Liquidity Coverage Ratio
NSFR- Net Stable Funding Ratio
IMF - International Monetary Fund
BCBS - Basel Committee on Banking Supervision
FSB – Financial Stability Board
GSIBs – Globally Systemic Important Banks
TLAC - Total Loss Absorbing Capacity
CET1 - Common equity tier 1
CVA - Credit Valuation Adjustment
GDP – Gross Domestic Product
ROA – Return on Asset
ROE –Return on Equity
NPLs – Non-performing loans
BIS – Bank for International Settlement

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1. CHAPTER ONE
Introduction
1.1 Background to the study
Banks are essential part of the financial sector of any economy (Econ, 2001). Banks perform
important activities by advancing loans (asset side), and provide liquidity to depositors
(liability side) (Vazquez and Federico, 2015). As a significant component of the economy,
banks allocate funds from depositors to borrowers. They deliver specialized financial services;
hence they are able to reduce the cost of acquiring information in relations to both savings and
borrowing opportunities. The prime functions of banks are to provide loans, accept deposits,
and grant overdraft facilities and other financial services (Culbertson 1958, p. 121). These
various functions carried out by banks expose them to liquidity risk, which may arise if many
depositors demand their funds at the same time, resulting in the fire sales of assets (Diamond
and Rajan, 2005). “Liquidity risk is a risk arising from a bank’s inability to meet its obligations
when they come due without incurring unacceptable losses” (Comptroller of the Currency,
2001, p.6). Liquidity risk can negatively affect both the bank’s profitability and also its capital
(Chaplin et al., 2000). Jenkinson (2008) conceded that liquidity risk does not only affect the
performance of banks but also has significant impact on its reputation. Diamond and Rajan
(2005) identified that when there is high demand for cash withdrawals by depositors, and a
bank is unable to meet its current obligations, this situation may lead to a bank run. The scenario
of Northern Rock in the 2000s made the bank to borrow substantially to finance mortgages. In
the fall of 2007, the global banking crisis began which resulted to the inability of Northern
Rock to generate substantial income as expected, resulting to the risk of not being able to meet
its current obligations. Consequently, the management of Northern Rock had to approach the
government for support (Telegraph, 2012). This information led to depositors of funds
withdrawing their monies from the bank due to the lack of confidence, and the bank failed
following a bank run. It was the first British bank to fail due to a bank run (Shrivastava et al
2003). Consequently, this led regulators to the view point that maturity transformation was
excessive (Tarullo, 2009). Some UK banks were bailed out in the course of the crisis with a
rescue package of £500 billion to bail out commercial banks (Financial Times, 2009).
However, the Bank of England has mandated the UK major banks to publicly present their self-
assessment as regards the ability to wind down in an orderly fashion which will not require
taxpayers’ bailout; this policy will commence in 2020, ensuring banks to issues new debt which

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is convertible to equity in distress times bailing in creditors instead of government bailout
(Financial times, 2018). Therefore, liquidity risk is an on-going concern for both banks and
regulators, and it is important to control this risk to guarantee customers and investors
accessibility to their funds. The previous bank regulation Basel II did not explicitly take
liquidity risk into account; however, the most recent bank regulation Basel III which was aimed
at improving the financial position of banks and to cushion the effect of economic shocks
accounted for liquidity risk by introducing two liquidity ratios; Liquidity Coverage Ratio
(LCR) and Net Stable Funding Ratio (NSFR). On the one hand, these new ratios should help
banks to withstand shocks and prevent liquidity risk; on the other hand, there is an argument
that higher liquidity will reduce bank profitability. For example, Wagner (2007) acknowledged
that if banks hold high liquidity in normal times, this would not have an impact on their stability
or bank’s probability of default. The upturn in liquidity primarily improves stability by
enabling the transfer of risk from the bank; this however, decreases the bank’s profits.
Meanwhile, a proliferation in banks’ liquidity in times of crisis reduces stability. Similarly,
there is an initial positive impact on stability, since it enables banks to be less vulnerable to
bank runs.
1.2 Research Aims/Objectives
This study aims to investigate the impact of liquidity on banks’ profitability in the U.K and
determine whether this impact is different in years before and after the global financial crisis
of 2007/08. The International Monetary Fund (2011) has acknowledged that the major cause
of the financial crisis in 2007 was due to poor liquidity risk management and over reliance on
short term wholesale funding which increased the probability of failure of banks. Under the
updated banking regulation Basel III banks are required to hold sufficient liquid assets to
prevent liquidity risk. While higher liquidity can improve bank stability it can also affect bank
profitability (Campgemini, 2011). Therefore, the study attempts to address the following
objectives:
The specific objectives of this study are:
1) To critically review the literature on the relationship between bank liquidity and
profitability and develop the hypothesis.
2) To assess and analyze the impact of liquidity on UK banks’ profitability, including
the years before during and after the global financial crisis.

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3) To examine other factors that affect bank profitability in the UK.
Research Questions
1) What is the impact of liquidity on UK banks’ profitability before and after the global
financial crisis?
2) How do other factors affect bank performance in the UK?
1.3 Rationale and Contribution
This study gives insight on the importance of liquidity management before and after the
financial crisis. Furthermore, this study evaluates the impact of the new regulatory liquidity
requirement on banks’ performance after the financial crisis. This study will serve as source of
information to banks, financial regulators and central banks with respect to policies to be
adopted. This study is distinctive as it evaluated evaluates UK’s 5 largest banks according to
asset size which was sourced from (AdvisoryHQ, 2017). A body of literature had previously
examined the impact of liquidity on banks profitability (Bourke 1989; Thorton 1992; Saunder
2000; Maudos 2004; Barth et al 2004; Goddard et al 2004; Molyneux and Wilson 2004;
Kosmidou 2008; Brunnermeier 2009; Adler 2012; Zhang and Daly 2013; Wang, 2018;
Mehrotra et al, 2018). Most of the earlier studies found a negative significant relationship
between liquidity and profitability, while the outcome of the latter studies found negative,
positive or no significant relationship.
To the best of my knowledge, there are not many recent studies that focused on the relationship
between liquidity and profitability, most of these studies evaluated the determinant and
measurement of liquidity risk and bank performance. Therefore, this study will contribute to
the current literature by specifically focusing on the relationship between liquidity and banks
profitability taking into account the years before and after the crisis.
The rest of the study is structured as follows: Chapter 2 provides a review of the main literature
that investigates the impact of liquidity on bank performance, Chapter 3 explains and justifies
research methodology, Chapter 4 provides discussion of the main results from the data analysis,
and Chapter 5 presents summary and conclusion.

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2. CHAPTER TWO
Literature Review
2.1 Introduction
This section reviews the literature relating to the importance of having higher liquidity to
sustain bank stability, changes in bank regulation and the impact of liquidity on bank’s
profitability. Furthermore, it evaluates the empirical framework with regards to bank and
liquidity profitability.
2.2 Bank regulation
The “saving and crisis” era in the 80’s which emanated due to the failure of banks, stimulated
the implementation of Basel I accord where banks all over the world were lending extensively
and country debts were increasing at an unsustainable rate, this resulted in the failure of many
international banks (Goodhart, 2011). To curtail this risk, The Basel Committee on Banking
Supervision implemented Basel I accord which focuses on strengthening the financial stability
of international banks and determining the minimum risk-based capital requirements
(Constancio, 2011).
Basel I accord explained capital in two perspectives; tier 1 (core capital) & tier 2
(supplementary capital) requiring banks to maintain at least 8% of its risk –weighted asset as
reserves (BCBS, 2010). Basel I became insufficient due to the fact that a minimum 8% capital
ratio was inadequate to protect banks during an economic downturn, the 8% capital ratio
became a stationary measure of default risks. More concerns were also raised as Basel 1 was
not sufficient to differentiate individual loans, opening up opportunities to regulatory arbitrage
(Tarullo 2008). These pitfalls resulted in the commencement of Basel II accord on the 11th of
January 1999, which added operational risk to the existing framework whilst improving on the
credit risk calculations (Berger and Bouwman, 2000). Basel II was structured to introduce
internal rating based approach for credit risk and the daily presentation of the market risk
positions to regulatory authorities using value at risk models (McAleer, 2009). These
amendments were organized to establish superior risk management framework in banks, but it
became an avenue to underestimate risk as large banks were being able to use their own models
in assessing risks although banks had to comply with the regulatory metrics (Financial Times,

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2008). This prompted banks comparative advantage over regulators as they became superior
in expertise and experience in the complex risk assessment (Financial Times, 2009).
The recent economic downturn in the financial market highlighted the shortcomings of Basel
II which prompted large banks to take substantial losses and search for new funding
opportunities. It was evidenced that the major factor which contributed to the crisis was the
evaporation of liquidity from the financial market, although regulators failed to incorporate
liquidity risk measures in Basel II framework. “During the early liquidity phase of the financial
crisis, many banks – despite adequate capital levels – still experienced difficulties because they
did not manage their liquidity in a prudent manner. The crisis again drove home the
importance of liquidity to the proper functioning of financial markets and the banking sector”
(BIS, 2010 p3). This inspired the conception of new liquidity rules by the Basel Committee on
Banking Supervision (BCBS) under the Basel III regulatory framework (Dietrich et al, 2014).
Basel III is a comprehensive set of reforms, developed by BCBS, which aims to augment the
regulations, supervision and risk management of the banking sector (Co-Pierre 2011). The
supervisory reforms of Basel III by BCBS, strengthens both micro-prudential and macro-
prudential guidelines, taking into account of the lesson learned during the crisis. The
committee also formulated two set of liquidity ratios which have not existed prior to the
financial crisis. These two liquidity ratios introduced are: Liquidity Coverage Ratio (LCR) and
Net Stable Funding ratio (NSFR). The major goal of LCR is to promote the short-term
resilience of bank’s liquidity risk profile by certifying that it has satisfactory unencumbered
high-quality liquid asset to survive a substantial stress situations, while NSFR ensures cover
for funding risk over a longer period by requiring banks to fund their activities with adequately
stable source of finance in the order to mitigate risk of future funding stress (Ravi et al, 2007).
These newly adopted ratios require banks to hold more capital in good times to cushion the
effect of economic downturns in bad times, thus justifying the fact that Basel III was developed
to strengthen bank capital requirements by increasing bank liquidity whilst decreasing bank
leverage (FSB, 2011). These capital requirements are measured by tier 1 common equity
capital ratio which is the comparison between bank’s core capital and its total risk-weighted
assets; this indicates the banks financial strength. The Basel Committee on Banking
Supervision increased the common equity tier 1 (CET1) from 2% to 4.5% of risk weighted
assets, the existing minimum capital requirement remained at 8% while the new minimum total
capital plus conservation buffer was 10.5% of risk weighted assets. In order to curtail the

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destabilizing pro-cyclical amplifications of the banking system during the financial crisis, the
Basel committee published the countercyclical buffer to be included in Basel III framework,
this capital buffer strengthens the existing framework which was not incorporated in Basel II.
The countercyclical buffer is purposed to achieve the macro-prudential goal of protecting the
banks, taking into account of the macro-economic and financial environment in which the
banks carry out it functions. This buffer protects the bank in the era of aggregate credit growth,
requiring banks to keep 2.5% extra capital (BCBS, 2010; 2011). Meanwhile, in Basel II there
were no buffers accounting for macro-prudential policy. Additionally, the Global Systemically
Important Banks (GSIBs) were mandated to hold extra capital buffer to improve the resilience
of banks and adequate risk-coverage mandating banks to comply from 1 January 2019 (BCBS,
2011). Furthermore, the GSIBs are also required to meet Total Loss Absorbing Capacity
(TLAC) standard alongside with the regulatory capital requirement in the Basel III framework
(FSB, 2012). The Basel committee indicated that international banks must deduct their TLAC
holdings which were not taken into account in their regulatory capital for tier 2 capital; this
reduced the chances of contagion in the banking system. The Credit Valuation Adjustment
(CVA) requirement was likewise captured in the Basel III framework to regulate trading,
derivatives and all securitization products.
These requirements are aimed at strengthening bank liquidity position and therefore improve
stability; however, it was argued that higher liquidity requirement may reduce bank
profitability (Diamond and Rajan, 2001). This trade-off between liquidity and profitability is a
function of holding regulatory capital and more high-quality liquid assets which cannot be
loaned to borrowers in the pursuit of increasing interest income, hence reducing profitability.
Likewise, funding assets with long maturity liabilities will reduce interest expenses resulting
to a decline in net interest margin. The importance of having adequate capital to maintain
liquidity for bank stability was evidenced during the financial crisis, as more capital was
injected to help banks recapitalize, providing the resources to recover from substantial losses
whilst restoring confidence in banks. It is assumed that higher capital levels reduce the
probability of stakeholders losing confidence in banks financial position. While lower levels
of liquidity increase the need for sufficient capital to safeguard the bank from a confidence
shock.
This section reviews the changes in the banking regulation before and after the global financial
crisis and also indicated the importance of holding higher liquidity for bank stability. The next

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section will evaluate findings from the empirical literature that examines the link between bank
liquidity and profitability, finally formulating hypothesis as a result of evidence deduced from
the reviewed literature.
2.3 Empirical Studies
This section will review the empirical literature on the impact of liquidity on banks profitability
and hypothesis will be formulated based on the evidences deduced from existing literature.
A broad evaluation of the impact of liquidity on banks profitability had erupted since the global
financial crisis which stimulated the interest of regulators, supervisors, independent researchers
and other players in the financial system to critically evaluate the activities of banks. Banks are
the most essential component of the financial system, saddled with the responsibility of
facilitating intermediation of funds between savers and borrowers (BIS, 2009). The
intermediation capacity of banks enhances the whole economy to be more efficient, resilient
and more profitable, furthermore being able to withstand adverse economic conditions (BCBS,
2010). At the same time, an adverse shock in the banking sector can spread to the wider
economy and result to financial crisis and economic recession. A very limited number of
studies had focused on liquidity as the main independent variable of bank performance. Earlier
studies (Bourke, 1989; Molyneux and Thornton, 1992) focused on the determining factors of
banks’ performance in which liquidity risk and other potential risks were evaluated. The recent
2007-2009 global financial crisis brought into light the inadequacies of the risk manageme nt
framework before the period. Ashby (2010) highlighted that prior to the financial crisis banks
recorded substantial amount of profit, but during the crisis banks profitability was challenged
as a result of liquidity risks. The crisis lingered as banks in many countries suffered liquidity
shortages as a result of the inaccessibility of wholesale bank funding market. Agreeing to
Achaya et al (2013), majority of the vulnerable banks financed their long-term assets with short
term debt whilst being incapable to rollover their borrowings, this had negative impact on the
global operational environment of banks., Brunnermeier et al (2013) clarified that if banks hold
illiquid assets which are funded by short term debt in a bank run scenario, it may lead to the
total collapse of the global banking system. This triggered the need for financial regulators to
upscale the supervisory framework in order to enhance financial and economic stability
(Kashyap et al 2008), leading to the advocacy for an active management of liquidity risk.

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2.3.1 Liquidity Risk
Liquidity is a measure of the degree at which assets can be converted to cash (Fed, 2012).
Liquidity played an essential role during the financial crisis, as uncertainty in the financial
market led funding sources to dry up resulting to the inability to finance their short-term
obligations (Bernanke, 2008). The International Monetary Fund (2011) established that, the
collapse of banks in the recent financial crisis was as a result of poor liquidity management and
high dependence on short term wholesale funding. Liquidity risk is the probability that over a
specific time period the bank will be incapable to meet its obligations with immediacy
(Drehmann and Nikolaou, 2009). According to Goodhart (2008), there are two fundamental
features of liquidity risk: maturity transformation, which emphasizes on the growth of banks
total liabilities and assets; and the ability to sell the bank’s assets without making any
significant loss on the value of the asset.
Loans are created through maturity transformation in which commercial banks borrow short
term in the form of demand deposits but lend to its customers on a long term. The financial
crisis called the attention of researchers to the inadequacies of maturity transformation which
resulted in the subprime crisis (Brunnermeier, 2009). Before the financial crisis in 2007, bank
managers paid little attention to the fundamental elements of liquidity risk (Committee of
European Banking Supervisors (CEBS, 2008)). However, liquidity risk has been identified as
one of the major causes of the financial crisis. The collapse of Lehman Brothers in September
2008 triggered a chain reaction in the financial markets and led to the fire sales of assets,
resulting to mistrust and lack of liquidity in the financial markets, although researchers blamed
the regulatory authorities and the Central bankers for mishandling the economy and ceasing to
implement appropriate policies to rescue the financial institutions (The Economist, 2018). One
of the indications of a crisis is the spread between LIBOR (London Interbank Offered Rate)
and OIS (Overnight Index Swap) rates. Prior to the financial crisis, in the first half of 2007, the
Libor-OIS (Overnight Index Swap) spreads were small. For example, the 1-month spread was
around 5 - 6 basis points, and the 3-month spread was 7 - 9 basis points (Angelini et al., 2011).
However, in August 2007 there was a sharp increase in the spreads and they have fluctuated
well above historical averages since then, rising to over 300 bps during the panic of 2008
(Tempelman, 2009). The amplified demand for liquidity and the sudden decrease in supply
exerted strong rising pressure on interest rates, which contributed to the widening of the spreads
(Kwan, 2009). Meanwhile, small banks were not able to secure loans from big banks since the

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high LIBOR rate regime exist; also, big banks refrained from lending to small banks due to
uncertainty during the financial crisis (Brunnermeier, 2009).
Bunda and Desquilet (2008), stated that the global financial crisis had a negative significant
impact on liquidity ratio and that banks experience greater liquidity risk exposures during the
crisis. Vadova (2011) using a sample of eight European banks, found that financial crisis has
a negative impact on the four measures of bank liquidity used in the analysis.
2.3.2 Liquidity and Profitability
Previous studies that examined the impact of banking liquidity and profitability found that,
liquidity may have significant impact on bank profitability. Shen, Kuo & Chen (2001)
evaluated the causes of liquidity risk and banks performance model using a sample of 12
advanced economies commercial banks including the United Kingdom, over 1994-2006. The
result showed that the causes of liquidity risk are the component of liquid assets and the
dependence on external funding, as well as the supervisory and regulatory factors and
macroeconomic factors. Furthermore, it was suggested that liquidity risk may reduce bank’s
interest margins.
Bordeleau et al (2010) examined the impact of liquidity on bank profitability using a sample
of large U.S and Canadian banks over a period of 1997 to 2009. Return on Assets (ROA) was
used to measure profitability, while liquid assets were used to measure liquidity, liquid assets
were estimated as a ratio of cash, government-issued and government-guaranteed securities
and inter-bank deposits in relations to banks total assets. This study controlled for other
determinants of banks performance, such as GDP, inflation and leverage. Results highlight that
profitability is enhanced by bank liquid assets, although, at some point further holding of liquid
assets reduces the banks’ profitability. The outcome presents empirical evidence that a
nonlinear relationship exists, however, this varies depending on a bank’s business model and
the state of economy, Therefore, after some point there is an indication that a trade-off
relationship exists between liquidity and banks’ ability to generate profits. Bordeleau et al
(2010) focuses on pre-crisis and during the crisis era, it is critical to observe if the same result
also applies after the financial crisis.
Bourke (1989) evaluated the performance of banks in twelve European, Northern American
and Australian countries from 1972-1981. His findings stated that capital and liquidity ratios
are positively related to profitability. Contrary to previous findings, this study shows that
capital in accounting term is referred to as a “free resource” as Rovell (1980) justified an
inverse relationship between capital ratios and cost of intermediation. It is assumed that well

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capitalized banks have access to very cheap (but less risky) source of capital. This implies that
when bank capital base is strong, it continuously attracts deposits reducing the need to invest
in liquid asset. Ibrahim (2017) evaluated the impact of liquidity on commercial banks
profitability in Iraq. He randomly selected five commercial banks in Iraq, analysed their annual
report over a period of 2005-2013 estimating profitability using return on asset (ROA) and loan
to deposit ratio (LTD), deposit to total asset ratio (DTA), cash to deposit ratio (CTD) for
liquidity. The outcome of the regression analysis suggested a positive significant relationship
between ROA and LTD, CTD, DTA. This study evaluated the impact of liquidity on banks
profitability prior, during and after the financial crisis although, it does not take into account
other important external determinant of liquidity and profitability. Likewise, these findings are
also consistent with these studies (Barth et al., 2003; Alshatti 2014; Alzorqan 2014) as they
found a positive relationship between liquidity and profitability.
Since the global financial crisis, the impact of liquidity on the profitability of banks has been
one of the most controversial topics in the banking sector. There have been conflicting results
in the findings of several authors. Molyneux and Thornton (1992) examined the determinants
of banks Liquidity and performance with a sample of 18 European banks including the United
Kingdom, between the periods of 1986 to 1989. Contrary to previous studies, Molyneux and
Thornton (1992) establish a negative association between liquidity holding and profitability.
Kosmidou et al (2008) studied the impact of liquidity and other variables on UK owned
commercial banks’ profits. They used return on average assets(ROAA) and net interest margins
(NIM) to measure profitability and liquid asset to total asset for liquidity measure. Their dataset
included 224 bank-year observations from 1995 to 2002. The main findings were that the
liquidity was negatively correlated with NIM but positively associated with ROAA, although
this study highlighted that equity ratio is the main determining factor of UK banks’ profitability
and that well capitalized banks can access external financing at lower costs of thereby
contributing to higher profits.
Tabari et al (2013) studied the effect of liquidity on the performance of commercial banks in
Iran over 2003 to 2010. In their study, return on equity and return on asset were used to proxy
profitability while liquid asset to total asset was used to measure liquidity. The result
highlighted that there is a negative relationship between liquidity and profitability of banks.
Tabari et al (2013) identified that when a bank has sufficient liquidity, it will not be able acquire
more source of funding to offset the high demand of short term obligations, therefore the bank
indulges in utilizing its cash assets and capital to supplement for the huge customer demand
resulting to the reduction in the level of loans, these aggregates to decrease in the bank’s

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performance. This study was comprehensive as it evaluated both bank-specific variables and
other macroeconomic variables. Furthermore, the outcome of the study illustrated that bank
assets, bank size, inflation rate and GDP increase the capacity of the banking institutions.
Alzorqan (2014) conducted a research on the impact of liquidity on banks performance. This
study examined the challenges of attaining optimal bank liquidity which confers bank stability
and profitable financial operations. He evaluated two banks out of 23 commercial banks in
Jordan over a period of 5 years (2008-2012). The outcome shows that there is considerable
influence of liquidity of banks on the financial performance. The sample size designated for
this study is quite small; more research could be done to incorporate the entire 23 banks in the
country so as to produce a more generalizable result. Kim Cuong Ly (2015) analysed the impact
of liquidity, regulations on commercial banks performance in 27 European countries within a
period of 2001-2011. The result shows that there is negative relationship between liquidity and
banks performance all through the regression analysis, this study indicates that banks with high
level of liquid asset are prone to the possibility of not earning higher profits. Furthermore, bank
capital regulation and supervision showed a positive relationship on banks performance.
Ashraf et al (2015) investigated the effectiveness of Basel III by linking the NSFRwith overall
financial stability, Financial data sourced from 948 banks from 85 countries from 2003 to 2013.
Profitability was measured by the expected return on assets, while the Net Stable Funding ratio
was used to proxy Liquidity. This study is different from other studies due to the fact that it
calculated the new regulatory ratio to assess banks liquidity, only few studies have done this
since the implementation of the new regulatory framework. However, this study also controlled
for capital which was measured as equity capital to asset ratio. The result showed evidence to
suggest that NSFR does increase the financial stability of banks. Banks having higher operating
profit are more stable, also banks with higher profitability are more resilient to short-term
shocks. Muriithi and Waweru (2017) examined the impact of liquidity risk on commercial
banks performance in Kenya using a sample of 43 commercial banks over a period of 2005-
2014. Two new regulatory ratios, liquidity coverage ratio (LCR) & net stable funding ratio
(NSFR) were used to measure liquidity, while banks performance was measured by return on
assets (ROA). It was hypothesized that both in long and short run, NSFR has negative
relationship with bank profitability, furthermore; there was nonlinear relationship between
ROA and LCR.
Generally, these studies have shown conflicting results on the impact of liquidity (and liquidity
risk) on commercial banks’ performance. On the one hand, high liquidity risk (i.e. low
proportion of liquid assets) can reduce bank profitability. On the other hand, holding a large

20
amount of liquid assets (i.e. low liquidity risk) can also have a negative impact on bank
profitability. However, majority of these studies supports the notion that there is a negative
relationship between liquidity and profitability of banks. Therefore, this study will provide
further evidence on how liquidity impacts bank profitability in the UK.
Hypothesis 1; There is a negative relationship between liquidity and profitability of banks.
This section examined the relationship between liquidity and profitability. However, there are
other variables, such as capital and bank size that affect bank performance. Previous studies
examined the relationship of capital, non-performing loan, bank size on bank profitability, and
they also used liquidity as one of their determinant. The next section reviews this literature.
2.4 Capital and Profitability
Capital is one of the most widely-researched determinants of bank profitability. It has attracted
more attention since the recent financial crisis of 2008. The evidence on the impact of capital
on bank profitability is however mixed.
Kosmidou et al (2008) studied the impact of bank-specific characteristics, macroeconomic
conditions and financial market structure on UK owned commercial banks’ profits This study
evaluated 8 UK commercial banks over a period of 7 years (1995-2002), they used equity to
total asset ratio as their proxy for capital. The outcome hereby highlighted that, the level of
capital owned by banks is the major determinant of UK banks profitability, justifying that well
capitalized banks tends to have low bankruptcy cost. This implies that capital has positive
relationship on profitability. Naceur and Kandil (2009) evaluated the impact of capital
requirements on banks cost of intermediation and performance using a sample of 28 Egyptian
banks over a period of 1989 to 2004. They estimated capital using capital adequacy ratio, and
profitability by ROA. They analysed the effect of the capital ratio on the cost of intermediation
and bank profitability. The findings highlighted that as the capital adequacy ratio internalizes
the risk to shareholders, banks increase the cost of intermediation, which supports higher
profitability (ROA and ROE). Generally, the result highlights that capital regulations have a
substantial role to play in the profitability of banks in Egypt. It is observed that according to
the studies earlier discussed there were evidence that capital had a positive relationship with
banks profitability before the crisis, this could be as a result of the focus on capital regulatory
framework.

21
According to the Basel Committee on Banking Supervision (2012), it is important for banks to
balance their reserve capital. These regulations are designed to provide banks protection which
may damage bank performance and impact the economy. In line with this capital requirement,
Lin et al. (2015) investigated 4828 syndicated loan of publicity bank over a period of twenty-
three years (1987-2010), the outcome highlighted that banks capital ratio has positive impact
on banks credit risk taking, this connotes that, the lower capital ratio will charge higher spread
for borrowers with fewer cash flows which suggests a negative relationship between capital
ratios and profitability.
Admati et al. (2013) reviewed the arguments which suggest that capital is expensive as large
banks are required to comply with regulatory capital requirements. They established that when
banks hold the required regulatory capital, they decrease the risk premium which in turn
reduces the expected profitability resulting to reduction in banks’ costs. They concluded that,
well capitalized banks are confronted with little or no challenges in making lending decisions
which enhances profit maximization.
Demirguc-Kunt et al. (2013) studied the impact of banks capital on stock returns during the
financial crisis of 2007-2009. They examined if banks with large capital were perceived
positively in the market during the crisis; therefore, prompting the result of increase in stock
returns. This study used an unbalanced panel sample of 381 banks listed on the stock exchange
of 12 developed economies between the periods of 2005 to second quarter of 2009: Q1. They
distinguish between the different types of variables used to measure capital while estimating
the regression for a subsample of big banks only. The outcome of this study highlighted that
there was no significant relationship between capital and stock returns before the crisis;
however, the relationship between sensitivity of stock returns to capital measures turn out to
be stronger during the crisis.
The result of this study suggests that banks with large capital were perceived to better during
the crisis due to its abilities to absorb losses and withstand external shocks. They also
established that the association between stock returns and capital is more significant when
leverage ratio is used to measure capital. Additionally, the outcome of this study highlighted
that during the global financial crisis, the stock returns of big banks showed more sensitivity
to leverage ratios than less capitalized banks. Before the crisis, well capitalized banks which
are of greater systemic importance in the financial sector held less capital, this support the
argument about the need to strengthen regulatory capital requirements and place more

22
emphasis on the liquidity positions of large banks. Based on the literature reviewed the
relationship between capital and profit is not very clear. Before the crisis, most studies found
a significant effect of capital on profitability. However, during the crisis shareholders capital
was eroded due to the financial and economic downturn, hence capital was utilized to rescue
banks and also to restore confidence in the entire financial system. Although, after crisis
researchers found negative relationship between capital ratios and profitability of banks.
2.5 Non-performing Loan and Profitability
Researchers have accumulated evidences that non-performing loan is a major stumbling block
to banks’ profitability; an increase in non-performing loans has continuously been an indicator
for bad performances (Davis and Karim 2008). After the recent financial crisis, non-performing
loans have become increasing matters of concern for banks in the UK and many European
countries (ECB, 2010). Financial Supervisory authorities have endeavoured to bring clarity to
what exactly non-performing loan is, by giving it a precise definition so as to be able to monitor
and supervise effectively. A loan is characterized as non-performing when the borrower is 90
days or more behind on the contractual payments (BCBS, 2009). Fofack (2005) identified that
non-performing loan can also be referred as bad loans, impaired loan or impaired asset. Bexley
and Nenninger (2012) also added that bankers and regulators sometimes refer to non-
performing loan as “problem loan” or “toxic asset”.
Shingjergji (2013) evaluated the impact of non-performing loans on banks performance. He
estimated data on 5 European banks over a period of 11years (2002 to 2012), some of the
determining factors of non-performing loan estimated were: capital adequacy ratios, return on
equity, loan to asset ratio, and net interest margin. The outcome of this study highlighted a
negative significant relationship between capital adequacy ratio and non-performing loan.
However, total loan and net interest margin correlated positively with non-performing loan.
This study validates previous studies which states that an increase in profitability ratios will
result to a decrease in non-performing loan ratios. Nyarko-Baasi (2018) studied the effect of
non-performing loan on the profitability of 5 commercial banks in Ghana, over a period of nine
years (2006 to 2015). This study measured profitability with return on equity (ROE), and non-
performing loan ratio (NPLR) to measure NPLs. They also included capital adequacy ratio
(CAR) in the analysis. The result of this study identified that there is a negative relationship
between NPLR and ROE, while a positive relationship exists between CAR and ROE. Bank

23
size also correlated positively with profitability supporting the argument which suggests bigger
banks are usually more profitable.
2.6 Bank Size
According to Goddard et al., (2004), the bank size is a measure of the total assets of banks. He
stated further that there is a positive impact on bank performance stimulated by growth in its
size. Bank total assets are commonly utilised as a measure of economies or diseconomies of
scale. Several studies have shown diverse outcome on the effect of bank size on performance.
Pasiouras and Kosmidou (2007) examined the factors affecting domestic and foreign banks
profitability. This study evaluated 584 commercial banks operating in 15 European Union
countries including the United Kingdom over a period of six years (1995-2001). Unlike
Goddard et al., (2004), this study shows that there is a negative significant relationship between
bank size and profitability, this relationship is reflected in both the foreign and domestic banks.
This result suggests that in both samples, larger banks tend to earn lower profits, while smaller
banks tend to earn higher profits. This also suggests that economies of scale exist for smaller
banks but diseconomies of scale for larger banks. Bank size was estimated by the log of total
assets and profitability by returns on average total assets of the banks. This outcome remained
constant even when the researchers classified the samples into two groups (domestic and
foreign) according to the ownership structures of the bank.
Chronopoulos et al. (2015) studied the impact of bank size on the bank performance; their
study evaluated 10 US banks during the period of 1984-2010, using ROA as a proxy for
performance and total assets for bank size. The outcome stated that there is a nonlinea r
relationship between bank size and performance; this implies that an increase in the return on
asset led to an increase in total assets of banks and then later decreases, this result show that as
size of banks increases, profitability first increases, and then decreases. This result provides
alternative definitions of bank size do not support the economies of scale hypothesis in US
banking industry. The outcome reveals that there is a significant positive relationship between
asset growth and profitability. Aladwan et al (2015) researched on the effect of bank size on
the profitability of commercial banks in Jordan, His study covered the 2007 crisis period up to
2012, categorized banks according to their total assets and their level of profitability. Return
on equity (ROE) was used to proxy profitability. The outcome of this study presented a positive
significant relationship between bank size and profitability. Eichengreen and Gibson (2001)
highlighted that the relationship between bank size and profitability may be positive to some

24
certain extent, although the relationship could be nonlinear if it extended beyond the certain
size level.
2.7 Macroeconomic Conditions
Several previous studies found that economic growth has positive effect on bank’s
performance. These studies include: Athanasoglou et al., (2008); Pasiouras and Kosmidou
(2007); Kosmidou, (2008); Bolt et al., (2012); Calaz et al (2006; Chamberlin (2016). They used
Gross domestic product as a measure of economic growth in an economy. Chamberlin (2016)
identified that the real GDP growth influence bank profitability through three main channels;
operating cost, net interest income and loan loss improvement. Bank profitability increases
during an economic boom and declines drastically during an economic downturn drawing
evidence from the recent global financial crisis. In a cross-country assessment Demirgic-Kunt
and Huizinga (1999) evaluated the impact of macroeconomic factors on bank profitability. The
result shows that there is a positive correlation between growth in real per capital GDP and
bank profitability. Beckman (2012) evaluated the impacts of cyclical variables on UK banks
profitability, the cyclical variables; short and long-term interest rates, the real GDP growth in
relations to bank specific variables represented as banks return on assets and the ratio of credit
to total assets. The outcome of this study highlights the positive impact of GDP growth on bank
performance and a negative impact of interest rates. Additionally, Albertazzi and Gambacorta
(2009), Koku, et al. (2015) found out that there is a positive relationship between banks return
on assets and the growth in gross domestic product. Abreu et al. (2003) studied the impact of
gross domestic product on banks profitability, their study was consistent with previous research
evaluated as they found a positive relationship between GDP growth and return on asset.
2.8 Chapter Summary
The empirical literature concerning the relationship between liquidity and banks performance
has been critically discussed in this chapter. Furthermore, the changes in the regulatory
framework of banks were also extensively reviewed; other factors that could determine banks
profitability were examined. Before the global financial crisis, financial regulators and other
financial market participants did not pay much attention towards liquidity risk. The financial
crisis aroused the interest of many researchers as the liquidity risk was identified to be a critical
factor to the cause of the crisis. After the crisis, regulatory authorities have upscale the risk
framework of banks to incorporate liquidity risks via the introduction of the two compulsory

25
ratios: LCR and NSFR. These ratios were introduced to improve bank liquidity position and
hence enhance stability. However, at the same time, higher liquidity can have an adverse impact
on bank profitability. This study examines whether liquidity can affect profitability on the
sample of 5 UK-owned banks from 2004-2017. The new liquidity rules have been introduced
in 2013 under the Basel III framework and will take full effect only in 2019. It is therefore an
important and current area for examination.

26
3. CHAPTER THREE
Research Methodology
3.1 Introduction
This chapter provides a discussion of the methodology used in evaluating the data related to
the impact of liquidity on commercial banks profitability, with regards to the research approach
and the model specifications explaining the variables to measure liquidity, profitability and
other macroeconomic variables.
3.2 Research Approach
As earlier stated, the main focus of this study is to analyse the impact of liquidity on bank
profitability. There are two main paradigms that underpin a research: positivism and
interpretivist. This study mainly focuses on the positivism. Positivism is employed in the
course of this study due to the fact that it distinguishes between the perspective of science and
individual experience and fact. It is also crucial to identify that positivist research seeks
objectivity and the use of consistently rational and logical approaches to carry out research
(Carson et al., 2001). Positivism can be described as a philosophical approach that believes that
reality is quantitatively given and it possess measurable properties which are independent of
the researcher (Pring, 2004).
Most positivist studies entail testing theories in order to intensify the predictive understanding
of a phenomenon (Orlikowski & Baroudi 1991, p.5). According to Bryman (2004) under
positivism, positivism approach is employed because it enables theories and hypothesis to be
tested empirically whilst interpretivist approach does not make it possible to see beyond
personal biases and experiences. According to Gephart (1999) positivism approach emphasizes
on gathering broader information outside of readily measured variables. Positivist approach
usually involves deductive reasoning. A deductive approach focuses on hypothesis testing
based on an existing theory, while inductive approach focuses on generation and postulation
of new theories. Deductive connotes thinking from the particular to general (Lakin et al., 2009).
This implies that deductive approach laid emphasis on drawing conclusions from premises or
propositions. Other reasons why this study adopts deductive approach is that, it facilitates the
generalization of outcomes of a particular empirical research.

27
This research uses quantitative approach to data analysis. Quantitative and qualitative research
are different methods in which research are being evaluated. Quantitative research establishes
the cause and effect between variables, explaining a phenomenon by gathering statistical data
which are evaluated using mathematical based methods (Gummesson, 2005). Therefore,
quantitative research will be employed in the course of this study to examine the relationship
between liquidity and banks profitability. This study will evaluate relevant financial and
macroeconomic data using a panel data regression model. The next section will give a detailed
account of the samples and variables to be appraised.
3.3 Sample Selection
The samples for this study consist of 5 largest banks in the UK: HSBC Holdings, Barclays Plc,
Royal Bank of Scotland Group, Lloyds Banking Group, and Standard Chartered Plc over a
period of 14 years from 2004 to 2017. The sample comprises of 70 bank-year observations,
including 5 largest UK banks according to asset size which was sourced from AdvisoryHQ
ranking in 2017. The rationale behind evaluating UK banks is that these banks are systemically
important, and they were one of the most affected banks in the UK in the 2008 global financial
crisis. Besides, the Bank of England has been in the forefront of formulating and implementing
banking reforms to enhance global financial and economic stability. Moreover, the sample
period was selected so as to evaluate the UK banking sector before, during and after the 2008
financial crisis. Conclusively, the sample of these banks is important to examine due to their
systemic influence and change in their balance sheet structure due to the financial crisis and
the new regulatory requirement.
3.4 Variable Selection
The two most important indicators to this study are liquidity and profitability, although there
are other variables which are also determinants of banks performance considered in this study.
These are additional independent variables or control variables.
3.4.1 Dependent Variable
Profitability
Previous studies evaluated in the literature review measured profitability using return on asset
(Bordeleau et al 2010; Molyneux and Thornton 1992); net interest margin (Kosmidou et al
2008). This study focuses on using return on asset (ROA) as it measures of profitability.
Return on asset (ROA) is calculated by dividing the net income over the period of one year by
the total asset of the bank (Khrawish, 2011). Vieira (2010) identified that ROA is a good

28
measure of profitability for companies within the same sector. Since all the samples evaluated
within the scope of this study are based in the banking sector, this makes this measure more
suitable to measure banks profitability. ROA explains how efficiently the organisation is
utilizing its total assets to generate adequate revenue (Wachtel, 2005).
3.4.2 Independent Variables
Liquidity
To ascertain the level of liquidity of the selected banks, two measures were adopted: Liquid
asset (cash and balances at central bank) to total asset, with an additional liquidity measure,
liquidity coverage ratio (LCR). Liquidity coverage ratio is collected from banks’ balance sheets
and is defined as high-quality liquid assets divided by net cash flows. Previous studies
evaluated in the literature also measured liquidity using these variables; Liquid asset to total
asset (Tabari et al 2013; Ibrahim 2017; Bordeleau et al 2010) LCR (Muriithi and Waweru,
2017).
Liquid asset to total asset as a measure of liquidity is preferable due to the fact that it enhances
easy comparison of liquidity positions between banks (Shen, 2001). The liquid asset ratio also
helps the banks to ascertain and meet short term needs of customers which are critical to
financial stability of the bank; liquid asset ratio also serves as an internal and external measure
of liquidity for banks (IMF, 2006). Conversely, Poorman and Blake (2005) acknowledged that
liquidity ratios generally are not sufficient measures of liquidity as it relies on some proportion
of the assets rather than also considering the quality of the asset, but in this case, we only
evaluated cash which is advantageous to this study. Hence, an additional liquidity measure,
one of the newly adopted Basel III liquidity ratios (LCR) is employed in this study. The
Liquidity Coverage ratio (LCR) is a measure of a bank’s exposure to short-run liquidity risk
(BCBS 2013, p. 1). According to BCBS (2012) Basel III described LCR as a measure of
liquidity which requires banks to hold high quality liquid asset to meet liquidity needs over a
30-day time horizon under an acute liquidity stress scenario. LCR was calculated by the banks
and collected from the financial statement of each selected banks.
This measure of liquidity is peculiar to this study as it helps to assess the impact of the newly
adopted regulatory framework. Although, Liquidity Coverage Ratio was introduced in 2010 by
the Basel Committee on Banking Supervision, most of the big banks used in the sample in this
study commenced adoption of the new regulatory framework in 2011, these results to
estimating small sample size which limits the result of this study. Researchers have found more
efficient liquidity measures such as maturity ladder method proposed by Basel Committee on

29
Banking Supervision in early 2000’s, liquidity index, financing gap (Saunders and Cornett,
2006).
Capital
This study uses equity to total asset ratio as a proxy of the capital strength of banks. This ratio
is calculated by dividing the yearly total shareholders’ equity by the total asset. It is assumed
that the higher the ratio presented, the more solvent the bank is; the lower the equity to total
asset ratio the more the bank is prone to insolvency risk thereby reducing the banks
creditworthiness. This ratio also indicates the banks’ leverage position (Naceur, 2008).
Previous literature, such as Kosmidou et al., (2005); Barth et al., (2004); Naceur and Kandil
(2009) also evaluated capital using equity to total asset.
Equity to total asset ratio is a suitable measure of the capital strength of banks as it presents an
overall outlook of a bank’s asset composition and also determines the level at which the bank
is reliant on external funding. This ratio also helps to determine whether the level of
capitalization is a critical factor of banks profitability. In liquidation scenario, banks with
higher capitalization are considered to be relatively safer (Shen, 2004).
Non-performing Loans
To examine whether the level of asset quality is a determining factor for banks performance,
this study uses the ratio of non-performing loan to total loans. According to the definition
earlier stated in the literature review non-performing loan is either in default or closes to default
also; Shingjergji (2013) measured the impact of non-performing loans on banks performance
using the ratio of non-performing loan to total loans. The increase in this ratio indicates
deterioration in asset quality.
Bank Size
To measure the bank size, the logarithm of total bank assets was used to reduce the distribution
and make it closer to normal. This is a dominant measure of banks size in most literature
relating to bank performance (Goddard et al., 2004). According to the literature reviewed in
this study, (Pasiouras and Kosmidou 2007; Aladwan 2015) studied the impact of bank size on
bank performance using the banks’ total asset. The first study found a negative relationship
while the second found that a positive relationship exists between bank size and banks
performance, although some other literature has shown that the relationship between bank size
and performance can be positive or negative (Naceur and Omran, 2011; Staikouras and Woods,
2004).

30
Gross Domestic product
This study captures the macroeconomic effect which could also significantly impair banks
profitability (Smith, 2010). The growth in UK Gross Domestic Product (GDP) was used to
measure the effect of macroeconomic conditions on banks performance. GDP is a measure of
the total economic activity in an economy (Lawn, 2013). According to the literature reviewed,
(Athanasoglou et al., 2008; Kosmidou, 2008; Bolt et al., 2012; Calaz et al 2006 Pasiouras and
Kosmidou 2007;) evaluated the impact of macroeconomic variables on banks performance.
These studies indicate that a positive relationship exists between the variables. It is assumed
that when the economy is performing well banks tends to lend more in order to charge more
margins on their loans. However, researchers have also pinpointed some limitations in using
GDP as a yardstick for economic growth of a country. Ivovic (2016) highlighted the
inadequacies of GDP as a tool to measure economic growth. He stated that, it was never
designed to be more than a monetary measure and does not reflected anything more than
productivity.
3.5 Model Specification & Method of Analysis
The dependent variable in this study is return on asset (ROA) while the independent variables
are liquidity, capital, bank size and non-performing loan, including the macroeconomic
independent variable - gross domestic product growth rate. In order to test the relationship
between liquidity and bank performance, the following model is estimated:
ROAit = α0 + β1 LIQit + β2 CAPit + β3 NPLR it+ β4 BSit + GDPit+ ℮it
α0 = Intercept; β = Correlation coefficient; ROA = Return on Assets; LIQ= Liquid assets to
total asset; CAP= Capital to total assets; NPLR= Non-Performing Loans to total loans; BS=
Bank Size; GDP= Gross Domestic Product; ℮= error term; i = individual banks; t = year. This
model is based on the previous studies that examined bank performance and liquidity risk
(Saunders and Cornett, 2006; Tabari et al., 2013; Alzorqan, 2014; Ashraf et al., 2015; Kim,
2015). The limitation of this model is that only 5 variables are used, other studies used net
interest margin, ROE, NSFR, inflation and other bank specific variables (Saunders and Cornett,
2006; Athanasoglou et al., 2008).

31
Table 1: Variable definitions
Table 3.5.1 presents how each variable in this study were measured.
3.6 Data Collection
Secondary data was obtained from the published financial statements of banks which were
made available on their individual official websites and Fame Database. Specific data obtained
were: cash and balances at central bank, total asset, total loans, non-performing loans,
shareholders’ equity, total deposits, and return on assets. These data were converted to ratios
to enable effective comparison and to avoid discrepancies. The study also includes a measure
of economic growth – GDP growth. Data concerning the macroeconomic variable (GDP) was
sourced from the World Bank website.
3.6.1 Data Estimation
Panel data estimation technique was used to address the heterogeneity, related to individual
banks by controlling for individual specific variables, merging both cross sectional and time
series observations. According to Muriithi et al., (2017) panel data provides more efficient and
explanatory data, more variability, less collinearity among other variables enhancing further
increase in degrees of freedom. Ogboi and Unuafe (2004) attested to the fact that, panel data
estimation technique improves empirical analysis in a manner that might not be possible when
estimating data with either only time series or cross sectional. The Data was analysed using
Microsoft excel and Econometric views (E-views) software.
A multiple regression analysis is carried out using fixed effects (FE) to assess the relationship
between dependent and independent variables. This is an appropriate method of estimation
when panel data is employed. FE is preferable, since the entities in the sample constitute the
entire population, which helps to eliminate error variance; moreover, its ability to control for
Variable Measurement
ROA Net income divided by total asset
Capital Total equity divided by total asset
Liquidity Liquid Assets to total assets
Liquidity LCR calculated as high-quality liquid asset divided by net cash flows
Asset Quality Non- performing loans to total loans
Bank Size Natural Log of Total assets
GDP Yearly Growth in Gross Domestic Product

32
other unobserved variables relevant to this study whilst eliminating bias makes it preferable
(Brooks, 2014).
However, fixed effect model requires the use of different dummy variables which could
increase the level of degrees of freedom; also, the relationship between the dependent and
independent variables remain unchanged even if there are alterations in the intercept (Brooks,
2017 pg. 516). Gelman (2007) justified that the more dummy variables introduced in the FE
estimation will result in more noise in the model resulting to distortion in the model. The
following studies also studies used FE in their estimations Goddard et al. (2004; Kosmidou et
al. 2008; Athanasoglou et al. 2005; Demirguc-Kunt and Huizinga 2001). It is also plausible to
note that in the random effects model the number of independent variables should be less than
the number of banks; however, in this study, the number of independent variable is the same
as the number of banks (5 banks and 5 variables).
This chapter has discussed extensively the research approaches, sample selection, model
estimation and other variables employed to ascertain the relationship between bank liquidity
and performance. The next chapter will discuss the result of the analysis.

33
4. CHAPTER FOUR
ANALYSIS AND DISCUSSION
4.1 Introduction
This section presents the findings of this study based on the results of financial analysis of 5
UK banks, assessing the relationship between liquidity and bank performance. The time series
analysis graphs are presented first alongside the descriptive statistics, and then the results from
correlation and regression analysis are discussed.
4.2 Times Series Analysis
Time series analysis shows how data changes over time due to a particular event Singh et al.,
(2018).
4.2.1 LIQUIDITY
Figure 1: Liquidity ratio of UK five biggest banks (consolidated statements)
Figure 1 highlights the liquidity ratio of the biggest UK banks according to market
capitalization (AdvisorHQ, 2017) over the period of 13 years (2004-2017). This figure shows
the liquidity position of banks before, during and after the recent global financial crisis. Before
the crisis, this graph shows that the liquidity ratio of all the banks was considerably low (3%-
6%) except for Standard Chartered Bank as its liquidity ratio continues to increase until during
the crisis. This peculiarity was as a result of the decentralized nature of the groups’ business,
as liquidity positions were locally managed by the head office in each country of operation. In
2009, the liquidity position of HSBC, RBS, Lloyds and Barclays bank increased substantially
due to the bank rescue package received by the government in 2008 (Financial Times, 2008).
The government provided more capital for the big banks to encourage liquidity in the financial
system. After the crisis, HSBC, RBS, Lloyds and Barclays bank sustained similar trend in their
0
5
10
15
20
25
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
LIQUIDITY
HSBC BARCLAYS RBS Lloyds Chartered

34
liquidity ratio while Standard Chartered attained the highest liquidity ratio of 20.83% in 2014.
It is important to also note that Standard Chartered bank’s liquidity ratio has been high over
the full period due to its diversified income streams, access to sustainable wholesale funding
market and its organizational culture of maintaining well diversified risk exposures.
Specifically, the substantial increase in Standard Chartered Bank’s liquidity position in 2014
was as a result of the reclassification of its high-quality residential mortgages made available
for sale. Before the crisis, the graph shows that banks maintained relatively low liquidity;
however, after the crisis banks implemented the new Basel III regulatory requirement which
focused more on the liquidity position of banks.
Figure 2: LCR of UK five biggest banks (consolidatedstatements)
Fig 2 represents the Liquidity coverage ratio of UK 5 biggest banks over a period of 6 years
(2011-2017). LCR was used as an alternative liquidity measure in this study. The data sourced
for this ratio did not cover the full period (2004-2017) due to the fact that LCR was first
published in 2010, however most of the big banks commenced adoption in 2011. According
to Basel (2013), 60% minimum LCR was required to be met by banks in 2015, 70% in 2016,
80% in 2017, 90% in 2018 and 100% in 2019. This graph shows that all the selected banks
complied with the regulation even from 2011 attaining the 60% minimum requirement. This
shows that years after the financial crisis the UK 5 largest banks have been more liquid and has
been able to meet their short-term obligations.
0
20
40
60
80
100
120
140
160
180
2011 2012 2013 2014 2015 2016 2017
LIQUIDITY COVERAGE RATIO

35
4.2.2 PROFITABILITY
Figure 3: ROA of UK five biggest banks (consolidated statements)
Figure 3 shows the return on assets of HSBC, Barclays, RBS, Lloyds and Standard Chartered
Bank over a period of 13 years (2004-2017). From 2004 until 2007 ROA of all the banks were
relatively high (0.24% to 1.64%). However, when the financial crisis began liquidity ratio of
HSBC, Barclays, RBS, and Lloyds began to fall, which continued to dip-in 2008. This reflected
the losses of banks during the global financial crisis. RBS reflected the highest level of losses
of -1.5% due to its poor decision and inadequate due diligence before acquiring ABN AMRO
which had substantial subprime mortgage exposures in its books (Telegraph, 2011). Amidst
the prevailing financial instability in the banking industry, Standard Chartered Bank continue
to record profitability figures during and after the crisis until 2015 where it recorded -0.34%
ROA, this is due to the desire of the bank to run a conventional balance sheet with respect to
capitalization and liquidity which results to profitability (Financial Times, 2011)
Generally, the return on assets of banks has been lower since the 2008 financial crisis, which
implies lower level of profitability. RBS continued to record negative return on assets until
2017 when it had a positive ROA of 0.18%. It can be suggested that the continued losses made
by RBS since the global financial crisis was due to its internal culture which focuses on
investing substantially in questionable financial products without necessary attention to the
risks inherent in such products (Business Insider, 2015).
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
RETURN ON ASSET

36
4.2.3 NON-PERFORMING LOAN
Figure 4: Non-performing loan of UK five biggest banks (consolidated statements)
Figure 4 shows represent the trends in the non-performing loan ratio of 5 biggest banks in the
UK over a period of 13 years (2004-2017). It was demonstrated that, before the crisis the NPLs
ratio of the banks were considerably followed the same pattern except for Standard Chartered
whose NPLs ratio was significantly low at below 1% before, during and after the financial
crisis due to the organizational culture of maintaining well diversified risk exposures. From
2008, the NPLs ratio of HSBC, Barclays, Lloyds and RBS increased substantially due to the
effect of the economic downturn which resulted to borrowers default their loans. NPLs ratio
for RBS increased to 5.08% in 2013 which makes it the highest throughout the period as a
result of its poor asset quality and bad management decisions. The sharp decline in RBS NPLs
ratio was as a result of the sale of the bad loans in 2014. The non-performing loan ratio of
Barclays, Lloyds also fell continuously while NPLs ratio for HSBC increased to 1.67% in 2016
which later decline in 2017.
0.00
1.00
2.00
3.00
4.00
5.00
6.00
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Non performing Loan

37
4.3.4 CAPITAL
Figure 5: Shareholders’ equity of UK five biggest banks (consolidated statements)
Figure 4 highlights the trend in the ratio of shareholders’ funds to total assets of 5 biggest banks
in the UK over a period of 13 years (2004-2017). Prior to the crisis the shareholders equity
ratio for all the banks were relatively low compared to other periods reflecting the inadequacies
of Basel II capital regulatory framework which existed before the crisis. Meanwhile, in
2007/2008 the equity ratio of all the banks fell substantially, this revealed how shareholders’
funds were eroded as a result of the global financial crisis. The V-shaped trend during the
financial crisis is a reflection of the systemic impact of the economic down turn on all the
banks. After the crisis, the shareholders’ funds to total asset ratio increased substantially due
to the impact of Basel III regulations (CET1 of 4.5% of risk weighted assets) and also the two
additional capital requirements; Capital conservative and the countercyclical buffers which
most UK banks has progressively complied with since implementation (Financial Times,
2016).
4.3 Descriptive Statistics
Table 4.3.1 below summarizes the descriptive statistics of 5 UK banks; this section is divided
into full period (2004-2017) and Sub-period (2009-2017). The full period covers years before,
during and after the global financial crisis, while the sub period only focuses on years after the
financial crisis. The periods were classified to determine if there are peculiar changes in the
variables after the implementation of the new regulation and higher requirements.
0
1
2
3
4
5
6
7
8
9
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Equity

38
Table 4.3.1: Descriptive Statistics of the banks from 2004-2017
Source: Calculation Based on financial statement of the banks (2004-2017)
In the full period, the numbers of observations are 70 except for LCR which is 35, due to the
unavailability of data for this variable in years before the global financial crisis, while in the
sub-period, the total observations reduced to 45, retaining LCR at 35. The classification of
periods helps to determine if there are distinctive differences in the variables in each period.
Statistics for banks performance shows that, the mean of return on assets (ROA) of the selected
banks are not very high throughout the period. ROA in the full period has a mean of 0.282%
with a median of approximately the same figure 0.281% while in the sub-period, the mean
value for ROA reduced to 0.158% with a median value of 0.165%. This demonstrates the
reduction in profitability of banks in years after the crisis which may be due to the effect of the
financial crisis hence the newly adopted regulatory requirement after the crisis. To determine
the shape of the distribution, the skewness and kurtosis of both periods must be examined; the
skewness for the two periods showed a negative skewness and data distribution which is close
to zero, although the skewness in the sub-period (-0.21) showed a closer value to zero compared
with the skewness in the full period (-0.571) which implies that the data is evenly distributed.
Kurtosis which measures the flatness or peaked-ness of a distribution shows ROA value of
4.79, -0.211 in the full period and sub-period respectively, this implies that this distribution is
slightly leptokurtic in the full period while in the sub-period platykurtic. However, both
skewness and kurtosis do not show substantial deviation from normality and therefore do not
require adjustment.
The result of the statistics related to the measure of liquidity (LIQ) in the full period, presented
a mean of 5.9% with the same value for its median, while in the sub-period 8.07% and 7.12%
median. This shows that the average liquidity position of all the banks were considerably high
2004-2017
DESCRIPTIVE STATISTICS
ROA LIQ LCR CAPITAL TA LNTA NPL GDP
Mean 0.282 5.900 116.057 5.254 9.88E+08 20.497 1.293 1.732
Median 0.281 5.900 124.000 5.452 9.38E+08 20.659 1.065 1.994
Maximum 1.636 20.897 171.000 7.821 2,401,645,586 21.599 5.080 4.448
Minimum -1.564 0.336 59.000 1.881 75,858,000 18.144 0.020 -4.188
Std. Dev. 0.501 4.467 29.664 1.550 5.52E+08 0.742 1.080 1.997
Skewness -0.571 0.843 -0.389 -0.485 0.275985 -1.004 1.139 -1.702
Kurtosis 4.799 3.590 2.190 2.549 2.237627 3.605 4.267 6.145
Jarque-Bera 13.247 9.309 1.838 3.335 2.583831 12.826 19.820 62.639
Probability 0.001 0.010 0.399 0.189 0.274744 0.002 0.000 0.000
Observations 70 70 35 70 70 70 70 70

39
over the years, although there are indications in the sub-period which suggest that the consistent
increase could be peculiar to years after the financial crisis as the failing banks were rescued.
The average value for Liquidity ratios looks higher in the post crisis era as compared with the
full period, the LIQ is 8% while it was 5.9% in the full period, and this is due to the compliance
of the banks with the new regulatory framework which placed more emphasis to liquidity. LIQ
in the full period is positively skewed 0.843 the kurtosis is 3.56 and the standard deviation is
4.467% while in the sub-period 1.100, 4.109 and 3.886% respectively. The distribution of data
in the two periods highlights the peculiarity of increase in banks liquidity after the financial
crisis. The alternative liquidity measures (LCR) also reflected the substantial increase in banks
liquidity as it presents a mean of 116% and a minimum value of 59% in both periods. The
standard deviation is 29% presenting a figure which is less than the mean. The distribution is
negatively skewed at -0.389 and the kurtosis is 2.190 – these values are very close to normal.
LCR is more efficient in presenting the liquidity positions of the banks as LCR includes in its
buffer, assets which are non-marketable in poor economic condition creating a leeway for
banks during liquidity shocks, while LIQ was calculated using only cash and balances at
central bank.
The capital ratio in the full period shows a mean of 5.254% with a median value of 5.452% -
these values are close to each other indicating that the distribution is close to symmetric, while
in the sub-period the capital ratio shows a mean of 5.8% and median 5.775%. The maximum
and minimum capitals of the banks in the full period are represented as 7.821% and 1.881%,
while in the sub period 7.821% and 3.414 respectively. The standard deviation, skewness and
kurtosis in the full period are 1.550, -0.485 and 2.549, while in the sub-period 1.12%, -0.179%
and 2.173 respectively. These values imply that the distributions in both periods are close to
normal. The maximum and minimum total asset in the full period, which represents banks size
(HSBC, Barclays, Lloyds, RBS and Standard Chartered) are 2,401,645,586 and 75,858,000;
while in the sub-period 1,924,938,777 and 270,000,000 respectively, A logarithmic
transformation (LNTA) was carried out on total asset (bank size) so as to reduce the distribution
and present a symmetrical distribution. LNTA has a mean of 20.497 in the full period with a
median of almost the same values 20.659, while in the sub-period 20.68 and 20.773
respectively which indicates that the distribution is close to symmetric, skewness and kurtosis
are -1.004and 3.065 in the full period, while -0.802 and 2.620 respectively, which also indicate
that the distribution is fairly close to normal. The mean value for the NPLs is 1.29% with a
maximum value of 5%, this maximum value of 5% could be attributable to the NPLs ratio of
RBS in the entire years of evaluation (see figure 4), although the NPLs of banks generally grew

40
substantially in years after the crisis - this will be highlighted in table 4.3.2, which focused
more on the years after the financial crisis. There is also an evidence of leptokurtic kurtosis of
4.267 and a positive skewness of 1.139 - however both figures are do not deviate substantially
from normal distribution.
The GDP has a mean value, standard deviation, skewness and kurtosis of 1.732%, 1.997%, -
1.702 and 6.145 in the full period, while the sub-period these values are 1.291%, 2.014%, -
2.206 and 6.441 respectively. GDP growth has the same minimum value of -4.2% in both
periods - this is the value in 2009 when the UK experienced the lowest GDP growth over this
time period. The distribution in both periods is negatively skewed. The negative skewness
could be as a result of the effect of financial crisis in 2009 which could justify why the ROA
of banks were still low even after the crisis. Kurtosis in both periods shows that distribution is
rather leptokurtic. Both skewness and kurtosis values are higher the bounds of the normal
distribution; however, these deviations are within the acceptable bounds of +/-2 for skewness
and +/-7 for kurtosis and therefore, in this case, do not need adjustment as this can result in
lower number of observations (Kline, 2015).
Table 4.3.2: Descriptive Statistics of the banks from 2009-2017
2009-2017
DESCRIPTIVE STATISTICS
ROA LIQ LCR CAPITA
L
Total asset LNTA NPL GDP
Mean 0.158 8.077 116.057 5.800 1090000000 20.698 1.496 1.291
Median 0.165 7.125 124.000 5.775 1050000000 20.773 1.170 1.787
Maximum 0.842 20.897 171.000 7.821 1924938777 21.378 5.080 3.054
Minimum -0.849 3.081 59.000 3.414 270000000 19.415 0.020 -4.188
Std. Dev 0.406 3.886 29.664 1.222 466000000 0.522 1.245 2.014
Skewness -0.211 1.100 -0.389 -0.179 -0.109 -0.802 0.770 -2.206
Kurtosis 2.539 4.109 2.190 2.173 1.876 2.620 3.044 6.441
Jarque-Bera 0.733 11.380 1.838 1.523 2.458 5.090 4.453 58.68
Probability 0.693 0.003 0.399 0.467 0.293 0.078 0.108 0.00
Observations 45 45 35 45 45 45 45 45
Table 4.3.2: Calculation Based on financial statement of the banks (2009 -2017)
This section examined descriptive statistics of the variables under study. However, the aim of
this study is to establish the relationship between the variables. According to Brooks (2008), a
simple evaluation of the variables for possible evidence for multicollinearity in the model can

41
be made through the examining the correlation matrix. Hence, the next section will evaluate
and discuss the result of the correlation matrix.
4.4 Correlation Analysis
Correlation is a technique of statistical analysis which evaluates the relationship between two
variables (Young, 2009). In this study, it is first important to establish the correlation between
dependent and independent variables. Dependent variable is (return on asset) ROA and the
independent variable are LCR, LIQ, CAPITAL, SIZE, NPL, and GDP. It is then important to
check for multicollinearity problems among the variables that is, correlation between the
independent variables.
Table 4.3.3: Correlation matrix (2004-2017)
2004-
2017
70 obs. Correlation Matrix
ROA LIQ LCR CAPITA
L
SIZE NPL GDP
ROA 1
LIQ -0.062 1
LCR -0.196 -0.032 1
CAPIT
AL
0.253 0.647 0.168 1
SIZE -0.421 -0.101 -0.067 -0.208 1
NPL -0.535 -0.205 -0.173 -0.341 0.480 1
GDP 0.181 -0.054 0.312 0.157 -0.279 -0.203 1
Table 4.3.4: Correlation matrix (2009-2017)
2009-2017 Correlation Matrix
45 Obs.
ROA LIQ LCR CAPITA
L
SIZE NPL GDP
ROA 1
LIQ 0.180 1
LCR -0.196 -0.032 1
CAPITAL 0.299 0.558 0.168 1
SIZE -0.205 -0.586 -0.067 -0.392 1
NPL -0.641 -0.500 -0.173 -0.625 0.463 1
GDP -0.206 0.315 0.312 0.328 -0.026 -0.124 1

42
Table 4.3.3 and 4.3.4 illustrates the relationship between variables over the full period from
2004 to 2017 and the sub-period from 2009 to 2017 respectively. Table 4.3.3 above, shows a
very weak negative correlation (-0.062) between liquidity and profitability. This result shows
there is almost no relationship between liquidity and profitability; hence there is not enough
evidence of the trade-off relationship reviewed in the literature. This indicates that according
to this result there is almost no relationship between liquidity as measured by cash and balances
at central bank and profitability. This result is consistent with Wagner (2009) who suggested
that even though banks had to increase their liquidity positions after the crisis it did not have
much impact on banks profitability.
It is important to note that there is no enough evidence to justify the trade-off in the full period
(2004-2017) which comprises of years before, during and after the global financial crisis. This
result however changes in the sub-period 2009-2017. The result is different in the sub-period
(2009-2017) as illustrated in table 4.3.4, the correlation between profitability and liquidity
presented a positively weak relationship (0.180).
A weak negative relationship of -0.196 exist with the additional liquidity measure (LCR) and
profitability. This provides some although weak support that the adoption of the new liquidity
regulatory framework can have a negative association with profitability, supporting the trade-
off between the banks holding substantial high-quality liquid assets at the expense of profit.
Table 4.3.3 and 4.3.4 illustrates the correlation between capital and profitability in both periods.
In the full period, a weak positive relationship (0.253) exists between ROA and Capital, the
same relationship was observed in the sub-period and the relationship between ROA and
Capital also presents a weak positive relationship (0.229). This suggests that, as the banks
complied with the increase in regulatory capital and liquidity requirements, banks became more
stable, which suggest higher liquidity positions to withstand economic shocks. (See figure 5).
Furthermore, a negative relationship (-0.421) was observed between ROA and bank size in the
full period and -0.205 in the sub-period. This outcome could be as a result of diseconomies of
scale which is related to big banks especially after a substantial growth period (see figure 5).
This study is consistent with Pasiouras and Kosmidou (2007).
The NPLs ratio of the banks in both full period and sub-period showed a negative relationship,
-0.535 and -0.64 respectively. This implies that NPLs impair banks profitability in both
periods, this relationship had existed in all the banks since the financial crisis except for
Standard Chartered. This increase in NPLs has been peculiar to RBS (see figure 4). Finally,
table 4.3.3 highlight that there a positively weak relationship (0.181) between GDP and ROA;

43
however, the relationship becomes negative in the sub-period 2009-2017. Usually, positive
relationship between GDP growth and profitability would be expected; however, negative
association could be due to the fact that some banks such as RBS suffered losses even when
economic conditions were relatively good (see figure 6, appendix).
It is critical to note that there are no potential multicollinearity problems between the
independent variable in the correlation analysis, both the full period (2004-2017) and the sub-
period (2009-2017). Hence there is no need for any adjustments (removal) to be made in the
model estimated. In order to analyse the relationship between liquidity risk and banks’
performance, the next section will explain the result of the multiple regression.
4.5 Regression Analysis
This section analyses the empirical result based on the model estimated. Regression analysis
helps to understand how the dependent variable in this case ROA changes when there is a one
of the independent variables changes by one unit, while other variables are constant (Dennis,
2005).
Table 4.5.1 Regression results including the three periods
Fixed Effects Models: - UK
Dependent variable: Return on Assets (ROA)
FULL PERIOD SUB-PERIOD SUB-PERIOD 2 (LCR)
Coefficient P-value Coefficient P-value Coefficient P-value
Independent variables
C 8.78874*** 0.00070 10.13317* 0.06230 7.47867 0.41540
LIQ -0.01929 0.23700 -0.00435 0.77990
LCR -0.00212 0.34030
CAP 0.0297 0.55330 -0.168113* 0.06030 -0.11093 0.36850
SIZE -0.409509*** 0.00100 -0.420017* 0.09770 0.29610 0.49480
NPL -0.101495** 0.04120 -0.16341*** 0.00700 -0.18145* 0.01470
GDP -0.014058 0.55310 -0.020757 0.41960 -0.03581 0.69520
R2
0.63536 0.68487 0.69580
Adjusted R2
0.58066 0.60383 0.58628
F-stat 11.61611 8.45174 6.35355
P-value (F-stat) 0.00000 0.00000 0.00012
Observations 70 45 35
***significant at the 1% level; **significant at the 5% level; *significant at the
10% level.
Table 4.5.1 shows the regression analysis results for each period, the statistics of interest are:
p-values of the coefficients, R2
, F-stat, p-values (F-stat). The number of observations varies

44
according to the number of years being evaluated in each sample. The level of significance is
determined by the probability values. The R2 is the amount of variation in ROA that is
explained by the independent variables in each model. F-statistics shows whether the
independent variables are jointly significant in explaining ROA, while the p-value of the F-stat
measures the statistical significance of the f-value (Dodd, 1997).
Liquidity and Profitability
Liquidity and Profitability figures highlighted in table 4.5.1 highlight that there is no
statistically significant relationship between both variables. In general, the regression analysis
shows that there is no significant relationship between liquidity measures and banks
profitability, both in the full period, sub-period and sub-period 2 (LCR).
In the full period, liquidity has a negative sign; however, the relationship is not statistically
significant. Therefore, there is not enough evidence to support the negative association between
liquidity and profitability. The p-value of the liquidity coefficient is not statistically significant
even at 10% level. In the sub-period, table 4.5.1 also highlighted that there is no significant
relationship between liquidity measure (LIQ) and banks performance (ROA) even in years
after the financial crisis; although this outcome highlight that there are other determining factor
of banks profitability such as capital, bank size and no-performing loan since we found
statistically significant relationship between these variables and profitability.
In the sub-period (2009-2017) which indicated years after the financial crisis, this result shows
that there is significant relationship between capital and profitability also this study found
statistically significant relationship between size and profitability in the full period and a
significant association in the sub-period, while non-performing loan was statistically
significant in all periods. This result is consistent with Jenkinson (2008) who concluded that
the liquidity risk does not have significant relationship with banks’ performance. Jenkinson
(2008) also evaluated the UK banks although, the number of banks selected were different and
he also evaluated years before the crisis and during the crisis. His study measured liquidity risk
by using liquid assets to total asset ratio while return on average asset (ROAA) was used to
measure profitability. The result of this study is consistent with (Eichengreen and Gibson,
2001; Bordeleau et al 2010. A recent study, (Mehrotra et al., 2018) also found no significant
relationship between liquidity and profitability, although this study evaluated more sample size
by considering 27 public sector banks and 20 private sector banks in India covering a period
of 5 years (2011-2016). They measured liquidity with different variables such as; cash-deposit
ratio, credit to deposit ratio and investment deposit ratio. Profitability was measure using both

45
ROA and ROE. This result of this period was not anticipated. However, the outcome of this
analysis could be as a result of a relatively small sample size.
In the second sub-period where LCR was included in the variables estimated due to its recent
adoption by banks. The Liquidity Coverage ratio was negative but not significant in this period.
This result is not anticipated; although this result is consistent with (Giordana et al, 2017;
Psillaki and Georgoulea, 2016) they evaluated the impact of Basel III standards on
Luxembourg banks performance using LCR as one of the variables evaluated. Hence, this
could not find evidence to justify the effect of the new liquidity tightening ratio (LCR) on banks
profitability. This study also suggests that there is no significant impact of LCR on banks
profitability. On the other hand, Muriithi and Waweru (2017) evaluated the impact of liquidity
on 43 banks in Kenya; they established that their finding suggested a nonlinear relationship
between LCR and ROA. Due to the limited data availability for the current study, the
insignificant coefficient on LCR could be as a result of the limited data in the selected samples
due to the number of years evaluated (2011-2017). Therefore, more years and observations
would be needed to check whether there is a relationship between liquidity and profitability.
Capital and Profitability
Relationship between Capital and Profitability show mixed results. In the full period, the
coefficient is positive, however, the p-value of the capital coefficient is not significant.
However, in the sub-period 1, the outcome of this analysis found a negative and significant
(10% level) relationship between profitability (ROA) and capital. This perhaps, provides some
support for the notion that higher capital requirements after the crisis might have had impact
on bank profitability. The evidence is however weak. In the sub-period 2 the coefficient is still
negative but is not significant. Therefore, there is some but weak evidence that in the period
after the crisis, higher capital ratios might have a negative impact on profitability. This
however, contradicts the result of the correlation analysis, which suggests that the correlation
between capital and profitability is positive but rather weak in both time-periods.
Size and Profitability
The p-values of bank size in the full period is statistically significant at 1% level, this indicates
that there is negative significant relationship between bank size and profitability. This could be
due to the fact that big banks had low profitability in the years around the crisis. Some of the
largest banks, like RBS had very low profitability during this period; hence the relationship
shows that bigger banks are less profitable. This could also provide evidence for the
diseconomies of scale argument reviewed in the literature (Shen et al., 2002). However,
Demirci et al (2018) evaluated the impact of size on Turkish banks profitability; the outcome

46
shows that there is a nonlinear relationship between banks sizes and their profitability. They
established that, even though the growth in bank size as measured by its total assets tends to be
positive at some point, it later became nonlinear and insignificant.
The p-value of size and profitability also presented a significant negative relationship, although
only at a 10% level. However, this relationship changes in the period after the crisis (2011-
2017) the relationship is now positive but not significant.
NPLs and profitability
Table 4.5.1 highlight the significant impact of non-performing loans on ROA before, during
and after the financial crisis - a statistically significant and negative relationship between NPLs
and ROA in all periods. This result indicates the substantial growth in the non-performing loan
ratio on banks’ balance sheet which could significantly impair the profitability of banks (ECB,
2010). The higher the ratio of NPLs, the lower the profitability ratio of banks, this ratio was
high after the financial crisis due to the poor quality of loan-asset composition and the desire
to improve the interest income of bank since the effect of the global financial crisis. This result
is consistent with (Shingjergji, 2013; Albulescu, 2015) as they evaluated European banks. This
result is consistent with the literature reviewed which suggest that researchers have
accumulated evidences that non-performing loan is a major stumbling block to banks
profitability (Davis and Karim 2008). The outcomes of this studies further support the evidence
according to (Fofack, 2005; Nyarko-Baasi, 2018). Other studies which found similar results
are; (Gwaula et al, 2016; Ozurumba, 2016; Makri et al., 2013)
GDP growth and Profitability
The p-values of GDP was not statistically significant in the full period, sub-period and sub-
period 2, although the p-values in all periods were positive. This implies that banks perform
better during good economic condition. However, during bad economic conditions the
profitability ratio of banks continues to diminish. The financial crisis of 2007/2008 justified
this notion, the outcome of this study is consistent with (Klein and Weill, 2016) who evaluated
the bank profitability and economic growth. They measured economic growth using GDP
growth and profitability using ROA.
Overall Model
The R2 in the full period is 63.5%; which implies that the independent variables (LIQ, CAP,
SIZE, NPLs, and GDP) account for 63.5% variation in the dependent variable (ROA), while in
the sub-period the R2 is 68%; however, in sub-period 2 the R2 is 69%. The outcome shows that,
the variation in dependent variable is best accounted for by the independent variable in the

THE IMPACT OF LIQUIDITY ON UK BANKS PROFITABILITY: A CASE STUDY OF THE 5 LARGEST UK BANKS.

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