master’s thesis by osei-tutu bismark
TRANSCRIPT
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.
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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
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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
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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
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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
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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
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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
..……………………. …………….. ………….
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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!
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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).
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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
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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.
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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.
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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.
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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
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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.
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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
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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.
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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-
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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
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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
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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
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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
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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
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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)