Master’s Thesis

Non-Performing Loans and Bank’s Profitability: Empirical Evidence from Ghana

by

OSEI-TUTU Bismark

52119006

March 2021

Master’s Thesis Presented to

Ritsumeikan Asia Pacific University

In Partial Fulfillment of the Requirements for the Degree of

Master of Business Administration.

ii

CONTENTS Page

TABLE OF CONTENT ...................................................................................................... ii

LIST OF TABLES .............................................................................................................. v

LIST OF FIGURES ........................................................................................................... vi

DECLARATION ............................................................................................................... vii

ACKNOWLEDGEMENT ............................................................................................... viii

ABSTRACT......................................................................................................................... 1

CHAPTER ONE ................................................................................................................. 2

INTRODUCTION ............................................................................................................... 2

1.1 Background to the Study .............................................................................................. 2

1.2 Problem Statement ....................................................................................................... 3

1.3 Research Objectives ..................................................................................................... 5

1.4 Research Questions ...................................................................................................... 5

1.5 Summary of Methodology ........................................................................................... 5

1.6 Significance of the Study ............................................................................................. 6

1.7 Scope of the Study ....................................................................................................... 6

1.8 Organisation of the Study............................................................................................. 7

CHAPTER TWO ................................................................................................................ 8

LITERATURE REVIEW ................................................................................................... 8

2.1 Introduction ................................................................................................................. 8

2.2 Overview of the Banking Sector in Ghana ................................................................... 8

2.3 Conceptual Review ...................................................................................................... 9

2.3.1 Non-Performing Loans (NPLs) ............................................................................... 10

2.3.1a Bank-Specific Determinants of NPLs .................................................................... 11

2.3.1b Macro-economic Determinants of NPLs................................................................ 12

2.3.2 Bank Profitability .................................................................................................... 13

2.3.2a Bank-Specific Determinant of Profitability ............................................................ 14

2.3.2a Macro-ecconomic Determinants of Profitability .................................................... 15

iii

2.4 Theoretical Review .................................................................................................... 15

2.4.1 Moral Hazard Theory .............................................................................................. 15

2.4.2 Information Asymmetry Theory .............................................................................. 16

2.5 Empirical Review and Hypothesis Development ........................................................ 17

2.5.1 Relationship between bank specific variable and Bank Profitability ........................ 17

2.5.2 Relationship between macroeconomic variables and Bank Profitability ................... 18

2.5.2 Relationship between macroeconomic variables and non-performing loans ............. 20

2.6 Summary of comments .............................................................................................. 21

2.6 Conceptual Framework .............................................................................................. 22

2.9 Research Gap ............................................................................................................. 23

CHAPTER THREE .......................................................................................................... 25

RESEARCH METHODOLOGY ..................................................................................... 25

3.1 Introduction ............................................................................................................... 25

3.2 Research Design ........................................................................................................ 25

3.3 Sources of Data .......................................................................................................... 25

3.4 Study Population........................................................................................................ 26

3.5 Theoretical Framework .............................................................................................. 26

3.6 Model Specification ................................................................................................... 26

3.6.1 Estimation Strategy ................................................................................................. 27

3.7 Variable Definition & Measurement, and Source ....................................................... 28

CHAPTER FOUR ............................................................................................................. 29

RESULTS AND DISCUSSIONS ...................................................................................... 29

4.1 Introduction ............................................................................................................... 29

4.2 Descriptive Analysis .................................................................................................. 29

4.3 Correlational Analysis ............................................................................................... 31

4.4 Trend Analysis of Non-Performing Loans (NPL) in Ghana ........................................ 32

4.5 Multiple Regression Analysis .................................................................................... 33

4.5.1 Analysis of factors affecting NPL ............................................................................ 33

4.5.2 Analysis of factors influencing ROA ........................................................................ 35

iv

4.5.3 Influential factors of ROE ....................................................................................... 36

CHAPTER FIVE............................................................................................................... 39

SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS ........... 39

5.1 Introduction ............................................................................................................... 39

5.2 Summary .................................................................................................................. 39

5.3 Conclusion ................................................................................................................. 40

5.4 Policy Implications and Recommendations ................................................................ 41

5.5 Limitations of the Study ............................................................................................. 42

REFERENCES .................................................................................................................. 43

APPENDIX…………………………………………………………………………………………………………………. 48

v

LIST OF TABLES

Table 2.1: Literature Validation and Falsification………………………………………………………………… 21

Table 4.1: Summary of Descriptive statistics ....................................................................... 29

Table 4.2: Correlation Analysis of the variables .................................................................. 31

Table 6.1: Variable Definition & Measurement ................................................................... 48

vi

LIST OF FIGURES

Figure 4.1: Trend Analysis of NPL ...................................................................................... 32

Table 4.2a: NPL Multivariate Regression Analysis ............................................................. 33

Table 4.2b: ROA Multivariate Regression Analysis ............................................................ 35

Table 4.2c: ROE Multivariate Regression Analysis ............................................................. 37

vii

DECLARATION

I, the under-signed, do hereby declare that this research work, under the supervision of

Professor Michael A. Cortez, is my own work towards the award of Degree of Master of

Business Administration; and that, to the best of my knowledge, it contains no materials already

published by someone else or materials which have been accepted for the honour of any other

Degree from the University, aside these, due references and acknowledgement have been made.

Name Index Number Signature

Bismark Osei-Tutu 52119006 signed

..……………………. …………….. ………….

viii

ACKNOWLEDGEMENT

I am most thankful to God Almighty, who by His grace, direction, protection and

wisdom, granted me the strength to carry out this research work. For all these, I say

thank you Father! This is how far you have brought me.

With sincere appreciation, I also acknowledge the immense commitment and

contributions of Professor Michael A. Cortez of the Graduate School of Management,

APU who supervised this research work and put forward his untiring efforts in ensuring

that it comes to an acceptable standard. His recommendation, advice and constructive

criticisms were extremely helpful. Indeed, I am profoundly indebted to him for his

supervision.

My profound gratitude, likewise, goes to the staff of APU and Akua Oforiwaa Antwi

of Fidelity Bank Ghana Limited who encouraged me to pursue the programme. Without

their participation and support, this work would not have come this far. To every one

of you, I say Thank You!

1

ABSTRACT

Non-Performing Loans (NPLs) are considered as part of the major issues affecting the banking sector

of many developed and developing economies and that financial sector reforms are usually tailored to

restructuring the loan policies of banks operating in those economies. Banks that adhere to financial

sector reforms mostly do well in their loan policies whereas banks that do not adhere to financial sector

reforms encounter numerous problems regarding loan policies. In this respect, the study focuses on the

factors that affect Non-Performing Loans and Profitability of banks. Further, the study looks at of bank-

specific factor and macro-economic factors that affect Bank Profitability and macro-economic

variables that affect Non-Performing Loans. In order to know the consequential effects of those

variables, the researcher used moral hazard and information asymmetry theories as the theoretical

bases. The study uses information collected from the financial statements of seven (7) indigenous

universal banks in Ghana. The financial statement spans from the year 2009 to 2019. The study adopts

explanatory research design and employs panel data analysis.

The study revealed that Interest Rate has significant positive relation with NPL. Annual Inflation was

found to have a significant inverse relation with NPL. However, GDP has no significant relation with

NPL.

With regards to bank profitability, the study revealed that the bank-specific factor (NPL) has no

significant influence on bank profitability (ROA & ROE)

On the country-specific variables, the results indicated that GDP, AI, and IR have no significant effect

on Bank Profitability (ROA & ROE).

2

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

In every resilient economy, the important role play by banks cannot be underestimated. Activities

perform by banks are furnishing and advancing cash to individuals and businesses as well as acting as

recipients of public savings. Banks generate revenue by accumulating interest that are charged on loans

and other non-interest income such as fees and commission received from services they render and

securities they own. In this regard, Kaaya & Pastory (2013) argued that the inability of banks to manage

or decrease its non-performing loans over a period of time consistently affects liquidity and solvency

of the banks and thus affecting the liquidity position of the financial sector. Accordingly, Kithinji

(2010) also posited that failure to prudently manage gross non-performing loans normally results in a

significant reduction in profits for many banks and gradually limits the banking sector capacity to

perform its function towards economic development of a nation.

In connection with the above description, Laryea et al. (2016) reasoned that the probability that banks

will fold-up, if the rate of unpaid loans by borrowers is very high. Isik et al. (2003) and Athanasoglou

et al. (2010) used the Turkish banking sector to buttress the arguments made by Laryea et al. They

claimed that the banking sector in Turkey has had a sharp rise in bad loans with the ratio of total loans

to NPLs increasing approximately by 54% between the period 2011–2016. Again, Ugoani (2016)

argued that approximately 80 percent of total loans portfolio of banks in Benin were non-performing

which led to the collapse of the three most important commercial banks in the country. His study in

Cameroon also revealed that NPLs portfolio got to 60-70 percent which actually led to the restructure

of five commercial banks and three others.

Following the statistics given by Isik et al. (2010), the ongoing or increase in NPLs disturbs the stability

and the strength of the banks or the financial industry. Interestingly, Saba et al. (2012) argued that

quality asset can be ensured by paying critical attention to NPLs in the sense that NPL performs a

3

decisive role and again acts as a pointer of stability in financial or the banking industry. In this regard,

Bloem et al. (2001) & Breuer (2006) emphasize the point that matters involving NPLs touch every

‘‘nook and cranny’’ of the economy with the financial or banking sector such as commercial banks

having the leading NPLs portfolio and the most affected. According to them, an increased NPLs in the

banking and the financial sector impede the progress of economic growth.

This notwithstanding, Saba et al. (2012) contended that banks life is dependent on loans; and that the

long run banks success is contingent upon keeping the level of bad loans at a required minimum by

ensuring that NPLs do not exceed a certain threshold. In support of this, Greenidge et al. (2010) argued

that NPLs are thus a measure of banking system stability leading to a country’s financial stability.

Therefore, averting the incidence of systematic banking problem of NPLs is certainly a key concern of

policymakers (Kunt et al., 1998).

With regard to the variety of arguments advanced, Rwegasira et al. (2011) stated that, although

international practices play a crucial role in defining or determining that a loan is not performing, the

criteria set in determination of NPLs in different economies is not the same. With these in mind,

Waweru et al. (2009); Tiwari et al. (2013) also argued that non-performing loans are loans which the

repayment period exceeds Ninety (90) days after the due date and do generate any interest income for

the banks.

1.2 Problem Statement

Evidence from Fukuda et al. (2012) says that one of the pre-conditions for economic development and

growth is the stability and resilience of the banking system where there is effective and efficient

allocation of capital from capital-sufficient economic agents to capital-insufficient economic agents in

the economic life of individuals in the society. Manove et al. (2001) and Jeong et al. (2013), however,

argued that banks efficient allocation of capital has been doused by the structural changes such as

deregulation and internationalisation of banks activities in the international financial market.

4

According to them, these structural changes have affected competition among banks and increased

credit risk by relaxing borrowing conditions leading to the destruction of the quality of the banks’

lending activities. That is, the rate at which NPLs increases.

In agreeing with Manove, Padilla et al. (2008) did an empirical study in Czech’s banking sector and

concluded that there is firm evidence in respect of bad management and proposed that regulatory bodies

in developing economies must concentrate on managerial performance by reducing NPLs to strengthen

and improve of the financial institutions. More importantly, Salas et al. (2002) did a study in Spanish

Commercial and Savings Banks. In their study, they used country-specific factors (macro) and bank-

specific factors (micro) factors to explain NPLs and profitability. They confirmed that lagged

efficiency insignificantly affects problem loans (perhaps as a result of the resistance of ‘’skimping’’

effects and bad management) and a negative effect on lagged solvency ratio to Non performing

advances.

The issue of NPLs in Ghana is not far from the truth revealed by Podpiera et al. (2008) and Salas et al.

(2002). Accordingly, Bank of Ghana (BoG) (2019) posited that the total stock of NPLs consistently

increased as it rose to GH¢7.19 billion in the year 2019 from GH¢7.14 billion recorded in the year

2018. Furthermore, BoG claimed that the three most important industries that are loan recipients

account for 56.2 percent with the commerce sector accumulating 24.2 percent, service sector 16.9

percent and manufacturing sector 15.1 percent.

In view of the above, the issue of how does bank-specific factors and macro-economic factors influence

profitability of banks; how does macro-economic factors affect Non-Performing loans in Ghana; what

is the trend of NPLs continue to remain unsolved problems in Ghana.

5

1.3 Research Objectives

The researcher’s main purpose or objective set for this investigation is to examine factors that affect

Non-Performing Loans and profitability of Banks in Ghana.

Specifically, the study seeks to:

1. Examine the bank-specific and macro-economic factors that affect Bank profitability in Ghana.

2. Establish the effect of macro-economic factors on NPLs in banking sector of Ghana.

3. To explore the trend of the NPLs in the banking sector of Ghana.

1.4 Research Questions

Regarding the objectives or purpose for the research, the study seeks to answer the following questions:

1. What bank-specific and macro-economic variables affect profitability of Banks in Ghana?

2. To what extent does macro-economic factors affect NPLs of Banks in Ghana?

3. What is the trend of the NPLs in the banking sector of Ghana?

1.5 Summary of Methodology

The researcher adopts the positivists research philosophy in the sense that the link between the variables

(independent and the dependent) will be discovered by related inferences (Cohen et al., 2011). Again,

the adoption of positivists research philosophy clarifies the understanding of the variables by empirical

tests and methods leading to high quality standard of validity and reliability (Cohen, 2007). The study

population consists of all the twenty-three (23) banks currently operating as universal banks in Ghana.

(Bank of Ghana, 2019). In effect, the researcher uses secondary data in estimating the results. The

secondary data will focus on NPLs and profitability variables figures of the various banks as well as

bank explicit variable figures. The researcher uses the dynamic panel data estimation in determining

time persistence structure of NPLs in Ghana. EVIEWS was used for data analysis.

6

1.6 Significance of the Study

Considering the dearth of literature that addresses the bank-specific variables and bank explicit factors

that affect profitability in the banking sector, and bank explicit factors that affect NPLs, the outcome

of the investigation will contribute to the literature and will also serve as a useful guide for those who

would want to work on this field in future. In this regard, the researcher will build a comprehensive

database on bank-specific variable for other researchers to draw information from. Again, the study

will highlight new knowledge to literature regarding bank-specific variables that influence profitability

and macroeconomic factors that influence NPLs. Furthermore, the findings from this work will enable

financial institutions know the importance of NPLs and bank profitability. The research will also be

beneficial to Bankers, Entrepreneurs and other corporate professionals since it will create awareness

of NPLs and Bank Profitability issues. Generally, theoretical and empirical reviews will furnish

theoretical and practical implications of NPLs and its effect on profitability. In this respect, the research

offers both managerial (from empirical literature) and theoretical (from theories) understanding of

NPLs and profitability of banks.

1.7 Scope of the Study

The extent of the investigation catches both the delimitation and impediments. Concerning the

delimitation, the examination focuses on Ghana's financial area on the grounds that the financial area

in Ghana has interesting attributes when contrasted with other financial areas in different nations. Once

more, the examination centres around Bank credit advances that do not perform and bank-explicit

factors that influence bank profitability and bank explicit factor that affect NPLs. Concerning bank

benefit, ROA and ROE are utilized. Assembling these, the investigation would not sum up the findings

to cover other nations financial area. One of the impediments of the examination will be time in that

there is time imperative with respect to the analyst who needs to consolidate scholastic exercises with

family life and work. Once more, there is the restriction of monetary limitations where the analyst

7

needs to submit individual assets into the task for transportation to the chosen association for data

gathering and the typesetting of the whole original copy of this examination. Ultimately, the trouble of

getting information from the banks.

1.8 Organisation of the Study

The research has been organized into five (5) main chapters. The first chapter gives detailed

background of the study, statement of problem, objectives, questions, summary of methodology,

significance, and scope of the research. Chapter two examines review of related literature. It provides

explanations and measures of NPLs and bank profitability. Again, the chapter documents the

relationship between the variables. The chapter will also outline theoretical and empirical literatures,

and finally concludes on the research gap. Chapter three also emphasizes the methodology used for

the study. Philosophical underpinnings, design, research setting, population, sources of data, empirical

model, statistical methods of estimating result, validity and reliability and data analysis. Chapter four

talks about analysis of the data and discussion of the results. The final chapter highlights a brief

synopsis of findings, conclusion, and recommendations of the research.

8

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This section presents a review of related studies on the topic under study and it’s organised into eight

(8) main categories. Section 2.2 gives an overview of the banking sector. Section 2.3 talks about a

conceptual review of literature and covers concepts of NPLs and profitability of banks. Section 2.4

covers theories that underpin the study. Section 2.5 develops hypothesis from the arguments and

propositions from the various studies. Section 2.6 presents summary of comments to cover literature

validated and falsified. Section 2.7 covers the conceptual framework that shows the link that exist

between the variables under study. Section 2.8 outlines the research gap.

2.2 Overview of the Banking Sector in Ghana

Bank of Ghana (BoG, 2020) asserted that the banks total assets amounted to GH¢129.06 billion in

December 2019. This shows a 22.8% increase compared with 12.3% upsurge in the year 2018. The

higher growth of total assets in December 2019 reveals a higher growth of both domestic and foreign

assets of the sector in Ghana. Domestic assets shot up by 23.1 percent representing GH¢118.69 billion

in December 2019 as compared with 12.5 percent rise recorded in the previous year. Meanwhile,

foreign assets also increased by 19.8% denoting GH¢10.38 billion during the same period as compared

with 9.6 percent growth in December 2018. The higher growth of domestic assets translates into a

significant rise in the share of domestic assets to 92.2% in December 2019 from 90.3% in December

2018. Share of foreign assets on the other hand declined accordingly from 9.7 percent to 7.8% within

the same comparative period.

Again, BoG (2020) reported that banks’ total investments comprising bills, securities and equity

increased to GH¢48.45 billion representing 27.0 increase in December 2019 as compared with 33.6

percent rise recorded in December 2018. The sharp growth in total investments in 2018 was largely

9

due to the special (long-term) resolution bonds issued to Consolidated Bank Ghana (CBG). This led to

long-term investments increasing by 115.8 percent in December 2018, while short term investments

contracted by 24.5 percent. A year after this development, growth in long-term investments (securities)

normalised to 30.1 percent (GH¢33.03 billion) in December 2019, while short-term investments (bills)

picked up by 21.1 percent which represents GH¢14.98 billion as at end of December 2019. However,

the larger growth in securities in December 2019 compared to the short-term bills reflected banks

preference for longer dated instruments in 2019.

More importantly, the credit growth rebounded strongly with a 23.8 percent increase in gross loans and

advances to GH¢45.17 billion in December 2019 which is a reversal of the 3.5 percent contraction a

year earlier. Similarly, net advances (gross loans adjusted for provisions and interest in suspense) grew

by 25.7 percent to GH¢39.96 billion following a marginal inch-up of 1.0 percent in December 2018.

The foreign currency component of net advances denominated in Ghana Cedis recorded a higher

growth of 21.5 percent to GH¢12.12 billion in December 2019 from GH¢9.97 billion (15.5% y/y

growth) in December 2018. This was partly due to the depreciation of the Ghana Cedi over the period.

2.3 Conceptual Review

Assessing banks quality of assets is very important measure of insolvency indications. This insolvency

indications, according to banking experts, affect productivity and stability. Mester (1996) & Berger et

al. (1997) pointed out the relevance of studying NPLs and finalised that non-performing loans

significantly and negatively impact banks productivity and stability with the reason that NPLs decline

the quality of assets in a bank. The study focuses on the effects of both bank internal and external

factors and their effect on bank profitability as well as bank external factors and their effect on NPLs.

10

2.3.1 Non-Performing Loans (NPLs)

Liberalisation of the financial institutions as well as the regulatory restrictions determine the risk nature

of the banking firms. Accordingly, Tsakalotos (1991) cited in Gibson et al. (1992) claimed that banks

are the foundation of “personal contacts and social pressure” that cause inefficiency regarding risk

management and NPLs associated problems. Banks are to survive by improving or achieving required

target of profitability in the mist of fierce competition by enhancing efficiency in risk management and

the adoption of sophisticated technology. In spite of these, the problem of bank bankruptcy is a crucial

issue in a lot of countries across the world.

Studies conducted in the Euro-area by Mester (1996) & Berger et al. (1997) argued that asset quality

is one of the critical reasons for bank liquidation. In establishing their fact, they said that the Euro-area

NPLs, that is loans beyond 90 days, exceeded 12% in 2015 putting excessive burden on banks financial

position that limits them from growth and properly performing their intermediation role. Mester &

Berger et al. posited that discovering the factors of non-performing loans is of foremost interest to

policy concentration.

In this regard, current studies have differentiated two main sources of factors of cumulative non-

performing loans - bank-implicit factors and country-specific factors (Berger et al., 1997). Berger et

al. (1997) who used the Granger-causality methods to examine related bank management constructs

vis-à-vis the existing relationship among loan quality, cost efficiency and bank capital found, that bad

moral hazard and bad management constructs described form substantial part of non-performing loans.

Again, Podpiera et al. (2008) assessed a connection between non-performing loans and cost efficiency

and concluded that cost efficiency has a significant impact on non-performing loans. In support of these

findings, Ghosh (2006) also said that lagged leverage affects non-performing loans. More importantly,

Cifter (2015) argued that bank concentration impacts non-performing loans.

Interestingly, Louzis et al. (2010) predicted the variables that have effect on non-performing loans for

each different categories of loans such as mortgage, business and consumer. They established that non-

11

performing loans are greatly affected by macro-economic variables and management quality. Ghosh

(2015) also concluded by saying that the factors affecting non-performing loans includes the size of

the bank, annual inflation rate, bad credit quality, liquidity risk, rate of inflation as well as

unemployment rate.

2.3.1a Bank Specific Determinants of NPLs

From the arguments from the above, it is recognised that the factors that influence non-performing

loans do not come from macroeconomic factors alone. The factors can also emanate from bank-specific

factors. These factors are seen by researchers as internal forces influencing the banking industry. They

posited that the unique attributes of the Ghana’s financial sector and the choice of a specific financial

institution in relations to maximum efficiency and enhancements in risk management are likely to cause

the evolution of non-performing loans. Particularly, Berger et al. (1997), in their seminal paper,

examined the relationship among loan quality, cost efficiency and bank capital. With a sample of

commercial banks in United States, they concluded on four propositions as crucial internal factors that

affect non-performing loans. The propositions are moral hazard, bad luck, skimping effect, and bad

management.

‘Bad luck’ proposition states that exogenous rises in non-performing loans results in a decline in

measured cost efficiency. The fundamental argument is that a significant rise in the number of loans

leads to additional operating costs associated with those costs.

‘Bad management’ proposition argues that low-cost efficiency is significantly and positively related

with rises in NPLs. The projected explanation connects ‘bad’ management with lack of skills in credit

scores, appraisal of pledged collaterals, and monitoring borrowing activities. This notwithstanding,

Podpiera et al. (2008) researched into the relationship that exist between cost efficiency and NPLs

using the banking sector in Czech spanning from 1994 to 2005. Their findings supported the bad

management proposition and affirmed the assertion that governing authorities in developing countries

12

must concentrate on managerial performance to augment stability of their financial institutions (by

decreasing NPLs).

‘Skimping’ proposition posits that increase in measured efficiency leads to cumulative numbers of

NPLs. Arguing from that perspective, banks that do not devote much effort and attention to ensuring

rising loan quality are more cost-efficient. Conversely, in the long run growing number of non-

performing loans will persist.

‘Moral hazard’ proposition claims that reduce capitalisation of banks results in a rise in NPLs. The

connection is found in the motivation provided by moral hazard on the part of managers who escalate

the riskiness of credit grant portfolio in situations where the banks are delicately capitalised.

‘Size effect’ proposition was developed by Salas et al. (2002). The ‘Size effect’ states that banks with

large size seem to encounter lesser NPLs. By integrating micro and macroeconomic variables, they

argued that lagged efficiency effect on credit grant and bad effect of lagged solvency ratio to non-

performing loans is in line with moral hazard proposition

2.3.1b Macroeconomic Determinants of NPLs

The relationship that exists between the macroeconomic variables such as business cycle and quality

level loans that reflect banking resilience is well researched in the literature (Kiyotaki et al.1997). They

further argued that the growth and the expansion stage of an economy is characterised by a comparative

reduced number of NPLs because both individuals and firms encounter an ample stream of income for

servicing debts. Geanakoplos (2009), nonetheless, posited that when flourishing period continues to

occur in an economy, credits are made available to lower-quality debtors and consequently non-

performing loans rise when depression sets in.

Remarkably, evidence from Cifter et al. (2009) confirmed the aforesaid linkage between business cycle

and credit default. In their studies, they asserted that business cycle affects the ratio of non-performing

loans. Again, a significant negative relationship exists between GDP growth and non-performing loans

13

ratio. This implies that GDP growth puts an economy in better shape and as a result that economic

agents either do not borrow at all or are in a better position to service their loans due to growth in

economic activities. They extended their argument further by including other variables such as

unemployment and interest rate because unemployment and interest rate affect household and firms.

A rise in unemployment rate significantly affects cash flow of individuals by increasing the debt

burden. In respect of firms, they reasoned that higher rate of unemployment indicates a decrease in

productivity has a negative effect on effective demand resulting in a reduction in revenues and a fragile

debt condition.

Cifter et al. genuinely argued that interest rate influences the strain in debt repayment in relation to

floating interest rate of loans. This implies that the interest charged would accumulate with respect to

the debt burden due to a rise of interest rate which will eventually lead to a higher number of non-

performing loans. In justifying the interrelationship that exist among GDP, unemployment, interest rate

and NPLs from the perspective of life-cycle consumption models, Lawrence (1995) argued that low-

income borrowers have higher probability of credit payment defaults. This is as a result of increased

unemployment which puts them in an unfavourable position to pay their debts. He further argued that,

in an equilibrium, financial institutions charge higher rate of interest to clients who are deemed risky

in repayment of loans. Rinaldi et al. (2006) agreed with Lawrence’s assertion and theorised the

optimization problem of an agent by saying that the likelihood of loan default greatly or largely depends

on the current status of income of the borrower, the rate of unemployment (which can be linked to

future uncertainty of income) as well as rate of borrowing.

2.3.2 Bank Profitability

Measuring a firm’s performance greatly depends on financial performance and operational policies

measured in monetary terms. In this regard, Kinyua et al. (2015) posited that the financial health of a

firm for a period should be compared with similar firms within the same industry or average of the

14

industry. Numerous institutions use the monetary measure as a base to read the records to embrace

only transaction data expressed in monetary terms (Kieso, 2010). According to Lawrence & Chad

(2012), monetary term is a uniform unit which is used to make evaluation between or among companies

in the same industry or similar industries. They further argued that a lot of companies have different

ways of measuring financial performance. Some use Return on Assets (ROA) and some others use

Return on Equity (ROE) or a combination of the two.

Profitability, according to Altunbas et al. (2001), is the capacity or ability of an organisation to preserve

its profit (revenue-expenditure) over a period of time. They further argued that profitability is very

crucial performance indicator to the investors and that profitability of the banks shows the progress or

success of management of firms. Moreover, they posited that variations in profitability enhances

economic progress because profits impact investment and savings decisions of businesses. To them,

profits offer greater flexibility and determine the cash flow situation of corporations through retained

earnings which is considered as a major source of finance.

Altunbas et al. (2001) argued that the internal and external determinants influence the structure and

performance of banks because current financial deregulation, technological changes, financial

innovation, and globalisation affect novel market contestants in the banking sector of an economy. This

makes the concept of efficient structure very significant for the banking industry.

2.3.2a Bank-specific determinants of profitability

There are many internal factors that influence bank. These factors as used by previous researchers

include bank size, capital adequacy ratio, Net interest margin, and non-performing loans. The effect

of these factors on profitability is not uniform in the sense that some of the variables lead to a positive

relation while others also lead to a negative relation. The current study focuses on non-performing

loans as a credit risk and bank internal factor and its effect on bank profitability (ROA and ROE). In

15

this regard, Altunbas et al. posited that the internal bank profitability determinants are components that

affect the management decisions and policy objectives. These emanate from management objectives,

policy decisions

2.3.2b Macro-economic determinants of bank profitability

Factors affecting profitability of banks do not only come from bank internal activities. They also

emanate from country specific factors such annual inflation, interest rate, gross domestic product,

exchange rate, and unemployment rate. These factors have influence on profitability of banks although

the banks have no or little control over them. Altunbas et al posited that macroeconomic factors, unlike

internal bank factors, are components that affect bank profitability and they are events that are not

under the control of the banks. The current study focuses annual inflation, interest rate, and gross

domestic product as external bank factors and their effect on profitability.

2.4 Theoretical Review

Researchers, (Akerlof, 1970; Berger et al., 1997), over the years, have advanced variety of theories and

propositions that describe factors connected with the incidence of growth of banks non-performing

credits. This is because non-performing loans play a crucial part in the financial deficit of commercial

banks in every nation. Following these researchers, the study used moral hazard and information

asymmetry theories to explain the rate at which non-performing credits grows in Ghana.

2.4.1 Moral Hazard Theory

Moral hazard is a concept which deals with a variety of principal-agent problems in the sense that

banks act as agent of depositors and shareholders. Accordingly, managers of financial institutions are

motivated to undertake risky activities because they are in a better position to gain a larger proportion

of upside risk such as profits, bonuses, market share (Jensen et al., 1976). The high downside risk such

16

as loses, and low dividends usually affect depositors and shareholders but not managers of the banks.

Likewise, bank managers who face capital pressure (undercapitalized banks) usually resort to

motivations provided by moral hazard with the assumption that underwriting high-risk loans at a high

rate of interest will help increase profit and capital base of their banks. High-risk loans, however, may

also lead to higher levels of NPLs because borrowers may also have similar adverse incentive to depend

on. Moral hazard is usually connected with bank management behaviour. Changes in items such as

bank size, loan growth, asset growth, deposit growth and capital adequacy ratio are associated with

decisions made bank management.

2.4.2 Information Asymmetry Theory

Asymmetry of information usually occurs in a situation where one party in a transactional relationship

possesses more material knowledge about the transaction than the other party. In making financial

decisions, asymmetry of information focuses on the impact of decisions made by both parties based on

the differences that exist in the information they possess (Mishkin, 1992). Banks granting credit

facilities to borrowers encounter uncertainties in repayment of the loan because it very difficult to

determine the true characteristics and actions of the borrowers in relation to their creditworthiness

(Ariccia, 1998). Asymmetry of information which is also referred to as the “lemon Principle” leads to

adverse selection. In a situation where lenders are not able to distinguish good from iniquitous

borrowers (highly immoral borrowers), there is a likelihood for all borrowers to be charged a normal

rate of interest which actually reflects their pooled experience (Evans et al., 2000; Catro, 2013).

However, if the rate of interest is too high for good borrowers to afford, then some good borrowers

would be compelled to exit the borrowing market thereby compelling the banks to apply a higher rate

of interest to the existing unqualified borrowers (Barron et al., 2008). Consequently, adverse selection

creates an opportunity for high-quality borrowers to be displaced by low-quality borrowers which

eventually leads to a rise and accumulation of NPLs (Bofondi et al., 2011; Makri et al., 2014;). In line

17

with information asymmetry, managers of financial institutions may also lack the capacity to

underwrite risk associated with credit and manage their operational costs. This phenomenon is

connected to Bad Management Hypothesis. According to Berger and De Young (1997). Reacting to

the NPLs rises as a result of imbalance of information between lenders and borrowers, bank managers

tend to inject more resources into managing and monitoring problem loans. This results in excess of

operating expenses over interest income in the long run. Accordingly, the ratio of higher cost-to-income

is an indication of weaker bank management practices in managing loans portfolio (Vardar et al. 2015;

Muratbek, 2017)

2.5 Empirical Review and Hypothesis Development

2.5.1 Relationship between bank specific factors and profitability

The outcome of NPL as a bank specific factor and profitability relationship is self-evident, not just

regarding the quality and worthiness of individual components of NPLs occurrence, but also in terms

of sign. Makri et al. (2014) conducted a research and concluded that ROA and ROE are best measure

of efficiency but he discovered different impact of these measures in relation to NPLs. In this regard,

Ozurumba (2016) and Bhattarai (2014) contended that there is a negative and a huge connection with

NPL and profitability. Amazingly, Berger et al. (1997) utilized Granger-causality techniques to

determine the relationship that exist among credit quality, cost productivity, and bank capital. They

arrived on bad management and moral hazard as significant causes non-performing credits.

Alexandri and Santoso (2015); Radivojevic and Jovovic (2017) did an investigation on the connection

between capital ampleness proportion and non-performing credits. They finished up per their

contention that there is a huge and a positive connection between capital sufficiency proportion and

non-performing advances.

Mester (1996); Berger & DeYoung (1997) argued from their studies that that NPLs have a negative

effect on banks efficiency and stability in the sense that non-performing credits worsen the quality of

assets of bank. Using NPLs as controlling variables or a bad output, Podpiera et al. (2008) and

18

Fukuyama et al. (2017) also argued that non-performing loan offers a negative benefit to bank

inefficiency. Arguing from microeconomic perspective, Assaf et al. (2013) looked into the relationship

between non-performing credits and size of a bank, capitalisation, and efficiency level of banks. The

findings revealed a positive relation between capitalisation and bank efficiency. The results, however,

found no meaningful correlation that exist between non-performing loans, size of the bank and the

efficiency level. Again, the study found that capitalisation from loan has a positive relationship between

loan capitalisation and technical efficiency of the banks. In relation to these assertions, it is proposed

that:

(H1a): Non-performing loans have significant positive impact on Return on Asset

(H1b) Non-Performing loans have significant positive impact on Return on Equity

2.5.2 Relationship between Macroeconomic variables and Bank Profitability

As posited by Altunbas et al, the internal bank profitability factors are components that affect the

management decisions and policy objectives. These emanate from management objectives and policy

decisions of the banks. On the other hand, external factors of bank profitability concentrate on the

events that are not under the control of the banks.

Zimmerman (1996) also established that managerial decisions concerning loan portfolio is a significant

factor in relation to performance of banks. Therefore, worthy bank performance occurs as a result of

quality management decisions which can be assessed in terms of top management consciousness and

control of policies of the banks. Further studies by Molyneux (1993) indicate that expense control is

one of the key determinants of profitability of financial institutions because it furnishes key and reliable

opportunity for improving banks profitability. They further suggested that staff expenses, as orthodox

knowledge suggests, is likely to be indirectly connected to profitability. The reason is that cost reduces

the total operations or ‘bottom line’ of organisations. The quantum of staff expenses also appears to

19

have a negative impact on banks return on asset. They, however, argued that staff expenses and total

profits are positively related. Interestingly, Molyneux (1993); Bourke (1989) also argued that

management incentives differ in many ways as a result of different bank ownership characteristics that

appears to have great influence on profitability

Concentrating on the external factors, Kaufman (1965) argued changes in population as well as income

practically have great positive relationship with bank earnings. He went further to state that growth in

income levels suggests a relatively small proportion of the differences in bank earnings. However,

Heggestad (1977) claimed that per capita income has no impact on profitability of banks since it is not

a proper measurable variable for economic shocks that can significantly impact bank earnings. Again,

Zimmerman (1996) posited that conditions of regional employment significantly affect bank asset

quality and return on asset.

More importantly, Tirtiroglou et al. (2000) in their study of US banking and its dynamics suggested

that regional heterogeneity greatly affect bank performance. Inarguably, Hoggarth et.al. (1998)

concluded that real GDP explains the greater variability of profit in banks of Germany with expectation

of positive sign. This translates to the fact that the higher growth in real GDP implies that there is a

lower probability of individual and corporate default with respect to credit. The following hypothesis

have been developed from the above arguments.

(H2a) GDP has positive and significant effect on ROA

(H2b): AI has positive and significant effect on ROA

(H2c): IR has positive and significant effect on ROA

(H2d) GDP has positive and significant effect on ROE

(H2e): AI has positive and significant effect on ROE

(H2f): IR has positive and significant effect on ROE

20

2.5.3 Relationship between macroeconomic variables and non-performing loans

Focusing on the external factors of NPLs, Espinoza et al. (2010) & Kauko (2012) studied non-

performing loans reduction growth using macroeconomic variables. The conclusion they arrived at was

that macro variables impact NPLs growth and increase in interest rate, fiscal, and external deficit.

Espinoza et al. (2010) also argued that macro variables impact NPLs for different categories of loans

separately. Also, they posited that not only macro variables affect NPLs category but also management

quality.

Moreover, Cifter (2015) did a study on how bank concentration influences NPLs. With ambiguous

results, he concluded that bank concentration has negative and significant effect on NPLs. Furthermore,

Beck et al. (2015) asserted that the most important causes and influential factors of NPLs are GDP

growth, share prices, interest rates and the exchange rate. Interestingly, Nkusu (2011) claimed that an

exacerbation in the macro-economic factors as proxied by slow growth, decline in asset prices, and

high unemployment are interrelated with loan payment associated problems. Improving

macroeconomic conditions help reduce NPLs. In this regard, Messai (2013) argued that GDP growth

and ROA have a negative impact on NPLs whilst unemployment and the real interest rate positively

affect NPLs. Ozili (2015) also addressed the interaction between non-performing loans and the stage

of business cycle and concluded that NPLs are really affected by business cycle in a period of recession

and boom. Regarding the forgoing arguments, it is proposed that:

(H3a): GDP has positive and significant effect on Non-Performing Loans

(H3b): AI has positive and significant effect on Non-Performing Loans

(H3c): IR has positive and significant effect on Non-Performing Loans.

21

2.6 Summary of Comments

Table 2.1: Literature Validation and Falsification

Author/Year Title Findings Method Relevance to

the study

Radivojevic

et al., 2017)

Examining determinants

of non-performing loans

Significant and a

positive relation is seen

in that of the capital

adequacy ratio and non-

performing loans.

Panel Data

Analysis

Hypothetical

validation

Fukuyama et

al. (2017)

"Non-performing loans

in Sub-Sahara Africa:

Causal analysis and

macroeconomic

implication".

non-performing loans

offer a negative benefit

to bank inefficiency

Time

Series

Analysis

Hypothetical

validation

Assaf et al.

(2013); &

Fukuyama et

al. (2011)

"Commercial bank net

interest margins, default

risk, interest-rate risk,

and off-balance sheet

banking".

non-performing loans

has a positive

correlation between

capitalization and bank

efficiency.

Time

Series

Analysis

Hypothetical

validation

Messai

(2013)

The treatment of non-

performing loans in

macroeconomic

statistics". I

GDP growth, ROA has

a negative effect on

NPLs,

Panel Data

Analysis

Hypothetical

validation

Alexandri et

al. (2015)

"Non-performing loan:

impact of Internal and

external factor evidence

in Indonesia".

NPL has a negative and

a significant

relationship with return

on asset & return on

equity

Panel Data

Analysis

Hypothetical

validation

Messai

(2013)

The treatment of non-

performing loans in

macroeconomic

statistics".

real interest rate

influence NPLs

positively.

Time

Series

Analysis

Hypothetical

validation

Ghosh (2015) "Forecasting non-

performing loans in

Barbados."

NPL rises are poor

credit quality, as well as

unemployment,

inflation, and public

debt

Panel Data

Analysis

Hypothetical

validation

22

2.7 Conceptual Framework

The framework is schematic model showing the relationship between bank specific variable and

macro-economic factors that affect profitability of banks and macro-economic variables that affect

NPLs. As gleaned from literature, the indicator of bank specific variables is Non-Performing Loans

(NPLs) and that of macro variables are Gross Domestic Product (GDP), Annual Inflation (AI) and

interest rate (IR). These are used as independent variables to show their relationship with profitability

Return on Asset (ROA) and Return on Equity (ROE). Further, macro-economic variables stated above

are used as independent variables to show their impact on NPLs. These are represented in the diagram

below.

Independent Variables Dependable Variables

NPL

Source: Panta (2018)

Bank Profitability

• ROA

• ROE

Bank Specific Variable

• NPL

NPL

Macroeconomic Variables

• GDP

• AI

• IR

Macroeconomic Variables

• GDP

• AI

• IR

23

2.8 Research Gap

Necessary and sufficient review of literature revealed the following gaps -concepts gap, research

setting gap, methodology gap (research design and analysis).

Concentrating on the concept gap, the review revealed that there is dearth of studies on non-performing

loans and bank profitability in the sense that most of the studies (Kauko, 2012; and Beck et al. 2015)

focused on only macroeconomic variables that affect non-performing loans and bank profitability.

These researches specifically argue on how macroeconomic variables such as share price index,

exchange rate and unemployment specifically influence non-performing loans of banks. In as much as

these studies well document refined issues on non-performing loans, the studies fail to link these

variables to bank profitability but rather focus on the effect of NPLs on general economy.

With the interesting arguments from the various studies on non-performing loans and bank

profitability, one would have thought that at least one study focused on one developing economy,

especially in Africa. Almost all of the studies (Fries et al., 2005; Park et al., 2006; Kumbhakar et al.,

2015) concentrate on the developed economies. In terms of research setting gap, the various studies,

again, fail to explain how non-performing loans drive poor performance of banks in the developing

economies.

Because vast majority of studies have failed to look at NPLs and bank profitability in developing

economies, most studies use the banks as case study (Partovi & Matousek, 2019). This type of research

design focuses on specific firm documenting particular characteristics of these firm.

Taken these together, the current study departs from the previous studies. The study concentrates on

bank specific and macroeconomic effect on bank profitability, and macroeconomic impact on non-

performing loans. Remarkably, the study focuses on Ghana, one of the developing economies in sub-

Saharan Africa. The study uses different banks rather than a single bank to understand how NPLs

emanating from bank specific and macro-economic factors affect bank profitability, and the impact of

macroeconomic factors on Non-Performing Loans.

24

Overall, the implications for the study will focus on theoretical implications and practical managerial

implications. In terms of theory, the study will contribute to the arguments of management styles, and

how these theories enable banks and other industries manage their portfolios for profitability and

performance. More importantly, the theoretical implications will consolidate the different perspectives

on managerial styles. With respect to practical managerial implications, the study will redirect the

minds of managers especially those in the banking sector to know how loans are disburse to customers

and the strategies to collect the monies. Knowing this, banks will be able to increase profitability and

perform better.

25

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Introduction

This chapter outlines the methodology used in this research. The chapter focuses on the research design

model specification, estimation strategy, sources of data, variables description, unit root test, and test

for co-integration.

3.2 Research Design

The researcher used the quantitative method of study. The study gleaned informational data from

secondary sources. Consequently, explanatory study design was used to understand the linkage

between NPLs and bank profitability in Ghana. Explanatory study design, according to Yin (2014) &

Merriam (2009), focuses on the scope and process of method used for the research by placing emphasis

on the nature of the research as being empirical. It is the study of complexity of a single case and

understanding its activity within an important scope. Despite its apparent flaws, the explanatory

design provides detailed descriptions of specific cases.

3.3 Sources of Data

This investigation adopted optional sources of information for its analysis. The time frame ranges from

2009 to 2019. Using financial statement and annual reports of the banks, the researcher obtained data

from Bank of Ghana, Ghana Stock Exchange, and World Bank. The selection criteria focused on banks

that are listed on Ghana Stock Exchange (GSE) and are mandated by the Central Bank of Ghana to

periodically publish their financial statement. They include Access Bank, Agricultural Development

Bank, Ecobank, Ghana Commercial Bank, Société General (Ghana), Standard Chartered Bank

(Ghana), and Cal Bank.

26

3.4 Study Population

The study used all the twenty-three (23) banks currently operating as universal banks in Ghana [Bank

of Ghana (BoG), 2019]. Regarding the sample size, the study agreed with Israel (2009) that when the

population required for the study is less than two hundred (200), the population should be taken to be

equal to the sample size.

3.5 Theoretical Framework

The main theoretical base for the study is Information Asymmetry Theory. This theory argues that

imbalance of information has impact on decisions made by parties in a transactional relationship

(Mishkin, 1992). Lenders granting credit facilities to borrowers encounter uncertainties regarding

repayment of credit because they are not in a better position to determine the true attributes of the

borrower (Ariccia, 1998). In a situation where lenders cannot differentiate good from iniquitous

borrowers, lenders may charge all borrowers the same normal interest rate (Evans et al., 2000; Catro,

2013). However, if this rate of interest is too high which the good borrowers cannot afford, they will

be compelled to leave the borrowing market (Barron et al., 2008). Therefore, adverse selection will

lead to replacement of high-quality borrowers with low-quality borrowers and cause a deterioration in

quality of loans (Bofondi et al., 2011; Makri et al., 2014).

3.6 Model Specification

Panel data was used in assessing the impact of NPLs on bank profitability of the seven (7) selected

banks out of twenty-three (23) banks operating as universal banks in Ghana. According to Baltagi

(2001) cited in Gujarati (2004), employing panel data analysis results in more variability and less

collinearity between or among the variables under study and offers more freedom and efficiency.

27

It is also suitable for the study of dynamics of change. In this study, seven (7) out of twenty-three (23)

different universal banks operating in Ghana are used which justifies the need to use panel regression.

Towing the line of Ezike et al. (2013) & Aymen (2013), the regression model is specified using

Ordinary Least Squares (OLS) as:

𝑌𝑖𝑡 = 𝑓(𝑋𝑖𝑡 , 𝑍𝑖𝑡 … ) + 𝜇𝑖𝑡

𝑌𝑖𝑡 represents the dependent variables NPL, and ROA and ROE which are used for measuring

profitability of banks at i and at time period t. 𝑋𝑖𝑡 , 𝑍𝑖𝑡 represent a set of independent variables bank-

specific and country-specific respectively of banks at i and also at time t. 𝜇𝑖𝑡 is the error term

incorporated to represent other factors that have been excluded in the model. Precisely, the model

hypothesized independent variables as Non-Performing Loans (NPL), Annual Inflation (AI), Gross

Domestic Product (GDP), and Interest Rate (IR). Operationally, the equation is simplified as:

𝑌𝑖𝑡 = 𝑓(𝑁𝑃𝐿𝑖𝑡, 𝐴𝐼𝑖𝑡, 𝐺𝐷𝑃𝑖𝑡 , 𝐼𝑅𝑖𝑡 )

𝑌𝑖𝑡 = 𝛾1 + 𝛾2𝑁𝑃𝐿𝑖𝑡 + 𝛾3𝐴𝐼𝑖𝑡 + 𝛾4𝐺𝐷𝑃𝑖𝑡 + 𝛾5𝐼𝑅𝑖𝑡 + 𝜇𝑖𝑡

Where 𝛾1 indicates a constant term and 𝛾2,𝛾3, 𝛾4, 𝛾5 indicates coefficients of the explanatory variable

measured their effect on profitability (ROA and ROE). 𝜏𝑡 captured the time effects where as 𝜇𝑖𝑡 is an

indicator of error term which was presumed not to correlate with the explanatory variables.

3.6.1 Estimation Strategy

Diagnostic Test Procedure

The Classical Linear Regression Model (CLRM) was used to estimate the values of a dependent

variables 𝑦 expressed as a function of independent variables 𝑋and 𝑧.

Following the CLRM equation, the equation for the present study was expressed in the equation below.

𝑦 = 𝛽0 + 𝛽1𝑋 + 𝜀

The error term 𝜀 was presumed to be independently identical and distributed with zero mean and

constant variance expectation.

28

3.7 Variable Definition & Measurement, and Source

The researcher used of secondary information obtained from the financial statements of seven out of

twenty-three (23) universal banks. The variables used for assessment are Return on Assets (ROA),

Return on Equity (ROE), Non-Performing Loans (NPL), Annual Inflation (AI), Gross Domestic

Product (GDP), and Interest Rate (IR). Data used for the research and its analysis span from the year

2009 to 2019. The time period was chosen based on the availability and accessibility of information

needed.

29

CHAPTER FOUR

RESULTS AND DISCUSSIONS

4.1 Introduction

This section presents analysis of the results in relation to the models specified in chapter three. It also

accounts for the research problem, stated objectives, and hypothesis established in the first and second

chapters. The chapter begins with presentation of descriptive and correlation analysis of variables of

interest and concludes on the results and interpretation of the analysis.

4.2 Descriptive Analysis

This section describes an overview of observations made from the study. The maximum and minimum

values, mean as well as the standard deviations of the variables have been described in this section.

Dependent and independent variables were selected and statistically observed. The table below gives

a brief summary of results of the variables used in the study.

Table 4.1: Summary of Descriptive statistics

Variables Minimum Maximum Mean Std. Dev

GDP (%) 2.178 14.047 6.6186 3.236

INF (%) 7.90 18.00 12.4236 3.8695

IR 12.50 26.00 18.2273 4.305

NPL (%) 1.78 49.29. 15.2003 11.280

ROA (%) 0.2 57.1 4.019 6.321

ROE (%) 0.8 50.0 22.586 11.240

Sample size (n) = 7

The summary of the descriptive analysis is depicted in Table 4.1. The variables under consideration

are analysed in the table. From the table, the mean score for NPL is 15.2003% indicating that the banks

have lent inexorably leading to accumulation of more loans that do not perform. The usual rule of BoG

says that banks need to maintain a minimum level of NPL which is 5% or low of the total loans. The

maximum and value of this ratio is 49.29%. The standard deviation of 11.280% indicating that there

30

is much difference of credit risk exposure among the banks. The higher NPL is a clear indication of

poor quality of loans as a result of numerous loan default.

ROA varies from 0.2% to 57.1%. The low ROA of the banks when contrasted with the business normal

shows inefficient utilization of banks resources. It shows a mean estimation of 4.019% and a deviation

of 6.321% from its mean worth. This shows that commercial banks in Ghana procure 4.019% profit

for midpoints from their resource every year. Also, ROE goes from 0.8% to 50.0%. The little worth

shows that investors are not increasing a lot but rather partners are procuring little incentive from their

speculation. Moreover, ROE gives an indication that the banks have been expanding gets back from

the productive designation of assets and lessening costs. Be that as it may, the other explanation behind

this may likewise be the lessening in investor value which makes the ROE goes up. The decline might

be because of a lot of the obligation taken which falsely blows up the ROE. It also indicates a mean

estimation of 22.586% and deviation of 11.240% from its mean worth. This also implies that

commercial banks acquire an average of 22.596% profit for midpoints from the value every year.

The GDP development rate goes from 2.1782 to 14.047 with a mean value of 6.617% and a little

deviation. The minimum value of GDP is broadly acknowledged due to the consequence of the

barricade and the higher is because of the bounce back from the bar. The Inflation changes with a limit

of 18.00% is likewise because of the barricade and the low swelling is an obvious indication of steady

market development. The minimum value of 12.50% in respect of financing cost term of credits also

implies that it is an exceptionally serious market demonstrating that loaning rate is similarly spread

inside the financial business, and the most extreme i.e., 26.00% showcases that the market isn't thought

if all things are considered.

31

4.3 Correlational Analysis

the commonest used and reported statistical methods is correlation analysis. It summarises scientific

research data. It is frequently used in determining the existence of relationship between and among

two or more dissimilar variables. The correlation analysis indicates the strengths and weaknesses of

the relations that exist between or among the variables (Taylor, 1990).

Table 4.2: Correlation Analysis of the variables

Variables ROA ROE NPL IR GDP AI

ROA 1

ROE .477** 1

NPL -.089 -.339** 1

IR -.043 .022 .328** 1

GDP -.004 -.070 -.194* -.812** 1

AI .068 .124 .081 .753** -.746** 1

Note: **. Indicates Correlation significance level at 0.01 (1-tailed) and *. indicates significance level

at 0.05 (1-tailed).

Table 2 displays the aftereffect of the correlational investigation test. Initially, NPL is fundamentally

and emphatically associated with ROE. An opposite connection of NPL with absolute resources places

that the introduction of non-performing advances drops as an ever-increasing number of credits are

given out. This is on the grounds that the dissolvability of the account holders crumbled which rises

the NPLs. This may also bring about an opportunity for the indebted individuals to get at higher loaning

rates which adds to the expansion of the premium pay and cause an upsurge in ROE. Curiously, the

relationship between NPL and ROA is negative meaning that increase in NPL decreases ROA. GDP

negatively relates to ROA and ROE meaning that GDP growth does not affect ROA and ROE. Annual

inflation positively correlates with ROA and ROE which means that increases in inflation help

commercial banks to increase their interest rate.

32

4.4 Trend Analysis of Non-Performing Loans (NPL) in Ghana

The analysis of the trend of NPL in Figure 1 revealed that NPL between 2010 and 2011 was very high

(50%). Again, between the periods of 2014 and 2015, NPL was high (48%). NPL in 2012 was very

low as compared to the other years. The minimum and the maximum values for the periods of NPL

are 1.78% and 49.29% respectively. In support of this revelation, World Bank (2018) indicated that

Ghana’s banking sector NPL has been increasing from 2015. According to the World Bank, the

average NPL ratio was 14.94%. This follows a minimum value of 7.68 percent in 2016 and maximum

of 21.59 percent in 2017. However, in 2018 the non-performing loans decreased to 18.19. Comparing

it with the world average of 6.78, Ghana had a slight increase in non-performing loans.

Figure 4.1: Trend Analysis of NPL

33

4.5 Multiple Regression Analysis

4.5.1 Analysis of factors affecting NPL

GDP, AI and IR were used as micro-economic factors to show their relationship with NPL.

Regarding the relationship between the dependent variable and independent variables, the figures of

Variance Inflation Factor (VIF) for the variables ranges from 2.636 to 3.430. This indicates that there

is no multicollinearity among the variables.

Table 4.2a: NPL Multivariate Regression Analysis

Variables B t P

value

VIF

Constant

GDP

-4.768

.247

.363

.717

3.347

AI -1.055 -2.095 .040 2.636

IR 1.725 3.340 .001 3.430

Significance

level

0.05

Adjusted R2 .139

F 5.088

D/W value 0.608

𝑵𝑷𝑳𝒊𝒕 = 𝜸𝟏 + 𝜸𝟐𝑮𝑫𝑷𝒊𝒕 + 𝜸𝟑𝑨𝑰 + 𝜸𝟒𝑰𝑹𝒊𝒕 + 𝝁𝒊𝒕

Sample size (n) = 7

The analysis suggests a positive (0.247) and insignificant relation (0.717) between GDP and NPL.

Therefore, the result does not support the hypothesis (H3a) that GDP has a significant positive

relationship with NPL. The positive sign means as GDP increases NPL also increases. This contradicts

general theory since a growing economy leads to a rise in income level of borrowers and enhance their

ability to repay their debt. However, many developing nations like Ghana are characterised by high

unemployment and disguised employment. In such circumstance, the unemployed are motivated by

34

GDP growth to borrow from banks to start businesses especially collateral loans. Again, businessmen

also take inspiration from GPD growth to borrow more to expand their businesses to enjoy normal or

super-normal profit. Any adverse change of the economy may affect their business negatively and

hence their inability to settle their debt. The insignificant relation of GDP and NPL is in line with the

finding of Farhan et al. (2012) who found insignificant relation between NPL and GDP.

With regards to inflation, the variable has a negative coefficient (-1.055) and statistical significance of

(0.040). The inverse and significant relationship with NPL and Inflation is consistent with the findings

of (Nkusu, 2011) and (Badar and Javid, 2013). Higher level of inflation can lead to easy servicing of

debt either by reducing the real value of outstanding debt or because it’s connected with low

unemployment as suggested by Phillip’s curve (Nkusu, 2011). Likewise, in an attempt to reduce high

level of inflation in a country, Central Banks usually increase their policy rate which compels

commercial banks to increase their interest rate and thereby preventing individuals and firms from

borrowing. In the same way, the banks also become selective of high-quality borrowers in a period of

high inflation and thereby decreasing the volumes of loans giving to customers (Al-Samad and Ahmad,

2009).

Regarding Interest Rate, the results reveal a positive (1.725) and significant (0.001) relation with NPL.

This implies that the banks that charge higher interest rate on loans are likely to encounter higher NPLs

due to the inability of borrowers to settle their high interest rate loans. The result is also consistent with

the hypothesis (H3c) which states that interest rate significantly and positively affects NPL. The result

also ties with observational discoveries of Louzis et al (2012) and Bofondi (2011) who found out in

their study that Interest Rate positively and significantly affects NPLs.

35

4.5.2 Analysis of factors influencing ROA

The study uses NPL, GDP, Interest rate, and inflation to demonstrate the influence of profitability. As

to the link between the dependent variable (ROA) and other independent variables, the figures of

Variance Inflation Factor (VIF) for the variables are from 1.209 to 3.954. The general threshold for

autocorrelation using the Durbin Watson Test is 0.0 to 4.0. Therefore, the VIF indicates that there is

no multicollinearity among the variable.

Table 4.2b: ROA Multivariate Regression Analysis

Variables B t P

value

VIF

Constant

NPL

GDP

5.904

-.024

-.044

-.335

-.105

.738

.917

1.209

3.353

AI .342 1.076 .285 2.794

IR -.300 -.885 .379 3.954

Significance

level

0.05

Adjusted R2 -0.027

F 0.499

D/W value 1.851

𝑹𝑶𝑨𝒊𝒕 = 𝜸𝟏 + 𝜸𝟐𝑵𝑷𝑳𝒊𝒕 +𝜸𝟑𝑮𝑫𝑷𝒊𝒕 + 𝜸𝟒𝑨𝑰 + 𝜸𝟓𝑰𝑹𝒊𝒕 + 𝝁𝒊𝒕

Sample size (n) = 7

NPL has no statistical impact on ROA even though it represents is negative sign. This negative

insignificant relation deviates from the assertion in H1a which argues that NPL has a positive and

significant relation with ROA. The negative sign implies that an increase in NPL results in a decrease

in ROA. The insignificant inverse relation is not consistent with the discoveries of (Ozurumba 2016;

Kadioglu et al., 2017; and Bhattarai 2014). Their discoveries indicate that there is a negative and a

36

critical connection between NPL and ROA. The insignificant connection could probably be as a result

of credit risk exposure and the inability of the banks to take risk in the relation to credit grant thereby

focusing much on other non-interest income. As suggested by moral hazard theory, banks are exposed

to credit risk and they undertake risk because of some benefits they stand to gain. However, if there

are no associated benefits, they are reluctant to take risk.

The results give an indication that there is a negative coefficient of GDP of -.044 and a p value of

0.917. This implies that GDP negatively and insignificantly affect ROA. Therefore, the results do not

support the argument in H2a which states that GDP significantly and positively relates with ROA. The

negative coefficient means that a unit change in GDP, results in -4.4% reduction in ROA.

With regards to inflation, the finding also reveals a positive and insignificant relation with ROA. This

means that a unit change (increase) in inflation result in a 34.2% increase in ROA. The finding also

does not validate the assertion that a positive and significant relationship exists between inflation and

ROA in respect of H2b.

Interest rate and ROA are negatively and insignificantly related. This is also not consistent with H2c.

The coefficient of -0.300 implies that a per unit rise of interest rate results in a 30% decrease in ROA.

4.5.3 Influential factors of ROE

NPL, GDP, Interest Rate and Inflation were used to demonstrate their influence on shareholders’

benefit. As regard the connections between the dependent variable (ROE) and independent variables,

the figures of Variance Inflation Factor (VIF) for the variables ranges from 1.209 to 3.954 The

threshold for autocorrelation is 0.00 to 4.00 according to Durbin Watson Test. Therefore, VIF indicates

that there is no multicollinearity among the variables.

37

Table 4.2c: ROE Multivariate Regression Analysis

Variables B t P value VIF

Constant

NPL

GDP

23.648

-.365

-.130

-.335

-.105

.349

.187

1.209

3.353

AI .281 1.076 .530 2.794

IR .102 -.885 .179 3.954

Significance

level

0.05

Adjusted R2 0.092

F 2.921

D/W value 0.995

𝑹𝑶𝑬𝒊𝒕 = 𝜸𝟏 + 𝜸𝟐𝑵𝑷𝑳𝒊𝒕 +𝜸𝟑𝑮𝑫𝑷𝒊𝒕 + 𝜸𝟒𝑨𝑰 + 𝜸𝟓𝑰𝑹𝒊𝒕 + 𝝁𝒊𝒕

Sample size (n) = 7

The findings reveal an inverse and insignificant connection between NPL and ROE. The insignificant

and negative relation is not consistent with H1b which suggests that NPL has a positive and significant

relation with ROE. The finding is also not consistent with the findings of (Ozurumba et al., 2016;

Kadioglu et al., 2017; and Bhattarai et al., 2014) who found that there is a significant and inverse

association between NPL and ROE. The insignificant connection may occur as result of the banks

focus on non- interest income due to credit risk exposure.

Similarly, GDP displays a negative coefficient of -.130 and an insignificant connection with ROE.

This also invalidates the assertion in H2d which indicates that GDP positively and significantly relates

with ROE.

Inflation is positive and insignificantly related with ROE and does not support the argument in H2e.

The positive coefficient of inflation indicates that a unit rise in inflation results in 28.1% increase in

ROE.

38

Interest Rate also shows a positive and insignificant relation with ROE and does not support H2f

which suggest that Interest rate significantly and positively affect ROE.

39

CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSIONS AND RECOMMENDATIONS

5.1 Introduction

This section contains a summary of findings, conclusions and recommendations. Section 5.2 explains

summary of findings, documenting the findings as gleaned from each of the slated objectives. Section

5.3 highlights conclusions in relation to the findings. Section 5.4 argues on the policy implications and

recommendations as a result of the findings and conclusions drawn from the study. Section 5.5 suggests

what other future researchers can do due to the challenges encountered in conducting this research.

5.2 Summary

The main purpose of the research was to examine the effect of bank-specific and macro-economic

factors on Bank profitability in Ghana; the effect of macro-economic factors on NPLs in banking

sector of Ghana; to explore the trend of the NPLs in the banking sector of Ghana.

The researcher adopted the use of two different measures of profitability namely Return on Assets

(ROA) and Return on Equity (ROE). Non-Performing Loans (NPL), GDP growth rate, Inflation rates

and Interest Rate (IR) were used as independent variables for dependent variables of profitability (ROA

& ROE). These independent variables were separated into bank-specific and macro-economic factors

to determine their impact on bank profitability. Further, the research also looked at the macro-economic

factors and their effect on NPLs.

In an anxious bid to achieve the objectives, the study answered the following questions: What bank-

specific variables and macro-economic variables affect Bank Profitability? What macro-economic

factors affect NPLs? What is the trend of NPLs in Ghana? Information used for analysis were obtained

from the financial statement of 7 commercial banks in Ghana. The data spanned from the period 2009

to 2019.

40

5.3 Conclusion

The study concludes that Interest rate has a significant positive relation with NPL. This implies that

the Banks that charge higher interest rate on loans limit the ability of borrowers to settle their high

interest rate loans.

Annual inflation has a significant and inverse relation with NPL. The inverse and relationship with

NPL and Inflation may be attributable to the fact that Central Banks usually increase their policy rate

to reduce high level of inflation. This compels the commercial banks to increase interest rate and

thereby preventing individuals and firms from borrowing. In the same way, the banks also become

selective of high-quality borrowers in a period of high inflation and thereby decreasing the volumes of

loans giving to customers (Al-Samad and Ahmad, 2009). In addition, a rise in inflation can also lead

to easy servicing of debt by either reducing the real value of outstanding debt or because it’s connected

with low unemployment as suggested by Phillip’s curve (Nkusu, 2011).

In line with Bank Profitability, the researcher found no significant relationship with NPL and Bank

Profitability (ROA & ROE). This may be as a result of the banks focus on other non-interest income

due to credit risk exposure. As suggested by moral hazard theory, banks undertake risk due to benefits

they stand to gain. The nature of credit risk may serve as a disincentive factor to credit grant. Likewise,

the study also found no significant relationship with GDP, IR, and AI with Bank Profitability (ROA &

ROE). Bank internal factors like asset size largely affect profitability than macro-economic factors as

discovered in the study of Panta (2018).

The Analysis of the data also revealed that the NPLs of the studied banks have been enhancing and

showing an increase in trend from 2010 to 2015. However, it slightly dropped from 21.59% in 2017 to

18.19% in 2018. The ratio of NPLs is gradually increasing in the past years. Therefore, the banks need

to keep a good loan-to-deposit ratio as required by the Central Bank of Ghana.

41

5.4 Policy Implications and Recommendations

Based on the outcome of the research, the following policy recommendations are worthy of notice:

Managerial Implications

Precisely, there is an indication that inefficiencies in performance measures can lead to future problem

in relation to credit grants. The study therefore recommends that the regulatory bodies should use these

measures in order to detect banks with potential rise in NPLs. Besides, oversight authorities must place

superior importance on managing risky activities engaged by the banks to prevent imminent instability

of finances.

The study further recommends that there must be a strong working capital policy which will prevent

or reduce working capital investment to ensure a significant growth in current liability to total asset

ratio. This is can be ensured if management ultimate goal is to increase profitability. However, strict

working capital policies also poses some risks should be pursued cautiously.

More so, the econometric framework demonstrated in the study can be used for testing and forecasting.

Alternative scenarios in macro-economic analysis can be used in to assess the position of NPLs to

determine whether they are likely to exceed the threshold indication of financial stability and to assess

the capacity of loan-loss provisions of banks. Besides, analogous exercises can be performed on a bank-

implicit factors to assess future problems that may occur in banks.

Theoretical Implications

Bank managers should take keen interest in managing credit risk and operational costs. Again, lenders

should be able to differentiate good from iniquitous borrowers so that normal interest rate are not

applied to all borrowers. (information asymmetry theory).

Equally, undercapitalized banks should resort to moral hazard motivations by underwriting high-risk

loans at high cost of credit. More importantly, bank managers should also be motivated by undertaking

risky activities due to larger proportion of upside risk such as profits (moral hazard theory).

42

5.5 Limitations of the Study

The research was limited to seven (7) banks out of twenty-three commercial banks operating in the

country. The rejection of most of the banks was due to non-availability of up-to-date information.

Again, the research was also limited to few variables especially bank-specific variables. They were

eliminated due to inconsistencies of information regarding those variables which resulted in their

elimination. The study also focused on commercial banks in Ghana and did not generalised the findings

to cover other nations.

43

REFERENCES

Aladwan, M. S "The impact of bank size on profitability: an empirical study on listed Jordanian

commercial banks". European Scientific Journal, 11(34), 217-236, 2015.

Alexandri, M. B., & Santoso, T. I. "Non-performing loan: impact of Internal and external factor

evidence in Indonesia". International Journal of Humanities and Social Science Invention, 4(1),

87-91, 2015.

Al-Samad, M.O. and N.H. Ahmad, 2009. Factors affecting banks credit risk: evidence from Jordan.

Malaysia: Collage of Business, University Utara Malaysia

Angbazo, L. "Commercial bank net interest margins, default risk, interest-rate risk, and off-balance

sheet banking". Journal of Banking & Finance, 55-87,1997.

Badar, M and A.Y. Javid, (2013). Impact of microeconomic forces on non-performing loans: An

empirical study of commercial banks in Pakistan. Wseas Transactions on Businesses and

Economics, 10(1): 40-48.

Bank of Ghana, 2017. Banking sector summary. Accra. Bank of Ghana Publication, 2.1/2017

Barron, J.M., and Staten, M.E., (2008). The Emergence of Captive Finance Companies and Risk

Segmentation in Loan Markets: Theory and Evidence. Journal of Money Credit and Banking.

40, 173-192.

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

and Banking, 27(2), 432-456, 1995.

Bhattarai, S. "Determinants of Non-Performing Loan in Nepalese Commercial Banks." Economic

Journal of Development Issues, 19-20, 2015.

Bhattarai, Y. R. "Effect of credit risk on the performance of Nepalese commercial banks." Journal of

Management and Finance, 1(1), 41-64, 2014.

Bloem, A. M., & Gorter, C. N. "The treatment of non-performing loans in macroeconomic statistics".

IMF working paper WP/01/209, 2001.

44

Bofondi, M. and Ropele, T. (2011), “Macroeconomic determinants of bad loans: evidence from Italian

banks”, No. 89, Bank of Italy, Economic Research and International Relations Area.

Boudriga, A., Taktak, N. B., & Jellouli, S. "Bank Specific, Business and Institutional, Environment

Determinants of Nonperforming Loans: Evidence from MENA Countries". Economic

Research Forum, (pp. 1-28). Cairo., 2010.

Breuer, J. B. "Problem bank loans, conflicts of interest, and institutions". Journal of Financial Stability,

2, 266-285, 2006.

EL-Maude, J. G., Abdul-Rahman, A., & Ibrahim, M. "Determinants of non-performing loans in

Nigeria’s deposit money banks". Archives of Business Research, 5(1), 74-88, 2017.

Farhan, M., Satta A., Chauhry, A. H., & Khalil, F., 2012, ‘Economic Determinant of Non-Performing

Loans: Perception of Parkistani Bankers’, European Journal of Business and Management, vol

4. no. 19, pp.87-99.

Fofack, H. "Nonperforming loans in Sub-Sahara Africa: Causal analysis and macroeconomic

implication". World Bank Policy Research Working Paper 3769, 2005.

Greenidge, K., & Grosvenor, T. "Forecasting non-performing loans in Barbados." Journal of Business,

Finance and Economics in Emerging Economies, 5, 80-107, 2010.

Hou, Y., & Dickinson, D. "The Non-performing Loans: Some Bank-level Evidences" Research

conference on safety and efficiency of the financial system, 2007, pp 1-34, 2007.

Hu, J.-L., Li, Y., & Chiu, Y.-H. "Ownership and nonperforming loans: Evidence from Taiwan's

banks". The Developing Economies, XLII (3), 405-420, 2004.

Jameel, K. "Crucial factors of nonperforming loans evidence from Pakistani banking sector."

International Journal of Scientific & Engineering Research, 5(7), 704-710, 2014.

Jimenez, G., & Saurina, J. "Credit cycles, credit risk, and prudential regulation". Banco de España. pp

1-38, 2005.

45

Kaaya, A. and Pastory, D (2013). Credit Risk and Commercial Banks Performance in Tanzania: A

Panel Data Analysis.Research Journal of Finance and Accounting. ISSN 2222-1697 (Paper)

ISSN 2222-2847, Vol.4, No.16 2013

Kadioglu, E., Telceken, N., & Ocal, N. "Effect of the Asset Quality on the Profitability." International

Journal of Economics and Finance, 9(7), 60-68, 2017.

Khemraj, T., & Pasha, S. "The determinants of non-performing loans: an econometric case study of

Guyana". Munich Personal RePEc Archive, 13(7), 1-9, 2009.

Kithinji, A. (2010). Credit Risk Management and Profitability of Commercial Banks in Kenya,

Business School, University of Nairobi.

Klein, N. "Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic

Performance". IMF Working Paper WP/13/72 ,2013.

Koirala, P. "A Consolidated Effort for Marketing in Commercial Banks". Nepal Rastra Bank Economic

Review, 19, 2007.

Kosmidou, K. "Determinants of performance of Greek banks during the period of EU financial

integration." Managerial Finance, 34(3), 146-159, 2008.

Kothari, C. (2004). Research Methodology Methods and Techniques. New Delhi: New Age

International Publisher, 2004.

Kunt, D., & Detragiache, E. "The Determinants of Banking Crises in Developing and Developed

Countries." International Monetary Fund, 45(1), 81-90, 1998.

Laryea, E., Ntow-Gyamfi, M., & Alu, A. A. "Nonperforming loans and bank profitability: evidence

from an emerging market." African Journal of Economic and Management Studies, 7 (4), 462-

481, 2016.

Li, Y. The Asian financial crisis and nonperforming loans: evidence from commercial bank In Taiwan.

International journal of management, 20(1), 69-73, 2003.

Lis, S. F., Pagés, J. M., & Saurina, J. "Credit Growth, Problem Loans and Credit Risk Provisioning".

Bank of Spain, Studies Service, Working Document no. 0018., 2000.

46

Louzis, D.P., Vouldis, A.T. and Metaxas, V.L. (2012), “Macroeconomic and bank-specific

determinants of non-performing loans in Greece: a comparative study of mortgage, business

and consumer loan portfolios”, Journal of Banking & Finance, Vol. 36 No. 4, pp. 1012-1027.

McNaughton, D., Dietz, C. T., Carlson, D. G., Falletti, P., & Ikramullah, K. Banking institutions in

developing markets: Building strong management and responding to change (Vol. 1).

Washington, DC: The WorldBank, 1997.

Nkusu, M. (2011), “Nonperforming loans and macro financial vulnerabilities in advanced economies”,

Working Papers No. 1-27, IMF.

Ozurumba, B. A. "Impact of Non-Performing Loans on the Performance of Selected Commercial

Banks in Nigeria." Research Journal of Finance and Accounting, 7(16), 95-109, 2016.

Panta, B. (2018). Non-Performing Loans and Bank Profitability: Study of Joint Venture Banks in

Nepal. International Journal of Sciences: Basic and Applied Research (IJSBAR), (2018)

Volume 42, No 1, pp 151-16

Radivojevic, N., & Jovovic, J. "Examining of determinants of non-performing loan." Prague Economic

Papers, 26(3), 300–316, 2017.

Ranjan, R., & Dhal, S. C. "Non-performing loans and terms of credit of public sector banks in India:

An empirical assessment." Reserve Bank of India Occasional Papers, 24(3), 81-121, 2003.

Saba, I., Kouser, R., & Azeem, M. "Determinants of non-performing loans: case of US banking sector."

The Romanian Economic Journal, 15(44), 141-152, 2012.

Salas, V., & Saurina, J. "Credit risk in two institutional regimes: Spanish commercial and savings

banks". Journal of Financial Services Research, 22(3), 203-224, 2002.

Sapkota, C. "Nepalese banking crisis explained." Journal of Institute of Chartered Accounts of Nepal,

13(4), 16-20, 2011.

Shingjergji, A."The Impact of Bank Specific Variables on the Non-Performing Loans Ratio in the

Albanian Banking System." Research Journal of Finance and Accounting, 4(7), 148-153, 2013.

47

Staikouras, C., & Wood, G. E. (2004). "The determinants of European Bank profitability."

International Journal of Economics and Business Research, 3(6), 57-68, 2004.

Taylor, R. "Interpretation of the correlation coefficient: a basic review." Journal of Diagnostic Medical

Sonography, 6, 35-39,1990.

Ugoani, J. N. "Non-performing loans portfolio and its effect on bank profitability in Nigeria."

Independent Journal of management and production, 7(2), 303-319, 2016.

Vatansever, M., & Hepşen, A. "Determining Impacts on Non-Performing Loan Ratio in Turkey." Journal

of Finance and Investment Analysis, 2(4), 119-129, 2013.

Zaini, M., Karim, A., Chan, S., and Hassan, S. (2010). Bank Efficiency and Non-Performing Loans:

Evidence from Malaysia and Singapore, Prague Economic Papers

48

APPENDIX

Table 6.1 Variable Description and Measurement

Variable Measurement Source

Return on

Asset

This indicates how much the banks earn from the

use of their assets.

The ROA was represented as the ratio of net

income (NI) to total assets (TA). It spelled out how

efficient the managers are in managing the funds

banks to generate income.

𝑅𝑂𝐴 =𝑁𝐼

𝑇𝑂𝑇𝐴𝐿 𝐴𝑆𝑆𝐸𝑇𝑆

Ara et al., (2009); Mills

& Amowine, (2013)

Return on

Equity

Return on equity measured each bank’s

profitability vis-a-vis equity. ROE revealed how

much profit each bank generates with the

shareholders’ funds. Return on Equity = Net

Income/Shareholder's Equity.

Sanusi, (2010).

Interest Rate This is the rate charge by banks on loans they grant

to their customers. It is also referred to as the cost

of borrowing.

Researcher’s own ideas

Gross

Domestic

Product

This was measured as the total market value of all

final goods and services produced within the

country over a given time period by factors of

production.

Landerfeld, Seskin &

Fraumeni (2008)

Inflation This was measured as the persistent rise in the

general price level of goods and services in the

Ghanaian economy, which is normally caused by

excess supply of money.

InvestorWords, (2015)

Non-

Performing

Loans

This occurs when, in a period of 90 days or

beyond, the borrower has not paid the agreed

principal plus the interest. Such loans are

considered to be non performing.

Bank of Ghana, (2018)