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UNIVERSITY OF GHANA
CREDIT RISK AND PROFITABILITY OF LISTED BANKS IN GHANA
BY
HADIRATT SHERIF
(10636518)
A LONG ESSAY SUMITTED TO THE UNIVERSITY OF GHANA, LEGON IN
PARTIAL FILFILMENT OF THE REQUIREMENT FOR THE AWARD OF AN
MASTERS DEGREE IN BUSINESS ADMINISTRATION (FINANCE OPTION)
MAY 2019
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DECLARATION
I do hereby declare that this work is the result of my own research and has not been presented by
anyone for any academic award in this or any other university. All references used in the work
have been fully acknowledged.
I bear sole responsibility for any shortcomings.
…………………………………. .…………………………
HADIRATT SHERIF DATE
(10636518)
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CERTIFICATION
I hereby certify that this long essay was supervised in accordance with procedures laid down by
the University of Ghana.
………………………………………… …………………………………..
SAINT KUTTU (PhD) DATE
(SUPERVISOR)
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DEDICATION
I dedicate this work to almighty ALLAH to Him be the Glory for the unimaginably great things
HE continues to do in my life.
Also I dedicate this to my family especially my son, Abdul Muhaymeen.
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ACKNOWLEDGEMENT
I wish to acknowledge all those who did contribute in diverse ways to make this thesis a success.
To my parents Mr. and Mrs Fathah Sheriff and my aunt Hajia Hamidiya Ismaila your spiritual and
physical support and above all your advice continue to shape my thoughts and actions. I am indeed
grateful.
To all other faculty members of University of Ghana Business School, I am sincerely grateful to
you for your support and assistance.
This thesis would not have been possible if it were not for the tireless guidance and support I got
from my supervisors Saint Kutu (PhD); I say thank you.
Finally, my appreciation goes to my colleagues; especially my study group mates, your consistent
support and encouragement gave me a source of hope in challenging and difficult periods through
the research.
Allah, bless you all.
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TABLE OF CONTENTS
DECLARATION ........................................................................................................................ i
CERTIFICATION ..................................................................................................................... ii
DEDICATION .......................................................................................................................... iii
ACKNOWLEDGEMENT ......................................................................................................... iv
TABLE OF CONTENTS ............................................................................................................v
LIST OF TABLES .................................................................................................................. viii
LIST OF FIGURES .................................................................................................................. ix
ABSTRACT ...............................................................................................................................x
CHAPTER ONE .........................................................................................................................1
INTRODUCTION ......................................................................................................................1
1.1 Background of Study .........................................................................................................1
1.2 Problem Statement .............................................................................................................3
1.3 Research Objectives ...........................................................................................................3
1.4 Research Questions ............................................................................................................3
1.5 Significance of Study .........................................................................................................4
1.6 Research Limitations .........................................................................................................5
1.7 Organisation of Study ........................................................................................................5
CHAPTER TWO ........................................................................................................................6
LITERATURE REVIEW ............................................................................................................6
2.1 Introduction .......................................................................................................................6
2.2 Theoretical review .............................................................................................................6
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2.2.1 Agency Theory and credit risk .....................................................................................6
2.2.2 Theory of Information Asymmetry and Information Sharing .......................................7
2.3 Empirical review................................................................................................................8
2.3.1 Determinants of Bank credit risk .................................................................................8
2.3.2 Some Literature on Bank Credit Management Practices...............................................9
2.4 Overview of the Ghanaian banking system ...................................................................... 10
2.5 Chapter summary ............................................................................................................. 11
CHAPTER THREE ................................................................................................................... 12
METHODOLOGY.................................................................................................................... 12
3.1 Introduction ..................................................................................................................... 12
3.2 Research Design .............................................................................................................. 12
3.3 Sampling and sources of data ........................................................................................... 12
3.4 Profitability Analysis ....................................................................................................... 13
3.4.1 Return on Assets (ROA) ............................................................................................ 13
3.5 Variable Measurements and Model Specification ............................................................. 14
3.5.1 Bank size ................................................................................................................... 15
3.5.2 Ownership structure .................................................................................................. 16
3.5.3 Leverage ................................................................................................................... 16
3.5.4 Competition............................................................................................................... 16
3.6 Data Analysis Technique ................................................................................................. 17
CHAPTER FOUR ..................................................................................................................... 18
DATA ANALYSES AND DISCUSSION OF FINDINGS ........................................................ 18
4.1 Introduction ..................................................................................................................... 18
4.2 Description of data ........................................................................................................... 18
4.3 Profitability of Listed Banks ............................................................................................ 19
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4.4 Correlation Analysis ........................................................................................................ 21
4.4 Regression Analyses ........................................................................................................ 23
4.4.1 Credit risk and Profitability ....................................................................................... 24
4.4.2 Ownership and Profitability ....................................................................................... 24
4.4.3 Size, Leverage and profitability ................................................................................. 25
4.4.4 Competition and Performance ................................................................................... 25
4.4.5 Interest rates and Profitability .................................................................................... 25
4.5 Summary ......................................................................................................................... 26
CHAPTER FIVE ...................................................................................................................... 27
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ............................................... 27
5.1. Introduction .................................................................................................................... 27
5.2 Summary of the study ...................................................................................................... 27
5.3 Summary of Findings ....................................................................................................... 28
5.4 Recommendations............................................................................................................ 29
REFERENCES ......................................................................................................................... 31
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LIST OF TABLES
Table 4.1 Descriptive statistics .................................................................................................. 19
Table 4.2 Return on Assets (ROA) for each Bank over Time Period .......................................... 20
Table 4.3 (Correlation Matrix) .................................................................................................. 22
Table 4.4 Regression Results ..................................................................................................... 24
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LIST OF FIGURES
Figure 4.1 Mean ROA of Listed Banks...................................................................................... 21
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ABSTRACT
This research assesses the relationship between credit risk and the performance of listed banks in
Ghana. The data sample consisted 8 listed banks covering a 7-year period from 2010 to 2016.
Findings from the study reveal that total assets and liabilities on average increased over the seven
(7) years period. But this increase has been almost in the same proportion, leading to an almost
constant leverage ratio over the period. On the average, the ROA of most of the banks increased
over time. An explanation for this could be that liquid assets, total assets, shareholder’s fund and
total comprehensive income have on average augmented over the years. Size formed the highest
correlation with ROA, followed by board independence and leverage in that order.
The regression result revealed a negative impact of credit risk on performance. A unit increase in
the credit risk of banks will lead to a 0.0324 units decrease in the return on assets. Bank size had
a positive impact on performance. The positive relationship implied that larger banks perform
better over time than smaller banks.
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CHAPTER ONE
INTRODUCTION
1.1 Background of Study
Financial institutions play a major role in the development of any country in both developed and
developing countries. By mobilizing domestic savings from depositors, banks provide resources
to individuals and business units who in turn use these resources for investment and productive
activities that promote economic development. Besides this intermediation role, banks continue to
provide innovative financial products to investors that lower transaction costs and facilitates
payment systems (Maxwell, 1995).
The performance of the financial sector is, therefore, pivotal to the growth of any contemporary
economy (Thankom Gopinath Arun & Turner, 2009; T. G. Arun & Turner, 2002; Demirgüç-Kunt,
2004). The activities of financial institutions like banks drive other sectors of the economy
particularly through money lending (PWC, 2017). Therefore their performance ripples into
economic growth (Ataullah & Le, 2006). Empirical studies have documented a positive
relationship between the performance of financial institutions and economic growth and
development (Beck & Levine, 2004; Beck, Levine, & Loayza, 2000). The financial sector’s
significance is apparent in its substantial contribution to gross domestic product (GDP) (Haldane,
Brennan, & Madouros, 2010). In Ghana, for example, the financial and insurance subsector
contributed 8.4%, 8.9% and 9.4% to GDP in 2014, 2015 and 2016 respectively(GSS, 2017). In the
financial sector, banks are considered the most significant due to the crucial role they play in
financial intermediation. Hence, their contribution to the economy cannot be overlooked. The
banking sub-sector in Ghana contributed about 75% of the assets of the financial sector in 2011
(Alhassan, 2015). Banks mobilize funds from surplus spending units (depositors) and allocate
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them to deficit spending units (borrowers), to enhance economic growth (Ataullah & Le, 2006;
Levine, Loayza, & Beck, 2002).
To be able to sustain the performance of these roles in an economy, banks must be able to generate
enough earnings in for business survival and continuity. Profitability is a concept that is at the
Centre stage of discussion when one talks of how banks should earn enough earnings to remain in
business. Profitability is in turn affected by several internal and external factors. Largely, banks
have control of internal factors also known as bank specific variables that tend to shape
profitability levels. Among other things, liquidity is one of the key internal factors that influence
profitability of banks. Generally, the term liquidity refers to the ability to fund increases in assets
and meet obligations as they fall due.
Credit risk can be described as the risk of default on the part of borrowers to pay back sums
borrowed (Hafsa Orhan Astrom, 2013; Richard, Chijoriga, Kaijage, Peterson, & Bohman, 2008).
Since the UT and Capital Bank takeovers, one growing phenomenon taking strides in the banking
sector is the credit risk management of banks in Ghana and how they impact their performance.
Banks generate more income from credit creation, but this comes with several risks. Amongst
those several risks, credit risk proves to be so inevitable in the credit creation process (Eccles,
Herz, Keegan, & Phillips, 2001), and can really interrupt the smooth running of a bank’s business.
Excessively high level of non-performing loans in the banks can be attributed to poor corporate
governance practices, loose and negligent credit administration processes and the absence or non-
adherence to credit risk management practices. Bad credit risk management has been identified to
be a recipe for disaster, a reason for which some banks go bankrupt. It is for this reason that this
study wants to determine the close nexus between credit risk management practices of banks in
Ghana and how they ultimately affect performance.
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1.2 Problem Statement
There are quite a proliferation of studies worldwide that have considered the link between credit
risk management (Andreou, Cooper, Louca, & Philip, 2017; Fayman & He, 2011; Freeman, Cox,
& Wright, 2006; Hafsa Orhan Astrom, 2013; J. Jin, Yu, & Mi, 2012; Kolapo, Ayeni, & Oke, 2012;
Treacy & Carey, 2000) but most of them are on developed economies. There’s only quite a few
domestically (Apanga, Appiah, & Arthur, 2016; Boahene, Dasah, & Agyei, 2012). Even though
the issue of credit risk and its management is becoming very essential in policy debates, research
in the area in developing countries is still in its infancy (Apanga et al., 2016). With the recent
bankruptcy issues and recent takeovers involving UT and Capital Banks, the merging of some
local banks, the financial soundness of the Ghanaian banking system has been called to question
and needs to be carefully assessed. One way of examining the financial soundness of these banks
is by examining their credit risk management practices and measuring the general immunity of the
banking industry toward such risk, hence further research needed in such sensitive area.
1.3 Research Objectives
The overall objective of this research is to assess the relationship between credit risk and the
profitability of listed banks in Ghana. The specific objectives include:
To measure the profitability of listed banks from 2010 to 2016.
To determine the linkage between credit risk and non-performing loans.
To evaluate the effect of credit risk and profitability of listed banks from 2010 to 2016.
1.4 Research Questions
The study seeks to answer the following questions:
a. In what way is credit risk linked with non-performing loans?
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b. To what extent does credit risk impact bank profitability?
1.5 Significance of Study
The outcomes of the research have direct implications for policy, practice and academic works.
Findings of the study will make relevant contribution to decision making among banks, investors,
and banking industry regulators alike. Among listed banks, findings of the study will serve as a
guide in decision making in particularly decision s regarding optimal decision levels that must be
held. From the bank perspective, findings of the study will lead to a better understanding of the
nature of relationship between credit risk and their performance. This empirical knowledge can
help to the formulation of financing decisions pertaining to credit risk that help improve the
profitability of banks. For investors, results of the study can enhance decision making regarding
which firms to invest. Investors can gauge the liquidity and profitability performance of listed
firms by dwelling on the results of the study in order to make informed decisions.
Policy wise, by empirically assessing the performance of the Ghanaian banking industry,
regulators, that is, the Bank of Ghana (BOG) is further informed about the financial health of the
industry. The BOG is well-informed regarding the trends and patterns as well as the potential
drivers of profitability. The analysis may provide insights towards potential avenues for policy
prescriptions and enhancement which can then be oriented towards specific less-performing and
more-performing banks and the whole industry.
To academic literature, the research contributions are in a few folds. First, the first study
contributes to the few credit risk management and performance literature in the Ghanaian banking
industry and Africa at large. Second, the study attempts to provide a new empirical evidence on
the nexus between credit risk, non-performing loans and bank performance. Again, the study will
make relevant contributions to existing literature on the effect of credit risk on performance of
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banks. The results of the study consequently will serve as a guide and source of reference material
for future researchers.
1.6 Research Limitations
Despite the contributions this study makes towards research, policy and practice, it still faces some
challenges. First, the performance of banks can be further examined by means of efficiency
measures other than ratios, which will be considered in another study. Second, the focus of this
study is limited to the banking sector while ignoring the other parts of the financial system such as
microfinance institutions, even though there seem to be little research focusing on such areas. Due
to data unavailability however, it is difficult to include such institutions in the study. That
notwithstanding, the study is representative since the Ghanaian financial system is dominated by
banks and is the driving force of the financial system (Buchs & Mathisen, 2005).
1.7 Organisation of Study
The study is categorised into five chapters, each with sections and potential subsections. The first
chapter focuses on the background of the study, problem statement, objectives, questions, research
significance, and the scope within which the study is confined. Chapter two reviews the relevant
literature on performance studies in banking in order to provide evidence to support the purpose
of the research and seek answers to research questions. It also gives a brief discussion of the
industry, the regulations, and the structure of the firms within the industry. In chapter three, the
methodology of the research is discussed, detailing main ratios to be deployed. Chapter four entails
data presentation, analyses of results, conducting of tests, and making graphical illustrations. The
final chapter discuses, summarises, concludes, makes recommendations and proposes directions
for further research.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter reviews theoretical underpinnings on credit risk management and some empirical
studies related to the current study. Whereas the theoretical review presents the theoretical basis
for the study with relation to credit risk management, the empirical review presents the current
state of research relating to credit risk management and the various methods employed in this study
and their relation to bank performance.
2.2 Theoretical review
This section presents the theoretical basis of the study. It explains the theories upon which this
research is grounded. There are quite a number of theories trying to explain the concept of credit
risk and its management, but this study focuses mainly on the agency theory (M. C. Jensen &
Meckling, 1976) and the theory of asymmetric information (Ncube & Senbet, 1997) and
information sharing.
2.2.1 Agency Theory and credit risk
M. C. Jensen and Meckling (1976) initiated the agency theory building on the earlier works of
Fama and Miller (1972). The theory spells out a conflict of interest between owners of firms who
act as principals, and managers who also act as agents. Managers tend to have the tendency to put
free cash flow to waste if not monitored. Hence, the higher a manager’s discretionary funds
available to him, the greater the likelihood of empire building (Michael C Jensen, 1986). This
implies that, managers can act in the bad interest of firm, engaging in poor projects.
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The agency problem in has been tagged as a mechanism that affects firm-level risk. Some authors
including L. Jin and Myers (2006) and Hutton, Marcus, and Tehranian (2009) observe that the
probability of cash risk (greater negative stock returns) is increased by accounting opacity. They
develop an imperfect information model where managers are willing to hide firm-specific negative
news when the cost of hiding outweighs the benefit, in which they proved the unsustainability of
hiding adverse news for a long period (Bleck & Liu, 2007; Kothari, Shu, & Wysocki, 2009).
Managers, within such an unfavorable environment, might be able to overstate and manipulate
financial performance by means of withholding bad news.
Conservatism within firms, in a similar vein, like banks can act as a CG mechanism to prevent the
accumulation of any hidden adverse news that could result in less crash risk. This linkage,
however, may be weaker within banks as a result of high regulatory supervision and scrutiny. In
addition, with the fact that banks are highly leveraged organizations, and due to their contracting
demands, litigation costs and preferences of regulators, we would also expect such firms to portray
and showcase higher levels of conditional conservatism (Armstrong, Guay, & Weber, 2010; Watts,
2003).
2.2.2 Theory of Information Asymmetry and Information Sharing
Previous reviews on the theories of credit management have asserted information asymmetry (IA)
to be the root cause of credit risks of banks or non- performing loans (Freimer & Gordon, 1965;
Freixas & Rochet, 1997, 2008; Stiglitz & Weiss, 1987). IA could be viewed as the lack of
comprehensive and complete credit information between surplus spending units (lenders) and
deficit spending units (borrowers) in the credit market (Aumann, 1987; Freixas & Rochet, 1997;
Myerson, 2013). IA breeds adverse selection and moral hazard in processing and issuance of bank
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loans (Kusi, Agbloyor, Ansah-Adu, & Gyeke-Dako, 2017). But some researchers have argued that
this problem can be reduced. In their respective studies, some notable researchers Gehrig and
Stenbacka (2007), Padilla and Pagano (1997), Padilla and Pagano (2000), Pagano and Jappelli
(1993) and Kallberg and Udell (2003) suggested that the effect of information asymmetry can be
reduced by means of information sharing in the credit market which tends to minimize moral
hazard and adverse selection (Gehrig & Stenbacka, 2007; Kallberg & Udell, 2003; Padilla &
Pagano, 1997, 2000; Pagano & Jappelli, 1993). Thus the absence of a comprehensive, reliable and
accurate credit information among parties of credit is reduced as lenders or banks share credit
information. The resulting effect of the reduced information asymmetry emanating from vital
credit information sharing among banks through private bureaus or public registries help banks to
lessen the rate of moral hazard and adverse selection. That is, collection of credit or financial data,
processing and reporting of data on credit worthiness of organizations and individuals by these
registries and bureaus at the request of banks. In theory, these credit bureaus and registries seem
to be perfect substitutes, empirical studies assert that private credit bureaus are more effective
(Miller, 2003; Singh, 2010; Triki & Gajigo, 2012).
2.3 Empirical review
2.3.1 Determinants of Bank credit risk
A few researchers have tried to draw a link between bank credit risk and some related causal
factors. One of those researchers are Gizycki and Gizycki (2001) who examined the overall
variability of Australian banks’ credit risk-taking in the1990s. Their study created a link between
bank credit risk, size and ownership. According to Gizycki and Gizycki (2001) the impaired asset
ratios of larger banks tend to be less than that of smaller banks. The study further revealed that
foreign banks with small assets bases within Australia experienced particularly high levels of
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impaired assets and low but variable profits between 1990 and 1992. To differentiate variation
across banks and variation through time, a decomposition of the dispersion of the full panel data
was carried out.
In other studies, Allen N Berger (1995) also argues that due to lower expected costs of bankruptcy,
greater number of capitalized banks are able to attract higher earnings and that enabled them to
pay lower interest on unsecured debt. Hortlund (2005) used data for Sweden for the period 1870
to 2001 and asserted that in the short run, successful banks could not only tend to be capitalized
but also more profitable, which abstruse the fundamental positive relationship between leverage
and returns.
2.3.2 Some Literature on Bank Credit Management Practices
A countable number of risk-adjusted performance measures proposals have existed (Heffernan,
1996; Kealhofer, 2003). However, the focus of these measures have been the risk-return trade-off,
that is measuring inherent risk in each product or activity and accordingly charging it for the capital
required in order to support it. This does not solve the problem of loanable amount recovery. In
dealing with asymmetric information issues and also in reducing excessive loan loss levels, an
effective system is critical to ensure repayment of loans by borrowers, and hence the long-term
success of any banking organization (Baker & Mathews, 2009). Effective CR management
involves establishing the Requisite CR environment; operating under a sound process of credit
granting; maintaining an appropriate credit administration involving the monitoring process as
well as sufficient controls over credit risk (Baker & Mathews, 2009; Van Greuning & Brajovic
Bratanovic, 2003).
Bennardo et al. (2009) revealed that credit information sharing among surplus spending units and
banks as individual deficit spending units reduce over-indebtedness, and this can be classified as
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greatly indebted to receive less credit and ultimately diminish the over-indebtedness of borrowers.
Luoto, McIntosh, and Wydick (2007) revealed that the employment of credit information sharing
tend to shift or move client portfolio toward better performing clients, and the treatment of
awareness induced a moderate improvement in repayment performance.
2.4 Overview of the Ghanaian banking system
The banking industry of Ghana comprises Bank of Ghana (BOG) as the regulating body. The
number of banks in the industry has augmented from seven (7) in 1987 to twenty-eight (28) in
2015, and twenty three (23) currently. Most new entrants from the period of 1987 to 2010 were
foreign banks (Adjei-Frimpong, Gan, & Hu, 2014). As at December 31st, 2016, the number of
foreign-controlled banks was recorded to be 17 with 16 being locally owned (PWC, 2017).
Since the mid-1980s, the banking industry has undergone regulatory and structural changes
(Alhassan & Ohene-Asare, 2013). In 1983, the government of Ghana, under the auspices of the
IMF, introduced the Economic Recovery Program (ERP) as a remedy to the economic crisis the
country was experiencing (Ohene-Asare & Asmild, 2012). In 1989, the Banking Law was enacted,
and this enabled domestic incorporated bodies who were suitable to file applications for licenses
to operate as banking institutions (Alhassan & Ohene-Asare, 2013). Later in 2004, the Banking
Act 2004 (Act 673) replaced the Banking Law (Aboagye, Akoena, Antwi-asare, & Gockel, 2008).
Since 2002, there have been notable developments in the industry. These include Universal
banking license ushered in 2003 which gave banks with GH¢7million in capital the opportunity to
become versatile in providing services to their customers by undertaking all the banking activities
unlike previously where they could only undertake the activities which they were specifically
licensed to perform (Bokpin, 2013; Quartey & Afful-Mensah, 2014). Second is the enactment of
the Banking Act 2004 (Act 673) under which the capital requirement of banks was required to
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augment to $US 8 million which was later increased to $US 30 million and $US 60 million in 2012
and 2013 respectively (Alhassan, 2015; KPMG, 2012; PWC, 2014) and currently at $US 100, the
equivalent of GHS 400 million. Another development was the abolishment of the secondary
reserves requirement by BOG in 2006 in order to free up significant liquidity for lending to
businesses (PWC, 2007). Following this development was the change of currency from the cedi to
the Ghana cedi in July 2007 (BOG, 2007). Others were the Whistle Blowers Act 2006 (Act 720),
the Foreign Exchange Act 2006 (Act 723), Banking (Amendment) Act 2007 (Act 738), Home
Mortgage Finance Act 2008 (Act 770), Credit Reporting Act 2007 (Act 726), Borrowers and
Lenders Act 2008 (Act 773), Non-Banking Financial Institutions Act 2008 (Act 774) , Anti-money
Laundering Act 2008 (Act 749) as well as significant mergers and acquisitions (Bokpin, 2013;
Isshaq & Bokpin, 2012).
2.5 Chapter summary
This chapter talked about two aspects of literature, the theoretical and the empirical. The theoretical
aspect hinges on relevant theories to this study. These are the information asymmetry and
information sharing theories. The empirical literature focused on studies in relation to bank risk
determinants and bank credit risk management practices. Lastly, this chapter gave a brief overview
of the Ghanaian banking industry.
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CHAPTER THREE
METHODOLOGY
3.1 Introduction
This chapter describes how the research questions will be answered by the methods and processes
used to collect and analyse the data. It basically answers the question “how do you intend to
achieve the stated objectives of the research?” More closely, the chapter considers the sampling
technique, the data source, the research design and the methods used in analysing the data.
3.2 Research Design
The quantitative approach is used in this study which allows for analysis of collected data using
statistical procedures and hypothesis testing (Creswell, 2008). Generally, the quantitative research
approach requires the determination of relationships between variables of a study using statistical
techniques and hence the use of quantitative research approach in the study. A panel is adopted in
this study as it suits the purpose of this study because of its character of requiring replications of
the same units over time which allows for assessment of behavioural changes (such as profitability)
over time (Wooldridge, 2013). In all, the study will gather and analyse data pertaining to listed
banks in Ghana covering the period from 2010 to 2016 for the benefit of current information and
ease of accessibility.
3.3 Sampling and sources of data
The population used for the study constitutes all listed banks in Ghana. The data sample consists
of seven (7) listed banks that have been in continuous existence from 2010-2016. These banks are
CAL Bank Limited, Ecobank Ghana Limited, Ghana Commercial Bank Ltd., HFC Bank Ltd, SG-
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SSB Ltd., Standard Chartered Bank Ltd., and Republic Bank. Ecobank Transnational was excluded
as it its operations transcend the borders of Ghana. Likewise, ADB was excluded as it was recently
listed.
Data is extracted from banks under consideration through their annual reports and cross-validated
with similar data from the Banking Supervision Department of the Bank of Ghana. The choice of
this data source is based on two main justifications. First, the annual reports are publicly available
and second, these reports have been used by previous studies in the Ghanaian banking literature.
3.4 Profitability Analysis
Assessing the profitability of listed firms has received a lot of attention in literature. This may be
attributed to the crucial role they play in economic growth and development. In addition, being
able to assess a firm’s performance will help management identify various sources of inefficiency
and subsequently improve future performance (Paradi & Zhu, 2013). Financial analysis or ratios
have historically been employed in performance assessment of firms by regulatory agencies,
researchers and management. A ratio is a quotient of two variables and was ideal because they are
relatively simple and easy to understand. Return on Assets (ROA) will be used to measure
performance of the selected listed companies.
3.4.1 Return on Assets (ROA)
For the purposes of this research, Return on assets is adopted to assess performance. Return on
assets (ROA) is an indicator of how profitable a company is relative to its total assets (Srivastava,
Shervani, & Fahey, 1998). ROA gives an idea as to how efficient management is at using its assets
to generate earnings. Calculated by dividing a company's annual earnings by its total assets, ROA
is displayed as a percentage. Sometimes this is referred to as "return on investment"(Bernstein &
Wild, 1998).
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The formula for return on assets is:
ROA = Profit before Interest and Tax (PBIT)/ Average Total Assets (1)
3.5 Variable Measurements and Model Specification
A regression equation requires the choice of variables and justification thereof. In this study, the
fundamental variables considered are liquidity and profitability. Though these variables are
primarily related to the current study, the study incorporates other extra additional variables.
Curwin, Roger and Slater (2008) suggest that in building a regression model, it is more practical
to include more variables because it is easy to get around the problem of increased variance than
the problem of biased prediction. Philips and Ghosh (2009) share a similar view arguing that the
inclusion of more explanatory variables in a study help to improve model estimates and improves
results of study. Hence, the model in this study incorporates other variables that potentially affect
profitability of listed firms.
The main variable to be adopted for the second stage analysis is credit risk which will be measured
by the ratio of non-performing loans to total credit (total loans and advances) and also the ratio of
total credit to total deposits, but other external variables such as bank size, ownership structure,
and leverage are also considered. A balanced panel regression model is adopted with return on
Assets (ROA) as the response variable and the regressors include credit risk, size, ownership
structure, and leverage. The model is estimated using a pooled OLS. The model is showed below:
Regression Model:
𝑅𝑂𝐴𝑖𝑡 = 𝛽0 + 𝛽1𝐶𝑅𝐸𝐷𝑅𝐼𝑆𝐾𝑖𝑡 + 𝛽2𝑆𝐼𝑍𝐸𝑖𝑡 + 𝛽3𝑂𝑊𝑁𝑖𝑡 + 𝛽4𝐿𝐸𝑉𝑖𝑡 + 𝛽5𝑆𝐺 + 𝛽6𝐶0𝑀𝑃𝑖𝑡 +
𝛽7𝑅𝐴𝑇𝐸𝑆 + 𝜇𝑖𝑡 (2)
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Definition of Variables:
CREDRISK: Credit risk
SIZE: Bank Size
OWN: Ownership
LEV: Leverage
COMP: Competition
SG: Sales Growth
Rates: Interest rates
𝜇𝑖𝑡 is a composite error term
3.5.1 Bank size
The size of the bank refers to the total assets of each bank over the period of 7 years. It is measured
as the natural logarithm of the total assets of the sampled microfinance firms used in the study. It
is an explanatory variable. This variable seeks to explain whether big banks enjoy certain
economies of scale which causes them to be more dynamically efficient than smaller banks or
whether smaller bank sizes is a pre-requisite for dynamic efficiency in the Ghanaian banking
industry. Past studies in literature has documented ambiguous results. While some studies recorded
a significantly positive impact of size on performance (Ataullah, Cockerill, & Le, 2004; Allen N.
Berger, Hunter, & Timme, 1993; Perera, Skully, & Wickramanayake, 2007; Srairi, 2010), others
found a significant negative impact (Allen & Rai, 1996; Altunbas, Carbo, Gardener, & Molyneux,
2007; Girardone, Molyneux, & Gardener, 2004; Isik & Hassan, 2002; Weill, 2004). Interestingly,
other researchers like Avkiran (1999) and Allen N. Berger and Mester (1997) have found no
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significant relationship in their study. In the Ghanaian banking industry, we expect to find a
positive relationship between large banks and dynamic efficiency.
3.5.2 Ownership structure
Ownership structure is a dummy variable. The objective is to test for differences in profitability
scores which could be attributed to the differences in bank ownership, i.e. between foreign owned
banks and domestic banks. As already stated, a bank is ‘foreign’ if more than sixty (60%) of its
equity share capital is held by foreigners and ‘domestic’ if otherwise. The impact of ownership
structure has been largely studied in the literature and it has generally been found that foreign
banks outperform domestic banks in developing economies while the opposite is the case for
developed countries (A. N. Berger, Deyoung, Genay, & Udell, 2000; Bokpin, 2013; Claessens,
Demirgüç-Kunt, & Huizinga, 2001; Deyoung & Nolle, 1996; Fries & Taci, 2005; Lensink,
Meesters, & Naaborg, 2008). We therefore expect a positive coefficient for foreign banks.
3.5.3 Leverage
Leverage is measured as the ratio of total liabilities to total assets. It measures the proportion of
banks assets funded by debt. On its impact on bank performance, a positive impact is expected.
Carvallo and Kasman (2005) and Casu and Girardone (2006) have however found leverage to
negatively impact bank efficiency.
3.5.4 Competition
HHI is employed in estimating competition in the Ghanaian banking industry. HHI estimates
competition via market concentration measures (Boone, 2008; Schaeck & Cihák, 2014). HHI is
computed as the sum of squares of each bank’s market share.
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N
i iMSHHI1
2)( (3)
Where MS represents the market share of bank i. The industry’s deposits, net advances and total
assets are the criteria for computing a bank’s market share.
3.6 Data Analysis Technique
Data in the study will be analysed using both descriptive and inferential statistical analytical
techniques. The descriptive statistics will comprise mean, kurtosis, and standard deviations. The
descriptive statistics will help to ascertain properties of the variables under consideration in the
study. The inferential statistics that will be used are correlation and regression. The technique of
correlation will help to determine the direction of relationship between the dependent and the
independent variables of the study. Regression technique on the other hand will help to determine
the strength of association between the variables of the study. Data is mainly analysed using Excel,
E-views 10, and R version 3.3
.
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CHAPTER FOUR
DATA ANALYSES AND DISCUSSION OF FINDINGS
4.1 Introduction
This chapter presents the results derived from analysing the data set for this study. First, it presents
and analyses the descriptive statistics of the variable used in estimating the profitability of banks
in Ghana. It then presents and provides preliminary analyses and discussions on the objectives of
the study. The correlation matrix of both the effect and causal variables in the econometric model
used in the study are discussed and shown. The general regression outputs are finally presented,
and discussions based on the obtained results are made.
4.2 Description of data
Data used in this study was sourced from BOG and the annual reports of the banks. The data
sample consists of 8 listed banks covering a 7-year period from 2010 to 2016 and giving 56
observations. Even though other listed banks existed prior to 2016, they were not included in the
sample because the study adopts a balanced panel. Table 4.1 and 4.2 present summary and
descriptive statistics of the variables used to carry out the analyses.
Evident from the Table 4.1, total assets and liabilities on average augmented over the seven (7)
years period. But this increase has been almost in the same proportion, leading to an almost
constant leverage ratio over the period. The minimum and maximum values of the variables,
particularly that of total assets and their relatively high dispersion, measured by the standard
deviations also leads to a very important revelation and conclusion that banks in Ghana have
different sizes and justifies that there exists variable returns to scale in the banking industry.
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Table 4.1 Descriptive statistics
Variable Observations Mean Std. Dev. Min Max
Total Assets 8 984.32 823.53 280.09 1015.46
Total liabs 8 865.44 633.41 110.11 190.27
Loans & Adv 8 713.24 130.16 250.39 900.26
Cred Risk 8 -1.03 0.60 -2.38 -0.51
Lev 8 0.84 0.11 0.56 0.92
Size 8 20.46 0.82 19.10 21.45
We could tell from the table that total assets and liabilities on average increased over the study
period. But this increase has been almost in the same proportion, leading to an almost constant
leverage ratio over the period.
The minimum and maximum values of the variables, particularly that of total assets and their
relatively high dispersion, measured by the standard deviations also leads to a very important
revelation and conclusion that banks in Ghana have different sizes and justifies that there exists
variable returns to scale in the banking industry.
4.3 Profitability of Listed Banks
To address the second objective of this study, the profitability of each selected bank is assessed by
computing the Return on Assets for the seven (7)-year period. The scores presented are an average
of all the seven (7) years. This is calculated by dividing a bank's annual earnings, that is Profit
before interest and tax (PBIT) by its total assets, and the results displayed in a percentage decimals.
ROA is preferred for performance measurement, because unlike other ratios of profitability
including return ROE, the former measurement includes all of the assets of the bank— even those
that arise out of liabilities to creditors as well as those that emanate from investor’s contributions,
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hence seen to be a more essential and robust internal tool for performance measurement (Burton,
Lauridsen, & Obel, 2002).
The formula for ROA is:
Return on Assets = Pre-Tax Profit
Total Assets
The results are presented in table 4.2 below but full details of the variables used in the computation
are given in the appendix:
Table 4.2 Return on Assets (ROA) for each Bank over Time Period
BANKS 2010 2011 2012 2013 2014 2015 2016 Mean
GCB 0.04 0.01 0.06 0.09 0.09 0.08 0.07 0.06
SCB 0.06 0.06 0.07 0.09 0.08 0.03 0.08 0.07
ADB 0.04 0.04 0.02 0.05 0.02 0.04 -0.03 0.02
SG-SSB 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.04
ECOBANK 0.06 0.05 0.06 0.06 0.08 0.07 0.06 0.06
CAL 0.02 0.03 0.06 0.08 0.07 0.06 0.00 0.05
HFC 0.03 0.03 0.03 0.05 0.05 0.02 -0.03 0.03
ACCESS 0.06 0.05 0.06 0.07 0.07 0.05 0.03 0.06
Mean 0.04 0.04 0.05 0.07 0.06 0.05 0.03
From the table, we could tell that even though there have been a few irregularities over the years
for some of the banks, the ROA of most of the banks on the average has increased over time (2011,
2015 and 2016 are exceptions to this). An explanation for this could be that liquid assets, total
assets, shareholder’s fund and total comprehensive income have on average augmented over the
years.
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Figure 4.1 Mean ROA of Listed Banks
We could also tell from the figure that when we take the means of the Returns, we could say that
Standard Chartered Bank (SCB) is the high performing bank in the industry with the highest ROA
of 0.064 over the years considered.
4.4 Correlation Analysis
The table and the correlation matrix below will give us other insights on how capital structure and
other variables might have influenced the behavior of the Return on Assets (ROA) by considering
their relationship with it.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
GCB SCB ADB SG-SSB ECOBANK CAL HFC ACCESS
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Table 4.3 (Correlation Matrix)
ROA CREDRISK OWN SIZE LEV SG Rates HHI
ROA 1
CREDRISK -0.63 1
OWN 0.47 -0.43 1
SIZE 0.64 -0.10 0.10 1
LEV 0.13 0.46 -0.31 0.77 1
SG 0.42 -0.66 0.30 0.29 -0.25 1
RATES 0.09 0.14 0.00 -0.05 -0.61 0.44 1
HHI -0.42 -0.66 0.30 -0.33 -0.61 -0.23 -0.30 1
Notes: ROA is return on asset and is computed as pre-tax profit divided by total assets. CREDRISK is credit risk and
it represents the ratio of non-performing loans to total credit (total loans and advances) and also the ratio of total
credit to total deposits, OWN is ownership, as to whether the bank is foreign or domestic, SIZE stands for Bank size
which is measured by a natural log of total assets, LEV is for leverage which is measured as the ratio of total liabilities
to total assets. HHI is a measure of competition.
The correlation matrix spells out various relationships that other variables have with ROA and
themselves. The matrix reveals that size forms the highest correlation with outcome variable,
ROA. The pair recorded the highest correlation coefficient of approximately 0.64, which shows a
positive relationship between the two. We could confirm that from the table that as size increases,
ROA increases likewise and as size decreases, ROA most often decreases as well. All the other
variables also form a positive relationship with ROA with the exception of credit risk, which
recorded the second highest relationship with ROA.
The positive relationship between ROA and ownership structure buttresses the point that foreign
banks outperform domestic banks in developing countries. This results provides support for the
global advantage hypothesis and is similar to the findings of Fries and Taci (2005) on 289 banks
in 15 post-communist countries and Bokpin (2013) on the Ghanaian banking industry. Possible
explanation of this finding is that these foreign banks have successfully been able to transfer their
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expertise and knowledge from their home countries and are exploiting local opportunities to
perform better than the domestic banks.
4.4 Regression Analyses
To achieve the second and third objectives of this study, the effect of credit risk and other variables
on banks profitability, measured by ROA, is assessed. Variables such as ownership, size, leverage,
sales growth, competition and interest rates are considered as control variables. Before conducting
this analysis however, a test is first performed to test for possible multicollinearity problems among
the regressors. Generally, there is low correlation among the independent variables.
The fitness of the regression model used for this analysis is tested and some of the results are seen
in the Regression statistics below. The coefficient of determination is used to test the model fitness.
Statistically the closer the coefficient of determination (R2) value is to 100%, the stronger the
regression model and its reliability. From the regression table 4.4 below, the R squared of 0.76
(76%) confirms the fitness and reliability of the regression model used.
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Table 4.4 Regression Results
Coefficients Standard Error t Stat P-value
Intercept -0.45 0.33 -1.36 0.03
CREDRISK -0.02 0.03 -0.52 0.02
OWN 0.01 0.02 0.25 0.06
SIZE 0.04 0.04 1.07 0.40
HHI -1.20 0.51 -2.34 0.02
LEV -0.42 0.57 -0.73 0.05
RATES 0.01 0.10 0.10 0.63
SG -0.01 0.00 -0.96 0.14
Regression Statistics
Multiple R 0.87
R Square 0.76
Adjusted R Square 0.17
Standard Error 0.02
Observations 8
4.4.1 Credit risk and Profitability
The regression result reveals a negative impact of credit risk on profitability. The coefficient of -
0.02 means that a unit increase in the credit risk of banks will lead to a 0.02 units decrease in the
return on assets, thus profitability.
4.4.2 Ownership and Profitability
Ownership has a positive effect on performance of listed banks in Ghana, which confirms the
positive effect indicated by some previous studies reviewed in the second chapter. The coefficient
of the variable suggests that all things being equal, ROA is expected to be approximately 0.01
units more if the listed bank is a foreign bank than domestic. The positive impact of ownership on
profitability buttresses the point that foreign banks outperform domestic banks in developing
countries. This results provides support for the global advantage hypothesis and is similar to the
findings of Fries and Taci (2005) on 289 banks in 15 post-communist countries and Bokpin (2013)
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on the Ghanaian banking industry. Possible explanation of this finding is that these foreign banks
have successfully been able to transfer their expertise and knowledge from their home countries
and are exploiting local opportunities to perform better than the domestic banks.
4.4.3 Size, Leverage and profitability
The regression output suggests a positive impact of bank’s size on performance, but insignificant
at 5%. The positive relationship implies that imply that larger banks perform better over time than
smaller banks, and also a unit increase in the size of a bank will lead to a 3.83 percent increase in
the return on assets of the bank. Leverage recorded a higher impact of -0.4
4.4.4 Competition and Performance
The impact of industrial competition (measured by the HHI) on the performance of banks is also
shown in the regression output. The results show a significant negative relationship between
competition and profitability. The negative relationship implies that a less competitive banking
industry will cause banks to perform poorly over time. In other words, the lower the competition,
the more likely it is to record a lower performance. This may be because reduced competition may
introduce less efficiency in banks operations, risk management behavior and laxed customer
services, these could lead to a rise in risk that affects cost of operations and overall management
of the bank, which will ultimately impact performance. The findings have significant implications
for bank regulators in drafting and implementing competition policies.
4.4.5 Interest rates and Profitability
With respect to interest rates, the regression results reveal a positive impact of on ROA, but it
insignificant, suggesting interest rates is not an important factor regarding profitability of listed
banks, which is rather against a priori expectation, since banks returns hinges on interest rates
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margins that they are able to obtain in the market, which should have an influence on their
performance
4.5 Summary
The chapter presented the results derived from analysing the data set for this study. First, it
presented and analysed the descriptive statistics of the variables used in estimating the profitability
of banks in Ghana. It then presented and provided preliminary analyses and discussions on the
objectives of the study. The correlation matrix of both the effect and causal variables in the
econometric model used in the study were discussed and shown. The general regression outputs
were finally presented, and discussions based on the obtained results were made.
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CHAPTER FIVE
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
5.1. Introduction
This chapter has been divided into two sub-sections. The first subsection highlights the objectives
of the study, the methods used in addressing the objectives, findings and conclusions. In the
following subsection, recommendations for policy and practice are provided as well as directions
for further research.
5.2 Summary of the study
The central objective of this research is to assess the relationship between credit risk and the
profitability of listed banks in Ghana. Return on Assets (ROA) was employed as a measure of
profitability and the impact of credit risk, ownership, bank size, leverage, sales growth,
competition and interest rates was assessed.
Credit risk was measured by the natural log of the ratio of loans to total asset of bank i in year t.
Ownership was considered a dummy variable. The objective was to test for differences in
profitability scores which could be attributed to the differences in bank ownership, i.e. between
foreign owned banks and domestic banks. Size was measured as the natural logarithm of total
assets. This variable sought to explain whether big banks enjoy certain economies of scale which
causes them to be more dynamically profitable than smaller banks or whether smaller bank sizes
is a pre-requisite for performance in the Ghanaian banking industry. Leverage was measured as
the ratio of total liabilities to total assets. It measured the proportion of banks assets funded by
debt. HHI was employed in estimating competition in the Ghanaian banking industry. HHI
estimated competition via market concentration measures.
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The data source for this study was the annual reports of all eight (8) listed banks for a seven year
period beginning from 2010 to 2016. The study’s population constitutes all listed banks operating
in Ghana. The choice of this data source was based on two main justifications. First, the annual
reports are publicly available and second, these reports have been used by previous studies in the
Ghanaian banking literature. The major findings of this study are summarised below.
5.3 Summary of Findings
Even though there have been a few irregularities over the years for some of the banks, on the
average, the ROA of most of the banks have increased over time. An explanation for this could be
that liquid assets, total assets, shareholder’s fund and total comprehensive income have on average
augmented over the years.
Standard Chartered Bank (SCB) was the high performing bank in the industry with the highest
ROA of 0.064 over the years considered.
A positive linear relationship between all other determinants considered and performance were
spelt out. Only credit risk and rates recorded negative relationships. Size formed the highest
correlation with ROA, followed by ownership, sales growth, competition and leverage in that
order.
The regression result revealed a negative impact of credit risk on profitability. A unit increase in
the credit risk of banks will lead to a 0.02 units decrease in the return on assets, thus profitability.
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Ownership had a significant positive effect on performance of listed banks in Ghana, which
buttressed the point that foreign banks outperform domestic banks in developing countries. A
Possible explanation for this is that these foreign banks have successfully been able to transfer
their expertise and knowledge from their home countries and are exploiting local opportunities to
perform better than the domestic banks.
There was also a positive impact of bank’s size on performance, but insignificant at 5%. The
positive relationship implied that imply that larger banks perform better over time than smaller
banks, and also a unit increase in the size of a bank will lead to a 5.13 percent increase in the return
on assets of the bank. Unlike size, leverage reported a negative impact on banks profitability, which
implies that highly levered banks have low profitability rates.
Competition also recorded a significant negative impact on profitability. The negative relationship
implies that a less competitive banking industry will cause banks to perform poorly over time. In
other words, the lower the competition, the more likely it is to record a lower performance. This
may be because reduced competition may introduce less efficiency in banks operations, risk
management behaviour and laxed customer services, these could lead to a rise in risk that affects
cost of operations and overall management of the bank, which will ultimately impact performance.
5.4 Recommendations
The findings and the conclusions of this study provide important implications for policy, practice
and further research.
For Practice and Policy,
a. ROA of most of the banks seems to have increased slightly over time, but not that
highly improved. To bring about significant improvements, it is incumbent on
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policy makers and management to enact policies and practices to minimize the
wastages in order to improve the current level. Reduction of operating expenses
and sound risk management procedures can be undertaken to achieve such
objectives.
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