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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
HHI  (MSi )
2
                                  (3) 
i1
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 
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
GCB SCB ADB SG-SSB ECOBANK CAL HFC ACCESS
  
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. 
 
 
 
 
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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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