UNIVERSITY OF GHANA CREDIT RISK AND PROFITABILITY OF

1

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

University of Ghana http://ugspace.ug.edu.gh

i

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)


ii

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)


iii

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.


iv

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.


v

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


vi

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


vii

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


viii

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


ix

LIST OF FIGURES

Figure 4.1 Mean ROA of Listed Banks...................................................................................... 21


x

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.


1

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


2

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.


3

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?


4

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


5

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.


6

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.


7

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


8

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


9

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


10

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


11

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.


12

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-


13

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).


14

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)


15

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


16

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.


17

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

.


18

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.


19

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,


20

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.


21

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


22

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


23

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.


24

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)


25

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


26

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.


27

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.


28

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.


29

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


30

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.


31

REFERENCES

Aboagye, Q. q. A., Akoena, S. k., Antwi-asare, T. o., & Gockel, A. f. (2008). EXPLAINING THE

MARKET POWER OF GHANAIAN BANKS1. South African Journal of Economics, 76(4), 569-

585. doi: 10.1111/j.1813-6982.2008.00221.x

Adjei-Frimpong, K., Gan, C., & Hu, B. (2014). Cost Efficiency of Ghana’s Banking Industry: A Panel

Data Analysis the International Journal of Business and Finance Research, 8(2), 69-86.

Alhassan, A. L. (2015). Explaining Bank Productivity in Ghana. Managerial and Decision Economics,

n/a-n/a. doi: 10.1002/mde.2748

Alhassan, A. L., & Ohene-Asare, K. (2013). HAS COMPETITION IMPACTED ON EFFICIENCY OF

GHANAIAN BANKS? 10th African Finance Journal.

Allen, L., & Rai, A. (1996). Operational efficiency in banking: An international comparison. Journal of

Banking & Finance, 20(4), 655-672. doi: http://dx.doi.org/10.1016/0378-4266(95)00026-7

Altunbas, Y., Carbo, S., Gardener, E. P. M., & Molyneux, P. (2007). Examining the Relationships between

Capital, Risk and Efficiency in European Banking. European Financial Management, 13(1), 49-

70. doi: 10.1111/j.1468-036X.2006.00285.x

Andreou, P. C., Cooper, I., Louca, C., & Philip, D. (2017). Bank loan loss accounting treatments, credit

cycles and crash risk. The British Accounting Review.

Apanga, M. A.-N., Appiah, K. O., & Arthur, J. (2016). Credit risk management of Ghanaian listed banks.

International Journal of Law and Management, 58(2), 162-178.


32

Armstrong, C. S., Guay, W. R., & Weber, J. P. (2010). The role of information and financial reporting in

corporate governance and debt contracting. Journal of accounting and economics, 50(2-3), 179-

234.

Arun, T. G., & Turner, J. (2009). Corporate Governance of Banks in Developing Economies: Concepts

and Issues’. Corporate Governance and Development, 96-106.

Arun, T. G., & Turner, J. D. (2002). Financial Sector Reforms in Developing Countries: The Indian

Experience. World Economy, 25(3), 429-445. doi: 10.1111/1467-9701.00440

Ataullah, A., Cockerill, T., & Le, H. (2004). Financial liberalization and bank efficiency: a comparative

analysis of India and Pakistan. Applied Economics, 36(17), 1915 - 1924.

Ataullah, A., & Le, H. (2006). Economic reforms and bank efficiency in developing countries: the case of

the Indian banking industry. Applied Financial Economics, 16(9), 653 - 663.

Aumann, R. (1987). Correlated Equilibrium as an Expression of Bayesian Rationality', Econo-Metrica 55

(1), 1–18. Google Scholar.

Avkiran, N. K. (1999). The evidence on efficiency gains: The role of mergers and the benefits to the

public. Journal of Banking and Finance, 23, 991-1013.

Baker, T., & Mathews, E. (2009). International association of insurance supervisors Handbook of

Transnational Economic Governance Regimes (pp. 377-384): Brill.

Beck, T., & Levine, R. (2004). Stock markets, banks, and growth: Panel evidence. Journal of Banking &

Finance, 28(3), 423-442. doi: http://dx.doi.org/10.1016/S0378-4266(02)00408-9

Beck, T., Levine, R., & Loayza, N. (2000). Finance and the Sources of Growth. Journal of financial

economics, 58(1), 261-300.


33

Berger, A. N. (1995). The relationship between capital and earnings in banking. Journal of Money, Credit

and Banking, 27(2), 432-456.

Berger, A. N., Deyoung, R., Genay, H., & Udell, G. F. (2000). Globalisation of financial institutions:

evidence from cross-border banking performance. Brookings–Wharton Papers on Financial

Service, 3, 23–120.

Berger, A. N., Hunter, W. C., & Timme, S. G. (1993). The efficiency of financial institutions: A review

and preview of research past, present and future. Journal of Banking & Finance, 17(2-3), 221-249.

doi: Doi: 10.1016/0378-4266(93)90030-h

Berger, A. N., & Mester, L. J. (1997). Inside the black box: What explains differences in the efficiencies

of financial institutions? Journal of Banking & Finance, 21(7), 895-947. doi: Doi: 10.1016/s0378-

4266(97)00010-1

Bernstein, L. A., & Wild, J. J. (1998). Financial statement analysis: theory, application, and interpretation:

Irwin Professional Publishing.

Bleck, A., & Liu, X. (2007). Market transparency and the accounting regime. Journal of Accounting

Research, 45(2), 229-256.

Boahene, S. H., Dasah, J., & Agyei, S. K. (2012). Credit risk and profitability of selected banks in Ghana.

Research Journal of finance and accounting, 3(7), 6-14.

BOG. (2007). Bank of Ghana Annual Report and Accounts. Accra.

Bokpin, G. A. (2013). Ownership structure, corporate governance and bank efficiency: an empirical

analysis of panel data from the banking industry in Ghana. Corporate Governance: The

international journal of business in society, 13(3), 274-287.


34

Boone, J. (2008). A New Way to Measure Competition. The Economic Journal, 118(531), 1245-1261.

doi: 10.1111/j.1468-0297.2008.02168.x

Buchs, T. D., & Mathisen, J. (2005). Competition and Efficiency in Banking: Behavioral Evidence from

Ghana. IMF Working Papers, 05/17.

Burton, R. M., Lauridsen, J., & Obel, B. (2002). Return on assets loss from situational and contingency

misfits. Management science, 48(11), 1461-1485.

Carvallo, O., & Kasman, A. (2005). Cost efficiency in the Latin American and Caribbean banking systems.

Journal of International Financial Markets, Institutions and Money, 15(1), 55-72. doi: DOI:

10.1016/j.intfin.2004.02.002

Casu, B., & Girardone, C. (2006). Bank competition, concentration and efficiency in the single European

market*. The Manchester School, 74(4), 441-468. doi: 10.1111/j.1467-9957.2006.00503.x

Claessens, S., Demirgüç-Kunt, A., & Huizinga, H. (2001). How does foreign entry affect domestic

banking markets? Journal of Banking & Finance, 25(5), 891-911.

Creswell, J. W. (2008). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (3rd

ed.). Los Angeles: Sage.

Demirgüç-Kunt, A. (2004). Financial structure and economic growth: A cross-country comparison of

banks, markets, and development: MIT press.

Deyoung, R., & Nolle, D. E. (1996). Foreign-Owned Banks in the United States: Earning Market Share

or Buying It? Journal of Money, Credit and Banking, 28(4), 622-636.

Eccles, R., Herz, R., Keegan, M., & Phillips, D. (2001). The risk of risk. Balance sheet, 9(3), 28-33.

Fama, E. F., & Miller, M. H. (1972). The theory of finance: Holt Rinehart & Winston.


35

Fayman, A., & He, L. T. (2011). Prepayment risk and bank performance. The Journal of Risk Finance,

12(1), 26-40.

Freeman, M. C., Cox, P. R., & Wright, B. (2006). Credit risk management: The use of credit derivatives

by non-financial corporations. Managerial Finance, 32(9), 761-773.

Freimer, M., & Gordon, M. J. (1965). Why bankers ration credit. the Quarterly Journal of economics,

79(3), 397-416.

Freixas, X., & Rochet, J.-C. (1997). Microeconomics of Banking: The MIT Press.

Freixas, X., & Rochet, J.-C. (2008). Microeconomics of banking: MIT press.

Fries, S., & Taci, A. (2005). Cost efficiency of banks in transition: Evidence from 289 banks in 15 post-

communist countries. Journal of Banking & Finance, 29(1), 55-81.

Gehrig, T., & Stenbacka, R. (2007). Information sharing and lending market competition with switching

costs and poaching. European Economic Review, 51(1), 77-99.

Girardone, C., Molyneux, P., & Gardener, E. P. M. (2004). Analysing the determinants of bank efficiency:

the case of Italian banks. Applied Economics, 36(3), 215 - 227.

Gizycki, M. C., & Gizycki, M. (2001). The effect of Macroeconomic conditions on banks' risk and

profitability: Reserve Bank of Australia.

GSS. (2017). Revised 2017 Annual Gross Domestic Product Ghana Statistical Service.

Hafsa Orhan Astrom, Z. (2013). Credit risk management pertaining to profit and loss sharing instruments

in Islamic banking. Journal of Financial Reporting and Accounting, 11(1), 80-91.


36

Haldane, A., Brennan, S., & Madouros, V. (2010). What is the contribution of the financial sector: Miracle

or mirage? The Future of Finance, 87.

Heffernan, S. (1996). Modern banking in theory and practice,(pp 97-106). John Wile and Sons, Chichester

institute for Research in Economic History, Stockholm Economies, Stockholm.

Hortlund, P. (2005). The long-term relationship between capital and earnings in banking: SSE/EFI

Working Paper Series in Economics and Finance.

Hutton, A. P., Marcus, A. J., & Tehranian, H. (2009). Opaque financial reports, R2, and crash risk. Journal

of financial economics, 94(1), 67-86.

Isik, I., & Hassan, M. K. (2002). Cost and Profit Efficiency of the Turkish Banking Industry: An Empirical

Investigation. [doi:10.0000/104454999315042]. The Financial Review, 37, 257-279.

Isshaq, Z., & Bokpin, G. A. (2012). Expansion and Efficiency in Banking: Evidence from Ghana.

Managerial and Decision Economics, 33(1), 19-28. doi: 10.1002/mde.1556

Jensen, M. C. (1986). Agency cost of free cash flow, corporate finance, and takeovers. Corporate Finance,

and Takeovers. American Economic Review, 76(2).

Jensen, M. C., & Meckling, W. (1976). Theory of the firm: Managerial behaviour, agency costs and capital

structure. [10.1016/0304-405X (76)90026-X]. Journal of Financial Economics, 3, 305-360.

Jin, J., Yu, Z., & Mi, C. (2012). Commercial bank credit risk management based on grey incidence

analysis. Grey Systems: Theory and Application, 2(3), 385-394.

Jin, L., & Myers, S. C. (2006). R2 around the world: New theory and new tests. Journal of financial

economics, 79(2), 257-292.


37

Kallberg, J. G., & Udell, G. F. (2003). The value of private sector business credit information sharing:

The US case. Journal of Banking & Finance, 27(3), 449-469.

Kealhofer, S. (2003). Quantifying credit risk I: default prediction. Financial Analysts Journal, 59(1), 30-

44.

Kolapo, T. F., Ayeni, R. K., & Oke, M. O. (2012). CREDIT RISK AND COMMERCIAL

BANKS'PERFORMANCE IN NIGERIA: A PANEL MODEL APPROACH. Australian journal

of business and management research, 2(2), 31.

Kothari, S. P., Shu, S., & Wysocki, P. D. (2009). Do managers withhold bad news? Journal of Accounting

Research, 47(1), 241-276.

KPMG. (2012). Doing Business in Ghana. Accra.

Kusi, B. A., Agbloyor, E. K., Ansah-Adu, K., & Gyeke-Dako, A. (2017). Bank credit risk and credit

information sharing in Africa: Does credit information sharing institutions and context matter?

Research in International Business and Finance.

Lensink, R., Meesters, A., & Naaborg, I. (2008). Bank efficiency and foreign ownership: Do good

institutions matter? Journal of Banking & Finance, 32(5), 834-844. doi: DOI:

10.1016/j.jbankfin.2007.06.001

Levine, R., Loayza, N., & Beck, T. (2002). Financial intermediation and growth: causality and causes.

Central Banking, Analysis, and Economic Policies Book Series, 3, 031-084.

Luoto, J., McIntosh, C., & Wydick, B. (2007). Credit information systems in less developed countries: A

test with microfinance in Guatemala. Economic Development and Cultural Change, 55(2), 313-

334.


38

Miller, M. J. (2003). Credit reporting systems and the international economy: Mit Press.

Myerson, R. B. (2013). Game theory: Harvard university press.

Ncube, M., & Senbet, L. W. (1997). Perspectives on financial regulation and liberalisation in Africa under

incentive problems and asymmetric information. Journal of African Economies, 6(1), 29-88.

Ohene-Asare, K., & Asmild, M. (2012). Banking efficiency analysis under corporate social

responsibilities. International Journal of Banking, Accounting and Finance, 4(2), 146-171. doi:

10.1504/ijbaaf.2012.048331

Padilla, A. J., & Pagano, M. (1997). Endogenous communication among lenders and entrepreneurial

incentives. The Review of Financial Studies, 10(1), 205-236.

Padilla, A. J., & Pagano, M. (2000). Sharing default information as a borrower discipline device. European

Economic Review, 44(10), 1951-1980.

Pagano, M., & Jappelli, T. (1993). Information sharing in credit markets. The journal of finance, 48(5),

1693-1718.

Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data

envelopment analysis. Omega, 41(1), 61-79. doi: http://dx.doi.org/10.1016/j.omega.2011.08.010

Perera, S., Skully, M., & Wickramanayake, J. (2007). Cost Efficiency in South Asian Banking: The Impact

of Bank Size, State Ownership and Stock Exchange Listings*. International Review of Finance,

7(1-2), 35-60. doi: 10.1111/j.1468-2443.2007.00067.x

PWC. (2007). Ghana Banking Survey. Is the Industry on the Bill? Accra: PricewaterhouseCoopers.

PWC. (2014). Ghana Banking Survey. The Future of Banking in Ghana... What Next? Accra:

PricewaterhouseCoopers.


39

PWC. (2017). Ghana Banking Survey. The Future of Banking in Ghana... What Next? Accra:

PricewaterhouseCoopers.

Quartey, P., & Afful-Mensah, G. (2014). Financial and monetary policies in Ghana: A review of recent

trends. Review of Development Finance, 4(2), 115-125.

Richard, E., Chijoriga, M., Kaijage, E., Peterson, C., & Bohman, H. (2008). Credit risk management

system of a commercial bank in Tanzania. International Journal of Emerging Markets, 3(3), 323-

332.

Schaeck, K., & Cihák, M. (2014). Competition, Efficiency, and Stability in Banking. Financial

Management, 43(1), 215-241. doi: 10.1111/fima.12010

Singh, D. D. (2010). Bank Specific and Macroeconomic Determinants of Bank Profitability: The Indian

Evidence. Paradigm, 14(1), 53-64.

Srairi, S. (2010). Cost and profit efficiency of conventional and Islamic banks in GCC countries. Journal

of Productivity Analysis, 34(1), 45-62. doi: 10.1007/s11123-009-0161-7

Srivastava, R. K., Shervani, T. A., & Fahey, L. (1998). Market-based assets and shareholder value: A

framework for analysis. The Journal of Marketing, 2-18.

Stiglitz, J. E., & Weiss, A. (1987). Credit rationing: reply. The American economic review, 77(1), 228-

231.

Treacy, W. F., & Carey, M. (2000). Credit risk rating systems at large US banks. Journal of Banking &

Finance, 24(1), 167-201.

Triki, T., & Gajigo, O. (2012). Credit bureaus and registries and access to finance: new evidence from 42

African countries: African Development Bank.


40

Van Greuning, H., & Brajovic Bratanovic, S. (2003). Analyzing and managing banking risk: a framework

for assessing corporate governance and financial risk management: Washington, DC: World Bank.

Watts, R. L. (2003). Conservatism in accounting part I: Explanations and implications. Accounting

Horizons, 17(3), 207-221.

Weill, L. (2004). Measuring Cost Efficiency in European Banking: A Comparison of Frontier Techniques.

Journal of Productivity Analysis, 21(2), 133-152. doi: 10.1023/B:PROD.0000016869.09423.0c

Wooldridge, J. M. (2013). Introductory Econometric: A modern approach (5th ed.). Australia: South-

Western Cengage Learning.


Documents

UNIVERSITY OF GHANA CREDIT RISK AND PROFITABILITY OF