Factors influencing

the profitability of

Domestic and Foreign

banks in Ghana

Master Thesis

MSc. Finance and International Business

By

Douglas Afoakwah Opoku-Agyemang

(201308805)

Supervisor,

Christian Schmaltz August, 2015

2015

i

Table of Contents

Contents Table of Contents .................................................................................................................................... i

Abstract ................................................................................................................................................. iv

List of Abbreviations .............................................................................................................................. v

List of Tables .......................................................................................................................................... vi

List of Equations .................................................................................................................................... vi

Chapter 1: Introduction .......................................................................................................................... 1

1.0. Introduction ............................................................................................................................ 1

1.1. Problem Statement ................................................................................................................ 1

1.2. Research Questions ................................................................................................................ 2

1.3. Scope of the Research ............................................................................................................ 2

1.4. Structure of the thesis ............................................................................................................ 3

Chapter 2: Literature Review ................................................................................................................. 4

2.0. Introduction ............................................................................................................................ 4

2.1. Theoretical Background: ........................................................................................................ 4

2.1.1. The Existence and Role of Banks.................................................................................... 4

2.2. Bank Profitability Hypothesis ................................................................................................ 5

2.2.1. Structure Conduct Performance (SCP) hypothesis ........................................................ 6

2.2.2. Efficient – Structure Hypothesis (ESH): .......................................................................... 6

2.2.3. Expense – Preference Hypothesis (ESH) ........................................................................ 7

2.3. Studies on Determinants of Bank Profitability in Ghana ...................................................... 7

2.4. Broad Studies on Determinants of Bank Profitability: .......................................................... 8

2.4.1. Internal (Bank-Specific) Determinants .......................................................................... 9

2.4.2. External Determinants: ................................................................................................ 13

2.5. Measures of Profitability (Dependent Variables) ............................................................... 16

2.5.1. Accounting Measures of Profitability .......................................................................... 16

2.5.2. Economic measures of Profitability ............................................................................. 17

2.6. Studies on Foreign versus Domestic Banks ......................................................................... 18

Chapter 3: Background of the Ghanaian Banking Industry ................................................................ 19

3.0. Introduction: ......................................................................................................................... 19

3.1. Economic Overview .............................................................................................................. 19

3.2. Overview of the Banking Industry ....................................................................................... 20

ii

3.2.1. Pre-Financial Sector Adjustment Period: ..................................................................... 21

3.2.2. Post Financial Sector Structural Adjustment Program (FINSAP) ................................ 22

3.2.3. Current Developments in the Banking System (2000 – 2015) .................................... 23

3.3. Contemporary Banking in Ghana - Universal Banking Business License (UBBL):............... 25

3.4. Foreign and Domestic Banks in Ghana ..................................................................................... 26

Chapter 4: Methodology ...................................................................................................................... 28

4.0. Introduction .......................................................................................................................... 28

4.1. Data: ...................................................................................................................................... 28

4.1.1. Data Source: ................................................................................................................. 28

4.1.2. Data Sampling Criteria: ................................................................................................ 28

4.1.3. Data Filtering: ............................................................................................................... 29

4.2. Hypothesis and Variables Justification: ............................................................................... 30

4.2.1. Justification of Chosen Variables and Hypothesis....................................................... 30

4.3. Specification of Econometric Model .................................................................................... 36

Chapter 5: Analysis and Discussion ..................................................................................................... 39

5.0. Introduction .......................................................................................................................... 39

5.1. Descriptive Statistics ............................................................................................................ 39

5.2. Correlation between Variables: ........................................................................................... 41

5.3. Unit Root Test ....................................................................................................................... 43

5.4. Hausman Test (Fixed Effects/Random Effects) ................................................................... 43

5.5. Test for Heteroscedasticity .................................................................................................. 43

5.6. Test for Auto-correlation ..................................................................................................... 44

5.7. Empirical Result .................................................................................................................... 44

5.8. Robustness Test .................................................................................................................... 51

5.8.1. The Generalized Methods of Moments: ...................................................................... 51

Chapter 6: Summary and Conclusions ................................................................................................. 55

6.0. Introduction: ......................................................................................................................... 55

6.1. Summary: .............................................................................................................................. 55

6.2. Recommendations ............................................................................................................... 56

6.3. Limitation of the Study: ....................................................................................................... 57

References: ........................................................................................................................................... 59

Appendices: .......................................................................................................................................... 68

Appendix 1: ....................................................................................................................................... 68

A. List of Commercial Banks in Ghana as at July 2015 ............................................................. 68

iii

B. Share of Industry Operating Assets ..................................................................................... 69

C. Share of Industry Deposits ................................................................................................... 70

Appendix 2: Unit Root Test Results ................................................................................................. 71

A. Dependent Variable - ROAA ................................................................................................ 71

B. Dependent Variable – ROAE ................................................................................................ 71

Appendix 3: Hausman Test .............................................................................................................. 72

A. Dependent Variable - ROAA ................................................................................................ 72

B. Dependent Variable – ROAE ................................................................................................ 72

Appendix 4: Stata Commands for ROAA Regression ...................................................................... 73

Appendix 5: Stata Commands for ROAE Regression ....................................................................... 73

Appendix 6: Stata Command Codes for Robustness Tests ............................................................. 73

A. GMM Estimation .................................................................................................................. 73

B. Fixed Effect Model ................................................................................................................ 74

C. Pooled OLS ............................................................................................................................ 74

D. Random Effects Model on Crises Sample ............................................................................ 74

E. Random Effects Model on Non-Crisis Sample ..................................................................... 74

Appendix 7: Full Data of Observations ............................................................................................ 75

iv

Abstract

This thesis examines the factors influencing the profitability of domestic and foreign banks in

the Ghanaian banking industry using a panel data of 27 banks from 2003 to 2013. Bank

profitability is measured by Return on Average Assets and Return on Average Equity. The

variables affecting bank profitability were categorised into Bank-specific variables, industry-

specific and macroeconomic variables. The bank-specific variables were operating efficiency,

credit risk, liquidity, bank size, bank growth, funding cost, years of experience and bank

ownership. The external variables included Bank Concentration and macro-economic

variables, which are Real GDP, Inflation and Money Supply.

The study uses the Generalised Least Square technique (GLS) to estimate random effect

regression model and adopt the Generalised Methods of Moments (GMM) as robustness check.

The findings revealed that foreign banks performed better than domestic banks within the

period but the difference is not substantial. Moreover, foreign banks are more capitalised than

domestic banks.

The study found that bank-specific variables are significant in explaining profitability but the

external variables are not significant apart from Money Supply. Operating efficiency, credit

risk, bank capitalisation and funding costs were the main variables that significantly influenced

bank profitability. Profitability in the industry was also affected negatively by the global

financial crisis of 2007.

Moreover, it was found that factors such as capitalisation, bank size and financial crisis were

significant for foreign banks while bank ownership and deposit growth had significant impact

on domestic banks.

v

List of Abbreviations

BOG Bank of Ghana

COI – Cost to Income

CPI – Consumer Price Index

ERP – Economic Recovery Program

ETA – Equity to Total Assets

FINSAP – Financial Sector Adjustment Program

GDP – Gross Domestic Product

GSE – Ghana Stock Exchange

HHI – Herfindahl-Hirschman Index

IBRD – International Bank for Reconstruction and Development

IDA – International Development Association

IETD – Interest Expense to Total Deposits

IMF – International Monetary Fund

LLPTL – Loan Loss Provision to Total Loans

LTA – Log of Total Assets

M2 – Money Supply

ROA – Return on Assets

ROAA – Return on Average Assets

ROAE – Return on Average Equity

ROE – Return on Equity

UBBL – Universal Banking Business License

YGD – Yearly Growth in Deposits

vi

List of Tables

Table 1: List of Banks used in the Study .............................................................................................. 29

Table 2: Selection of Determinants of Profitability .............................................................................. 35

Table 3: Descriptive Statistics ............................................................................................................... 39

Table 4: Correlation Matrix of All Variables .......................................................................................... 42

Table 5: Results for ROAA (Model 1) .................................................................................................... 45

Table 6: Results for ROAE (Model 2) ..................................................................................................... 47

Table 7: Results for Robustness Tests ................................................................................................... 53

List of Equations

Equation 1: General Model for Regression ........................................................................................... 37

Equation 2: General Model with ROAA as dependent Variable ........................................................... 37

Equation 3: General Model with ROAE as dependent Variable ........................................................... 37

Equation 4: Dynamic Model for GMM Estimation ................................................................................ 52

1

Chapter 1: Introduction

1.0. Introduction

The productivity of an economy today depends largely on the soundness of the financial

system. The health of the financial sector is critical in any development paradigm making the

role of banks even more critical. The banking system is therefore seen as an essential part of

an economy and represents one of the most important components of a nation’s capital. In their

basic roles, commercial banks serve as financial intermediaries between savers and investors,

they are the means through which the central bank (government) implements its monetary

policy, and they serve as the main medium of payment for businesses.

Since the start of the new millennium, growth and competition within the Ghanaian banking

sector has increased due to the liberalization of the financial system that has led to the rapid

flock of foreign banks into the Ghanaian industry.

Looking from the increasing levels of Foreign banks activities in Ghana vis-a-vis the domestic

banks and knowing the crucial role that commercial banks play within the Ghanaian economy,

the performance and profitability of these banks become of paramount interest to not only

stakeholders of these banks but of the economy as whole.

This study seeks to identify factors that affect profitability of foreign and domestic commercial

banks in Ghana. It aims to develop recommendations that could prove useful for management

decision making and policy objectives within the banking industry of Ghana.

1.1. Problem Statement

The competitive landscape of banking is transforming over the years. Factors such as

deregulation, technological changes, globalization of goods and services, financial markets and

financial crises are having direct impact on the global banking industry (Trujillo-Ponce, 2013).

These developments have affected the operations, efficiency, productivity, margins and

profitability of banks.

In their IMF working paper, Flamini et al (2009) reported that banks in Sub-Saharan Africa

make higher profits compared to those in other regions. Moreover, PWC (2014) reports that

despite increased minimum capital requirement for new entrants into the banking industry,

financial services providers in other countries are still highly interested in entering the

Ghanaian banking industry. This indicates the extent of opportunities and profitability in the

Ghanaian banking industries in spite of increasing levels of banking regulations and

requirements.

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In addition to the perceived profitability of Banks in Sub-Sahara Africa, the Ghanaian Banking

sector serves as an interesting context to study the factors influencing Bank profitability. There

have been major structural changes in the banking sector over the decades that have affected

the competitive nature of banking in Ghana. The structural phases are grouped into three

phases. The first phase started in 1987 where the legal and regulatory environment of the

banking industry was reviewed with the aim of making the banks more efficient and

economically viable. The second phase was rolled out in the 1990s that gave more supervisory

control to the Central Bank of Ghana and liberalized the industry. The third phase took off in

the 2000s and dealt with increased banking regulations and requirements, (Antwi-Asare and

Addison, 2000 and Owusu-Antwi, 2009).

The structural changes led to liberalization of the banking industry allowing the influx of

foreign banks especially at the start of the millennium. As a popular assertion, foreign banks

tend to perform better than local banks (Berger, 2007) especially in developing countries

(Demirguc-Kunt and Huizinga, 2000; Claessens et al, 2001, Berger et al, 2009) such as Ghana

(Figueria et al, 2006). Amidst the major structural reforms and influx of foreign banks in

Ghana, this study seeks to investigate the factors affecting profitability and accounting for

differences in profitability among foreign and domestic banks in Ghana.

1.2. Research Questions

In the light of the problem statement above, the thesis will seek to answer the following

questions:

Which category of profit drivers are key in explaining profitability of banks in Ghana?

Is it bank-specific factors, external factors, or both factors pulled together?

How significant are the drivers or variables identified in each of the categories above?

How do the factors affecting profitability differ among foreign and domestic banks in

Ghana?

1.3. Scope of the Research

The study covers the commercial banking sector of Ghana and identifies factors that affect their

profitability and performance of these banks. The period used in the study expands from 2003

to 2013. The research covers the total commercial banks available in the Ghanaian banking

industry as at December 2013.

3

1.4. Structure of the thesis

The study is presented in six (6) chapters. Chapter one (1) gives a general introduction and

motivation for the thesis. In doing this, the problem statement and research questions are

presented. The scope of the research is stated alongside the structure of the whole thesis.

Chapter two (2) covers the theoretical and empirical literature on the topic. It highlights the

main categories of factors affecting bank profitability and the theoretical and empirical

researches that have been conducted on them.

Chapter three (3), talks about the Ghanaian Banking industry; It starts with a brief background

of the macroeconomic environment. It gives a background to the banking industry and

highlights major developments and structural changes that have occurred in the industry.

Chapter four (4) illustrates the research approach and methodology. It covers data description,

hypothesis formulation and enumerates the methodology employed in testing the hypothesis.

The analyses of the data are outlined in Chapter five (5) along with the results and Robustness

checks.

Finally, Chapter six (6) concludes the research by providing a summary of the research and

important findings. Recommendations from the study and limitations in the study are

presented.

4

Chapter 2: Literature Review

2.0. Introduction

The Chapter reviews theoretical and empirical studies conducted in the field of bank

profitability. The sections cover theoretical concepts, bank profitability hypothesis, empirical

studies conducted in Ghana and studies undertaken beyond Ghana.

2.1. Theoretical Background:

2.1.1. The Existence and Role of Banks

Generally, income and spending needs between entities in an economy are mismatched. In an

aggregate economy, there exist entities (i.e. households and firms) that tend to have more funds

than they have to consume (savers) and others having fewer funds than they wish to invest

(borrowers). The mismatch in savings and consumption needs creates the traditional reason for

the existence of financial markets. Financial markets bridge this gap in two main ways: Direct

financing and indirect financing. For direct financing, the savers interact directly with

borrowers by purchasing financial assets issued by the borrower and consequently hold claim

against the borrower. (Mishkin, 2012).

The fundamental role of banks comes to play in the second form of financing i.e. indirect

financing. This is the mechanism whereby funds are channeled from savers to borrowers

through financial intermediaries. Banks serve as the main institution that perform the financial

intermediation role.

According to Santos (2001), there would be no need for financial intermediaries if not for

frictions that exist in the capital market. This presupposes that within a perfect capital market

of Modigliani and Miller (1958) where there is no information asymmetry, transaction costs,

taxes and monitoring costs; savers and borrowers would be able to interact effectively and fully

allocate resources with no costs. This rational informs the traditional theory of financial

intermediation. Santomero (1984) and Bhattacharya and Thakor (1993) argue that the

existence of transaction costs and information asymmetry justifies the existence of financial

intermediaries like banks.

Banks as financial intermediaries are able to overcome the issues faced by individuals in the

capital market through the following functions1: (1) The brokerage function: banks serve as a

1 The book of Saunders and Cornett (2003) and the work of Santomero (1984) served are the main sources for the review.

5

one-stop shop to match savers and lenders. As a result they able to significantly reduce

transaction and information costs for the lenders and savers. (2) Asset transformation: this

function has two views: asset diversification function and an asset evaluation function. With

asset diversification, the essential role of intermediaries is transforming large-denomination

financial assets into smaller units (Klein, 1973). With the ability to break down assets into

smaller units, they are able to fit provide tailored loans that meet each individuals demand, they

reduce transaction costs and diversify their operations for the benefits of their customers and

shareholders. The asset evaluation function means they act as evaluators of credit risk for

depositors (Santomero, 1984). Due to information asymmetry, individual face difficulty in

assessing the quality of signals in the financial environment. Banks then function as a filter to

evaluate these signals. (3) Delegated Monitoring: Not all savers have the time and resources to

monitor borrowers for default risks. As a result, they participate in indirect financing to

delegate this role to banks who have the requisite expertise and resources to play this role. (4)

Liquidity Transformation: This is the transformation process of using short-term debts like

deposits to finance long-term investments. Therefore, they are able to meet the high liquidity

needs of depositors while holding relatively illiquid and risky assets. They achieve this through

diversification of their portfolio risk unlike savers who hold relatively undiversified portfolio.

(5) Maturity Transformation: In the process of liquidity transformation, banks face the risk of

mismatch in their maturities because they are mostly made of long-term assets and short-term

liabilities. This exposes the banks to interest rate risk. Banks are able to manage this risk

through its access to various markets and expertise in risk management instruments.

2.2. Bank Profitability Hypothesis

Various profitability theories have evolved over the years to establish the existence or

inexistence of a link between market structure and profitability. The traditional microeconomic

concept founded in neoclassical economics popularly known as ‘theory of the firm’ states that

firms exist and make decisions to maximize profits. Based on the traditional assumption,

researchers have come out with a great deal of testable predictions on the behavior of profit

maximizing firms upon which the performance of industries can be derived. A countless

number of theories are modelled to explain performance and profitability of commercial banks,

however according to Rasiah (2010); the Structure Conduct Performance (SCP) has gained

prominence among them besides its criticisms. Other theories include Efficient-Structure

Hypothesis (ESH) and Expense-Preference (EPH) Hypothesis.

6

2.2.1. Structure Conduct Performance (SCP) hypothesis

Mason (1939) initially proposed the structure-conduct-performance (SCP) hypothesis and Bain

(1951) subsequently modified it. The SCP hypothesis is based on the proposition that: when a

few firms have a large percentage of market shares, this fosters collusion among firms in the

industry. The possibility of collusive behaviour increases when the market is concentrated in

the hands of a few firms, and the higher the market concentration ratio, the higher will be the

profitability performance of the firms (Gilbert, 1984). The SCP hypothesis assumes a positive

correlation between the degree of market share concentration and the firm’s performance and

due to monopolistic or collusive reasons, irrespective of efficiency, the firms in a concentrated

market will make more profit than firms in a less concentrated market (Lloyad-Williams et al,

1994).

The SCP relationship in the banking sector are well explored in the literature and a number of

empirical studies provide support in favour of the SCP hypothesis. Noticeable studies that

supported the SCP hypothesis included: Rose and Fraser (1976), Gilbert (1984) and Lloyad-

Williams et al (1994). Lloyad-Williams et al (1994) tested in the light of the Spanish banking

industry and found evidence of support for the SCP hypothesis. Gilbert (1984) also reported

that as many as 32 out of 44 studies reviewed found evidence to support that market

concentration significantly and positively affected bank performance.

On the contrary, other researchers did not find evidence to the SCP such as Smirlock, (1985)

and Miller & VanHoose (1993). They found results that either do not support or reject the

hypothesis that market concentration has a positive impact on performance of banks.

2.2.2. Efficient – Structure Hypothesis (ESH):

The Efficient-Structure Hypothesis (ESH) is argued by some researchers as an outcome of

traditional Structural-Conduct Performance hypothesis (Aguirre et al, 2008), however it was

hypothesized as a challenge and alternative to the SCP by its main proponents [Demsetz (1973),

and McGee (1974)]. The ESH asserts that firms that are scale and managerially efficient

eventually increase their size and market concentration because of their ability to generate

higher profits (Demsetz, 1973). The driving force behind the process of gaining a large market

share is the efficiency of the firm. The most efficient firms will gain market share and earn

economic profits (Samad, 2008).

7

Various empirical studies have been conducted on the ESH. According to Rasiah (2010),

Smirlock (1985) was the first to apply the ESH in the banking sector in USA and found

evidence of no relationship between concentration and profitability, but rather between bank

market share and bank profitability. He stated that market concentration is not a random event

but rather the result of firms with superior efficiency obtaining a large market share.

Other researchers such as Gillini et al (1984), and Evanoff and Fortier (1988) tested the two

competing hypotheses, SCP and ESH, and found that firm-specific efficiency was a factor for

explaining the profitability in the United States banking industry.

2.2.3. Expense – Preference Hypothesis (ESH)

The Expense Preference theory was developed as an extension to the ‘theory of the firm’ (Blair

and Placone, 1988). The theory posits that firms’ managers maximize utility rather than profit

and that managers have a positive preference for expenditures on items such as staff size, office

furnishings, and the luxuriousness of the firm's premises (Hannan and Mavinga, 1980). The

circumstances that make such behavior possible are the separation of ownership from control

and imperfections in goods and capital markets (Hannan and Mavinga, 1980).

The hypothesis has been tested extensively in the savings and loan, banking, and utility

industries (Edwards 1977; Hannan and Mavinga 1980; and Blair and Placone 1988). Edwards

(1977) found that size of staff, wage and salary expenditures in banking increased with

monopoly power in the US and that indicated the existence of expense-preference behavior.

Others like Hannan and Mavinga (1980) and Verbrugge and Jahera (1981) supported the theory

using similar tests like Edwards (1977) and concluded in the same vein that number of

employees of banks in markets which exhibited monopoly power were higher than the banks

in a competitive environment.

2.3. Studies on Determinants of Bank Profitability in Ghana

While extensive empirical literature exist on the determinants of Bank Profitability across

various countries, a few empirical studies have been done in Ghana. The studies are mostly

based on selected banks rather than the whole industry. Examples include: Kakrah and

Ameyaw (2010), Bentum (2012). The ones that involved the whole banking industry were

Kutsienyo (2011), Owusu-Antwi et al (2015) and Gyamerah and Amoah (2015).

Kakrah and Ameyaw (2010) studied MBGL and GCB from 1990 to 2009. They concluded that

bank specific variables such as non-interest income, expense and bank size were significant

key drivers of bank profitability while credit risk did not have any significant impact. Bentum

8

(2012) differentiated by finding out how four banks’ profit determinants changed during the

Global financial crisis. He found evidence that macro-economic variables had greater influence

on profit during the crisis period while bank-specific variables were significant outside the

crises period.

Kutsienyo (2011) used a Generalised Least Square technique to analyse 26 commercial banks

over the period 2000-2009. He found significant impact of bank specific variables such as

capital adequacy, liquidity and bank size and macro variables like money supply had positive

significant impact on Return on Assets. Owusu-Antwi et al (2015) on the other hand used the

Generalised Methods of Moments estimation to evaluate the determinants of profitability of

the existing banks from 1988 to 2011 using Economic Value Added (EVA) as their dependent

variable. Their conclusion was that variables like Cost-to-income ratio, Liquidity and Total

Assets are significant influencers when EVA is used to proxy profit but not when ROAA and

ROAE are used as profit measures.

Finally, Gyamerah and Amoah (2015) used endogenous and exogenous data of commercial

banks in Ghana from 1999 to 2010 to determine profitability and found that cost management

inversely affect profitability while bank sizes and credit risks positively affected the

profitability of banks.

There are several differences in the results of the studies above. This can be attributed to

differences in approach, methodology, cross-section and period under study. However, one

consistent conclusion among all was the variable Cost-to-Income had significant influence on

profitability of Banks in Ghana.

2.4. Broad Studies on Determinants of Bank Profitability:

The works of Rasiah (2010) and Kutsienyo (2011) and Dietrich and Wanzenried (2011) served

as the main source of inspiration in reviewing the general determinants of Bank Profitability

found in this section.

Rasiah (2010) did a broad review of literature on the determinants of Commercial bank

profitability and grouped them into internal and external determinants. The internal factors are

defined as those determinants that are directly within the control and power of management of

the banks. External determinants are the ones not directly within the control of management.

9

2.4.1. Internal (Bank-Specific) Determinants

Internal determinants of Bank profitability refers to the factors within the control of

management of the individual banks and account for the inter-bank profit differences, Rasiah

(2010) grouped them into financial statement variables and non-financial statement variables.

The financial statement variables directly affect the balance sheet and profit and loss account

of the banks while the non-financial statement variables do not have a direct impact. Examples

of Internal determinants include bank size, marketing strategy, management expertise,

operational efficiency, among others.

According to Kutsienyo (2011, p.26), Internal determinants may be difficult to assess but since

they are implicitly reflected in the operating performance of banks, they can be extracted from

the financial statements of banks. As a result, studies conducted on bank profitability tends to

use financial statement ratios as proxies for internal measurements. The following internal

variables are reviewed in the light of the Ghanaian banking industry:

2.4.1.1. Credit Risk

Flamini et al (2009) argued that credit risk is a major risk for banks in Sub-Saharan Africa.

Credit risk is the risk that a borrower will default on their debt by failing to make required

payments as scheduled. It is identified as a major issue for banks and financial institutions in

Ghana. PWC (2014) reported that banks in Ghana have been very aggressive in their loan

underwriting practices especially between 2006 and 2009, and as such suffered high defaults

rates. High-risk borrowers also tend to take advantage of the weak legislative environment and

absence of a central credit reference system to borrow and default across banks.

In curbing the problem, the Central BOG embarked on legislative changes in 2008. It resulted

in the establishment of three credit reference bureaux, collateral registry and the Borrowers and

Lenders Act for effective credit administration. The changes have contributed to the

improvement in quality of loan books within the industry.

The asset quality of loan portfolio is used as proxy for credit risk. It is measured by the ratio of

loans loss provision to Gross Loans like Athanasoglou et al (2008). Dietrich and Wanzenried

(2011) argue that when the number of defaulters within a portfolio of loans is anticipated to be

high, it reflects a lower credit quality of the loans. A lower credit quality has negative influence

on bank profitability because the real impairment costs of non-repayment are likely to be higher

for banks with lower asset quality than those with higher asset quality.

10

Miller and Noulas (1997) also add that credit risk will affect profitability negatively because

the higher the level of risky loans, the higher the level of loan defaults.

In Ghana, Gyamerah and Amoah (2015) found a significant negative effect of Credit Risk on

commercial banks’ profitability. Athanasoglou et al. (2008) and Dietrich and Wanzenried

(2011) found negative significant effects of the variable on bank profitability in Greece and

Switzerland respectively.

Other studies found positive relationship and these include Al-Haschimi (2007) and Flamini et

al (2009). Flamini et al used the ratio of loans to deposit & Short Term fund and found a positive

and significant effect of credit risk on Profitability in sub-Sahara Africa.

2.4.1.2. Liquidity

Liquidity risk is a major concern for banks since poor management of liquidity can result in

run-on-the-bank and bank failure. Liquidity refers to the capacity of the bank to fulfil all

payment obligations as and when they fall due. Banks in Ghana are reported to be very cautious

in maintaining liquid funds to meet contractual obligations when it falls. Moreover, the industry

as a whole is risk averse with over 80% of banks holding enough liquid assets to meet at least

50% of customer deposits and withdrawal demands (PWC, 2014).

Kashyap et al (2002) state that a bank which holds highly liquid assets tend to have relatively

lower income since liquid assets are less risky and therefore attract lower rates of returns.

Moreover, liquidity holdings imposed by banking supervisors represent cost to the bank

especially if the demand for liquidity from depositors is not highly correlated with demand for

liquidity from borrowers.

The ratio of Net Loans to Total Assets is used to proxy for liquidity. This ratio indicates what

percentage of the assets of the bank is tied up in loans.

There are mixed results among empirical studies on the effect of liquidity on profitability.

While some find negative relationship (Molyneux and Thorton, 1992; Guru et al, 1999 and

Gyamerah and Amoah, 2015), others find a positive relationship (Bourke, 1989 and Pasiouras

& Kosmidou; 2007). In the same studies, Pasiouras & Kosmidou (2007) found significant

positive effects on profitability among domestic banks in the EU but negative relationship for

foreign banks.

11

2.4.1.3. Bank Size

The impact of Bank Size on profitability is largely discussed among researchers and it is

considered as a relevant determinant of bank.

Bank Size captures potential economies of scale of a bank. Large size banks are expected to

reduce cost because the cost of producing a unit of banking service should be cheaper to them.

In addition, large bank size is associated with diversification opportunities that should allow

them to increase returns while reducing risks and costs (Garcia-Herrero et al., 2009). Flamini

et al (2009) reason that large-sized banks having a greater share of the market are able to

operate in a non-competitive environment. They tend to offer high lending rates while their

deposit rates are low because they are perceived safer than smaller banks. Thereby enjoying

higher profits from the high interest spreads.

On the other hand, large sized banks may negatively affect profitability because of difficulty

in management, management inefficiencies, bureaucratic processes and agency costs. Garcia-

Herrero (2009) claims that certain banks may also embark on aggressive growth strategies at

the expense of profitability.

Total Assets is widely used as the measure of bank size. The empirical results on bank size are

mixed. Researchers such as Short (1979), Smirlock (1985) and Flamini et al (2009) have found

a positive relationship between bank size and bank profitability. On the other hand, Pasiouras

& Kosmidou (2007) and Dietrich & Wanzenried (2011) found negative relationship between

size and profitability respectively in Europe and Switzerland.

2.4.1.4. Operating Efficiency

The ratio of Cost-to-Income is extensively used as measure of operating efficiency. The ratio

encompasses major elements of operating costs such as administrative costs, staff salaries and

benefits, property costs, etc. It reflects the cost of running a bank.

A negative relationship is expected out of cost-to-income ratio and profitability because

improved management of operating costs should increase efficiency. Athanasoglou et al.

(2008) found evidence of negative relationship between cost-to-income and ROAA on greek

banks. Liu and Wilson (2010) also found a negative relationship for cost-to-income on

Japanese banks regardless of dependent variable whether ROA, ROE or NIM. Dietrich and

Wanzenried (2011) found similar results for Banks in Switzerland. Guillen et al. (2014) studied

12

bank profitability in 12 South American countries and concluded on the same negative effects

of cost-to-income.

In Ghana, Kakrah and Ameyaw (2010), Kutsienyo (2011) and Gyamerah and Amoah (2015)

found that expense has a negative effect on profitability proving that operating efficiency

affects profitability positively. Owusu-Antwi et al (2015) on the other hand found a positive

relationship between cost-to-income ratio and Economic Value Added (EVA) dependent

variable indicating that inefficient banks perform better.

2.4.1.5. Bank Capitalisation

Capital refers to the amount of own funds available in the bank’s business. Capitalisation within

the Ghanaian banking industry has experienced increments over the years. In 2008, the

minimum stated capital for commercial banks were increased to GH¢60million ($16 million).

Foreign banks were given a year up to 2009 to meet the requirement while domestic banks

were given up to 2012 (BOG, 2008).

Bank capitalisation serve as cushion to increase shares of risky assets since well-capitalised

banks need to borrow less to support a given level of assets and face lower costs of funding

because of the low prone to bankruptcy risks. High levels of capitalisations also sends positive

signals about the solvency of the bank thereby lowering the risks of bankruptcy and credit

default. Therefore, highly capitalised banks are able to reduce their costs of financing, as they

pay relatively lower interest rates on their debts (Athanasoglou et al., 2005).

In Ghana, there exists no deposit insurance scheme like the U.S. Federal Deposit Insurance

Commission (FDIC)2. As a result, the level of bank capitalisation should be able to send strong

signals to depositors about the banks solvency and guarantee the safety of deposits.

Capital is proxied as Equity-to-Total Assets like Dietrich and Wanzenried (2011) and

Gyamerah and Amoah (2015). Empirical studies by Bourke (1989), Demirguc-Kunt and

Huizinga (1999), Goddard et al. (2004), Pasiouras and Kosmidou (2007) and García-Herrero

et al. (2009) indicate a positive relationship between capitalisation and profitability.

2 The FDIC is an independent agency in the United States created to provide deposit insurance that guarantees the safety of depositor’s accounts.

13

2.4.1.6. Bank Growth and Funding Costs

Bank growth is measured by the annual growth of the deposits as Dietrich & Wanzenried

(2011). Theoretically, it is expected that a faster growing bank would be able to expand its

business and thus generate greater profits. According to Dietrich & Wanzenried (2011), profit

increments that are derived from increases in deposits depend on other factors such as - the

bank’s ability to convert deposit liabilities into income-earning assets, which reflects the

operating efficiency of the bank. Moreover, it also depends on the credit quality of the assets..

On the other hand, high growth rate within an industry serves as incentive for competitors

especially if barriers to entry are weak and this can again reduce profits within the industry

(Porter, 2008). Dietrich & Wanzenried (2011) found negative significant relationship between

growth in deposits and bank profitability in Switzerland.

Funding costs are measured by interest expenses over average total deposits. According to

Dietrich & Wanzenried (2011), Funding costs are mainly determined by the bank’s credit

rating, competition, market interest rates and by the composition of the sources of funds.

Empirically, Dietrich & Wanzenried (2011) found negative significant impact of funding costs

on profitability since banks that raise funds cheaply are more profitable.

2.4.2. External Determinants:

External determinants are those factors beyond the control of management that influences the

bank’s performance and profitability. The onus then falls on management to undertake

strategies in order to adapt and adjust to them.

The literature further divides external determinants into industry-specific factors and

macroeconomic factors. Industry-specific factors are those factors that are specific to the

banking industry but beyond the control of an individual bank. Such factors include financial

regulation, bank concentration, competitive conditions, industry growth and developments.

Macroeconomic factors are the economy wide phenomena affecting businesses in the

economy. It covers inflation, interest rates, Gross Domestic Product, Money Supply,

unemployment, etc.

Among the widely reviewed external variables are concentration, market growth, inflation,

interest rates, Business Cycle and money supply. In this study, those factors will be adopted,

as they are widely studied within the Ghanaian industry and worldwide at large.

14

2.4.2.1. Bank Concentration

Bank Concentration refers to the number and size of banks in the market and reflects the market

power in the industry. The relationship between concentration and profitability is developed

from the Structure Conduct Performance (SCP) hypothesis. The SCP hypothesis proposes that

‘market concentration fosters collusion among firms in the market and earn monopoly profits’

(Gilbert, 1984). Collusion may result in high interest spreads because higher rates may be

charged on loans while lower rates are paid on deposit, therefore it is expected that bank

concentration positively affects profitability.

On the other hand, a higher bank concentration may stem from tougher and increased

competition in the banking industry resulting in price cuts (Boone and Weigand, 2000) that

would result in negative effect of concentration.

The Herfindahl-Hirschman index (HHI) is the commonly accepted measure of concentration.

The index is useful in measuring concentration in a various contexts: concentration of income

or wealth, degree of concentration of the output of firms in banking or industrial markets and

it is also useful in analysing horizontal mergers since such mergers affect market concentration

(Rhoades, 1993). The HHI accounts for concentration by incorporating relative size or market

share of all firms in a market. It is calculated by squaring the market shares of all firms in a

market and then summing the squares.

Empirically, Demirguc-Kunt and Huizinga (2000) found evidence of direct relationship

between concentration and profitability. Smirlock (1985) used a three-bank deposit

concentration ratio in place of the HHI and found no positive significant relationship with

profitability. Garcia et al (2009) found evidence that a more concentrated banking system was

associated with lower pre-provision profit in China.

In Ghana, Kutsienyo (2011) found negative effect of concentration on profitability. Gyamerah

and Amoah (2015) however found concentration to be insignificant in determining

profitability.

2.4.2.2. Inflation

Inflation is the aggregate increase in prices of goods and services in a community. Perry (1992)

as cited in Pasiouras and Kosmidou (2007) argues that the relationship between inflation and

profitability depends largely on whether inflation is anticipated or unanticipated. With

anticipated inflation, banks can have ample time to adjust interest rates accordingly and that

15

can favourably result in their revenues increasing faster than costs. Pasiouras and Kosmidou

(2007) reported a positive relationship between inflation and profitability of domestic banks in

Europe. Unanticipated inflation would affect banks negatively since they may not adjust in real

time and be exposed to negative effects. Pasiouras and Kosmidou (2007) reported that inflation

negatively affected the profitability of foreign banks in Europe.

Gyamerah and Amoah (2015) found positive influence of inflation in Ghana while Owusu-

Antwi et al (2015) found no significant impact of inflation on profitability.

Studies such as Molyneux and Thornton (1992), Demirgüc-Kunt and Huizinga (1999),

Athanasoglou et al. (2008) and Dietrich and Wanzenried (2011) found direct effect of inflation

on profitability.

2.4.2.3. Money Supply

Money supply refers to the stock of money available in an economy. Growth in money supply

is used as a proxy for market growth (Bourke, 1989; Molyneux & Thorton, 1992). Growth and

expansion in money supply enables banks to increase profitability especially in the presence of

entry barriers (Bourke, 1989).

Bourke (1989) found evidence of a positive relationship between growth in money supply and

profitability however Molyneux and Thorton (1992) researching on European banks found no

evidence in support of significant positive relationship for money supply and profitability. Both

Kutsienyo (2011) and Gyamerah and Amoah (2015) found money supply to positively affect

ROAA in Ghana.

2.4.2.4. Gross Domestic Product (GDP)

Real GDP is a macroeconomic measure of the value of economic output adjusted for price

changes. The relationship between economic growth (GDP) and financial sector development

has been a long standing topic. Two theories are in support of a positive relationship between

economic growth and financial sector development (Patrick, 1996): the supply-leading

hypothesis and demand following hypothesis. The Supply-leading hypothesis theorizes a

causal relationship from financial development to economic growth. In other words, a

deliberate creation of financial markets & institutions increases the supply of financial services

that leads to real economic growth (McKinnon 1973 and Calderon and Liu, 2003). The

demand-following hypothesis that explains the expected positive relationship between GDP

and Bank performance posits that an increasing demand for financial services induces

16

expansion in the financial sector as the real economy grows. In a nutshell, financial sector

responds to economic growth. (Gurley and Shaw, 1967 and Goldsmith, 1969).

Specifically on GDP and Banking performance, Pasiouras and Kosmidou (2007) find a positive

relationship for domestic banks in Europe but a negative relationship for foreign banks. They

explained that since GDP growth is assumed to have an impact on numerous factors related to

the supply and demand of loans and deposits, domestic and foreign banks tend to serve different

customers who may react differently under the same macroeconomic conditions.

Demirguc-Kunt et al (1999) and Kosmidou et al. (2005) found positive relationship for GDP

and bank performance. Kutsienyo (2011) found a direct relationship while Gyamerah and

Amoah (2015) found an inverse relationship between real GDP growth and bank profit in

Ghana.

2.5. Measures of Profitability (Dependent Variables)

Ommeran (2011) broadly categorizes the measures of profitability into two groups: traditional

accounting based measures and economic based measures.

2.5.1. Accounting Measures of Profitability

These are measures obtained from public disclosed information. Lots of accounting based

measures of profitability exist but the ones extensively used are Return on Assets (ROA),

Return on Equity (ROE) and Net Interest Margin (NIM). The Net Interest Margin measures

the spreads between the rates paid on deposits and that charged on loans. García-Herrero et al

(2009) describes NIM as an imperfect measure of profitability because it is an ex-ante measure

that does not factor how the bank is run. The Return on Assets (ROA) and Return on Equity

(ROE) are the most widely accepted measures of profitability. García-Herrero et al (2009)

support ROA and ROE as more comprehensive measures of bank profitability since they

include operational efficiency and loan loss provision.

Return on assets (ROA) is the ratio of Net Income (after Taxes) to Total Assets. The ROA

shows managerial efficiency – how effective and efficient the management of banks have been

at using the assets to generate earnings. A higher ratio indicates a higher performance of the

bank. Several studies adopt ROA as a comprehensive profitability measure (Bourke (1989);

Molyneux and Thornton (1992); Demirguc-Kunt et al., (1998); Athanasoglou et al (2008);

García-Herrero et al (2009), Dietrich and Wanzenried (2011), Chen and Liao (2011),

Gyamerah and Amoah (2015)).

17

However, a major downside of ROA is that it is distorted by the Off Balance Sheet activities

of the Bank. Returns from a bank’s OBS activities are incorporated into the net income but its

accompanying assets are not incorporated in the assets. As a result the ROA is biased upwards

due to the exclusion of OBS assets (Ommeran, 2011).

Return on Equity (ROE) is the alternative measure, calculated as the ratio of Net Income to

Equity. It gives an indication of management’s effective utilisation of equity funds, and gives

a sense of banks’ judgement on asset composition, liquidity positions, and effective cost

management.The shortfall of ROE as a measure is that banks with high financial leverage can

generate a higher ratio since ROE is inversely related to Equity. ROE may at times fail to show

the true financial health of the banks due to its relation with leverage and equity itself. A high

ROE may either reflect a healthy profitability or low capital adequacy (European Central Bank,

2010).

Researchers mostly prefer ROA to ROE however the two are mostly used together in the

literature. Rivard and Thomas (1997) argued for a preference of ROA over ROE because high

equity multiplier cannot distort ROA. This study uses Return on Average Assets (ROAA) and

Return on Average Equity (ROAE) as measures of profitability.

2.5.2. Economic measures of Profitability

The economic measures of profitability are based on economic profit unlike the traditional

accounting measures based on accounting profit. Popular examples of economic measures are

Risk-adjusted Return on Capital (RAROC) and Economic Value Added (EVA), other

examples include price-earnings ratio and market to book ratio. The Economic based measures

are value-based performance measure that focus on shareholder value creation. According to

Kimball (1998), economic measures take risks and opportunity costs into account unlike the

traditional measures.

Academic literature does not often use the economic-based measures to analyze bank

profitability. This is because they are mostly subject to internal policies that differ between

banks (European Central Bank, 2010) and it is difficult to calculate with only the publicly

available information, as there is the need for additional internal data.

In the Ghanaian banking industry, Owusu-Antwi et al (2015) employed Economic Value

Added (EVA) as their measure of profitability as they studied factors affecting profitability in

18

the industry. This study does not use the Economic measures of Profitability but the accounting

measures.

2.6. Studies on Foreign versus Domestic Banks

There are a great deal of literature in the field of foreign and domestic firms performance. In

Ghana, Tetteh (2014) studied differences in bank characteristics among foreign and local

banks. He found evidence indicating that foreign and domestic banks differ in terms of

profitability, size, interest, income generation and revenue and the foreign banks performed

better than the domestic ones in all contexts. Foreign banks are generally seen to perform better

than local ones especially in developing countries (Asheghian, 1982, Figueira et al,. 2006).

Figuiera et al (2006) found that banks in Africa with more foreign ownership outperform their

local partners. Demirguc-Kunt and Huizinga (1999) showed that foreign banks were

disadvantaged in developed countries but had advantages over domestic peers in developing

countries.

Chang et al. (1998) found foreign banks in the US to be less cost efficient than domestic banks.

In the EU, Kosmidou et al. (2004) found domestic banks to show higher overall performance

than foreign banks operating in the UK.

Nonetheless, advantages by foreign banks are found in developed economies as well. Williams

(2003) found that foreign banks used their resources more efficiently than their domestic

counterparts in Australia.

19

Chapter 3: Background of the Ghanaian Banking Industry

3.0. Introduction:

This chapter gives an overview of economic developments over the period of study and mainly

talk about the banking industry showing the major structural changes and developments in the

Ghanaian banking Industry. Finally, the chapter profiles the existing banks in the industry.

3.1. Economic Overview

Ghana is a lower middle-income country found in Sub-Sahara Africa within the West Africa

Sub-region. It has an estimated population of about 26.44 billion as at 2014 (World Bank,

2015). Politically, Ghana is a democratic multi-party state and “one of the more stable nations

in Africa, with a good record of power-changing hands peacefully” (BBC, 2015).

Ghana’s economy has been growing steadily for more than a decade and is one of the fastest

growing economies in Africa. The growth largely emanates from its endowed natural

resources. It is currently the world’s second largest producer of Cocoa behind its neighbour

Ivory Coast, and Africa’s biggest gold miner after South Africa. It recently discovered oil in

2010 and has started commercial production of the oil (African Business Magazine, 2011;

BBC, 2015).

Figure 1: Macroeconomic Indicators

Data Source: World Bank’s World Economic Indicators

The macroeconomic performance overview shows a steady growth in Real GDP from the start

of the new millennium as shown in figure1. Real GDP rose to its maximum high of 15% in

2011. The spike in GDP growth in 2011 was mainly spurred by the commercial production of

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Real GDP Growth - %growth

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oil that started in last quarter of 2011 (PWC, 2014). Before that, the average growth of GDP

was around 5.6%. The high growth in 2011 could not be sustained and has been declining

sharply since 2012 towards ‘its equilibrium’. The decline in growth is largely attributed to

energy disruptions in the country, rising inflation and rising fiscal and external imbalances

(World Bank, 2015). Agriculture used to be the major contributor to the GDP in the country

but this has declined, with the Services sector (which includes the Banking and financial sector)

being the major contributor. It contributes about half of the GDP (49.8% and 49.6% in 2013

and 2014 respectively). The industrial sector is the second largest contributor (28.1% in 2013,

28.4% in 2014) mainly due to the crude oil production. Agricultural sector remains the major

source of employment but the third largest contributor to GDP (21.3% in 2013, 21.5% in 2014)

[Ghana Statistical Service, 2015].

Inflation over the last decade has been a major issue for the country. For it being a major driver

of interest rates in the country, the banking and financial sector is quite affected by inflation

movements. Inflation averaged 30% at the start of the millennium. This was gradually

controlled amidst high volatility to a record low in 2011 where a single digit inflation of 8.70%

was achieved. Unfortunately, inflation been back in the double digit range since 2013. The rate

for 2014 was 15.50%. Increases in utility and petroleum prices are considered major factors for

the upsurge in inflation [Dzawu and Dontoh, 2015].

3.2. Overview of the Banking Industry

The current banking industry comprises 28 Commercial banks (15 foreign banks and 13 local

banks), 138 rural and community banks, 503 Microfinance Institutions, 3 credit reference

bureaux and 60 non-banking financial institutions which include finance houses, savings and

loans, leasing and mortgage firms (BOG, 2014 p. 21). Appendix 1 shows a list of the existing

Commercial Banks. The Bank of Ghana (BOG) is the head of the banking sector and has the

responsibility of formulating and implementing monetary policies as well as supervising and

regulating the banking industry.

The banking and financial sector of Ghana has undergone major structural changes since the

late 19th century. Based on the major Financial Sector Adjustment Programme (FINSAP) of

1988, Antwi-Asare and Addison (2000) separates the period of Banking in Ghana into Pre-

Financial Sector Period and Post-Financial Sector Period. The Financial Sector Adjustment

Program (FINSAP) were reform policies that was rolled out in 1986 as part of the then

Economic Recovery Program. It sought to address the institutional deficiencies of the financial

21

system by restructuring distressed banks, reforming prudential legislations and financial

liberalization (Owusu-Antwi, 2009).

3.2.1. Pre-Financial Sector Adjustment Period:

According to Antwi-Asare and Addison (2000), the modern banking system in Ghana began

in the late nineteenth century with the Post Office Savings Bank (POSB) in 1888 rode on the

back of post office facilities. The first real bank was Bank of British West Africa (currently,

Standard Chartered Bank) which was established in 1896, followed by the Barclays Bank

Dominion, Colonial and Overseas (now Barclays Bank Ghana Limited) in 1917. These Banks

were established by the colonial masters to finance trade between the Gold Coast (currently

Ghana) and the United Kingdom. The two banks favoured foreign nationals over the indigenes;

as a result, it led to the establishment of a third bank in 1953 known as the Bank of Gold Coast.

After Ghana’s independence in 1957, the Bank of Gold Coast split into two Banks that became

the Central Bank of Ghana and Ghana Commercial Bank Limited (BOG, 2011a). Additional

Banks sprang up afterwards for specific purposes. Example; the National Investment Bank

(which still maintains its name) was established as Development Bank in 1963. The

Agricultural Credit and Co-Operative Bank (now Agricultural Development Bank) was formed

in 1965 as development Bank for the Agricultural Sector. The first merchant bank was also

established in 1971 as National Merchant and Finance Bank Limited (currently, Universal

Merchant Bank Limited).

Antwi-Asare and Addison (2000) explain that: “the banking sector that existed before the mid-

1980s was largely the result of a conscious government-driven effort to bring into being

institutions which it felt could fill gaps within the financial sector. Their processes was largely

financed by the government, either directly with the provision of capital or indirectly through

public institutions like the BOG”.

Prior to 1983, the then existing banking industry had been greatly affected by the nation’s

economic decline. Aryeetey (1994) describes that due to the socialist ideologies of the then

existing government, Ghana pursued a growth strategy that was based on inward-oriented trade

led by the public sector and aimed at achieving social welfare objectives. Owusu and Odhiambo

(2015) adds that it resulted in budgetary pressures and quick exhaustion of external reserves

that brought shortages in the economy. The government then resulted to price control policies

for rationing scarce goods and services. The price control policies affected the financial sector

where banks were forced to provide credit solely on political and social basis. Exchange rates

22

and interest rates were in turn fixed by the government as a way of coping with the cost of

credit and the economy apparently experience high rates of inflation (Owusu and Odhiambo,

2015). The government’s control and high rates of inflation brought about financial repression.

Antwi-Asare and Addison (2000) explained that the banks had built up large portfolio of non-

performing loans that had piled up from year to year. The BOG also failed to perform its role

as supervisor. The World Bank (1994) as cited in Antwi-Asare and Addison (2000) reported

that Ghana’s three largest banks had never undergone a comprehensive examination.

The deteriorating financial sector and wailing economy led to the call for Structural

Adjustments and Economic Reforms under the supervision of the IMF and World Bank.

3.2.2. Post Financial Sector Structural Adjustment Program

(FINSAP)

According to the IMF (1998), Ghana launched an Economic Recovery Program (ERP) in 1983

that was aimed at reversing a protracted period of serious economic decline characterized by

lax financial management, inflation rates over 100 percent, and extensive government

involvement in the economy. In 1988, a Financial Sector Adjustment Progam (FINSAP) was

launched as part of the ongoing ERP. The financial reform involved institutional restructuring,

enhancement of the legal and regulatory framework for banking operations, and liberalizing

interest rates (Sowah, 2002). The three components of the FINSAP that most directly affects

the banks were bank restructuring, reforms of the prudential system, and the liberalization of

financial markets (Owusu-Antwi, 2009).

The bank restructuring involved the overhaul of credit policies and strengthening of credit

appraisal, loan monitoring, and loan recovery systems (World Bank, 1994).

The reforms to the prudential system brought about revisions to the banking legislation. A new

banking law was enacted in 1989 that specified capital adequacy (6%) and minimum capital

requirements (200million old cedis for local banks and 500million old cedis for foreign banks),

prudential lending guidelines, and financial reporting procedures. The BOG’s examination and

supervisory roles were upgraded as well (World Bank, 1994 p. 53-54).

Financial Liberalisation was introduced with the aim of enhancing efficiency in resource

allocation and promoting competition. Liberalization involved the removal of government’s

control and easing entry restriction into the banking sector. Other liberalisations included

removal of interest rate control, the sectoral composition of bank lending, and the introduction

of market-based instruments of money control (Owusu-Antwi, 2009). FINSAP engendered

healthy growth and competition in the financial sector, the private sector also gained

23

prominence and flourished. The performance of the financial sector has been substantial since

the reforms.

3.2.3. Current Developments in the Banking System (2000 – 2015)

The FINSAP which lasted up to 1995 was market-oriented. It liberalized the financial sector

opening it up to healthy competition and growth that continued into the new millennium. In

Summary, new banking acts that improves regulation and supervision were enacted within the

period. The Bank of Ghana introduced Universal Banking Business License in 2003, which

eroded the traditional three-pillar banking model in Ghana thereby eliminating restrictions for

each bank. The currency known as Cedi was redenominated in 2007 that introduced a new

currency known as Ghana Cedi. Minimum Capital Requirements were increased at various

points to the current GH¢60 million for existing banks but GH¢120 million for new banks.

Eight foreign banks entered the industry within the period with seven of them entering between

2005 and 2007.

Some of the major developments that affected the banking industry over the period are as

follows:

2002:

Bank of Ghana Act 2002 (Act 612) replaced Bank of Ghana Law, 1992 (PNDCL 291)

and strengthened BOG in its regulatory and supervisory functions

2003:

In February of 2003, the BOG formally introduced the Universal Banking Business

license (UBBL). The UBBL gave freedom to the banks to engage in all permissible

banking business without restrictions and thereby eliminate compartmentalization. It

replaced the previous three-pillar banking model – development, merchant and

commercial banking. It has levelled the playing field, and opened up the system to

competition, product innovation and entry (Bank of Ghana, 2011b).

Existing banks had to have a minimum net worth of ¢70billion cedis to qualify for

UBBL and new banks had to have the same to operate under UBBL.

2004:

A new Banking Act 2004 (Act 673) was passed in October 2004 to replace the Banking

Law 1989 (PNDCL 225). The Act was incorporated current and international standards

24

and ensured more effective supervision and regulation of the banking industry (Bank

of Ghana, 2004).

The Act 673 was in consonance with Basel II that expected Banks to upgrade and report

on credit and operational risk capabilities and Disclose Standard procedures for routine

operations as well as market risk positioning.

The Act 673 increased minimum capital adequacy from 6% to 10%.

2006:

The BOG abolished secondary reserve requirement of 15% as a way of making more

funds available for private sector lending. It however kept the primary reserve

requirement of 9%. (BOG, 2006).

The Foreign Exchange Act 2006 (Act 723) and Whistle Blowers Act 2006 (Act 720)

came into effect.

All existing banks were in compliance with the minimum capital of ¢70billion required

for Universal Banking Business.

2007:

Banking Amendment Act 2007 (Act 738) and the Credit Reporting Act 2007 (Act 726)

were enacted. The Amendment Act serves as an overall legislative reform aimed at

developing an efficient financial services industry in Ghana.

On July 3rd 2007, the BOG redenominated the existing currency, cedi by the

introduction of a new currency designated as the Ghana Cedi and Ghana pesewa. Ten

thousand cedis was set to One Ghana Cedi which is equivalent to one hundred Ghana

pesewas i.e.¢10,000 = GH¢1.00 = 100Gp.

The re-denomination of the cedi was designed to the lingering legacy of past inflation

and macroeconomic instability. The legacy of the past episodes of high inflation had

been the rapid increases in the numerical values of prices as well as foreign currency

exchange in local currency terms [BOG, 2007].

2008:

The BOG firmed up its policy to raise the minimum capital of banks from GH¢7million

to GH¢60 million after due consultation with the banking industry. All foreign owned

banks were required to attain the new level by December 2009, while Ghanaian owned

25

banks had up to December 2012 to attain the capital. However, the Ghanaian owned

banks were required to reach GH¢25 million by end 2009.

A common electronic platform known as E-zwich was established to develop the

payment and settlement system. This made it possible to link all banking institutions

with a biometric smartcard as a vehicle for inclusion of all segments of the population.

(BOG, 2008).

2012:

All banks had been able to meet the 31 December 2012 deadline for the GH¢60million

($16million) minimum capital requirement set by the Bank of Ghana.

2013:

The Bank of Ghana reviewed upwards the minimum capital required for new banks to

operate in the country. New commercial banks are required to have a minimum stated

capital of GH¢120million ($32million) but Existing banks are only required to maintain

a stated capital of GH¢60million ($16million).

3.3. Contemporary Banking in Ghana - Universal Banking Business

License (UBBL):

The traditional banking model of Ghana was classified into three (3) known as Commercial

Banking, Development Banking and Merchant banking. Commercial banks provided retail-

banking services to individuals, businesses and households while merchant banks provided

wholesale services to large corporations. Development banks were focused on providing

banking services to a specific sector of the economy. The 3-model of banking prevailed in the

industry until 2003 when the BOG introduced Universal Banking License.

It integrated the financial system and made the old divisions anachronistic. According to BOG

(2011b), “the UBBL eliminated restrictions and allowed banks to engage in all permissible

banking businesses. It has levelled the playing field, and opened the financial system up to

competition, product innovation and entry”.

Under UBBL, Banks are allowed to offer products that were previously the preserve of other

traditional banking sectors. The Bank of Ghana raised the minimum capital requirement as at

2003 to ¢70 billion as a requirement for UBBL. They argued that well-capitalised and well-

managed universal banks will encourage a more competitive and dynamic banking system

26

capable of effective intermediation on the scale needed to support growth in the economy

(Bank of Ghana, 2004 p. 46). By 2006, all the banks had complied with the minimum capital

requirement for UBBL (Bank of Ghana, 2006) however, Hinson et al (2006, p.71) claimed that

all the banks had started practicing universal banking even before meeting the capital

requirement.

3.4. Foreign and Domestic Banks in Ghana

Foreign banks currently dominate the banking sector in Ghana with 15 out of the total 28 banks.

The presence of foreign banks is not only seen in their quantity but they make up the history

of the banking industry. The pioneering banks in the industry were foreign banks established

by the colonial masters. These were the Bank of British West Africa in 1896 and Barclays Bank

DCO in 1917. The two pioneers still operate in the country under the names Standard Chartered

Bank and Barclays Bank Ghana Limited. The presence of foreign banks directly and indirectly

influenced the establishment of domestic banks. Indirectly in the sense that, it was

discrimination in the operations against the locals that served as motivation for a bank that

could better serve the indigenous Ghanaians (Antwi-Asare and Addison, 2000). As a result, the

first domestic bank was set up in 1953 as Bank of Gold Coast. The number of Domestic banks

increased thereafter especially after Ghana’s Independence in 1957. The government then

made conscious efforts to set up institutions that to fill gaps in the financial sector hence created

domestic banks in the forms of development banks, merchant banks and commercial banks.

The financial sector after independence was highly regulated and controlled by the government

as a result new foreign entrants seized until reforms were done in 1988. The entry of foreign

banks grew substantially between 1990 and 2008. Out of 16 new banks that were established,

11 were foreign banks (PWC, 2009). This brought keen competition in the industry.

27

Figure 2: 2013 Share of Industry Assets and Deposits. Source: PWC (2014) [Full Names of Banks in Table 1]

Industry analysis shows a dominance of the foreign banks not only in terms of quantity but also

in shares of assets and deposits. Foreign Banks own an average of 60% of both the industry

operating assets and Deposits. Ecobank Ghana Limited (EBG), a foreign bank is the Industry

Leader in terms of both assets and deposits followed by Ghana Commercial Bank Ltd (GCB)

that is a local bank. As at 2013, fifty percent of both the industry’s assets and deposits were

owned by six banks and five out of the six are foreign banks. According to PWC (2014), the

market shares of the industry has not changed significantly over the last 3 years because there

is limited differentiation in the products offered by the banks to give any bank a strong edge

over the others. Indications within the industry shows that foreign banks will continue to

dominate.

Market shares of Assets and deposits for the year 2013 is shown in Figure 2. Moreover, a 4-

year overview of the industry shares of operating assets and deposits from 2010 to 2013 are

presented in Appendix 1B and 1C respectively.

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

ECO

GC

B

SCB

STA

NB

IC

BB

G

ZEN

Fid

elit

y

AD

B

CA

L

UB

A

UTB

UN

IBA

NK

SGG

H

NIB

AC

CES

S

HFC

GTB PB

L

UM

B

BO

A

FCP

FAM

BL

ICB

RO

YAL

ENER

GY

BSI

C

BA

RO

DA

Share of Industry Assets and Deposits

Assets Deposits

28

Chapter 4: Methodology

4.0. Introduction

The research methodology used in this study is presented in this chapter. This Chapter discusses

the data, data source, profitability determinants and the justification of the chosen variables. It

also discusses the statistical and econometric tools used to analyse the data for the purpose of

the study.

4.1. Data:

4.1.1. Data Source:

Data can be grouped into two main sources based on the origin of the data: Primary Data and

Secondary Data. Primary Data are original data collected first hand by the researcher while

secondary data are already existing data that has been collected probably for another purpose.

This study employs secondary data, which are annual financial statements of individual banks

from the Ghanaian banking industry. Various financial ratios are calculated from the annual

statements. The Financial ratios are for variables that are hypothesized as determinants of

profitability (The variables are presented in section 4.4.1.).

The data of financial statements were collected from the Banking and Supervision Department

of the Central Bank of Ghana. In addition, the researcher did a random cross-check from some

individual bank’s annual reports. While the primary source of data was from the Central Bank

of Ghana, the information could not be used extensively to calculate all the required ratios

needed for this study. As a result, additional data were sourced from the PricewaterHouse

Coopers’ annual reports on Banking Survey in Ghana (PWC Ghana, various years). The

researcher examined the common data between the two sources and found them to be similar

therefore rendered the additional data collected from PWC survey as valid and reliable as those

from the central Bank of Ghana.

In addition, macroeconomic indicators were obtained from the World Bank Database of

International Bank for Reconstruction and Development (IBRD) and International

Development Association (IDA).

4.1.2. Data Sampling Criteria:

The Data consists of all Universal Banks in the Ghanaian Banking Industry as at December

2013. This comprised of 27 Banks made up of 15 Foreign Banks and 12 Local Banks. The

period under study was from 2003 to 2013. The period marks a significant period of reform

29

within the Ghanaian banking industry and the introduction of Universal Banking Business

License (UBBL) in 2003 that created a level-playing service platform for all commercial banks

in the country. The UBBL replaced the traditional 3-pillar banking model in Ghana which were

commercial, development and merchant banking. It gives freedom to all banks to engage in all

permissible banking business and products without restrictions nor compartmentalization. All

existing banks in Ghana operate under UBBL (Bank of Ghana, 2006).

Table 1: List of Banks used in the Study

Name of Bank Symbol Year of Incorporation

Majority Ownership

1 Access Bank (Ghana) Limited ACCESS 2008 Foreign

2 Agricultural Development Bank Limited ADB 1965 Local

3 Bank for Africa BARODA 1997 Foreign

4 Bank of Baroda (Ghana) Limited BOA 2007 Foreign

5 Barclays Bank of Ghana Limited BBG 1917 Foreign

6 BSIC (Ghana) Limited BSIC 2008 Foreign

7 CAL Bank Limited CAL 1990 Local

8 Ecobank Ghana Limited ECO 1990 Foreign

9 Energy Bank (Ghana) Limited ENERGY 2010 Foreign

10 Fidelity Bank Limited FAMBL 2006 Local

11 First Atlantic Bank Limited FCP 1994 Foreign

12 First Capital Plus Bank Limited Fidelity 2009 Local

13 GCB Bank Limited GCB 1953 Local

14 Guaranty Trust Bank (Ghana) Limited GTB 2004 Foreign

15 HFC Bank Ghana Limited HFC 1990 Local

16 International Commercial Bank Limited ICB 1996 Foreign

17 National Investment Bank Limited NIB 1963 Local

18 Prudential Bank Limited PBL 1993 Local

19 Societe-Generale (SG) Ghana Limited ROYAL 1975 Foreign

20 Stanbic Bank Ghana Limited SCB 1999 Foreign

21 Standard Chartered Bank Ghana Limited SGGH 1896 Foreign

22 The Royal Bank Limited STANBIC 2011 Local

23 UniBank (Ghana) Limited UBA 1997 Local

24 United Bank for Africa (Ghana) Limited UMB 2004 Foreign

25 Universal Merchant Bank Ghana Limited UNIBANK 1971 Local

26 UT Bank Limited UTB 1995 Local

27 Zenith Bank (Ghana) Limited ZEN 2005 Foreign Twenty-seven Banks existed in the Ghanaian banking industry as at 2013 and are involved in the study. All the

banks operate under Universal Banking License with no restriction to compartmentalize their operations.

4.1.3. Data Filtering:

With 27 banks within an 11-year period, the whole expected balanced panel would have been

297 but due to later entrants, mergers, and acquisition within the period, an unbalanced data

30

panel of 236 observations were obtained for the study. There were a minimum of 16 banks as

at 2006 and a maximum of 27 banks in 2013. The maximum observations from a single bank

is 11 and a minimum of one. The full data of observations is displayed in appendix 7.

Out of the 236 observations, one was deleted (2007 observation for BSIC) due to insufficient

information to calculate most of the requisite variables for the study. A total of 235

observations are used for the regressions.

4.2. Hypothesis and Variables Justification:

The study seeks to find the determinants of bank profitability in Ghana. Chapter 2 covered the

theoretical and empirical literature of the determinants of bank profitability. Based on the

literature review, variables selected for this study will be justified and the expected sign for the

relationship between the explanatory variables and profitability will be hypothesized in the

next sections. The variables selection were based on a review of the literature, the Ghanaian

banking industry and inspired by the works of Dietrich and Wanzenried (2011), Kutsienyo L.

(2011) and Gyamerah and Amoah (2015).

4.2.1. Justification of Chosen Variables and Hypothesis

4.2.1.1. Dependent Variables:

As shown in the literature review, profitability measures are grouped into traditional accounting

measures such as ROA and economic-based measures such as EVA. This study measures

profitability by the traditional accounting measures. The Return on Average Assets (ROAA)

and Return on Average Equity (ROAE) are used as profitability measures hence the dependent

variables. This is similar to Bourke (1989); García-Herrero et al (2009), Chen and Liao (2011)

and Gyamerah and Amoah (2015). In the Ghanaian banking industry, ROAA will be

considered more appropriate than ROAE because bank equity is low and has suffered artificial

changes due to the continuous recapitalization programs by the BOG.

4.2.1.2. Independent Variables

The independent variables are the determinants that are to be used in estimating the dependent

variables outline above. Per the literature review in Chapter 2, the variables are grouped into

internal determinants and external determinants. Internal determinants are the bank-specific

factors that affect profitability. The external determinants are further grouped into industrial

and macroeconomic factors.

31

4.2.1.2.1. Bank Specific Variables

Bank-Specific variables are those variables within the control of management of the banks.

These variables are selected by using some key drivers of profitability which are earnings,

efficiency, risk taking and leverage (European Central Bank, 2010).

Cost-to-Income ratio (COI): this ratio is used as a proxy for operating efficiency. It is

calculated as total operating cost/Total Income. The ratio encompasses major elements

of operating costs such as administrative costs, staff salaries and benefits, property

costs, etc. It generally shows the costs of running the bank relative to the earnings of

the bank. A negative relationship is expected out of cost-to-income ratio and

profitability since the higher the costs and expenses, the more inefficient the bank

would be, culminating in low profitability. The ratio was adopted by Liu and Wilson

(2010), Dietrich and Wanzenried (2011) and Guillen et al (2014). In Ghana, Both

Gyamerah and Amoah (2015) and Owusu-Antwi et al (2015) employed cost-to-income

as measures of efficiency. All the researchers found a negative relationship between

cost-to-income and their measures of profitability as expected with the theoretical

review. Based on theoretical and empirical literature, a negative relationship is expected

between this variable and the dependent variables.

Loan Loss Provision to Total Loans (LLPTL): The ratio is used as a proxy for credit

risk in the study. It reflects the asset quality of the loan portfolio. When the number of

defaulters within a portfolio of loans is anticipated to be high, it reveals a lower credit

quality of the loans. A lower credit quality subsequently could influence bank

profitability negatively because the real impairment costs of non-repayment are likely

to be higher for banks with lower asset quality than those with higher asset quality.

Athanasoglou et al. (2008) and Dietrich and Wanzenried (2011) used the ratio of Loan

Loss Provision to Total Loans as proxy for credit risk and found negative significant

effects on profitability and performance. According to Miller and Noulas (1997), credit

risk will affect profitability negatively because the higher the level of risky loans, the

higher the level of loan defaults. A negative relationship is expected between loan loss

provision to total loans and profitability.

Net Loans to Total Assets (NLTA): The ratio of net loans to Total Assets is used as

proxy for measuring liquidity. This ratio indicates how much of the total assets of the

company are tied up in loans. The higher the ratio, the more illiquid the bank is. The

relationship between liquidity and profitability is not clearly defined. A bank which

32

holds highly liquid assets tends to have relatively lower income since liquid assets are

less risky hence attract lower rates of returns. Moreover, liquidity holdings imposed by

banking supervisors represent cost to the bank especially if the demand for liquidity

from depositors is not highly correlated with demand for liquidity from borrowers

(Kashyap et al, 2002). Certain researchers empirically identified a negative correlation

between liquidity and profitability (Molyneux and Thorton, 1992; Guru et al, 1999). In

Ghana, Gyamerah and Amoah (2015) found a negative relationship between liquidity

and profitability.

A positive relationship has also been established between banks’ profitability and

liquidity (Bourke, 1989; Pasiouras & Kosmidou; 2007). Pasiouras & Kosmidou (2007)

had a significant positive relation between profitability and liquidity of domestic banks

in EU but found a negative relationship for foreign banks. Empirically, the relationship

between liquidity and profitability is indeterminate but theoretical literature argues for

a negative relationship due to the cost of liquidity and lower rates of returns on liquid

assets. As a result, we expect a negative relationship between liquidity and profitability.

Log of Total Assets (LTA): Total Assets is extensively used as the measure of bank

size. Relation between bank size and profitability is widely seen as positive however

inefficiencies on a bigger scale can cause negative effects. The theoretical literature

shows that economies of scale are beneficial to companies as they grow in size but

beyond a level without efficient management, diseconomies of scale set in. The

expected relationship between size and profitability is not explicitly defined. A positive

relation is expected for economies of scale and diversification benefits while a negative

relation is expected for diseconomies of scale and bureaucratic procedures.

The study uses logarithm of Total Assets in order to capture the potential non-linear

effect of size as used by Athanasoglou et al (2008) and Trujillo-Ponce (2011). The

empirical results on bank size are mixed as well. Researchers such as Smirlock (1985),

and Gyamerah and Amoah (2015) found a positive relationship. Studies by Dietrich &

Wanzenried (2011) and Pasiouras & Kosmidou (2007) found negative effects of size

on profitability.

Equity to Total Assets Ratio (ETA): The ratio of Equity to Total Assets (ETA) is

incorporated in the regression model as a proxy for capital adequacy. The equity-to-

assets measures the amount of bank’s assets that are funded with owners. Capital

adequacy refers to the sufficiency of the amount of equity to absorb any shocks that the

33

bank may experience. High levels of capitalisations also serve as a positive signalling

effect about the solvency of the bank. Highly capitalised banks also face lower risks of

bankruptcy, which reduces their cost of financing.

Empirical evidence provided by researchers such as Bourke (1989), Demirguc-Kunt

and Huizinga (1999), Goddard et al. (2004), Pasiouras and Kosmidou (2007) and

García-Herrero et al. (2009) indicate a positive relationship between highly capitalised

banks and profitability or performance. Thus, the expected relationship between equity-

to-total assets and profitability is positive based on the empirical and theoretical

literature.

Annual Growth of Deposits (YGD): The yearly growth in deposits is used as a proxy

for growth of the bank as Dietrich & Wanzenried (2011). All things being equal, it is

expected that a fast growing bank would be able to expand its business and thus

generate greater profits leading to a positive effect of the variable on Profitability.

However, for growth in deposits to affect profit, it depends on the bank’s ability to

convert the deposits into income earning assets (Dietrich & Wanzenried, 2011).

Moreover, high growth rate within an industry may attract new entrants that may

eventually erode the industrial profit (Porter, 2008). As a result the sign of relationship

between this variable and Profitability is not clearly defined theoretically.

Interest Expenses to Average Total Deposits (IETD): This ratio is used as the proxy

for funding costs as Dietrich & Wanzenried (2011). A negative relationship is expected

between the variable and profitability since banks that are able to achieve funds more

cheaply are expected to have better profits.

Bank Age (AGE): A dummy variable is used to represent bank’s age. The banks within

the Ghanaian banking industry are grouped into three categories based on their years of

existence. The first group are banks that were established after year 2000. The second

group include those established between 1980 and 2000 and the third group are those

banks established before 1980. It is expected that older banks are more profitable as a

result of their years of experience and longer period of service within which they have

built up good reputation. Dietrich & Wanzenried (2011) found a positive significant

impact of age on bank profitability.

Bank Ownership: For this study, the bank ownership is classified as either foreign or

domestic bank based on its majority ownership (PWC Ghana, 2014). A bank is

classified as foreign if other nationals hold more than 50% share and have control of

34

the bank. A long standing assertion is that foreign banks tend to perform better than

local banks especially in developing countries (Demirguc-Kunt and Huizinga, 2000;

Claessens et al, 2001 and Berger et al, 2009) such as Ghana (Figueria et al, 2006).

Foreign investors tend to be more efficient in their resource usage, less dependent on

the government (Chen and Liao, 2011) and bring on board innovative ideas and new

technologies from their home countries to the host country making them more efficient

(Asheghian, 1982). In Ghana, Tetteh (2014) states that there was dormancy, lack of

healthy competition and technological development among the domestic banks prior to

the arrival of more multinational subsidiary banks in the country. Tetteh (2014) found

foreign banks to perform better and were more profitable than their local peers. A

dummy variable (FOREIGN) is used for foreign banks to ascertain whether banks with

majority foreign investors are more profitable than their domestic peers. From the above

review, it is expected for foreign banks to be more profitable than domestic ones.

Listed Banks: it is found out whether a bank being listed on the Ghana Stock Exchange

(GSE) has an impact on profitability. For a potentially positive impact, listed banks are

able to raise additional funds from the capital market; they also face additional pressures

for profitability from shareholders, analysts and the overall financial markets. On the

other hand, the stock market can cause negative impacts as listed banks face several

reporting and other requirements that create significant additional costs. A dummy

variable (LISTED) is used for listed banks on the GSE. The overall effect of this

variable is indeterminate.

4.2.1.2.2. Industry-Specific Variable

This involves factors that are specific to the Ghanaian banking industry but are external to

individual banks. Managers cannot change those variables immediately and they are in relation

to the characteristics of other banks.

Herfindahl-Hirschman Index (HHI): The HHI is used as proxy for bank concentration. This

is to measure the market structure. The Herfindahl-Hirschman index is defined as the sum of

the squares of the market shares of all the banks within the industry and the market shares are

expressed as fractions. According to the Structure-Conduct Hypothesis highlighted at the

literature review, it is expected that banks in highly concentrated markets earn monopoly rents

because they collude (Gilbert, 1984). As a result, a positive relation is expected between market

concentration measured by HHI and profitability. Empirically, Demirguc-Kunt et al. (2000)

found evidence of direct relationship between concentration and performance or profitability.

35

In Ghana, Gyamerah and Amoah (2015) found a positive but insignificant effect of

concentration on profitability.

4.2.1.2.3. Macro-Economic Variables:

Macroeconomic factors are the economy wide phenomena affecting businesses in the

economy. They are external for management of bank and they have no direct control over.

Real GDP growth: Real GDP growth is used as proxy for business cycles within the country.

Growth in Gross Domestic Product (GDP) is expected to have a positive influence on

profitability. This is due to the relationship between economic growth and financial sector

development. Demand for lending increases during times of cyclical upswings (Dietrich &

Wanzenried, 2011). Kosmidou et al. (2005) and Demirguc-Kunt and Huizinga (1999) all find

positive relationship for GDP and bank performance.

Inflation: The relationship between inflation and profitability depends largely on whether

inflation is anticipated or unanticipated (Perry, 1992). With anticipated inflation, banks can

anticipate and adjust interest rates which can favourably result in revenues increasing faster

costs therefore having a positive impact on profitability. Unanticipated inflation on the other

hand means that the banks can be exposed to negative effects of inflation resulting in faster

increases of bank costs than bank revenues. The annual Consumer Price Index (CPI) growth

rate for Ghana is used as proxy for inflation. Negative relationship is expected between

inflation and bank profitability.

Money Supply (M2): As Bourke (1989) and Molyneux & Thorton (1992), Growth in money

supply is used as a proxy for market growth. Changes in money supply can induce changes in

the nominal GDP and price levels. Growth and expansion in the money supply enables banks

to increase profitability especially in the presence of entry barriers (Bourke, 1989). Bourke

(1989) found evidence of a positive relationship between growth in money supply. A direct

relationship is expected between growth in money supply and bank profitability.

Financial Crises: A dummy variable for the financial crises period is created to control for

variation in profitability during the crises period. The period period from 2007 to 2011 were

used as financial crises period. Due to the negative effects of the global financial crises on

business and economies in general, a negative relationship is expected for this variable and

profitability. Summary of the variables are shown in Table 2 below.

Table 2: Selection of Determinants of Profitability

36

SYMBOL Definition Measurement Expected Sign

Source

Dependent Variables

ROAA Return on Average Assets

Net Income/ Average Total Assets (%)

N/A BOG

ROAE Return on Average Equity

Net Income/ Average Total Equity (%)

N/A BOG

Bank Specific Variables

COI Operational Efficiency

Cost to Income Ratio (%) (-) BOG

LLPTL Credit Risk Loan loss Provision to Gross Loans (%)

(-) PWC

NLTA Liquidity Net Loans to Total Assets (%) (+/-) BOG

LTA Bank Size Log of Total Assets (+/-) BOG

ETA Bank Capitalization

Equity/Total Assets (%) (+) BOG

YGD Bank Growth Annual Growth in Deposits (%) (+/-) BOG

IETD Funding Costs Interest expenses over Average Total Deposits (%)

(-) BOG

AGE Bank Age Dummy Variable for different bank age groups AGE1 = Banks between 1980 and 2000 AGE2 = Banks before 1980.

(+) PWC

FOREIGN Bank Ownership Dummy Variable for foreign banks in the sample.

(+) PWC

LISTED Bank Ownership Dummy Variable for banks listed on the Ghana Stock Exchange

(+/-) GSE

Industry-Specific Variable

HHI Market Concentration

Herfindahl-Hirschman Index based on market shares.

(+) BOG

Explanatory Variables(Macroeconomic)

GDP Gross Domestic Real GDP growth (%) (+) World Bank Data

CPI Inflation Current period CPI growth rate (%)

(-) World Bank Data

M2 Money Supply Growth in Money Supply (%) (+) World Bank Data

Crisis Financial Crises Dummy variable for the global Financial Crises period (2007-2011)

(-) World Bank

The table shows the variables selected in the determinant of Profitability in Ghana and their expected relationship

with Profitability. Section 3.4.1 gives details of the variables. The variables selected were inspired by the works

of Dietrich and Wanzenried (2011), Kutsienyo (2011) and Gyamerah and Amoah (2015).

4.3. Specification of Econometric Model

Based on existing literature, a linear regression is used and this choice of function was due to

several credible studies in the field. Short (1979) and Bourke (1989) showed that linear

37

analysis produced results as interesting as any other type of functions. The linear model of

Athanasoglou et al (2008) for which Dietrich and Wanzenried (2011) adopted is used for the

study. This is to test the statistical effect of the variables specified as determinants of banking

performance and profitability in Ghana. The general model is in the following form:

Equation 1: General Model for Regression

𝛱𝒊𝒕 = 𝜶 + ∑ 𝜷𝒋𝑿𝒋𝒊𝒕

𝑱

𝒋=𝟏

+ ∑ 𝜷𝒍𝑿𝒍𝒊𝒕

𝑳

𝒍=𝟏

+ ∑ 𝜷𝒎𝑿𝒎𝒊𝒕

𝑴

𝒌=𝟏

+ 𝜺𝒊𝒕 (𝟏)

Where Πit is the dependent variable measuring profitability and estimated by ROAA and

ROAE for bank i at time t, with i =1, . . .,N, and t =1, . . ., T, N denotes the number of cross-

sectional observations and T the length of the sample period. There is a constant term measured

by the scalar α. Xit’s are the explanatory variables and ɛit is the disturbance. The Xit’s are

grouped into 1 × k factors of bank-specific (𝑋𝑗𝑖𝑡

), industry-specific (𝑋𝑙𝑖𝑡

) and macroeconomic

variables (𝑋𝑚𝑖𝑡

), where 𝑘 refers to the number of slope parameters for the different variables

classes.

As there are two dependent variables, there will be two linear models with each dependent

variable as a function of the explanatory variables.

Equation 2: General Model with ROAA as dependent Variable

𝑅𝑂𝐴𝐴𝒊𝒕 = 𝜶 + 𝛽1𝑪𝑶𝑰 + 𝛽2𝑳𝑳𝑷𝑻𝑳 + 𝛽3𝑵𝑳𝑻𝑨 + 𝛽4𝑳𝑻𝑨 + 𝛽5𝐸𝑇𝐴 + 𝛽6𝒀𝑮𝑫

+ 𝛽7𝑰𝑬𝑻𝑫 + 𝛽8𝑨𝑮𝑬 + 𝛽9𝑭𝑶𝑹𝑬𝑰𝑮𝑵 + 𝛽10𝑳𝑰𝑺𝑻𝑬𝑫 + 𝛽11𝑯𝑯𝑰

+ 𝛽12𝑮𝑫𝑷 + 𝛽13𝑪𝑷𝑰 + 𝛽14𝑴𝟐 + 𝜺𝒊𝒕, (𝟐)

Equation 3: General Model with ROAE as dependent Variable

𝑅𝑂𝐴𝐸𝒊𝒕 = 𝜶 + 𝛽1𝑪𝑶𝑰 + 𝛽2𝑳𝑳𝑷𝑻𝑳 + 𝛽3𝑵𝑳𝑻𝑨 + 𝛽4𝑳𝑻𝑨 + 𝛽5𝐸𝑇𝐴 + 𝛽6𝒀𝑮𝑫

+ 𝛽7𝑰𝑬𝑻𝑫 + 𝛽8𝑨𝑮𝑬 + 𝛽9𝑭𝑶𝑹𝑬𝑰𝑮𝑵 + 𝛽10𝑳𝑰𝑺𝑻𝑬𝑫 + 𝛽11𝑯𝑯𝑰

+ 𝛽12𝑮𝑫𝑷 + 𝛽13𝑪𝑷𝑰 + 𝛽14𝑴𝟐 + 𝜺𝒊𝒕, (𝟑)

In static relationship, the literature applies the Least Squares Methods on fixed effects (FE) or

random effects (RE) models. Example, Pasiouras and Kosmidou (2007) used a pooled ordinary

least squares (OLS) technique in which differences between the observations and estimations

are minimized in terms of sum of squares. The model is estimated through either fixed effect

38

or random effect regression and the choice between the two is made based on the Hausman

Test.

For each model, the sample is divided into Foreign and Domestic Samples and the model is

estimated for each of the sample. This is to identify the differences in the determinants of

Profitability among Foreign and Domestic Banks.

Finally, to address the risk of omitted variables, I follow the procedure used by García-Herrero

et al (2009). They followed a general to specific strategy by first estimating the general

equation. They then use a wald test to conduct a joint hypothesis that the individual non-

significant variables are equal to zero. If the hypothesis is not rejected, they re-estimate the

model only with the controls which were significant in the general regression. They argue that

the coefficients obtained are more efficient.

39

Chapter 5: Analysis and Discussion

5.0. Introduction

The empirical evidence on the determinants of profitability of foreign and domestic

commercial banks in Ghana based on panel data of banks over the period 2003-2013 is

presented in this chapter. The hypotheses are tested in this chapter to examine which factors

significantly influence profitability. The process of the analyses are presented along with the

results. Finally, various robustness tests are carried out to make the results comparable to the

various studies carried on the Ghanaian banking industry.

5.1. Descriptive Statistics

Table 3 summarizes the descriptive statistics of the variables captured in the regression model.

These statistics were generated to give overall description of the data used in the model. The

key descriptive measures are the mean, standard deviation, the minimum and the maximum

values.

Table 3: Descriptive Statistics

Total Sample Domestic Foreign

Obs. Mean St. Dev

Median Max Min Obs Mean St. Dev

Obs Mean St. Dev

Dependent Variable

ROAA 235 0.023 0.035 0.026 0.148 -0.214 103 0.022 0.023 132 0.024 0.042

ROAE 235 0.186 0.276 0.196 1.402 -1.095 103 0.188 0.219 132 0.184 0.314

Bank-Specific Variables

COI 235 0.664 0.405 0.603 4.145 0.001 103 0.670 0.220 132 0.658 0.506

LLPTL 235 0.052 0.057 0.037 0.344 -0.009 103 0.050 0.061 132 0.054 0.054

NLTA 235 0.415 0.143 0.420 0.701 0.030 103 0.485 0.128 132 0.361 0.130

LTA 235 19.575 1.384 19.776 22.255 12.264 103 19.605 1.504 132 19.552 1.289

ETA 235 0.153 0.110 0.126 0.842 0.009 103 0.121 0.050 132 0.178 0.135

YGD 235 0.452 0.939 0.309 12.162 -0.402 103 0.366 0.345 132 0.520 1.213

IETD 235 0.089 0.058 0.076 0.402 0.015 103 0.111 0.064 132 0.071 0.046

Industry-Specific Variable

HHI 235 0.081 0.018 0.076 0.116 0.060 103 0.082 0.019 132 0.080 0.018

Macroeconomic variables

GDP 235 7.638 2.922 7.585 15.009 3.992 103 7.516 2.846 132 7.733 2.988

CPI 235 13.384 4.816 11.608 26.675 8.727 103 13.560 4.945 132 13.248 4.727

M2 235 0.291 0.071 0.273 0.393 0.195 103 0.290 0.071 132 0.292 0.071

The table shows the descriptive statistics of the whole sample and the sub-samples. Macro-economic

variables are in percentages. For the Notation of variables, see Table 2

40

The table shows descriptive statistics of all the variables for both the total samples and the sub-

samples. The average profitability returns in the industry are positive for both ROAA and

ROAE. Profitability measured by ROAA is generally not high within the industry. The banks

are able to generate 2.3% return on their Total Assets with a standard deviation of 3.5%.

Foreign banks on the average perform better than domestic banks however; the difference is

not that substantial. Besides, the variation of profitability is higher for foreign banks than their

domestic peers indicated by the higher standard deviations (4.2% > 2.3%).

ROAE is significantly higher than ROAA showing that the banks are able generate substantial

returns for their equity holders. The mean value for the whole sample is 18.6% with domestic

banks generating more return on equity than foreign banks. The higher equity does not

necessarily depict a healthy profitability but may reflect a low capital adequacy because of the

relationship between leverage and equity (European Central Bank, 2010).

Looking at capital adequacy (ETA), foreign banks are better capitalized (17.8%) than domestic

banks (12.1%). The difference of 5.7% is quite substantial. A reason could be the fact that BOG

gives stricter for foreign banks to meet minimum capital requirements than local banks. In spite

of the significant differences, the mean value of the total sample (15.3%) shows the industry

within that period complied with and even performed better than the Central Bank capital

adequacy requirement of 10%.

The mean of the variable cost-to-income (COI) is very high. It tells that expenses are high

within the industry and that operating efficiency is weak within the industry. Furthermore, there

is a large range between the minimum and maximum value (414.4%) meaning that the most

efficient has a quite substantial cost advantage compared to the least efficient bank.

For liquidity, NLTA shows a mean value of 41.5% for the total sample. The domestic banks

are more illiquid than the foreign banks. On the average, 48.5% of the total assets of domestic

banks are tied up in loans compared to 36.1% for foreign banks.

For the industry-specific variable, the mean value of HHI (0.081) shows an unconcentrated and

competitive market. The minimum (0.060) and maximum (0.116) shows the industrial has

grown more competitive over the period of 2003 to 2013. This can be attributed to the changes

in capital requirements and the increase in foreign banks within the period.

For the macro-economic variables, Real GDP growth has been positive and growing at an

average of 7.6%. The growth has relatively been stable and positive judging from the standard

41

deviation of 2.9%. Inflation is relatively high with a mean value of 13.38%. The maximum

value of inflation is 26.67% while the minimum value is 8.72%. This goes to show that inflation

over the period is substantially high and can be detrimental for the banks if not anticipated well.

5.2. Correlation between Variables:

Table 4 shows the correlations between all the variables both the dependent and independent

variables. The coefficient of correlation provides an index of the direction and the magnitude

of the relationship between two set of scores without implying causality. The sign of the

coefficient is an indication of the direction of the relationship. The absolute value of the

coefficient indicates the magnitude. A correlation of -1 represents a perfect negative correlation

in which variables move in exactly the opposite direction while 1 represents variables moving

in the same direction (Stockburger, 1996).

Table 4 shows the correlation matrix. From the table, the highest correlation exists between the

two dependent variables – ROAA and ROAE (0.835). This is not surprising judging from the

relationship between Equity and Assets. The relationship is a positive one and this high

correlation is not a problem for our model since the two are both dependent variables used

separately in the models. Moreover, it is assumed that the high correlation makes it possible to

expect similar relationships between the explanatory variables and both dependent variables.

The main aim of the correlation matrix is to find the existence of multicollinearity among the

independent variables. Econometric references have indicated that collinearity increases

estimates of parameter variance, yields high R-square in the face of low parameter significance,

and results in parameters with incorrect signs and implausible magnitudes (Mela & Kopalle,

2002). Green et al. (1988) and Lehmann et al. (1998) respectively suggest 0.9 and 0.7 as a

threshold of bivariate correlations for the harmful effect of collinearity. The results displayed

from the correlation matrix shows the absence of multicollinearity among the independent

variables based on the thresholds.

42

Table 4: Correlation Matrix of All Variables

ROAA ROAE COI LLPTL NLTA LTA ETA YGD IETD AGE FOREIGN LISTED HHI GDP CPI M2

ROAA 1.000

ROAE 0.835 1.000

COI -0.779 -0.569 1.000

LLPTL -0.146 -0.227 -0.031 1.000

NLTA 0.145 0.206 -0.149 0.018 1.000

LTA 0.301 0.215 -0.280 0.177 0.264 1.000

ETA 0.031 -0.142 0.010 0.022 -0.373 -0.176 1.000

YGD 0.009 -0.007 0.026 -0.061 -0.131 -0.108 0.134 1.000

IETD -0.135 -0.119 -0.009 -0.014 0.127 -0.359 -0.069 0.054 1.000

AGE 0.254 0.255 -0.206 0.088 0.390 0.315 -0.191 -0.209 -0.240 1.000

FOREIGN 0.029 -0.007 -0.015 0.034 -0.432 -0.019 0.260 0.081 -0.341 -0.300 1.000

LISTED 0.225 0.256 -0.173 -0.184 0.230 0.199 -0.143 -0.094 -0.013 0.340 -0.129 1.000

HHI -0.031 0.133 0.076 -0.278 -0.131 -0.521 -0.263 -0.053 0.103 0.195 -0.055 0.071 1.000

GDP 0.042 -0.065 -0.071 0.226 0.031 0.305 0.121 0.000 -0.245 -0.092 0.037 -0.034 -0.540 1.000

CPI -0.081 0.022 0.003 -0.157 -0.127 -0.317 -0.105 -0.113 0.257 0.106 -0.032 0.041 0.563 -0.572 1.000

M2 -0.127 -0.060 0.189 -0.120 0.110 -0.067 -0.133 0.137 -0.106 -0.017 0.015 -0.014 0.046 0.313 -0.281 1.000

Correlation matrix among all variables, the main focus is the degree of correlation among the independent variables. The correlation coefficients show no

strong correlation among the independent variables which is an indication of no multi-collinearity among the variables. Notation of the Variables in Table 2.

43

5.3. Unit Root Test

The econometric analysis of models (1) and (2) may suffer from the issue of stationarity. The

existence of unit root in the dependent variables will give spurious regression and results. As a

result, there is the need to test for stationarity of the panel using a unit root test for unbalanced

panels. Maddala and Wu (1999) suggest the use of the Fisher test. They argue that the Fisher

test performs better than other tests for unit root in a panel data. The Fisher test has the added

benefit of not requiring a balanced data unlike most of the other tests. Given that the data is

unbalanced, the Fisher test then serves as the best option. The results of the Unit root tests is

shown in Appendix 2. The test was made for the two dependent variables. The null hypothesis

of unit root or non-stationarity is rejected at 5% level for both dependent variables (ROAA and

ROAE). Given that the dependent variables are both stationary, it shows it is less likely to get

spurious results with our model estimation.

5.4. Hausman Test (Fixed Effects/Random Effects)

The next issue is the choice between Fixed Effects and Random Effects Model. The Hausman

Test is employed on model (2) to identify the appropriate Effects model. The result of the

Hausman Test is shown in appendix 3. The rule is that if the chi square statistic obtained by

the Hausman test is larger than the critical chi-square χ0.05,122 = 21.03, then the fixed effects

estimator is the appropriate choice. The results showed a chi square value for ROAA and

ROAE as 20.38 and 9.87 respectively. They are both lower than the chi critical value at 5%

significant level. This tells that the difference in co-efficients are systematic and provides

evidence in favor of the Random Effects Model. The test results can be found at Appendix 3.

5.5. Test for Heteroscedasticity

The test for Heteroscedasticity was based on a Likelihood-Ratio (LR) test. Wiggins and Poi

(2013) developed the test. They argued that LR test could be used to test heteroscedasticity

since iterated Generalized Least Squares (GLS) with only heteroscedasticity produces

maximum-likelihood parameter estimates. The Stata® commands, xtgls and lrtest are used for

the test of heteroscedasticity in Panel data (Wiggins and Poi, 2013). The null hypothesis of this

test is homoscedasticity or No heteroscedasticity.

44

5.6. Test for Auto-correlation

The models were tested for autocorrelation since autocorrelation biases the standard errors and

make the results less efficient. The test for autocorrelation was made using Wooldridge (2002)

test. Drukker (2003) argues that Wooldridge’s test is very attractive because it requires

relatively few assumptions and easy to implement. Wooldridge test is applicable to both

balanced and unbalanced datasets and the test is proven to have good size and power properties

with samples of moderate sizes like this study. Drukker (2003) developed a new Stata®

command, xtserial that implements the Wooldridge’s test for serial correlation in panel data.

This command was used to test for serial correlation in all models. The null hypothesis for the

test is ‘No First-Order Autocorrelation’.

5.7. Empirical Result

The study investigates empirically which are the determinants of bank profitability among

domestic and foreign banks in Ghana with an annual panel data for a maximum of 27 banks

during the period 2003 – 2013. It uses two measures of bank profitability that are ROAA and

ROAE. Results for ROAA are shown in table 5 while that of ROAE are shown in table 6. The

stata regression commands are displayed in Appendixes 4 and 5 respectively for ROAA and

ROAE. Robust Standard Errors were used since the Wiggins and Poi (2013) LR Test and

Wooldridge test detected heteroskedasticity and autocorrelation respectively in the samples.

45

Table 5: Results for ROAA (Model 1)

(A) All Banks in

Sample

(B) Domestic Banks in

Sample

(C) Foreign Banks in

Sample

Dependent Variable: ROAA

Jointly non-significant variables excluded

Jointly non-significant

variables excluded

Jointly non-significant

variables excluded

Bank-Specific Variables

Cost to Income - COI -0.0634*** (0.0119)

-0.0622*** (0.0117)

-0.0302** (0.01539)

-0.0330** (0.0139)

-0.0641*** (0.0138)

-0.0661*** (0.0136)

Loan Loss Provision to Total Liabilities- LLPTL

-0.1049** (0.0445)

-0.1222*** (0.0368)

-0.0727 (0.05495)

-0.0954* (0.0489)

-0.1149** (0.0479)

Net Loans to Total Assets -NLTA

0.0087 (0.0173)

0.0166 (0.02561)

-0.0030 (0.0225)

Log of Total Assets - LTA 0.0030 (0.0030)

0.0031* (0.0017)

0.0094* (0.0053)

0.0063** (0.0026)

Equity to Total Assets – ETA

0.0285 (0.0213)

0.0270** (0.0130)

0.1263*** (0.04562)

0.1434*** (0.0544)

0.0486** (0.0215)

0.0327** (0.0164)

Annual Growth in Deposits – YGD

0.0020 (0.0016)

0.0255* (0.01520)

0.0242* (0.0144)

0.0008 (0.0015)

Interest Expense to Total Deposits – IETD

-0.0405 (0.0268)

-0.0568** (0.02706)

-0.0504 (0.0313)

-0.0675** (0.0319)

-0.0658** (0.0313)

AGE1 0.0057 (0.0042)

0.0060** (0.0030)

-0.0029 (0.00498)

0.0036 (0.0029)

AGE2 0.0065 (0.0096)

0.0089 (0.0054)

0.0025 (0.0056)

Foreign Banks 0.0017 (0.0025)

Listed Banks 0.0017 (0.0042)

0.0059** (0.00280)

0.0053** (0.0025)

-0.0040 (0.0051)

46

Industry-Specific Variable

Concentration – HHI 0.0676 (0.2709)

0.0996 (0.21556)

0.2201 (0.227)

Macroeconomic Variables

Real Gross Domestic Product - GDP

-0.0002 (0.0004)

-0.0004 (0.0007)

Inflation – CPI -0.0004 (0.0005)

Money supply – M2 0.0308 (0.0381)

-0.0040 (0.03868)

0.0769* (0.0459)

0.0695 (0.0435)

Financial Crisis - CRISIS -0.0106*** (0.0040)

-0.0086*** (0.0033)

-0.0067 (0.00585)

-0.0182*** (0.0064)

-0.0193*** (0.0067)

Constant -0.0007 (0.0791)

0.0041 (0.0353)

0.0149 (0.03427)

0.0217 (0.0157)

-0.1447 (0.1286)

-0.0605 (0.0626)

Observations 235 235 103 103 132 132

Number of Groups 27 27 12 12 15 15

R-Squared 0.6951 0.6770 0.4074 0.3342 0.8343 0.8282

F-Value 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Heteroskedasticity (P-Value)

0.0000 0.0000

Autocorrelation (Wooldridge Test P-Value)

0.0003 0.0167 0.0002

Hausman Test (P-Value)

0.0603 0.6978 0.4475

Robust Standard Errors are in Parentheses. For Notation of the variables, refer to Table 2. The Significant parameters are shown with *, ** and *** for 10%,

5% and 1% significant levels respectively.

47

Table 6: Results for ROAE (Model 2)

(A) All Banks in

Sample

(B) Domestic Banks

in Sample

(C) Foreign Banks in

Sample

Dependent Variable: ROAE Jointly non-significant variables excluded

Jointly non-significant variables excluded

Jointly non-significant variables excluded

Bank-Specific Variables

Cost to Income - COI -0.3433*** (0.0911)

-0.3396 (0.0933)

-0.3206** (0.1605)

-0.38343** (0.1816)

-0.2982*** (0.0950)

-0.3088*** (0.1053)

Loan Loss Provision to Total Liabilities- LLPTL

-0.9262* (0.5124)

-1.0183 (0.3608)

-0.7079 (0.7556)

-0.91624* (0.5560)

-1.2583** (0.5856)

-1.2335** (0.5251)

Net Loans to Total Assets -NLTA

0.2336 (0.2298)

0.1078 (0.3241)

0.2619 (0.2954)

Log of Total Assets - LTA 0.0293 (0.0278)

0.0512 (0.0246)

0.0965*** (0.0300)

0.0903*** (0.0173)

Equity to Total Assets – ETA

-0.0643 (0.2541)

-0.1651 (0.6223)

-0.33536 (0.3976)

0.2721 (0.1982)

Annual Growth in Deposits – YGD

0.0171 (0.0160)

0.224 1 (0.1426)

0.0066 (0.0083)

Interest Expense to Total Deposits – IETD

-0.5219** (0.2635)

-0.6335* (0.3598)

-0.41963 (0.2952)

-0.1337 (0.2683)

AGE1 0.0537 (0.0525)

0.0528 (0.0265)

0.0616* (0.0365)

0.0618** (0.0250)

AGE2 0.0131 (0.1044)

-0.0022 (0.1105)

0.0069 (0.0590)

Foreign Banks 0.0247 (0.0372)

Listed Banks 0.0286 (0.0456)

0.0401 (0.0446)

-0.0239 (0.0353)

48

Industry-Specific Variable

Concentration – HHI 2.7848 (2.5766)

3.3485 (1.4460)

1.3229 (2.8305)

5.5634*** (1.4118)

5.0488*** (1.0307)

Macroeconomic Variables

Real Gross Domestic Product - GDP

-0.0027 (0.0053)

Inflation – CPI -0.0020 (0.0050)

-0.0018 (0.0051)

Money supply – M2 0.1082 (0.3996)

-0.0775 (0.3114)

0.3320 (0.4125)

Financial Crisis - CRISIS -0.0503 (0.0368)

-0.0349 (0.0324)

-0.0513 (0.0657)

-0.0915* (0.0523)

Constant -0.3964 (0.7475)

-0.8141 (0.5963)

0.3181 (0.4169)

0.578167*** (0.1037)

-2.0682*** (0.6895)

-1.7407*** (0.4359)

Observations 235 235 103 103 132 132

Number of Groups 27 27 12 12 15 15

R-Squared 0.4724 0.4419 0.3280 0.1882 0.5948 0.5635

F-Value 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Heteroskedasticity (P-Value)

0.0000 0.0000 0.0000

Autocorrelation (Wooldridge Test P-Value)

0.0002 0.0032 0.0072

Hausman Test (P-Value) 0.6572 0.4815 0.0731

Robust Standard Errors are in Parentheses. For Notation of the variables, refer to Table 2. The Significant parameters are shown with *, ** and *** for 10%,

5% and 1% significant levels respectively.

49

Table 5 and table 6 present the results for ROAA and ROAE respectively. It can be seen that

Operational Efficiency (Cost-to-Income) and Credit Risk (LLPTL) are the main determinants

of ROAA and ROAE in most cases due to their relatively high significant coefficients. The

variable Cost-to-Income (COI) is very significant at all stages of the regression. The co-

efficient shows a negative relationship between both COI and ROAA and COI and ROAE.

This relationship comes as expected from the literature review. Since Cost-to-Income was used

as a proxy to measure Operational efficiency, the result shows the extent to which operational

inefficiency is negatively affecting profitability in the Ghanaian industry. The co-efficient of

COI for foreign banks is slightly lower than that for domestic banks which shows that high

operational costs are affecting the domestic banks’ profitability more than foreign banks. The

negative relationship effect of COI is a finding that is consistent with most of the empirical

literature. Kutsienyo (2011) and Gyamerah and Amoah (2015) found similar results for the

Ghanaian Banking Industry. Athanasoglou et al (2008) found similar positive relationship for

Greek banks. Similar results were concluded from Liu and Wilson (2010) and Dietrich and

Wanzenried (2011).

Credit risk measured by Loan Loss Provision to Total Liabilities show a significant bearing on

ROAA in the whole industry. In their IMF working paper, Flamini et al (2009) argued that

credit risk is a major source of bank risk in Sub-Saharan Africa. The result of this study

confirms their statement. The literature review showed that banks in Ghana has been aggressive

in its loan underwriting practices and suffered from high credit risk and loan defaults. This in

turn affects their profitability negatively as shown in the negative coefficients between the

credit risk variable and both dependent variables. The findings however show that while credit

risk is significant within Foreign Banks, it is not significant for domestic Banks. This finding

should confirm the theory that domestic firms are more informed lenders than foreign banks

because they have better knowledge of the market. As a result, the foreign banks are exposed

to higher credit risks than the domestic banks. The negative relationship between credit risk

and profitability confirms the hypothesis. This finding is consistent with studies by Miller and

Noulas (1997), Dietrich and Wanzenried (2011) and Gyamerah and Amoah (2015).

Bank Size proxied by Log of Total Assets is very significant for foreign banks but insignificant

for Local Banks. This effect is the same whether ROAA or ROAE is used as dependent

variable. The Co-efficient is positive indicating that size has a positive influence on

profitability among foreign banks in the industry. A plausible explanation is that foreign banks

are able to adequately leverage the positive effects of bank size to their advantage unlike the

50

domestic banks. Also most of the large sized banks are foreign banks hence the result can be

understood for ROAA.

The positive relationship between Bank Size and ROAE for foreign banks can be understood

from the direction that – when the BOG increase capital requirements, it tends to give local

banks a longer period of time to meet the requirements unlike the foreign banks. Example,

when the BOG increased capital requirements to GH¢60million ($16million) in 2008, it gave

the foreign banks one year to raise the new capital but domestic banks were given 4years. The

finding shows that the foreign banks after meeting these capital requirements in the earliest

possible time are able to benefit from their size through economies of scale and diversification.

The positive relationship between Bank Size and Profitability is also reported by studies such

as Smirlock (1985) and Flamini et al (2009).

The ratio of Equity-to-Total Assets (ETA) is significant for both domestic sample and foreign

sample when ROAA is used as measure of profitability. ETA has a positive impact on ROAA.

The results show that level of capitalization has a positive impact on profitability within the

Ghanaian Banking industry. As hypothesized, a well capitalized bank is able to absorb shocks

and can also reduce their cost of financing due to their lower risks of bankruptcy. Kutsienyo

(2011) and Gyamerah and Amoah (2015) both found similar positive relationship within the

Ghanaian banking industry. Other external external studies that confirm this result are Bourke

(1989), Demirguc-Kunt and Huizinga (1999), Goddard et al. (2004), Pasiouras and Kosmidou

(2007) and Garcia-Herrero et al (2009).

Similarly, the variable Funding Cost, which is proxied by the ratio of Interest Expense to Total

Deposit (IETD) is significant for both ROAA and ROAE. The Coefficients are negative. This

result is consistent with Dietrich and Wanzenried (2011). The negative relationship

presupposes that banks which are able to source funds more cheaply make better profits.

The Annual Growth in Deposits (YGD) proxies Bank Growth. For the ROAA model, It is

positive and significant at 10% level for domestic banks but insignificant for foreign banks.

The positive relationship shows that domestic banks are able to convert growth in deposits into

interest-earning assets that are able to increase profitability.

Lastly, Bank Age in the ROAA model is shown to have positive and significant results for the

whole sample. However, this result is treated with care given that the variable became

significant in the restricted regression.

51

The Macro-economic variables appear not to have significant effect on profitability in the

models. Inflation is insignificant and this result is in line with Owusu-Antwi et al (2015) who

also found inflation to be insignificant in determining profitability in Ghana. Looking at the

ROAA results in Table 5, Money Supply shows a positive significant effect on foreign banks

but insignificant effect on domestic banks. The Herfindahl-Hirschman Index (HHI) is only

positively significant in the ROAE model for foreign banks.

The Variable Crisis is very significant in the ROAA model for the total sample and foreign

banks. The sign of the co-efficient is negative which is in line with the hypothesis. The highly

significant effect of the crisis on foreign banks is well understood from the angle that these

foreign banks are subsidiaries of their parent companies in the developed countries. Since the

Financial crises mostly affected the financial industry of the developed countries, it had direct

bearing on their subsidiaries in developing countries such as Ghana. This result is in

contradiction with Bentum (2012) who reported that Ghanaian banks were experiencing

profitability during the financial crisis.

5.8. Robustness Test

This part of the thesis discusses whether the results as presented in Table 5 and 6 are valid

explanations for bank profitability in Ghana. The robustness tests will check the validity of the

results under different conditions and make this study more comparable to the ones done in the

Ghanaian banking that used various methodologies.

The main model used the Random Effects Regression Model to estimate equation 1. The

robustness checks are made using the system GMM estimator (presented in column A), Fixed

Effects model (column B) and a Pooled OLS (Column C). Finally, the sample is split into two

based on dummy variable, CRISIS to test differences in the results due to the financial crisis of

2007-2011 (columns D and E).

The robustness test are made using only ROAA as dependent variable and in all cases, robust

standard errors are used to correct for heteroscedasticity.

5.8.1. The Generalized Methods of Moments:

The first robustness test will apply the system Generalized Methods of Moments (GMMM)

estimator to estimate the variables. To use the GMM, a dynamic model of equation (1) is

adopted. The dynamic model includes a one-period lagged dependent variable among the

regressors. This will be used to check whether profitability shows a tendency to persist over

52

time within the Ghanaian banking industry. Researchers such as Athanasoglou et al (2008),

Garcia-Herrero et al, (2009) and Dietrich and Wanzenried (2011) used the system GMM to

investigate profitability determinants in the banking sector. This robustness test follows their

approach. First, the dynamic model of equation (1) is as follows:

Equation 4: Dynamic Model for GMM Estimation

𝛱𝒊𝒕 = 𝜶 + 𝜹𝛱𝒊,𝒕−𝟏 + ∑ 𝜷𝒋𝑿𝒋𝒊𝒕

𝑱

𝒋=𝟏

+ ∑ 𝜷𝒍𝑿𝒍𝒊𝒕

𝑳

𝒍=𝟏

+ ∑ 𝜷𝒎𝑿𝒎𝒊𝒕

𝑴

𝒌=𝟏

+ 𝜺𝒊𝒕 (𝟒)

Where 𝝅𝒊,𝒕−𝟏 is the one-period lagged profitability and δ the speed of adjustment to

equilibrium. A co-efficient (δ) of the one period lagged dependent variable measures the speed

of adjustments of banks’ profitability to equilibrium. A value between 0 and 1 implies that

profits persist, but they will eventually return to their normal (average) level. A value close to

0 means that the industry is fairly competitive (high speed of adjustment), while a value of δ

close to 1 implies less competitive structure (very slow adjustment) [Athanasoglou et al.

(2008)].

As described above, the system GMM estimator described by Arellano and Bover (1995) is

used for the estimation. It uses lagged values of the dependent variable in levels and differences

as instruments, and also, lagged values of other regressors which could potentially suffer from

endogeneity (Garcia-Herrero et al, 2009).

The system GMM estimator makes choices between endogenous, predetermined and

exogenous variables. In estimating the dynamic model (4) with system GMM, the variable COI

is treated as predetermined, LLPTL and NLTA are treated as endogenous and the rest as

exogenous.

System GMM estimator is consistent when there is no second-order autocorrelation and when

the model is not over-identified. The Arrelano and Bond Test for first and second order

autocorrelation is used. From table 7, first-order autocorrelation is rejected but it does not reject

second-order autocorrelation. The Sargan’s test is used to test over-identification which fails

to reject the null of no over-identification. In summary, the specified model passes the

specification test.

The results of the regression is presented in Table 7. The Stata commands for the regressions

are shown in Appendix 6.

53

Table 7: Results for Robustness Tests

(A) GMM

(B) Fixed Effects

(C) Pooled OLS

(D) RE – CRISIS

Sample

(E) RE –

NO CRISIS Sample

Dependent Variable: ROAA

L.ROAA 0.1125 (0.0853)

Cost to Income - COI

-0.0702*** (0.0136)

-0.0624*** (0.0107)

-0.0635*** (0.0109)

-0.0817*** (0.0087)

-0.0470*** (0.0113)

Loan Loss Provision to Total Liabilities- LLPTL

-0.1773** (0.0839)

-0.1354* (0.0688)

-0.1066*** (0.0387)

-0.1033** (0.0473)

-0.1198** (0.0643)

Net Loans to Total Assets - NLTA

-0.0229 (0.0361)

0.0142 (0.0250)

0.0094 (0.0122)

0.0130 (0.0161)

-0.0114 (0.0247)

Log of Total Assets - LTA

0.0017 (0.0022)

-0.0001 (0.0023)

0.0030 (0.0021)

0.0015 (0.0011)

0.0112* (0.0066)

Equity to Total Assets – ETA

0.1253* (0.0721)

0.0494 (0.0318)

0.0282 (0.0179)

0.0133 (0.0146)

0.0804* (0.0451)

Annual Growth in Deposits – YGD

0.0044* (0.0024)

0.0018 (0.0015)

0.0021 (0.0017)

0.0017** (0.0009)

0.0228* (0.0131)

Interest Expense to Total Deposits – IETD

-0.3017 (0.3662)

-0.0024 (0.0340)

-0.0401 (0.0275)

-0.0105 (0.0495)

-0.0269 (0.0381)

AGE1

0.0057 (0.0036)

0.0061 (0.0046)

0.0063 (0.0058)

AGE2

0.0067 (0.0063)

0.0074 (0.0085)

0.0059 (0.0148)

Foreign Banks

0.0018 (0.0028)

-0.0005 (0.0036)

0.0037 (0.0037)

Listed Banks

0.0017 (0.0032)

0.0034 (0.0047)

-0.0044 (0.0051)

Concentration – HHI

0.5443 (1.1473)

0.3233 (0.4162)

0.1104

(0.5406)

Real Gross Domestic Product - GDP

0.0008 (0.0007)

0.0004 (0.0009)

0.0003 (0.0009)

-0.0039 (0.0029)

Inflation – CPI

-0.0003* (0.0002)

-0.0017 (0.0036)

-0.0008 (0.0011)

0.0005 (0.0004)

Money supply – M2

-0.1189 (0.3034)

-0.0269 (0.0489)

0.0332 (0.0374)

Financial Crisis - CRISIS

-0.0003 (0.0118)

-0.0094 (0.0072)

0.0005 (0.0223)

Constant

0.6050 (0.0460)

(0.0118) (0.0659)

0.0273 (0.0421)

-0.1676 (0.1836)

Observations 235 235 114 121

Number of Groups 27 27 25 27

R-Squared 0.6510 0.6970 0.7954 0.7021

54

F-Value 0.0000 0.0000 0.0000 0.0000

Arrelano-Bond test for AR(1)

0.0432

Arrelano-Bond test for AR(2)

0.3450

Sargan-Test for Over-Identification.

1.0000

Robust Standard Errors are in Parentheses. For Notation of the variables, refer to Table 2. The Significant parameters are shown with *, ** and *** for 10%, 5% and 1% significant levels respectively.

Year dummies are used as control in GMM and Fixed effects regressions but not reported.

Table 7 presents the results of the robustness tests. First, it is worth noting that the lagged

dependent variable from the GMM estimation is insignificant which can be explained that

profit persistency does not prevail in the Ghanaian banking industry within the period of study.

From the table, most of the findings in the main results hold. The variable Cost-to-Income

maintains its highly significant effect throughout all the tests. It has consistently been

significant at 1% significantly value with a negative coefficient as hypothesized. This goes to

affirm that the main bank-specific variable that affects profitability of banks in Ghana is the

Cost-to-Income.

The other variable that has withstood the robustness checks is credit risk measured by the Loan

Loss Provision to Total Liabilities. Its level of significance tends to change with the various

tests but it has consistently been significant least at 10% significant level. The variable has

negative effect on profitability in all the robustness tests just as in the main results.

Also, the GMM regression shows that ETA is positive and significant at 10% significant level.

The variable is also significant in the main model for both Domestic and Foreign Samples. In

addition, bank growth in deposits (YGD) remains significant when estimated with the GMM

and during both the crisis and non-crisis period. Finally, during the period of non-crisis, Bank

size and capitalisation were significant but became insignificant during the crisis.

Overall, the bank-specific variables are significant in explaining variations in profitability but

the external variables are mostly not significant in explaining the variations. Both the main

model and the robustness checks confirm this finding.

55

Chapter 6: Summary and Conclusions

6.0. Introduction:

This chapter gives a summary of the thesis and its findings. It gives recommendations based

on the findings and presents limitations to the study.

6.1. Summary:

The paper sought to identify the factors influencing the profitability of foreign and domestic

banks in the Ghanaian banking industry from the period 2003 to 2013. Based on literature

review on empirical works and the Ghanaian banking industry, a set of variables were

accumulated and categorized into Bank-Specific, Industry-Specific and Macro-economic

variables. These Variables were hypothesized to influence profitability either positively or

negatively. Profitability as used in the study was measured by Return on Average Assets

(ROAA) and Return on Average Equity (ROAE). The following Bank-Specific variables were

hypothesized: Operational efficiency measured by Cost-to-Income; Credit Risk measured by

Loan-Loss Provision to Total Liabilities, Liquidity measured by Net Loans to Total Assets,

Bank Size, Bank Growth, Funding Cost, Bank Years of Experience and Bank Ownership.

External variables that were factored included: Bank Concentration measured by Herfindahl-

Hirschman index and Macro-economic variables which where: Real GDP, Inflation and Money

supply.

The findings based on the study showed that foreign banks within the period performed better

than domestic banks but the average difference between the two are not substantial. Also,

foreign banks are adequately capitalised than domestic banks.

The study revealed that Bank-Specific variables are more significant in explaining variations

in profitability than external variables. The two most significant variables that affect

profitability of all banks (whether domestic or foreign) are Operational efficiency and Credit

Risk. The findings prove that operational inefficiencies and credit risks are significantly

eroding profitability of banks in the industry.

Funding costs was significant and positive which presupposes that banks, which are able to

source funds more cheaply are able to make better profits. Bank capitalization also had

significant positive influence for both domestic and foreign banks.

56

Certain variables affected Domestic banks more than foreign banks and vice versa. It was

found that factors such as capitalisation, bank size and the 2008 Global financial crisis

specifically affected the profitability of foreign banks. Capital adequacy was positive and

significant in explaining profits of foreign banks but insignificant for domestic banks. Bank

Size positively influenced foreign banks and finally, the Global financial crisis was found to

have had significant negative impact on the profitability of foreign banks but not on domestic

banks.

For the Domestic banks, the findings revealed that ownership status influenced their

profitability. Domestic Banks that are listed on the Ghana Stock Exchange enjoyed significant

positive variation in profitability than their counterparts that are not listed. In addition, growth

in deposits had positive influence on the profitability of the domestic banks.

From the study, external variables such as GDP, Money supply and Inflation did not have

significant influence on profitability in the industry as a whole but money supply had positive

significant influence on foreign banks.

The study sheds a new light onto profitability determinants within the Ghanaian banking

industry by bringing into fore new perspectives on profitability determinants in the banking

industry. However, most of the findings are in line with other empirical studies in Ghana and

the world at large.

6.2. Recommendations

From the findings in the thesis, the following recommendations are suggested:

Primarily, it is revealed that credit risk significantly affect profits negatively, therefore banks

must adopt and adhere to comprehensive credit risk management practices to reduce the rate

of credit risks they face. The Bank of Ghana in its supervisory role should ensure that all banks

provide information about their creditors to the established credit reference bureaux so credit

officers can make adequate background checks on potential borrowers.

Operational efficiency is one most important factor eroding profit in the industry as a result

management should effectively strategize to control their levels of Operational expenses.

Furthermore, the BOG directive of giving longer period for local banks to attain new capital

requirements is found to benefit the foreign banks more because of their higher level of

57

capitalization at certain points. As a result, it will be in the interest of domestic banks if they

willingly grow their capital at the same pace like the foreign banks.

6.3. Limitation of the Study:

From the studies, it was realised that the sample sizes were somehow small when the data was

divided into foreign samples and domestic samples. Additional periods of data could have

drawn more significant variables than the ones provided. While this was the quest of the

researcher, it was impossible to get significant accounting data on the Ghanaian banking

industry before the millennium. As a result, the period started with year 2003 to match the

introduction of Universal Banking License in Ghana.

Similarly, the accumulated data sourced from the BOG did not provide all necessary

information as an individual financial statement will provide. Since it was very impossible to

accumulate all the individual annual financial statements of the banks because of their

unavailability, the BOG data was the best alternative.

As mentioned, because it did not contain all necessary information as the individual statements,

certain proxies were chosen solely on data availability. Example, in measuring for the proxy

that measures Bank liquidity, the ratio of Net Loans to Customer & Short-term Funding is

argued as the best measure since it shows the relationship between comparatively illiquid assets

which is loans to comparatively stable funding sources that is deposits and other short term

funding (Pasiouras & Kosmidou, 2007). However the ratio of Net Loans to Total Assets was

rather used due to data availability.

Furthermore, even though the Bank of Ghana authenticates the data as fully audited, there were

instances that I traced and cross-checked some individual bank statements and realised certain

transposition errors. Since not all financial statements are available for cross-checking and

corrections, it could be possible that there are some errors which could not be traced and

corrected. The researcher would like to state here that he is aware of the BankScope® database

but this contains more missing data on Ghanaian Banks than the available for the period of

study. Using all efforts, the Bank of Ghana Data was the best option available.

Finally, the data may not reflect the reality in the Ghanaian Banking industry. This is in allusion

to IMF Country report on Ghana in 2011 (IMF, 2011). The report stated there was weakness

in financial accounts of banks in the country. Their findings report practices of overstatement

of capital, profitability and liquidity in the banking sector. Factoring the report, since this thesis

58

uses financial information that spans beyond the report, the findings may necessarily reflect

the data provided rather than the reality in the industry.

59

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68

Appendices:

Appendix 1:

A. List of Commercial Banks in Ghana as at July 2015

Name of Bank Symbol Year of Incorporation

Majority Ownership

1 Access Bank (Ghana) Limited ACCESS 2008 Foreign

2 Agricultural Development Bank Limited

ADB 1965 Local

3 Bank for Africa BARODA 1997 Foreign

4 Bank of Baroda (Ghana) Limited BOA 2007 Foreign

5 Barclays Bank of Ghana Limited BBG 1917 Foreign

6 BSIC (Ghana) Limited BSIC 2008 Foreign

7 CAL Bank Limited CAL 1990 Local

8 Ecobank Ghana Limited ECO 1990 Foreign

9 Energy Bank (Ghana) Limited ENERGY 2010 Foreign

10 Fidelity Bank Limited FAMBL 2006 Local

11 First Atlantic Bank Limited FCP 1994 Foreign

12 First Capital Plus Bank Limited Fidelity 2009 Local

13 GCB Bank Limited GCB 1953 Local

14 GN Bank Limited GNB 2014 Local

15 Guaranty Trust Bank (Ghana) Limited GTB 2004 Foreign

16 HFC Bank Ghana Limited HFC 1990 Local

17 International Commercial Bank Limited

ICB 1996 Foreign

18 National Investment Bank Limited NIB 1963 Local

19 Prudential Bank Limited PBL 1993 Local

20 Societe-Generale (SG) Ghana Limited ROYAL 1975 Foreign

21 Stanbic Bank Ghana Limited SCB 1999 Foreign

22 Standard Chartered Bank Ghana Limited

SGGH 1896 Foreign

23 The Royal Bank Limited STANBIC 2011 Local

24 UniBank (Ghana) Limited UBA 1997 Local

25 United Bank for Africa (Ghana) Limited

UMB 2004 Foreign

26 Universal Merchant Bank Ghana Limited

UNIBANK 1971 Local

27 UT Bank Limited UTB 1995 Local

28 Zenith Bank (Ghana) Limited ZEN 2005 Foreign

69

B. Share of Industry Operating Assets

Source: PricewaterHouse Coopers – 2014 Ghana Banking Survey

70

C. Share of Industry Deposits

Source: PricewaterHouse Coopers – 2014 Ghana Banking Survey

71

Appendix 2: Unit Root Test Results

A. Dependent Variable - ROAA

Panel unit root test: Summary

Series: ROAA

Date: 06/06/15 Time: 14:10

Sample: 2003 2013

Exogenous variables: Individual effects

User-specified lags: 1

Newey-West automatic bandwidth selection and Bartlett kernel

Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -5.00645 0.0000 20 169

Null: Unit root (assumes individual unit root process)

Im, Pesaran and Shin W-stat -1.41268 0.0789 20 169

ADF - Fisher Chi-square 61.8576 0.0149 20 169

PP - Fisher Chi-square 78.6538 0.0003 20 189 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

B. Dependent Variable – ROAE

Panel unit root test: Summary

Series: ROAE

Date: 06/06/15 Time: 14:13

Sample: 2003 2013

Exogenous variables: Individual effects

User-specified lags: 1

Newey-West automatic bandwidth selection and Bartlett kernel

Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -9.19758 0.0000 20 169

Null: Unit root (assumes individual unit root process)

Im, Pesaran and Shin W-stat -2.82992 0.0023 20 169

ADF - Fisher Chi-square 73.3326 0.0010 20 169

PP - Fisher Chi-square 66.5585 0.0052 20 189

72

** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

Appendix 3: Hausman Test

A. Dependent Variable - ROAA

Dependent Variable: ROAA

Correlated Random Effects - Hausman Test

Equation: Untitled

Test cross-section random effects

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 20.376754 12 0.0603

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob.

COI -0.062478 -0.063481 0.000002 0.4831

LLPTL -0.130900 -0.113391 0.000252 0.2700

NLTA 0.013285 0.010458 0.000051 0.6930

LTA 0.000001 0.002261 0.000002 0.0912

ETA 0.048045 0.029194 0.000274 0.2547

YGD 0.001805 0.001763 0.000001 0.9548

IETD -0.004742 -0.028156 0.000631 0.3512

HHI -0.077017 0.041194 0.005226 0.1020

GDP 0.000237 -0.000096 0.000000 0.0511

CPI -0.000446 -0.000459 0.000000 0.8181

M2 0.021296 0.027810 0.000015 0.0953

CRISIS -0.011698 -0.010852 0.000001 0.2500

B. Dependent Variable – ROAE

Dependent Variable: ROAE Correlated Random Effects - Hausman Test Equation: Untitled Test cross-section random effects

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob. Cross-section random 9.529183 12 0.6572

Cross-section random effects test comparisons:

Variable Fixed Random Var(Diff.) Prob.

73

COI -0.378007 -0.360134 0.000181 0.1843

LLPTL -1.196189 -1.011798 0.022408 0.2180 NLTA 0.216165 0.247447 0.004744 0.6497 LTA 0.003058 0.017735 0.000169 0.2594 ETA 0.307414 -0.010832 0.026943 0.0525 YGD 0.022541 0.015387 0.000051 0.3149 IETD -0.115757 -0.284582 0.058071 0.4836 HHI 1.724674 2.524418 0.491597 0.2540 GDP 0.000431 -0.001185 0.000003 0.3253 CPI -0.002872 -0.002677 0.000000 0.7049 M2 0.087025 0.095581 0.001313 0.8134

CRISIS -0.060710 -0.054603 0.000051 0.3917

Regressions in the thesis performed using Stata (StataCorp) statistical software package using

the following commands:

Appendix 4: Stata Commands for ROAA Regression

Regression A

Xtreg ROAA COI LLPTL NLTA LTA ETA YGD IETD AGE1 AGE2 FOREIGN LISTED HHI GDP

CPI M2 CRISIS, re VCE(Cluster ID)

Regression B

Xtreg ROAA COI LLPTL NLTA ETA YGD IETD AGE1 LISTED HHI M2 CRISIS if FOREIGN

==0, re VCE(cluster ID).

Regression C

Xtreg ROAA COI LLPTL NLTA LTA ETA YGD IETD AGE1 AGE2 LISTED HHI GDP M2

CRISIS if FOREIGN ==1, re Cluster (ID)

Appendix 5: Stata Commands for ROAE Regression

Regression A

Xtreg ROAE COI LLPTL NLTA LTA ETA YGD IETD AGE1 AGE2 FOREIGN LISTED HHI GDP

CPI M2 CRISIS, re Cluster(ID)

Regression B

Xtreg ROAE COI LLPTL NLTA ETA YGD IETD AGE2 LISTED HHI M2 CRISIS if FOREIGN

==0, re cluster(ID)

Regression C

Xtreg ROAE COI LLPTL NLTA LTA ETA YGD IETD AGE1 AGE2 LISTED HHI CPI M2 CRISIS

if FOREIGN ==1, re Cluster(ID)

Appendix 6: Stata Command Codes for Robustness Tests

A. GMM Estimation

Xtabond ROAA COI NLTA ETA LLPTL LTA YGD IETD CRISIS i.Year, noconstant lags(1) twostep

pre(COI, lagstruct (0, .)) endog (LLPTL NLTA, lagstruct (0, .)) vce (robust) artests (2)

74

Estat abond

B. Fixed Effect Model

Xtreg ROAA COI LLPTL NLTA LTA ETA YGD IETD AGE1 AGE2 FOREIGN LISTED HHI GDP CPI M2

CRISIS i.Year, fe cluster(ID).

C. Pooled OLS

Regress RoAA COI LLPTL NLTA LTA ETA YGD IETD AGE1 AGE2 FOREIGN LISTED HHI GDP CPI

M2 CRISIS i.Year, robust

D. Random Effects Model on Crises Sample

Xtreg ROAA LLPTL NLTA LTA ETA YGD IETD AGE1 AGE2 FOREIGN LISTED HHI GDP CPI M2 if

CRISIS ==1, re cluster(ID)

E. Random Effects Model on Non-Crisis Sample

Xtreg ROAA COI LLPTL NLTA LTA ETA YGD IETD AGE1 AGE2 FOREIGN LISTED HHI GDP CPI M2

if CRISIS == 0, re cluster(ID)

75

Appendix 7: Full Data of Observations

ROAA ROAE COI LLPTL NLTA LTA ETA YGD IETD HHI GDP CPI M2 FORE

IGN 1. ACCESS - 09 0.004887 0.005805 0.815575 0.0400 0.138900 18.34929 0.841811 0.000000 0.246346 0.075906 3.991571 19.25071 0.247391 1 2. ACCESS - 10 0.057131 0.100344 0.426813 0.0620 0.092543 19.09762 0.440442 12.16208 0.114265 0.069937 8.008593 10.70757 0.319177 1 3. ACCESS - 11 0.035258 0.091012 0.447302 0.0260 0.272649 19.45288 0.350219 0.799619 0.088297 0.064397 15.00889 8.726837 0.339950 1 4. ACCESS - 12 0.064250 0.258080 0.460162 0.1520 0.344132 20.49673 0.213296 2.299445 0.119470 0.062994 8.785039 9.160778 0.251392 1 5. ACCESS - 13 0.050964 0.236342 0.440323 0.0260 0.437481 20.71456 0.217522 0.333051 0.062218 0.059902 7.585001 11.60833 0.195006 1 6. ADB - 03 0.026313 0.169832 0.492377 0.0838 0.289052 19.51721 0.154935 0.000000 0.063155 0.116019 5.200000 26.67495 0.232408 0 7. ADB - 04 0.036321 0.216002 0.524745 0.0976 0.273175 19.55034 0.180935 0.056323 0.065924 0.106536 5.600000 12.62457 0.272752 0 8. ADB - 05 0.022883 0.126605 0.177708 0.0474 0.367938 19.65374 0.180567 0.131481 0.061727 0.106243 5.900004 15.11819 0.194675 0 9. ADB - 06 0.028574 0.163767 0.652251 0.0717 0.367819 19.83245 0.169383 0.291851 0.053221 0.095964 6.399912 10.91517 0.393405 0 10. ADB - 07 0.021196 0.116460 0.787855 0.0140 0.479230 19.95796 0.193139 0.156178 0.044709 0.089943 6.459591 10.73273 0.368345 0 11. ADB - 08 0.027417 0.150457 0.791597 0.0170 0.593664 20.25209 0.174093 0.178859 0.069345 0.081494 8.430638 16.52214 0.391782 0 12. ADB - 09 0.018644 0.110008 0.826239 0.0360 0.507600 20.41479 0.165563 0.330657 0.106368 0.075906 3.991571 19.25071 0.247391 0 13. ADB - 10 0.038168 0.250809 0.819485 0.0410 0.573604 20.72915 0.142407 0.260933 0.077842 0.069937 8.008593 10.70757 0.319177 0 14. ADB - 11 0.039435 0.273053 0.828283 0.0240 0.562922 20.91037 0.146102 0.544021 0.057033 0.064397 15.00889 8.726837 0.339950 0 15. ADB - 12 0.020148 0.143003 0.781496 0.0480 0.535716 21.09084 0.136543 0.165878 0.045925 0.062994 8.785039 9.160778 0.251392 0 16. ADB - 13 0.052596 0.337223 0.639821 0.0590 0.563801 21.20678 0.173265 0.099567 0.054969 0.059902 7.585001 11.60833 0.195006 0 17. BARODA - 09 0.056929 0.103617 0.399943 0.0100 0.280686 16.54831 0.549416 0.000000 0.068057 0.075906 3.991571 19.25071 0.247391 1 18. BARODA - 10 0.012341 0.027539 0.516329 0.0160 0.126502 17.97821 0.423889 5.418092 0.053181 0.069937 8.008593 10.70757 0.319177 1 19. BARODA - 11 0.034195 0.057834 0.260696 0.0000 0.157632 18.33081 0.708888 -0.402215 0.029231 0.064397 15.00889 8.726837 0.339950 1 20. BARODA - 12 0.076275 0.114606 0.142859 0.0000 0.262310 18.56172 0.631134 0.818124 0.028090 0.062994 8.785039 9.160778 0.251392 1 21. BARODA - 13 0.096540 0.161321 0.117275 0.0000 0.328722 18.81974 0.573165 0.672881 0.032949 0.059902 7.585001 11.60833 0.195006 1 22. BOA - 03 0.006592 0.180333 0.692254 0.0675 0.145721 17.21987 0.036554 0.000000 0.211819 0.116019 5.200000 26.67495 0.232408 1 23. BOA - 04 0.018797 0.312674 0.646371 0.0273 0.221979 17.41618 0.079481 0.814935 0.205921 0.106536 5.600000 12.62457 0.272752 1 24. BOA - 05 0.011480 0.140422 0.756739 0.0276 0.258892 17.50339 0.083842 0.395904 0.105752 0.106243 5.900004 15.11819 0.194675 1 25. BOA - 06 -0.007849 -0.076670 0.863729 0.0513 0.276973 18.01045 0.113537 0.574996 0.068576 0.095964 6.399912 10.91517 0.393405 1 26. BOA - 07 0.009874 0.100912 0.741558 0.0210 0.459933 18.82203 0.090885 1.259084 0.090250 0.089943 6.459591 10.73273 0.368345 1 27. BOA - 08 0.030405 0.369514 0.602776 0.0110 0.396138 19.52257 0.078013 1.069680 0.084808 0.081494 8.430638 16.52214 0.391782 1 28. BOA - 09 0.019939 0.256641 0.601716 0.0270 0.468938 19.66443 0.077414 0.156957 0.145232 0.075906 3.991571 19.25071 0.247391 1 29. BOA - 10 -0.043948 -1.095422 0.880170 0.1020 0.453176 19.84283 0.008919 0.231753 0.136216 0.069937 8.008593 10.70757 0.319177 1 30. BOA - 11 -0.034015 -0.588739 0.888774 0.1850 0.504939 19.77805 0.109904 -0.202593 0.089260 0.064397 15.00889 8.726837 0.339950 1 31. BOA - 12 0.004354 0.039842 0.673944 0.1720 0.537538 20.15690 0.108834 0.231436 0.092437 0.062994 8.785039 9.160778 0.251392 1 32. BOA - 13 -0.004757 -0.039547 0.329917 0.2010 0.527703 20.26592 0.130572 0.101128 0.125666 0.059902 7.585001 11.60833 0.195006 1 33. BSIC - 08 -0.087890 -0.117903 1.745101 0.0000 0.149052 16.52894 0.595045 0.000000 0.048164 0.081494 8.430638 16.52214 0.391782 1 34. BSIC - 09 -0.214119 -0.691288 3.203458 0.0210 0.314019 17.04363 0.139216 2.788173 0.092529 0.075906 3.991571 19.25071 0.247391 1

76

35. BSIC - 10 -0.073195 -0.297787 1.330722 0.0240 0.352042 18.04444 0.303577 1.158685 0.097258 0.069937 8.008593 10.70757 0.319177 1 36. BSIC - 11 0.011889 0.037020 0.842167 0.0410 0.405848 18.37727 0.333727 0.476032 0.055913 0.064397 15.00889 8.726837 0.339950 1 37. BSIC - 12 0.024265 0.067658 0.670850 0.0840 0.364679 19.00102 0.371999 0.749973 0.093261 0.062994 8.785039 9.160778 0.251392 1 38. BSIC - 13 -0.021252 -0.060135 0.804659 0.2480 0.424786 19.17864 0.337835 0.166678 0.111119 0.059902 7.585001 11.60833 0.195006 1 39. BBG - 03 0.056391 0.547886 0.394770 0.0308 0.421182 19.75151 0.102924 0.000000 0.025667 0.116019 5.200000 26.67495 0.232408 1 40. BBG - 04 0.056706 0.587724 0.378179 0.0204 0.431200 19.98702 0.092096 0.418030 0.024073 0.106536 5.600000 12.62457 0.272752 1 41. BBG - 05 0.066003 0.660644 0.417777 0.0264 0.557049 20.01576 0.108712 -0.110430 0.026372 0.106243 5.900004 15.11819 0.194675 1 42. BBG - 06 0.063374 0.624632 0.535192 0.0092 0.556034 20.29678 0.095982 0.400867 0.060164 0.095964 6.399912 10.91517 0.393405 1 43. BBG - 07 0.034101 0.398121 0.456449 0.0080 0.535392 20.90261 0.080019 0.470256 0.022703 0.089943 6.459591 10.73273 0.368345 1 44. BBG - 08 -0.005265 -0.061867 0.771048 0.0600 0.518876 21.04890 0.089484 0.283065 0.067325 0.081494 8.430638 16.52214 0.391782 1 45. BBG - 09 -0.012714 -0.117432 0.802527 0.0980 0.355463 21.09152 0.126264 0.010857 0.059955 0.075906 3.991571 19.25071 0.247391 1 46. BBG - 10 0.038807 0.299481 0.559688 0.2010 0.266262 21.21624 0.132560 0.171077 0.025417 0.069937 8.008593 10.70757 0.319177 1 47. BBG - 11 0.047252 0.313361 0.465299 0.1410 0.474508 21.36877 0.166161 0.217735 0.016537 0.064397 15.00889 8.726837 0.339950 1 48. BBG - 12 0.054141 0.304722 0.440917 0.0880 0.452004 21.40446 0.188780 0.091985 0.015018 0.062994 8.785039 9.160778 0.251392 1 49. BBG - 13 0.066508 0.349676 0.409803 0.0830 0.498344 21.56746 0.191406 0.094918 0.016689 0.059902 7.585001 11.60833 0.195006 1 50. CAL - 03 0.035174 0.279126 0.473256 0.0307 0.370695 17.88935 0.126014 0.000000 0.128182 0.116019 5.200000 26.67495 0.232408 0 51. CAL - 04 0.043757 0.265253 0.494135 0.0236 0.365046 18.22882 0.192704 0.615404 0.105972 0.106536 5.600000 12.62457 0.272752 0 52. CAL - 05 0.031226 0.165677 0.510452 0.0373 0.410358 18.38990 0.184875 0.125756 0.089887 0.106243 5.900004 15.11819 0.194675 0 53. CAL - 06 0.035822 0.232059 0.479379 0.0302 0.545503 18.87218 0.135529 0.396389 0.093577 0.095964 6.399912 10.91517 0.393405 0 54. CAL - 07 0.030872 0.239787 0.599431 0.0140 0.488934 19.26682 0.124176 0.381397 0.124288 0.089943 6.459591 10.73273 0.368345 0 55. CAL - 08 0.028049 0.247898 0.622020 0.0110 0.577359 19.63158 0.105491 0.369241 0.155490 0.081494 8.430638 16.52214 0.391782 0 56. CAL - 09 0.022587 0.192119 0.629338 0.0150 0.476647 19.92580 0.126566 0.658108 0.194509 0.075906 3.991571 19.25071 0.247391 0 57. CAL - 10 0.018543 0.131952 0.527780 0.0540 0.513524 20.02962 0.153114 0.029859 0.120789 0.069937 8.008593 10.70757 0.319177 0 58. CAL - 11 0.028524 0.216454 0.482994 0.0600 0.524850 20.48255 0.118211 1.048305 0.082094 0.064397 15.00889 8.726837 0.339950 0 59. CAL - 12 0.050840 0.333049 0.368075 0.0430 0.644661 20.87112 0.175999 0.253815 0.096237 0.062994 8.785039 9.160778 0.251392 0 60. CAL - 13 0.067697 0.378457 0.335384 0.0380 0.629367 21.16729 0.181013 0.129403 0.163781 0.059902 7.585001 11.60833 0.195006 0 61. ECO - 03 0.035617 0.396342 0.468712 0.0201 0.370913 18.94081 0.089865 0.000000 0.038318 0.116019 5.200000 26.67495 0.232408 1 62. ECO - 04 0.033227 0.377784 0.522415 0.0096 0.296707 19.26640 0.086569 0.111389 0.044353 0.106536 5.600000 12.62457 0.272752 1 63. ECO - 05 0.040954 0.517206 0.495089 0.0131 0.366739 19.56882 0.073725 0.371156 0.050334 0.106243 5.900004 15.11819 0.194675 1 64. ECO - 06 0.043470 0.494153 0.499832 0.0019 0.375688 19.88361 0.098365 0.628205 0.039773 0.095964 6.399912 10.91517 0.393405 1 65. ECO - 07 0.036171 0.437726 0.512121 0.0020 0.430032 20.31460 0.072411 0.304832 0.036110 0.089943 6.459591 10.73273 0.368345 1 66. ECO - 08 0.042392 0.505487 0.632686 0.0140 0.436591 20.63955 0.092137 0.558865 0.047481 0.081494 8.430638 16.52214 0.391782 1 67. ECO - 09 0.047262 0.377036 0.485846 0.0200 0.329938 21.03019 0.147824 0.263811 0.063308 0.075906 3.991571 19.25071 0.247391 1 68. ECO - 10 0.040757 0.279459 0.468920 0.0470 0.326853 21.13971 0.144065 0.259762 0.031764 0.069937 8.008593 10.70757 0.319177 1 69. ECO - 11 0.038471 0.298735 0.530164 0.0160 0.398711 21.47845 0.117886 0.417444 0.025827 0.064397 15.00889 8.726837 0.339950 1 70. ECO - 12 0.051997 0.404770 0.487800 0.0410 0.412853 21.94080 0.135119 0.562706 0.039259 0.062994 8.785039 9.160778 0.251392 1 71. ECO - 13 0.046447 0.366717 0.456551 0.0420 0.459417 22.25461 0.120471 0.337746 0.026281 0.059902 7.585001 11.60833 0.195006 1 72. ENERGY - 11 0.018251 0.057422 0.511770 0.0101 0.030063 19.11520 0.317839 0.000000 0.016338 0.064397 15.00889 8.726837 0.339950 1 73. ENERGY - 12 0.028276 0.091671 0.556080 0.0080 0.073641 19.23444 0.300110 0.078825 0.059930 0.062994 8.785039 9.160778 0.251392 1 74. ENERGY - 13 0.023520 0.080214 0.659934 0.0000 0.055821 19.31771 0.286878 -0.199950 0.120995 0.059902 7.585001 11.60833 0.195006 1

77

75. FIDELITY - 06 -0.012137 -0.151175 1.222231 0.0132 0.040344 18.19091 0.080282 0.000000 0.102945 0.095964 6.399912 10.91517 0.393405 0

76. FIDELITY - 07 0.002726 0.046974 0.872883 0.0150 0.235763 18.79698 0.045900 1.065369 0.113268 0.089943 6.459591 10.73273 0.368345 0

77. FIDELITY - 08 0.012747 0.287816 0.821388 0.0010 0.394157 19.20579 0.043216 0.300050 0.107297 0.081494 8.430638 16.52214 0.391782 0

78. FIDELITY - 09 0.006976 0.098131 0.820589 0.0090 0.487515 19.70740 0.087962 0.870571 0.159803 0.075906 3.991571 19.25071 0.247391 0

79. FIDELITY - 10 0.009542 0.139434 0.724467 0.0320 0.325751 20.29394 0.057569 0.856791 0.107287 0.069937 8.008593 10.70757 0.319177 0

80. FIDELITY - 11 0.011542 0.208507 0.674441 0.0320 0.397677 20.75275 0.053953 0.636124 0.076732 0.064397 15.00889 8.726837 0.339950 0

81. FIDELITY - 12 0.023423 0.314156 0.618445 0.0380 0.477680 21.01072 0.090480 0.205483 0.095011 0.062994 8.785039 9.160778 0.251392 0

82. FIDELITY - 13 0.029032 0.319553 0.594267 0.0490 0.476997 21.24780 0.091144 0.254504 0.111568 0.059902 7.585001 11.60833 0.195006 0

83. FAMBL - 03 0.004998 0.075054 0.557201 0.0684 0.389061 17.53611 0.066593 0.000000 0.150247 0.116019 5.200000 26.67495 0.232408 1 84. FAMBL - 04 0.015308 0.226538 0.580180 0.0800 0.430249 17.73722 0.068374 0.010374 0.160748 0.106536 5.600000 12.62457 0.272752 1 85. FAMBL - 05 0.012895 0.168473 0.537642 0.0723 0.471048 18.24247 0.081465 0.390968 0.218230 0.106243 5.900004 15.11819 0.194675 1 86. FAMBL - 06 0.010604 0.156456 0.543227 0.0688 0.399739 18.74045 0.059453 0.619474 0.191555 0.095964 6.399912 10.91517 0.393405 1 87. FAMBL - 07 0.013287 0.224453 0.584681 0.0530 0.407570 18.94025 0.058985 0.603178 0.137243 0.089943 6.459591 10.73273 0.368345 1 88. FAMBL - 08 0.009752 0.265077 0.644492 0.0110 0.465499 19.72424 0.026655 1.566193 0.088183 0.081494 8.430638 16.52214 0.391782 1 89. FAMBL - 09 -0.003654 -0.107386 0.870807 0.0260 0.300524 19.50276 0.043216 -0.267141 0.131068 0.075906 3.991571 19.25071 0.247391 1 90. FAMBL - 10 0.026567 0.404638 0.468671 0.1860 0.530157 19.05937 0.100615 -0.235719 0.176070 0.069937 8.008593 10.70757 0.319177 1 91. FAMBL - 11 0.005335 0.028982 0.825676 0.1790 0.560771 19.02948 0.270091 -0.072380 0.086883 0.064397 15.00889 8.726837 0.339950 1 92. FAMBL - 12 0.017585 0.062916 0.747375 0.1540 0.500641 19.41134 0.285928 0.344461 0.146729 0.062994 8.785039 9.160778 0.251392 1 93. FAMBL - 13 0.015751 0.069271 0.658864 0.1770 0.347719 19.88332 0.190873 0.206814 0.148661 0.059902 7.585001 11.60833 0.195006 1 94. FCP - 13 0.011437 0.075567 0.754277 0.0230 0.419703 20.03478 0.151351 0.000000 0.264877 0.059902 7.585001 11.60833 0.195006 0 95. GCB - 03 0.018419 0.395098 0.573586 0.0548 0.345883 20.04440 0.093237 0.000000 0.056985 0.116019 5.200000 26.67495 0.232408 0 96. GCB - 04 0.031051 0.315712 0.637333 0.0398 0.375746 20.13911 0.103004 0.339812 0.045398 0.106536 5.600000 12.62457 0.272752 0 97. GCB - 05 0.018989 0.172896 0.772452 0.0244 0.469771 20.46965 0.114737 0.487605 0.026416 0.106243 5.900004 15.11819 0.194675 0 98. GCB - 06 0.032913 0.286861 0.676342 0.0044 0.469771 20.46965 0.114737 0.000000 0.023616 0.095964 6.399912 10.91517 0.393405 0 99. GCB - 07 0.025910 0.195785 0.769107 0.0010 0.650281 20.85615 0.144302 0.322752 0.030323 0.089943 6.459591 10.73273 0.368345 0 100. GCB - 08 0.026547 0.200746 0.639342 0.0080 0.660543 21.22149 0.123869 0.227219 0.053074 0.081494 8.430638 16.52214 0.391782 0 101. GCB - 09 0.010170 0.089980 0.698375 0.0280 0.660126 21.37407 0.103715 0.222660 0.117324 0.075906 3.991571 19.25071 0.247391 0 102. GCB - 10 0.027555 0.249706 0.510062 0.1100 0.476509 21.46821 0.116387 0.250749 0.072807 0.069937 8.008593 10.70757 0.319177 0 103. GCB - 11 0.007316 0.080473 0.860578 0.2210 0.194010 21.62121 0.069044 0.308586 0.027392 0.064397 15.00889 8.726837 0.339950 0 104. GCB - 12 0.051098 0.613446 0.526498 0.1470 0.285280 21.81252 0.095067 0.132541 0.023194 0.062994 8.785039 9.160778 0.251392 0 105. GCB - 13 0.070099 0.597126 0.473477 0.1070 0.282160 21.94846 0.136884 0.124375 0.037182 0.059902 7.585001 11.60833 0.195006 0 106. GTB - 06 -0.105375 -0.326584 4.144583 0.0289 0.165598 16.87088 0.322660 0.000000 0.039578 0.095964 6.399912 10.91517 0.393405 1 107. GTB - 07 -0.055347 -0.264937 1.581170 0.0140 0.307860 17.53439 0.150320 1.683100 0.060745 0.089943 6.459591 10.73273 0.368345 1

78

108. GTB - 08 0.034847 0.464564 0.353131 0.0200 0.236474 18.97487 0.057175 1.711126 0.090261 0.081494 8.430638 16.52214 0.391782 1 109. GTB - 09 0.052893 0.247823 0.220918 0.0200 0.375340 19.47089 0.308578 0.972711 0.108359 0.075906 3.991571 19.25071 0.247391 1 110. GTB - 10 0.033348 0.125883 0.570936 0.0500 0.330588 19.84355 0.234828 0.594014 0.078926 0.069937 8.008593 10.70757 0.319177 1 111. GTB - 11 0.032085 0.136619 0.092122 0.0920 0.255735 19.91200 0.234867 -0.072798 0.060347 0.064397 15.00889 8.726837 0.339950 1 112. GTB - 12 0.069137 0.314342 0.429674 0.0830 0.356107 20.34196 0.210232 0.789887 0.055760 0.062994 8.785039 9.160778 0.251392 1 113. GTB - 13 0.064997 0.319362 0.410028 0.0730 0.316533 20.65906 0.198634 0.388561 0.052915 0.059902 7.585001 11.60833 0.195006 1 114. HFC - 03 0.034882 0.201101 0.487514 0.0199 0.417569 17.75105 0.173454 0.000000 0.402158 0.116019 5.200000 26.67495 0.232408 0 115. HFC - 04 0.029542 0.175664 0.522769 0.0093 0.388732 17.89821 0.163614 0.603341 0.344916 0.106536 5.600000 12.62457 0.272752 0 116. HFC - 05 0.011050 0.070849 0.027486 0.0051 0.422515 18.07049 0.149518 0.621030 0.207118 0.106243 5.900004 15.11819 0.194675 0 117. HFC - 06 0.013741 0.114357 0.721524 0.0093 0.614877 18.49050 0.100873 0.979440 0.147291 0.095964 6.399912 10.91517 0.393405 0 118. HFC - 07 0.023918 0.269160 0.604073 0.0110 0.636998 18.89603 0.080856 0.506220 0.157628 0.089943 6.459591 10.73273 0.368345 0 119. HFC - 08 0.021159 0.275321 0.675848 0.0160 0.396829 19.75203 0.075152 0.019696 0.211466 0.081494 8.430638 16.52214 0.391782 0 120. HFC - 09 0.017411 0.194259 0.736422 0.0110 0.603164 19.38042 0.111014 0.379621 0.270827 0.075906 3.991571 19.25071 0.247391 0 121. HFC - 10 0.024604 0.154726 0.669653 0.0490 0.498851 19.70553 0.193064 0.334526 0.157242 0.069937 8.008593 10.70757 0.319177 0 122. HFC - 11 0.024965 0.137570 0.730048 0.0520 0.496193 19.88145 0.171745 0.470665 0.098900 0.064397 15.00889 8.726837 0.339950 0 123. HFC - 12 0.025572 0.129371 0.666776 0.0490 0.577378 20.19188 0.216670 0.356522 0.085847 0.062994 8.785039 9.160778 0.251392 0 124. HFC - 13 0.046563 0.249709 0.496148 0.0500 0.534058 20.69596 0.168226 0.449337 0.102831 0.059902 7.585001 11.60833 0.195006 0 125. ICB - 03 0.020717 0.144944 0.655546 0.0214 0.158541 16.89888 0.142934 0.000000 0.104326 0.116019 5.200000 26.67495 0.232408 1 126. ICB - 04 0.027648 0.158575 0.589678 0.0296 0.197101 17.32679 0.194838 0.547185 0.086874 0.106536 5.600000 12.62457 0.272752 1 127. ICB - 05 0.023528 0.132614 0.538300 0.0620 0.217499 17.62863 0.164537 0.388279 0.108933 0.106243 5.900004 15.11819 0.194675 1 128. ICB - 06 0.018281 0.126982 0.696108 0.0352 0.233836 18.05604 0.130545 0.399599 0.075591 0.095964 6.399912 10.91517 0.393405 1 129. ICB - 07 0.019449 0.144223 0.717042 0.0140 0.296638 18.21555 0.138533 0.238982 0.083657 0.089943 6.459591 10.73273 0.368345 1 130. ICB - 08 0.035342 0.252729 0.391418 0.0010 0.298245 18.45287 0.140876 0.186126 0.087512 0.081494 8.430638 16.52214 0.391782 1 131. ICB - 09 0.003289 0.011664 0.747269 0.0620 0.179102 19.05379 0.359306 0.156533 0.151793 0.075906 3.991571 19.25071 0.247391 1 132. ICB - 10 0.025112 0.072193 0.614558 0.2220 0.220383 19.18387 0.337789 0.294554 0.108473 0.069937 8.008593 10.70757 0.319177 1 133. ICB - 11 0.022420 0.070562 0.593201 0.1420 0.327411 19.36953 0.301073 0.345636 0.067735 0.064397 15.00889 8.726837 0.339950 1 134. ICB - 12 0.000523 0.001892 0.763062 0.1320 0.396957 19.54214 0.255661 0.328246 0.076716 0.062994 8.785039 9.160778 0.251392 1 135. ICB - 13 0.024646 0.091380 0.461950 0.1890 0.395792 19.52861 0.283958 -0.162171 0.089409 0.059902 7.585001 11.60833 0.195006 1 136. NIB - 03 0.031541 0.249651 0.467510 0.0625 0.358150 18.46219 0.126341 0.000000 0.112295 0.116019 5.200000 26.67495 0.232408 0 137. NIB - 04 0.040909 0.341398 0.502919 0.0368 0.528634 18.80878 0.115223 0.294386 0.146242 0.106536 5.600000 12.62457 0.272752 0 138. NIB - 05 0.120507 1.019723 0.760367 0.0621 0.474293 19.06306 0.120466 1.808414 0.090602 0.106243 5.900004 15.11819 0.194675 0 139. NIB - 06 0.018842 0.142483 1.695783 0.0070 0.492269 19.44958 0.140244 0.353131 0.075330 0.095964 6.399912 10.91517 0.393405 0 140. NIB - 07 0.019194 0.144770 1.490673 0.1310 0.566246 19.65689 0.126354 0.440808 0.071478 0.089943 6.459591 10.73273 0.368345 0 141. NIB - 08 -0.075691 -0.741710 0.779041 0.2400 0.584850 19.83351 0.081681 0.039659 0.094635 0.081494 8.430638 16.52214 0.391782 0 142. NIB - 09 -0.046889 -0.475745 0.778495 0.2970 0.525337 20.10992 0.111361 0.324186 0.155973 0.075906 3.991571 19.25071 0.247391 0 143. NIB - 10 0.003839 0.038355 0.864548 0.2690 0.453419 20.39634 0.091651 0.481367 0.108811 0.069937 8.008593 10.70757 0.319177 0 144. NIB - 11 0.009475 0.101054 0.526786 0.3440 0.528412 20.59579 0.095494 0.449250 0.072643 0.064397 15.00889 8.726837 0.339950 0 145. NIB - 12 0.013244 0.126298 0.487744 0.1240 0.512758 20.59195 0.114273 -0.030933 0.063632 0.062994 8.785039 9.160778 0.251392 0 146. NIB - 13 0.037281 0.199830 0.467425 0.1450 0.433315 20.89718 0.239839 0.083801 0.062870 0.059902 7.585001 11.60833 0.195006 0 147. PBL - 03 0.019356 0.395116 0.681482 0.0302 0.225207 17.93490 0.048988 0.000000 0.161624 0.116019 5.200000 26.67495 0.232408 0

79

148. PBL - 04 0.024272 0.462025 0.656958 0.0187 0.337682 18.28426 0.055036 0.647358 0.132630 0.106536 5.600000 12.62457 0.272752 0 149. PBL - 05 0.024364 0.411604 0.673775 0.0160 0.476803 18.43669 0.062761 0.435779 0.103417 0.106243 5.900004 15.11819 0.194675 0 150. PBL - 06 0.013509 0.239153 0.751178 0.0094 0.577112 18.85642 0.052365 0.442310 0.079095 0.095964 6.399912 10.91517 0.393405 0 151. PBL - 07 0.016271 0.332797 0.646987 0.0160 0.472191 19.30606 0.046679 0.704898 0.084758 0.089943 6.459591 10.73273 0.368345 0 152. PBL - 08 0.016791 0.322363 0.732322 0.0040 0.558390 19.45379 0.056750 0.097594 0.092383 0.081494 8.430638 16.52214 0.391782 0 153. PBL - 09 0.009782 0.148744 0.843416 0.0080 0.545999 19.64403 0.073212 0.286699 0.134500 0.075906 3.991571 19.25071 0.247391 0 154. PBL - 10 0.010739 0.125685 0.806560 0.0630 0.554784 19.82307 0.095670 0.350319 0.101160 0.069937 8.008593 10.70757 0.319177 0 155. PBL - 11 0.017227 0.191525 0.724397 0.0720 0.533416 20.12503 0.085718 0.354131 0.066513 0.064397 15.00889 8.726837 0.339950 0 156. PBL - 12 0.015700 0.145453 0.742066 0.0710 0.629791 20.33045 0.126038 0.208364 0.069908 0.062994 8.785039 9.160778 0.251392 0 157. PBL - 13 0.018743 0.155671 0.637532 0.0730 0.632653 20.53767 0.115817 0.248442 0.069280 0.059902 7.585001 11.60833 0.195006 0 158. SGGH - 03 0.042074 0.269376 0.535117 0.0720 0.357113 19.15773 0.156192 0.000000 0.068301 0.116019 5.200000 26.67495 0.232408 1 159. SGGH - 04 0.046876 0.305540 0.564997 0.0376 0.301395 19.31234 0.151046 0.250721 0.050238 0.106536 5.600000 12.62457 0.272752 1 160. SGGH - 05 0.034689 0.242578 0.648503 0.0098 0.425799 19.49039 0.136268 0.131226 0.046775 0.106243 5.900004 15.11819 0.194675 1 161. SGGH - 06 0.030112 0.203968 0.651412 0.0301 0.386901 19.71845 0.156678 0.323847 0.040344 0.095964 6.399912 10.91517 0.393405 1 162. SGGH - 07 0.029565 0.200181 0.636387 0.0240 0.508414 19.85065 0.139820 0.182313 0.029980 0.089943 6.459591 10.73273 0.368345 1 163. SGGH - 08 0.036324 0.242345 0.693530 0.0210 0.657379 19.89491 0.159517 0.068341 0.024203 0.081494 8.430638 16.52214 0.391782 1 164. SGGH - 09 0.038074 0.216541 0.633739 0.0140 0.513649 20.17282 0.188179 0.300438 0.031477 0.075906 3.991571 19.25071 0.247391 1 165. SGGH - 10 0.030683 0.172360 0.655511 0.0720 0.435552 20.34626 0.169474 0.274673 0.023055 0.069937 8.008593 10.70757 0.319177 1 166. SGGH - 11 0.029957 0.171379 0.684924 0.0700 0.409648 20.55019 0.179144 0.263273 0.024182 0.064397 15.00889 8.726837 0.339950 1 167. SGGH - 12 0.031364 0.188876 0.637894 0.0500 0.477627 20.80846 0.155947 0.372729 0.023518 0.062994 8.785039 9.160778 0.251392 1 168. SGGH - 13 0.031546 0.200069 0.599024 0.0550 0.608607 20.91929 0.159221 0.078042 0.027061 0.059902 7.585001 11.60833 0.195006 1 169. STANBIC -

03 0.020074 0.102339 0.661622 0.0271 0.245201 17.54781 0.196148 0.000000 0.040371 0.116019 5.200000 26.67495 0.232408 1 170. STANBIC -

04 0.026322 0.170292 0.658571 0.0573 0.268640 18.12337 0.131187 0.865774 0.043534 0.106536 5.600000 12.62457 0.272752 1 171. STANBIC -

05 0.020276 0.156383 0.574616 0.0568 0.296941 18.29485 0.128368 0.115308 0.055551 0.106243 5.900004 15.11819 0.194675 1 172. STANBIC -

06 0.147575 1.237008 0.005478 -0.0082 0.534176 18.72215 0.113386 0.609733 0.049989 0.095964 6.399912 10.91517 0.393405 1 173. STANBIC -

07 0.116204 1.401755 0.000619 0.0030 0.686953 19.67695 0.071165 1.574416 0.064740 0.089943 6.459591 10.73273 0.368345 1 174. STANBIC -

08 0.036404 0.417510 0.513893 0.0180 0.626682 19.94480 0.099457 0.389570 0.027500 0.081494 8.430638 16.52214 0.391782 1 175. STANBIC -

09 0.001693 0.014987 0.555341 0.1090 0.484086 20.38639 0.121647 0.597643 0.075572 0.075906 3.991571 19.25071 0.247391 1 176. STANBIC -

10 0.023791 0.193563 0.586453 0.0700 0.500182 20.59900 0.123935 0.232490 0.045103 0.069937 8.008593 10.70757 0.319177 1 177. STANBIC -

11 0.028489 0.198109 0.535751 0.0450 0.442816 20.85159 0.159239 0.171202 0.025649 0.064397 15.00889 8.726837 0.339950 1 178. STANBIC -

12 0.039898 0.273258 0.529917 0.0430 0.386443 21.25834 0.137200 0.502370 0.036741 0.062994 8.785039 9.160778 0.251392 1

80

179. STANBIC - 13 0.046219 0.367372 0.441270 0.0280 0.332867 21.79856 0.119173 0.215392 0.043651 0.059902 7.585001 11.60833 0.195006 1

180. SCB - 03 0.046030 0.432602 0.491529 -0.0085 0.368422 19.76333 0.106403 0.000000 0.048941 0.116019 5.200000 26.67495 0.232408 1 181. SCB - 04 0.046807 0.452952 0.483073 0.0032 0.372167 19.90180 0.100667 0.173424 0.041444 0.106536 5.600000 12.62457 0.272752 1 182. SCB - 05 0.048667 0.425554 0.460346 0.0487 0.420150 20.05821 0.126075 -0.015707 0.042866 0.106243 5.900004 15.11819 0.194675 1 183. SCB - 06 0.050190 0.422741 0.468042 -0.0058 0.605022 20.38220 0.113410 0.368922 0.047597 0.095964 6.399912 10.91517 0.393405 1 184. SCB - 07 0.043479 0.390913 0.515140 0.0060 0.555255 20.51097 0.109300 0.200420 0.062684 0.089943 6.459591 10.73273 0.368345 1 185. SCB - 08 0.037005 0.373192 0.611092 0.0040 0.467375 20.70810 0.090829 0.387873 0.053454 0.081494 8.430638 16.52214 0.391782 1 186. SCB - 09 0.048132 0.461751 0.458696 0.0350 0.290937 21.06274 0.113642 0.122316 0.045796 0.075906 3.991571 19.25071 0.247391 1 187. SCB - 10 0.047009 0.406166 0.472129 0.0540 0.280087 21.23482 0.117503 0.311323 0.063560 0.069937 8.008593 10.70757 0.319177 1 188. SCB - 11 0.042692 0.362500 0.429992 0.0500 0.302742 21.40184 0.117995 0.354476 0.035280 0.064397 15.00889 8.726837 0.339950 1 189. SCB - 12 0.062492 0.501128 0.372193 0.0380 0.401390 21.59485 0.130234 0.151729 0.033281 0.062994 8.785039 9.160778 0.251392 1 190. SCB - 13 0.077344 0.521133 0.308031 0.0400 0.378216 21.81799 0.162960 0.043956 0.054572 0.059902 7.585001 11.60833 0.195006 1 191. ROYAL - 13 0.033445 0.085096 0.662896 0.0000 0.204857 19.44349 0.393029 0.000000 0.069282 0.059902 7.585001 11.60833 0.195006 0 192. UNIBANK -

03 0.007818 0.091579 0.871147 0.0257 0.312277 16.07980 0.085365 0.000000 0.099989 0.116019 5.200000 26.67495 0.232408 0 193. UNIBANK -

04 0.010075 0.080461 0.738048 0.0762 0.264251 16.65720 0.147592 0.525399 0.090109 0.106536 5.600000 12.62457 0.272752 0 194. UNIBANK -

05 0.023533 0.176435 0.718148 0.0449 0.427503 16.90270 0.122261 0.405744 0.089524 0.106243 5.900004 15.11819 0.194675 0 195. UNIBANK -

06 0.028513 0.171541 0.820228 0.0369 0.424151 17.43069 0.192139 0.591273 0.134329 0.095964 6.399912 10.91517 0.393405 0 196. UNIBANK -

07 0.027505 0.186181 0.859585 0.0160 0.564344 18.05308 0.123898 1.244606 0.167180 0.089943 6.459591 10.73273 0.368345 0 197. UNIBANK -

08 0.027191 0.194313 0.884062 0.0040 0.558898 18.57458 0.149453 0.595858 0.280613 0.081494 8.430638 16.52214 0.391782 0 198. UNIBANK -

09 0.028207 0.272349 0.786504 -0.0030 0.503557 19.20905 0.080163 1.055904 0.203340 0.075906 3.991571 19.25071 0.247391 0 199. UNIBANK -

10 0.015477 0.164306 0.786504 0.0040 0.560512 19.79058 0.102560 0.665574 0.113389 0.069937 8.008593 10.70757 0.319177 0 200. UNIBANK -

11 0.021814 0.229538 0.708881 0.0100 0.629011 20.15396 0.089803 0.446651 0.090735 0.064397 15.00889 8.726837 0.339950 0 201. UNIBANK -

12 0.022507 0.227693 0.699131 0.0120 0.597068 20.61583 0.104547 0.559264 0.115934 0.062994 8.785039 9.160778 0.251392 0 202. UNIBANK -

13 0.023682 0.212728 0.710815 0.0130 0.635331 20.98484 0.116014 0.284848 0.179093 0.059902 7.585001 11.60833 0.195006 0 203. UBA - 05 -0.134032 -0.673327 2.708378 0.0600 0.248034 17.11241 0.199060 0.000000 0.025189 0.106243 5.900004 15.11819 0.194675 1 204. UBA - 06 -0.040266 -0.255229 1.105589 0.0600 0.453519 17.80717 0.137152 1.455264 0.057896 0.095964 6.399912 10.91517 0.393405 1 205. UBA - 07 -0.023860 -0.269879 0.997542 0.0600 0.322739 18.35746 0.060296 0.667975 0.095354 0.089943 6.459591 10.73273 0.368345 1 206. UBA - 08 -0.043761 -0.945566 1.528138 0.0320 0.188615 19.08811 0.039531 1.066397 0.090863 0.081494 8.430638 16.52214 0.391782 1 207. UBA - 09 0.002998 0.019846 0.819211 0.0860 0.140874 19.41245 0.231740 0.329773 0.073977 0.075906 3.991571 19.25071 0.247391 1 208. UBA - 10 0.027473 0.137380 0.602545 0.0750 0.217271 19.81027 0.178645 0.554366 0.051769 0.069937 8.008593 10.70757 0.319177 1

81

209. UBA - 11 0.045995 0.270327 0.458396 0.0740 0.298966 20.16662 0.164192 0.302995 0.034817 0.064397 15.00889 8.726837 0.339950 1 210. UBA - 12 0.074364 0.433407 0.350219 0.0150 0.394542 20.37401 0.177585 0.059291 0.042908 0.062994 8.785039 9.160778 0.251392 1 211. UBA - 13 0.079797 0.597415 0.273730 0.0390 0.111202 21.16186 0.113552 0.519891 0.079993 0.059902 7.585001 11.60833 0.195006 1 212. UMB - 03 0.016488 0.162152 0.463728 0.0725 0.369682 18.41390 0.101685 0.000000 0.074933 0.116019 5.200000 26.67495 0.232408 0 213. UMB - 04 0.042695 0.387480 0.387143 0.0518 0.466488 18.75246 0.116246 0.352385 0.052699 0.106536 5.600000 12.62457 0.272752 0 214. UMB - 05 0.038729 0.326917 0.482436 0.0297 0.596819 19.07011 0.120084 0.316255 0.059892 0.106243 5.900004 15.11819 0.194675 0 215. UMB - 06 0.035207 0.336835 0.474320 0.0279 0.651448 19.63077 0.095641 0.569695 0.058950 0.095964 6.399912 10.91517 0.393405 0 216. UMB - 07 0.026606 0.280085 0.492494 0.0800 0.623488 19.97240 0.094532 0.441387 0.110270 0.089943 6.459591 10.73273 0.368345 0 217. UMB - 08 0.050536 0.466386 0.379929 0.0540 0.700745 19.90254 0.123180 -0.010563 0.096602 0.081494 8.430638 16.52214 0.391782 0 218. UMB - 09 0.009912 0.101982 0.445428 0.0960 0.478470 20.37141 0.080929 0.607724 0.132739 0.075906 3.991571 19.25071 0.247391 0 219. UMB - 10 0.006131 0.078142 0.505512 0.0170 0.555893 20.50827 0.076317 0.280413 0.124600 0.069937 8.008593 10.70757 0.319177 0 220. UMB - 11 -0.029632 -0.456909 1.127119 0.0000 0.303026 20.46689 0.052904 -0.082868 0.063134 0.064397 15.00889 8.726837 0.339950 0 221. UMB - 12 -0.025992 -0.395397 1.001817 0.0000 0.404961 20.55561 0.077480 0.167966 0.073890 0.062994 8.785039 9.160778 0.251392 0 222. UMB - 13 -0.067179 -0.639374 0.916399 0.0000 0.452954 20.40648 0.137097 -0.237052 0.113848 0.059902 7.585001 11.60833 0.195006 0 223. UTB - 09 0.035490 0.337582 0.481828 -0.0020 0.652512 12.26397 0.105129 0.000000 0.222284 0.075906 3.991571 19.25071 0.247391 0 224. UTB - 10 0.027191 0.270016 0.580614 0.0850 0.610293 13.15509 0.098885 1.282777 0.149030 0.069937 8.008593 10.70757 0.319177 0 225. UTB - 11 0.036628 0.426403 0.600491 0.0480 0.666652 20.38480 0.085892 1.445669 0.183938 0.064397 15.00889 8.726837 0.339950 0 226. UTB - 12 0.024628 0.220717 0.619149 0.0470 0.688666 20.71008 0.130139 0.461653 0.108100 0.062994 8.785039 9.160778 0.251392 0 227. UTB - 13 0.008399 0.075899 0.698747 0.0590 0.686244 21.01320 0.096286 0.153524 0.135306 0.059902 7.585001 11.60833 0.195006 0 228. ZEN - 06 -0.046038 -0.415390 2.069347 0.0094 0.216013 17.98582 0.110831 0.000000 0.017946 0.095964 6.399912 10.91517 0.393405 1 229. ZEN - 07 0.000637 0.009759 0.923292 0.0130 0.424613 18.86696 0.046369 1.563589 0.077666 0.089943 6.459591 10.73273 0.368345 1 230. ZEN - 08 0.035102 0.412673 0.555875 0.0090 0.354790 19.77640 0.100642 1.332851 0.080067 0.081494 8.430638 16.52214 0.391782 1 231. ZEN - 09 0.026119 0.220276 0.513920 0.0520 0.338687 20.13387 0.131113 0.398345 0.109636 0.075906 3.991571 19.25071 0.247391 1 232. ZEN - 10 0.018042 0.136887 0.596742 0.0730 0.412655 20.29868 0.132394 0.178612 0.052215 0.069937 8.008593 10.70757 0.319177 1 233. ZEN - 11 0.034642 0.238516 0.591884 0.1130 0.286591 20.36630 0.157249 0.044653 0.066087 0.064397 15.00889 8.726837 0.339950 1 234. ZEN - 12 0.036853 0.242664 0.516376 0.0890 0.343730 20.67143 0.147904 0.353352 0.037314 0.062994 8.785039 9.160778 0.251392 1 235. ZEN - 13 0.051295 0.383371 0.405618 0.0540 0.352376 21.37592 0.126826 0.366100 0.052960 0.059902 7.585001 11.60833 0.195006 1