Factors influencing the profitability of Domestic and Foreign banks in [PDF]

2015

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

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. 2.2.

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

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

i

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. 4.3.

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

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 ii

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

iii

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, industryspecific 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.

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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

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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

vi

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.

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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.

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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.

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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.

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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 LloyadWilliams 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).

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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 7

(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.

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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. 9

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.

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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. GarciaHerrero (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

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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.

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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.

13

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 14

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 OwusuAntwi 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 15

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)). 16

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

17

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.

18

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). 35.00 30.00 25.00 20.00

Real GDP Growth - % growth

15.00

Inflation - % growth

10.00 5.00

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0.00

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 19

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 PreFinancial 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 20

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 mid1980s 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

21

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 nonperforming 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 22

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 23

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 24

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 retailbanking 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 wellmanaged universal banks will encourage a more competitive and dynamic banking system 25

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.

26

Share of Industry Assets and Deposits 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0%

ECO GCB SCB STANBIC BBG ZEN Fidelity ADB CAL UBA UTB UNIBANK SGGH NIB ACCESS HFC GTB PBL UMB BOA FCP FAMBL ICB ROYAL ENERGY BSIC BARODA

0.0%

Assets

Deposits

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 4year overview of the industry shares of operating assets and deposits from 2010 to 2013 are presented in Appendix 1B and 1C respectively.

27

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 28

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Name of Bank

Symbol

Access Bank (Ghana) Limited Agricultural Development Bank Limited Bank for Africa Bank of Baroda (Ghana) Limited Barclays Bank of Ghana Limited BSIC (Ghana) Limited CAL Bank Limited Ecobank Ghana Limited Energy Bank (Ghana) Limited Fidelity Bank Limited First Atlantic Bank Limited First Capital Plus Bank Limited GCB Bank Limited Guaranty Trust Bank (Ghana) Limited HFC Bank Ghana Limited International Commercial Bank Limited National Investment Bank Limited Prudential Bank Limited Societe-Generale (SG) Ghana Limited Stanbic Bank Ghana Limited Standard Chartered Bank Ghana Limited The Royal Bank Limited UniBank (Ghana) Limited United Bank for Africa (Ghana) Limited Universal Merchant Bank Ghana Limited UT Bank Limited Zenith Bank (Ghana) Limited

ACCESS ADB BARODA BOA BBG BSIC CAL ECO ENERGY FAMBL FCP Fidelity GCB GTB HFC ICB NIB PBL ROYAL SCB SGGH STANBIC UBA UMB UNIBANK UTB ZEN

Year of Incorporation 2008 1965 1997 2007 1917 2008 1990 1990 2010 2006 1994 2009 1953 2004 1990 1996 1963 1993 1975 1999 1896 2011 1997 2004 1971 1995 2005

Majority Ownership Foreign Local Foreign Foreign Foreign Foreign Local Foreign Foreign Local Foreign Local Local Foreign Local Foreign Local Local Foreign Foreign Foreign Local Local Foreign Local Local 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 29

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.

30

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 31

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-toassets 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

32

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 equityto-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

33

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. 34

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 35

SYMBOL

Definition

Dependent Variables ROAA Return on Average Assets ROAE Return on Average Equity Bank Specific Variables COI Operational Efficiency LLPTL Credit Risk NLTA LTA ETA YGD IETD

Liquidity Bank Size Bank Capitalization Bank Growth Funding Costs

AGE

Bank Age

FOREIGN

Bank Ownership

LISTED

Bank Ownership

Measurement

Expected Sign

Source

Net Income/ Average Total Assets (%) Net Income/ Average Total Equity (%)

N/A

BOG

N/A

BOG

Cost to Income Ratio (%)

(-)

BOG

Loan loss Provision to Gross Loans (%) Net Loans to Total Assets (%) Log of Total Assets Equity/Total Assets (%)

(-)

PWC

(+/-) (+/-) (+)

BOG BOG BOG

Annual Growth in Deposits (%) Interest expenses over Average Total Deposits (%) Dummy Variable for different bank age groups AGE1 = Banks between 1980 and 2000 AGE2 = Banks before 1980. Dummy Variable for foreign banks in the sample. Dummy Variable for banks listed on the Ghana Stock Exchange

(+/-) (-)

BOG BOG

(+)

PWC

(+)

PWC

(+/-)

GSE

(+)

BOG

(+)

World Bank Data World Bank Data World Bank Data World Bank

Industry-Specific Variable HHI Market Herfindahl-Hirschman Index Concentration based on market shares. Explanatory Variables(Macroeconomic) GDP Gross Domestic Real GDP growth (%) CPI

Inflation

M2

Money Supply

Crisis

Financial Crises

Current period CPI growth rate (%) Growth in Money Supply (%)

(-)

Dummy variable for the global Financial Crises period (20072011)

(-)

(+)

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 36

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 𝑱

𝛱𝒊𝒕

𝑳

𝑴

𝒎 𝒋 𝒍 = 𝜶 + ∑ 𝜷𝒋 𝑿 + ∑ 𝜷𝒍 𝑿 + ∑ 𝜷𝒎 𝑿 + 𝜺𝒊𝒕 𝒊𝒕 𝒊𝒕 𝒊𝒕 𝒋=𝟏

𝒍=𝟏

(𝟏)

𝒌=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 crosssectional 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

𝑗 𝑙 grouped into 1 × k factors of bank-specific (𝑋 ), industry-specific (𝑋 ) and macroeconomic 𝑖𝑡 𝑖𝑡 𝑚 variables (𝑋 ), where 𝑘 refers to the number of slope parameters for the different variables 𝑖𝑡 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 + 𝛽1 𝑪𝑶𝑰 + 𝛽2 𝑳𝑳𝑷𝑻𝑳 + 𝛽3 𝑵𝑳𝑻𝑨 + 𝛽4 𝑳𝑻𝑨 + 𝛽5 𝐸𝑇𝐴 + 𝛽6 𝒀𝑮𝑫 + 𝛽7 𝑰𝑬𝑻𝑫 + 𝛽8 𝑨𝑮𝑬 + 𝛽9 𝑭𝑶𝑹𝑬𝑰𝑮𝑵 + 𝛽10 𝑳𝑰𝑺𝑻𝑬𝑫 + 𝛽11 𝑯𝑯𝑰 + 𝛽12 𝑮𝑫𝑷 + 𝛽13 𝑪𝑷𝑰 + 𝛽14 𝑴𝟐 + 𝜺𝒊𝒕 ,

(𝟐)

Equation 3: General Model with ROAE as dependent Variable

𝑅𝑂𝐴𝐸𝒊𝒕 =

𝜶 + 𝛽1 𝑪𝑶𝑰 + 𝛽2 𝑳𝑳𝑷𝑻𝑳 + 𝛽3 𝑵𝑳𝑻𝑨 + 𝛽4 𝑳𝑻𝑨 + 𝛽5 𝐸𝑇𝐴 + 𝛽6 𝒀𝑮𝑫 + 𝛽7 𝑰𝑬𝑻𝑫 + 𝛽8 𝑨𝑮𝑬 + 𝛽9 𝑭𝑶𝑹𝑬𝑰𝑮𝑵 + 𝛽10 𝑳𝑰𝑺𝑻𝑬𝑫 + 𝛽11 𝑯𝑯𝑰 + 𝛽12 𝑮𝑫𝑷 + 𝛽13 𝑪𝑷𝑰 + 𝛽14 𝑴𝟐 + 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 37

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 nonsignificant 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.

38

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 Obs. Mean Dependent Variable ROAA 235 0.023 ROAE 235 0.186 Bank-Specific Variables COI 235 0.664 LLPTL 235 0.052 NLTA 235 0.415 LTA 235 19.575 ETA 235 0.153 YGD 235 0.452 IETD 235 0.089 Industry-Specific Variable HHI 235 0.081 Macroeconomic variables GDP 235 7.638 CPI 235 13.384 M2 235 0.291

Total Sample St. Median Max Dev 0.035 0.276 0.405 0.057 0.143 1.384 0.110 0.939 0.058 0.018 2.922 4.816 0.071

0.026 0.196

0.148 1.402

Min

Domestic Obs Mean St. Dev

Foreign Obs Mean St. Dev

-0.214 -1.095

103 103

0.603 4.145 0.001 0.037 0.344 -0.009 0.420 0.701 0.030 19.776 22.255 12.264 0.126 0.842 0.009 0.309 12.162 -0.402 0.076 0.402 0.015 0.076

0.022 0.188

0.023 0.219

132 132

0.024 0.184

0.042 0.314

103 0.670 103 0.050 103 0.485 103 19.605 103 0.121 103 0.366 103 0.111

0.220 0.061 0.128 1.504 0.050 0.345 0.064

132 0.658 132 0.054 132 0.361 132 19.552 132 0.178 132 0.520 132 0.071

0.506 0.054 0.130 1.289 0.135 1.213 0.046

0.116

0.060

103

0.082

0.019

132

0.080

0.018

7.585 15.009 11.608 26.675 0.273 0.393

3.992 8.727 0.195

103 7.516 103 13.560 103 0.290

2.846 4.945 0.071

132 7.733 132 13.248 132 0.292

2.988 4.727 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

39

The table shows descriptive statistics of all the variables for both the total samples and the subsamples. 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 40

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.

41

Table 4: Correlation Matrix of All Variables ROAA ROAE COI ROAA

1.000

ROAE

0.835

LLPTL NLTA LTA

ETA

YGD

-0.779 -0.569

LLPTL

-0.146 -0.227 -0.031 0.206 -0.149

0.018

1.000

LTA

0.301

0.215 -0.280

0.177

0.264

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

0.127 -0.359 -0.069

0.054

-0.135 -0.119 -0.009

AGE

0.254

FOREIGN

0.029 -0.007 -0.015

LISTED

0.225

GDP CPI

0.255 -0.206

CPI

M2

-0.014 0.088

0.390

1.000

0.034 -0.432 -0.019

0.260

0.133

-0.278 -0.131 -0.521 -0.263 -0.053

0.022

0.003

0.226

0.031

0.199 -0.143 -0.094 -0.013

0.305

0.121

1.000

0.081 -0.341 -0.300

-0.184

0.076

0.230

1.000

0.315 -0.191 -0.209 -0.240

0.256 -0.173

0.042 -0.065 -0.071 -0.081

GDP

1.000

0.145

-0.031

FOREIGN LISTED HHI

1.000

NLTA

HHI

AGE

1.000

COI

IETD

IETD

1.000

0.340

-0.129

1.000

0.195

-0.055

0.071

0.000 -0.245 -0.092

0.037

-0.157 -0.127 -0.317 -0.105 -0.113

0.103

0.257

0.106

-0.032

1.000

-0.034 -0.540 0.041

1.000

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. 42

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 χ20.05,12 = 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.

43

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.

44

Table 5: Results for ROAA (Model 1) (A) All Banks in Sample Dependent Variable: ROAA

(B) Domestic Banks in Sample Jointly nonsignificant variables excluded

(C) Foreign Banks in Sample Jointly nonsignificant variables excluded

Jointly nonsignificant variables excluded

Bank-Specific Variables Cost to Income - COI Loan Loss Provision to Total Liabilities- LLPTL Net Loans to Total Assets -NLTA Log of Total Assets - LTA Equity to Total Assets – ETA Annual Growth in Deposits – YGD Interest Expense to Total Deposits – IETD AGE1 AGE2 Foreign Banks Listed Banks

-0.0634*** (0.0119) -0.1049** (0.0445) 0.0087 (0.0173) 0.0030 (0.0030) 0.0285 (0.0213) 0.0020 (0.0016) -0.0405 (0.0268) 0.0057 (0.0042) 0.0065 (0.0096) 0.0017 (0.0025) 0.0017 (0.0042)

-0.0622*** (0.0117) -0.1222*** (0.0368)

0.0031* (0.0017) 0.0270** (0.0130)

0.0060** (0.0030) 0.0089 (0.0054)

-0.0302** (0.01539) -0.0727 (0.05495) 0.0166 (0.02561)

-0.0330** (0.0139)

0.1263*** (0.04562) 0.0255* (0.01520) -0.0568** (0.02706) -0.0029 (0.00498)

0.1434*** (0.0544) 0.0242* (0.0144) -0.0504 (0.0313)

0.0059** (0.00280)

0.0053** (0.0025)

45

-0.0641*** (0.0138) -0.0954* (0.0489) -0.0030 (0.0225) 0.0094* (0.0053) 0.0486** (0.0215) 0.0008 (0.0015) -0.0675** (0.0319) 0.0036 (0.0029) 0.0025 (0.0056)

-0.0040 (0.0051)

-0.0661*** (0.0136) -0.1149** (0.0479)

0.0063** (0.0026) 0.0327** (0.0164)

-0.0658** (0.0313)

Industry-Specific Variable Concentration – HHI

0.0676 (0.2709)

0.0996 (0.21556)

0.2201 (0.227)

Macroeconomic Variables Real Gross Domestic Product - GDP Inflation – CPI Money supply – M2

-0.0002 (0.0004) -0.0004 (0.0005) 0.0308 (0.0381) -0.0106*** (0.0040) -0.0007 (0.0791) 235

-0.0004 (0.0007)

-0.0086*** (0.0033) 0.0041 (0.0353) 235

-0.0040 (0.03868) -0.0067 (0.00585) 0.0149 (0.03427) 103

27

27

R-Squared

0.6951

F-Value

0.0000

Financial Crisis - CRISIS Constant Observations Number of Groups

0.0217 (0.0157) 103

0.0769* (0.0459) -0.0182*** (0.0064) -0.1447 (0.1286) 132

0.0695 (0.0435) -0.0193*** (0.0067) -0.0605 (0.0626) 132

12

12

15

15

0.6770

0.4074

0.3342

0.8343

0.8282

0.0000

0.0000

0.0000

0.0000

0.0000

Heteroskedasticity (P0.0000 0.0000 Value) Autocorrelation 0.0003 0.0167 0.0002 (Wooldridge Test PValue) Hausman Test 0.0603 0.6978 0.4475 (P-Value) 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.

46

Table 6: Results for ROAE (Model 2) (A) All Banks in Sample Dependent Variable: ROAE

(B) Domestic Banks in Sample Jointly nonsignificant variables excluded

(C) Foreign Banks in Sample Jointly nonsignificant variables excluded

Jointly nonsignificant variables excluded

Bank-Specific Variables Cost to Income - COI Loan Loss Provision to Total Liabilities- LLPTL Net Loans to Total Assets NLTA Log of Total Assets - LTA Equity to Total Assets – ETA Annual Growth in Deposits – YGD Interest Expense to Total Deposits – IETD AGE1 AGE2 Foreign Banks Listed Banks

-0.3433*** (0.0911) -0.9262* (0.5124) 0.2336 (0.2298) 0.0293 (0.0278) -0.0643 (0.2541) 0.0171 (0.0160) -0.5219** (0.2635) 0.0537 (0.0525) 0.0131 (0.1044) 0.0247 (0.0372) 0.0286 (0.0456)

-0.3396 (0.0933) -1.0183 (0.3608)

-0.3206** (0.1605) -0.7079 (0.7556) 0.1078 (0.3241)

-0.38343** (0.1816) -0.91624* (0.5560)

-0.0022 (0.1105)

-0.2982*** (0.0950) -1.2583** (0.5856) 0.2619 (0.2954) 0.0965*** (0.0300) 0.2721 (0.1982) 0.0066 (0.0083) -0.1337 (0.2683) 0.0616* (0.0365) 0.0069 (0.0590)

0.0401 (0.0446)

-0.0239 (0.0353)

0.0512 (0.0246) -0.1651 (0.6223) 0.224 1 (0.1426) -0.6335* (0.3598) 0.0528 (0.0265)

47

-0.33536 (0.3976)

-0.41963 (0.2952)

-0.3088*** (0.1053) -1.2335** (0.5251)

0.0903*** (0.0173)

0.0618** (0.0250)

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)

0.578167*** (0.1037) 103

-0.0018 (0.0051) 0.3320 (0.4125) -0.0915* (0.0523) -2.0682*** (0.6895) 132

-1.7407*** (0.4359) 132

Macroeconomic Variables Real Gross Domestic Product - GDP Inflation – CPI

-0.0027 (0.0053) -0.0020 (0.0050) 0.1082 (0.3996) -0.0503 (0.0368) -0.3964 (0.7475) 235

-0.0349 (0.0324) -0.8141 (0.5963) 235

-0.0775 (0.3114) -0.0513 (0.0657) 0.3181 (0.4169) 103

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 (PValue) Autocorrelation (Wooldridge Test P-Value) Hausman Test (P-Value)

0.0000

0.0000

0.0000

0.0002

0.0032

0.0072

0.6572

0.4815

0.0731

Money supply – M2 Financial Crisis - CRISIS Constant Observations Number of Groups

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.

48

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 coefficient 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 49

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. 50

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 51

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 𝑱

𝛱𝒊𝒕

𝑳

𝑴

𝒎 𝒋 𝒍 = 𝜶 + 𝜹𝛱𝒊,𝒕−𝟏 + ∑ 𝜷𝒋 𝑿 + ∑ 𝜷𝒍 𝑿 + ∑ 𝜷𝒎 𝑿 + 𝜺𝒊𝒕 𝒊𝒕 𝒊𝒕 𝒊𝒕 𝒋=𝟏

𝒍=𝟏

(𝟒)

𝒌=𝟏

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.

52

Table 7: Results for Robustness Tests

Dependent Variable: ROAA L.ROAA Cost to Income COI Loan Loss Provision to Total LiabilitiesLLPTL Net Loans to Total Assets - NLTA Log of Total Assets - LTA Equity to Total Assets – ETA Annual Growth in Deposits – YGD Interest Expense to Total Deposits – IETD AGE1

(A) GMM

(B) Fixed Effects

(C) Pooled OLS

0.1125 (0.0853) -0.0702*** (0.0136)

-0.0624*** (0.0107)

-0.0635*** (0.0109)

-0.0817*** (0.0087)

-0.0470*** (0.0113)

-0.1773** (0.0839) -0.0229 (0.0361) 0.0017 (0.0022) 0.1253* (0.0721) 0.0044* (0.0024)

-0.1354* (0.0688) 0.0142 (0.0250) -0.0001 (0.0023) 0.0494 (0.0318) 0.0018 (0.0015)

-0.1066*** (0.0387) 0.0094 (0.0122) 0.0030 (0.0021) 0.0282 (0.0179) 0.0021 (0.0017)

-0.1033** (0.0473) 0.0130 (0.0161) 0.0015 (0.0011) 0.0133 (0.0146) 0.0017** (0.0009)

-0.1198** (0.0643) -0.0114 (0.0247) 0.0112* (0.0066) 0.0804* (0.0451) 0.0228* (0.0131)

-0.3017 (0.3662)

-0.0024 (0.0340)

-0.0401 (0.0275) 0.0057 (0.0036) 0.0067 (0.0063) 0.0018 (0.0028) 0.0017 (0.0032) 0.5443 (1.1473)

-0.0105 (0.0495) 0.0061 (0.0046) 0.0074 (0.0085) -0.0005 (0.0036) 0.0034 (0.0047) 0.3233 (0.4162)

-0.0269 (0.0381) 0.0063 (0.0058) 0.0059 (0.0148) 0.0037 (0.0037) -0.0044 (0.0051)

0.0004 (0.0009) -0.0017 (0.0036) -0.1189 (0.3034) 0.0005 (0.0223) (0.0118) (0.0659) 235 27 0.6970

0.0003 (0.0009) -0.0008 (0.0011) -0.0269 (0.0489)

-0.0039 (0.0029) 0.0005 (0.0004) 0.0332 (0.0374)

0.0273 (0.0421) 114 25 0.7954

-0.1676 (0.1836) 121 27 0.7021

AGE2 Foreign Banks Listed Banks Concentration – HHI Real Gross Domestic Product GDP Inflation – CPI Money supply – M2 Financial Crisis CRISIS Constant Observations Number of Groups R-Squared

0.0008 (0.0007) -0.0003* (0.0002)

-0.0003 (0.0118)

-0.0094 (0.0072) 0.6050 (0.0460) 235 27 0.6510 53

(D) RE – CRISIS Sample

(E) RE – NO CRISIS Sample

0.1104 (0.5406)

F-Value 0.0000 0.0000 0.0000 0.0000 Arrelano-Bond test 0.0432 for AR(1) Arrelano-Bond test 0.3450 for AR(2) Sargan-Test for 1.0000 Over-Identification. 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.

54

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 HerfindahlHirschman 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.

55

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

56

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 57

uses financial information that spans beyond the report, the findings may necessarily reflect the data provided rather than the reality in the industry.

58

References: 

African Business Magazine (2011) Is Ghana Entering A Sweet, Golden Era? [Online] Available

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Appendices: Appendix 1: A. List of Commercial Banks in Ghana as at July 2015 Name of Bank 1 Access Bank (Ghana) Limited 2 Agricultural Development Bank Limited 3 Bank for Africa 4 Bank of Baroda (Ghana) Limited 5 Barclays Bank of Ghana Limited 6 BSIC (Ghana) Limited 7 CAL Bank Limited 8 Ecobank Ghana Limited 9 Energy Bank (Ghana) Limited 10 Fidelity Bank Limited 11 First Atlantic Bank Limited 12 First Capital Plus Bank Limited 13 GCB Bank Limited 14 GN Bank Limited 15 Guaranty Trust Bank (Ghana) Limited 16 HFC Bank Ghana Limited 17 International Commercial Bank Limited 18 National Investment Bank Limited 19 Prudential Bank Limited 20 Societe-Generale (SG) Ghana Limited 21 Stanbic Bank Ghana Limited 22 Standard Chartered Bank Ghana Limited 23 The Royal Bank Limited 24 UniBank (Ghana) Limited 25 United Bank for Africa (Ghana) Limited 26 Universal Merchant Bank Ghana Limited 27 UT Bank Limited 28 Zenith Bank (Ghana) Limited

Symbol ACCESS ADB

Year of Incorporation 2008 1965

Majority Ownership Foreign Local

BARODA BOA BBG BSIC CAL ECO ENERGY FAMBL FCP Fidelity GCB GNB GTB HFC ICB

1997 2007 1917 2008 1990 1990 2010 2006 1994 2009 1953 2014 2004 1990 1996

Foreign Foreign Foreign Foreign Local Foreign Foreign Local Foreign Local Local Local Foreign Local Foreign

NIB PBL ROYAL SCB SGGH

1963 1993 1975 1999 1896

Local Local Foreign Foreign Foreign

STANBIC UBA UMB

2011 Local 1997 Local 2004 Foreign

UNIBANK

1971 Local

UTB ZEN

1995 Local 2005 Foreign

68

B. Share of Industry Operating Assets

Source: PricewaterHouse Coopers – 2014 Ghana Banking Survey

69

C. Share of Industry Deposits

Source: PricewaterHouse Coopers – 2014 Ghana Banking Survey

70

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 CrossMethod

Statistic

Prob.**

sections

Obs

0.0000

20

169

-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

Null: Unit root (assumes common unit root process) Levin, Lin & Chu t*

-5.00645

Null: Unit root (assumes individual unit root process) Im, Pesaran and Shin W-stat

** 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 CrossMethod

Statistic

Prob.**

sections

Obs

0.0000

20

169

-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

Null: Unit root (assumes common unit root process) Levin, Lin & Chu t*

-9.19758

Null: Unit root (assumes individual unit root process) Im, Pesaran and Shin W-stat

71

** 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.

20.376754

12

0.0603

Random

Var(Diff.)

Prob.

Cross-section random

Cross-section random effects test comparisons: Variable

Fixed

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

Chi-Sq. Statistic

Chi-Sq. d.f.

Prob.

9.529183

12

0.6572

Random

Var(Diff.)

Prob.

B. Dependent Variable – ROAE Dependent Variable: ROAE Correlated Random Effects - Hausman Test Equation: Untitled Test cross-section random effects Test Summary Cross-section random Cross-section random effects test comparisons: Variable

Fixed

72

COI LLPTL NLTA LTA ETA YGD IETD HHI GDP CPI M2 CRISIS

-0.378007 -1.196189 0.216165 0.003058 0.307414 0.022541 -0.115757 1.724674 0.000431 -0.002872 0.087025 -0.060710

-0.360134 -1.011798 0.247447 0.017735 -0.010832 0.015387 -0.284582 2.524418 -0.001185 -0.002677 0.095581 -0.054603

0.000181 0.022408 0.004744 0.000169 0.026943 0.000051 0.058071 0.491597 0.000003 0.000000 0.001313 0.000051

0.1843 0.2180 0.6497 0.2594 0.0525 0.3149 0.4836 0.2540 0.3253 0.7049 0.8134 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) 73

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)

74

Appendix 7: Full Data of Observations

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34.

ROAA ACCESS - 09 0.004887 ACCESS - 10 0.057131 ACCESS - 11 0.035258 ACCESS - 12 0.064250 ACCESS - 13 0.050964 ADB - 03 0.026313 ADB - 04 0.036321 ADB - 05 0.022883 ADB - 06 0.028574 ADB - 07 0.021196 ADB - 08 0.027417 ADB - 09 0.018644 ADB - 10 0.038168 ADB - 11 0.039435 ADB - 12 0.020148 ADB - 13 0.052596 BARODA - 09 0.056929 BARODA - 10 0.012341 BARODA - 11 0.034195 BARODA - 12 0.076275 BARODA - 13 0.096540 BOA - 03 0.006592 BOA - 04 0.018797 BOA - 05 0.011480 BOA - 06 -0.007849 BOA - 07 0.009874 BOA - 08 0.030405 BOA - 09 0.019939 BOA - 10 -0.043948 BOA - 11 -0.034015 BOA - 12 0.004354 BOA - 13 -0.004757 BSIC - 08 -0.087890 BSIC - 09 -0.214119

ROAE 0.005805 0.100344 0.091012 0.258080 0.236342 0.169832 0.216002 0.126605 0.163767 0.116460 0.150457 0.110008 0.250809 0.273053 0.143003 0.337223 0.103617 0.027539 0.057834 0.114606 0.161321 0.180333 0.312674 0.140422 -0.076670 0.100912 0.369514 0.256641 -1.095422 -0.588739 0.039842 -0.039547 -0.117903 -0.691288

COI 0.815575 0.426813 0.447302 0.460162 0.440323 0.492377 0.524745 0.177708 0.652251 0.787855 0.791597 0.826239 0.819485 0.828283 0.781496 0.639821 0.399943 0.516329 0.260696 0.142859 0.117275 0.692254 0.646371 0.756739 0.863729 0.741558 0.602776 0.601716 0.880170 0.888774 0.673944 0.329917 1.745101 3.203458

LLPTL 0.0400 0.0620 0.0260 0.1520 0.0260 0.0838 0.0976 0.0474 0.0717 0.0140 0.0170 0.0360 0.0410 0.0240 0.0480 0.0590 0.0100 0.0160 0.0000 0.0000 0.0000 0.0675 0.0273 0.0276 0.0513 0.0210 0.0110 0.0270 0.1020 0.1850 0.1720 0.2010 0.0000 0.0210

NLTA 0.138900 0.092543 0.272649 0.344132 0.437481 0.289052 0.273175 0.367938 0.367819 0.479230 0.593664 0.507600 0.573604 0.562922 0.535716 0.563801 0.280686 0.126502 0.157632 0.262310 0.328722 0.145721 0.221979 0.258892 0.276973 0.459933 0.396138 0.468938 0.453176 0.504939 0.537538 0.527703 0.149052 0.314019

LTA 18.34929 19.09762 19.45288 20.49673 20.71456 19.51721 19.55034 19.65374 19.83245 19.95796 20.25209 20.41479 20.72915 20.91037 21.09084 21.20678 16.54831 17.97821 18.33081 18.56172 18.81974 17.21987 17.41618 17.50339 18.01045 18.82203 19.52257 19.66443 19.84283 19.77805 20.15690 20.26592 16.52894 17.04363

75

ETA YGD 0.841811 0.000000 0.440442 12.16208 0.350219 0.799619 0.213296 2.299445 0.217522 0.333051 0.154935 0.000000 0.180935 0.056323 0.180567 0.131481 0.169383 0.291851 0.193139 0.156178 0.174093 0.178859 0.165563 0.330657 0.142407 0.260933 0.146102 0.544021 0.136543 0.165878 0.173265 0.099567 0.549416 0.000000 0.423889 5.418092 0.708888 -0.402215 0.631134 0.818124 0.573165 0.672881 0.036554 0.000000 0.079481 0.814935 0.083842 0.395904 0.113537 0.574996 0.090885 1.259084 0.078013 1.069680 0.077414 0.156957 0.008919 0.231753 0.109904 -0.202593 0.108834 0.231436 0.130572 0.101128 0.595045 0.000000 0.139216 2.788173

IETD 0.246346 0.114265 0.088297 0.119470 0.062218 0.063155 0.065924 0.061727 0.053221 0.044709 0.069345 0.106368 0.077842 0.057033 0.045925 0.054969 0.068057 0.053181 0.029231 0.028090 0.032949 0.211819 0.205921 0.105752 0.068576 0.090250 0.084808 0.145232 0.136216 0.089260 0.092437 0.125666 0.048164 0.092529

HHI 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.081494 0.075906

GDP 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 8.430638 3.991571

CPI 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 16.52214 19.25071

M2 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.391782 0.247391

FORE IGN 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74.

BSIC - 10 -0.073195 BSIC - 11 0.011889 BSIC - 12 0.024265 BSIC - 13 -0.021252 BBG - 03 0.056391 BBG - 04 0.056706 BBG - 05 0.066003 BBG - 06 0.063374 BBG - 07 0.034101 BBG - 08 -0.005265 BBG - 09 -0.012714 BBG - 10 0.038807 BBG - 11 0.047252 BBG - 12 0.054141 BBG - 13 0.066508 CAL - 03 0.035174 CAL - 04 0.043757 CAL - 05 0.031226 CAL - 06 0.035822 CAL - 07 0.030872 CAL - 08 0.028049 CAL - 09 0.022587 CAL - 10 0.018543 CAL - 11 0.028524 CAL - 12 0.050840 CAL - 13 0.067697 ECO - 03 0.035617 ECO - 04 0.033227 ECO - 05 0.040954 ECO - 06 0.043470 ECO - 07 0.036171 ECO - 08 0.042392 ECO - 09 0.047262 ECO - 10 0.040757 ECO - 11 0.038471 ECO - 12 0.051997 ECO - 13 0.046447 ENERGY - 11 0.018251 ENERGY - 12 0.028276 ENERGY - 13 0.023520

-0.297787 0.037020 0.067658 -0.060135 0.547886 0.587724 0.660644 0.624632 0.398121 -0.061867 -0.117432 0.299481 0.313361 0.304722 0.349676 0.279126 0.265253 0.165677 0.232059 0.239787 0.247898 0.192119 0.131952 0.216454 0.333049 0.378457 0.396342 0.377784 0.517206 0.494153 0.437726 0.505487 0.377036 0.279459 0.298735 0.404770 0.366717 0.057422 0.091671 0.080214

1.330722 0.842167 0.670850 0.804659 0.394770 0.378179 0.417777 0.535192 0.456449 0.771048 0.802527 0.559688 0.465299 0.440917 0.409803 0.473256 0.494135 0.510452 0.479379 0.599431 0.622020 0.629338 0.527780 0.482994 0.368075 0.335384 0.468712 0.522415 0.495089 0.499832 0.512121 0.632686 0.485846 0.468920 0.530164 0.487800 0.456551 0.511770 0.556080 0.659934

0.0240 0.0410 0.0840 0.2480 0.0308 0.0204 0.0264 0.0092 0.0080 0.0600 0.0980 0.2010 0.1410 0.0880 0.0830 0.0307 0.0236 0.0373 0.0302 0.0140 0.0110 0.0150 0.0540 0.0600 0.0430 0.0380 0.0201 0.0096 0.0131 0.0019 0.0020 0.0140 0.0200 0.0470 0.0160 0.0410 0.0420 0.0101 0.0080 0.0000

0.352042 0.405848 0.364679 0.424786 0.421182 0.431200 0.557049 0.556034 0.535392 0.518876 0.355463 0.266262 0.474508 0.452004 0.498344 0.370695 0.365046 0.410358 0.545503 0.488934 0.577359 0.476647 0.513524 0.524850 0.644661 0.629367 0.370913 0.296707 0.366739 0.375688 0.430032 0.436591 0.329938 0.326853 0.398711 0.412853 0.459417 0.030063 0.073641 0.055821

18.04444 18.37727 19.00102 19.17864 19.75151 19.98702 20.01576 20.29678 20.90261 21.04890 21.09152 21.21624 21.36877 21.40446 21.56746 17.88935 18.22882 18.38990 18.87218 19.26682 19.63158 19.92580 20.02962 20.48255 20.87112 21.16729 18.94081 19.26640 19.56882 19.88361 20.31460 20.63955 21.03019 21.13971 21.47845 21.94080 22.25461 19.11520 19.23444 19.31771

76

0.303577 1.158685 0.333727 0.476032 0.371999 0.749973 0.337835 0.166678 0.102924 0.000000 0.092096 0.418030 0.108712 -0.110430 0.095982 0.400867 0.080019 0.470256 0.089484 0.283065 0.126264 0.010857 0.132560 0.171077 0.166161 0.217735 0.188780 0.091985 0.191406 0.094918 0.126014 0.000000 0.192704 0.615404 0.184875 0.125756 0.135529 0.396389 0.124176 0.381397 0.105491 0.369241 0.126566 0.658108 0.153114 0.029859 0.118211 1.048305 0.175999 0.253815 0.181013 0.129403 0.089865 0.000000 0.086569 0.111389 0.073725 0.371156 0.098365 0.628205 0.072411 0.304832 0.092137 0.558865 0.147824 0.263811 0.144065 0.259762 0.117886 0.417444 0.135119 0.562706 0.120471 0.337746 0.317839 0.000000 0.300110 0.078825 0.286878 -0.199950

0.097258 0.055913 0.093261 0.111119 0.025667 0.024073 0.026372 0.060164 0.022703 0.067325 0.059955 0.025417 0.016537 0.015018 0.016689 0.128182 0.105972 0.089887 0.093577 0.124288 0.155490 0.194509 0.120789 0.082094 0.096237 0.163781 0.038318 0.044353 0.050334 0.039773 0.036110 0.047481 0.063308 0.031764 0.025827 0.039259 0.026281 0.016338 0.059930 0.120995

0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.064397 0.062994 0.059902

8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 15.00889 8.785039 7.585001

10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 8.726837 9.160778 11.60833

0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.339950 0.251392 0.195006

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1

75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100. 101. 102. 103. 104. 105. 106. 107.

FIDELITY 06 FIDELITY 07 FIDELITY 08 FIDELITY 09 FIDELITY 10 FIDELITY 11 FIDELITY 12 FIDELITY 13 FAMBL - 03 FAMBL - 04 FAMBL - 05 FAMBL - 06 FAMBL - 07 FAMBL - 08 FAMBL - 09 FAMBL - 10 FAMBL - 11 FAMBL - 12 FAMBL - 13 FCP - 13 GCB - 03 GCB - 04 GCB - 05 GCB - 06 GCB - 07 GCB - 08 GCB - 09 GCB - 10 GCB - 11 GCB - 12 GCB - 13 GTB - 06 GTB - 07

-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

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

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

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

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

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

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

0.029032 0.004998 0.015308 0.012895 0.010604 0.013287 0.009752 -0.003654 0.026567 0.005335 0.017585 0.015751 0.011437 0.018419 0.031051 0.018989 0.032913 0.025910 0.026547 0.010170 0.027555 0.007316 0.051098 0.070099 -0.105375 -0.055347

0.319553 0.075054 0.226538 0.168473 0.156456 0.224453 0.265077 -0.107386 0.404638 0.028982 0.062916 0.069271 0.075567 0.395098 0.315712 0.172896 0.286861 0.195785 0.200746 0.089980 0.249706 0.080473 0.613446 0.597126 -0.326584 -0.264937

0.594267 0.557201 0.580180 0.537642 0.543227 0.584681 0.644492 0.870807 0.468671 0.825676 0.747375 0.658864 0.754277 0.573586 0.637333 0.772452 0.676342 0.769107 0.639342 0.698375 0.510062 0.860578 0.526498 0.473477 4.144583 1.581170

0.0490 0.0684 0.0800 0.0723 0.0688 0.0530 0.0110 0.0260 0.1860 0.1790 0.1540 0.1770 0.0230 0.0548 0.0398 0.0244 0.0044 0.0010 0.0080 0.0280 0.1100 0.2210 0.1470 0.1070 0.0289 0.0140

0.476997 0.389061 0.430249 0.471048 0.399739 0.407570 0.465499 0.300524 0.530157 0.560771 0.500641 0.347719 0.419703 0.345883 0.375746 0.469771 0.469771 0.650281 0.660543 0.660126 0.476509 0.194010 0.285280 0.282160 0.165598 0.307860

21.24780 17.53611 17.73722 18.24247 18.74045 18.94025 19.72424 19.50276 19.05937 19.02948 19.41134 19.88332 20.03478 20.04440 20.13911 20.46965 20.46965 20.85615 21.22149 21.37407 21.46821 21.62121 21.81252 21.94846 16.87088 17.53439

77

0.091144 0.254504 0.066593 0.000000 0.068374 0.010374 0.081465 0.390968 0.059453 0.619474 0.058985 0.603178 0.026655 1.566193 0.043216 -0.267141 0.100615 -0.235719 0.270091 -0.072380 0.285928 0.344461 0.190873 0.206814 0.151351 0.000000 0.093237 0.000000 0.103004 0.339812 0.114737 0.487605 0.114737 0.000000 0.144302 0.322752 0.123869 0.227219 0.103715 0.222660 0.116387 0.250749 0.069044 0.308586 0.095067 0.132541 0.136884 0.124375 0.322660 0.000000 0.150320 1.683100

0.111568 0.150247 0.160748 0.218230 0.191555 0.137243 0.088183 0.131068 0.176070 0.086883 0.146729 0.148661 0.264877 0.056985 0.045398 0.026416 0.023616 0.030323 0.053074 0.117324 0.072807 0.027392 0.023194 0.037182 0.039578 0.060745

0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.095964 0.089943

7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 6.399912 6.459591

11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 10.91517 10.73273

0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.393405 0.368345

0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1

108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142. 143. 144. 145. 146. 147.

GTB - 08 GTB - 09 GTB - 10 GTB - 11 GTB - 12 GTB - 13 HFC - 03 HFC - 04 HFC - 05 HFC - 06 HFC - 07 HFC - 08 HFC - 09 HFC - 10 HFC - 11 HFC - 12 HFC - 13 ICB - 03 ICB - 04 ICB - 05 ICB - 06 ICB - 07 ICB - 08 ICB - 09 ICB - 10 ICB - 11 ICB - 12 ICB - 13 NIB - 03 NIB - 04 NIB - 05 NIB - 06 NIB - 07 NIB - 08 NIB - 09 NIB - 10 NIB - 11 NIB - 12 NIB - 13 PBL - 03

0.034847 0.052893 0.033348 0.032085 0.069137 0.064997 0.034882 0.029542 0.011050 0.013741 0.023918 0.021159 0.017411 0.024604 0.024965 0.025572 0.046563 0.020717 0.027648 0.023528 0.018281 0.019449 0.035342 0.003289 0.025112 0.022420 0.000523 0.024646 0.031541 0.040909 0.120507 0.018842 0.019194 -0.075691 -0.046889 0.003839 0.009475 0.013244 0.037281 0.019356

0.464564 0.247823 0.125883 0.136619 0.314342 0.319362 0.201101 0.175664 0.070849 0.114357 0.269160 0.275321 0.194259 0.154726 0.137570 0.129371 0.249709 0.144944 0.158575 0.132614 0.126982 0.144223 0.252729 0.011664 0.072193 0.070562 0.001892 0.091380 0.249651 0.341398 1.019723 0.142483 0.144770 -0.741710 -0.475745 0.038355 0.101054 0.126298 0.199830 0.395116

0.353131 0.220918 0.570936 0.092122 0.429674 0.410028 0.487514 0.522769 0.027486 0.721524 0.604073 0.675848 0.736422 0.669653 0.730048 0.666776 0.496148 0.655546 0.589678 0.538300 0.696108 0.717042 0.391418 0.747269 0.614558 0.593201 0.763062 0.461950 0.467510 0.502919 0.760367 1.695783 1.490673 0.779041 0.778495 0.864548 0.526786 0.487744 0.467425 0.681482

0.0200 0.0200 0.0500 0.0920 0.0830 0.0730 0.0199 0.0093 0.0051 0.0093 0.0110 0.0160 0.0110 0.0490 0.0520 0.0490 0.0500 0.0214 0.0296 0.0620 0.0352 0.0140 0.0010 0.0620 0.2220 0.1420 0.1320 0.1890 0.0625 0.0368 0.0621 0.0070 0.1310 0.2400 0.2970 0.2690 0.3440 0.1240 0.1450 0.0302

0.236474 0.375340 0.330588 0.255735 0.356107 0.316533 0.417569 0.388732 0.422515 0.614877 0.636998 0.396829 0.603164 0.498851 0.496193 0.577378 0.534058 0.158541 0.197101 0.217499 0.233836 0.296638 0.298245 0.179102 0.220383 0.327411 0.396957 0.395792 0.358150 0.528634 0.474293 0.492269 0.566246 0.584850 0.525337 0.453419 0.528412 0.512758 0.433315 0.225207

18.97487 19.47089 19.84355 19.91200 20.34196 20.65906 17.75105 17.89821 18.07049 18.49050 18.89603 19.75203 19.38042 19.70553 19.88145 20.19188 20.69596 16.89888 17.32679 17.62863 18.05604 18.21555 18.45287 19.05379 19.18387 19.36953 19.54214 19.52861 18.46219 18.80878 19.06306 19.44958 19.65689 19.83351 20.10992 20.39634 20.59579 20.59195 20.89718 17.93490

78

0.057175 1.711126 0.308578 0.972711 0.234828 0.594014 0.234867 -0.072798 0.210232 0.789887 0.198634 0.388561 0.173454 0.000000 0.163614 0.603341 0.149518 0.621030 0.100873 0.979440 0.080856 0.506220 0.075152 0.019696 0.111014 0.379621 0.193064 0.334526 0.171745 0.470665 0.216670 0.356522 0.168226 0.449337 0.142934 0.000000 0.194838 0.547185 0.164537 0.388279 0.130545 0.399599 0.138533 0.238982 0.140876 0.186126 0.359306 0.156533 0.337789 0.294554 0.301073 0.345636 0.255661 0.328246 0.283958 -0.162171 0.126341 0.000000 0.115223 0.294386 0.120466 1.808414 0.140244 0.353131 0.126354 0.440808 0.081681 0.039659 0.111361 0.324186 0.091651 0.481367 0.095494 0.449250 0.114273 -0.030933 0.239839 0.083801 0.048988 0.000000

0.090261 0.108359 0.078926 0.060347 0.055760 0.052915 0.402158 0.344916 0.207118 0.147291 0.157628 0.211466 0.270827 0.157242 0.098900 0.085847 0.102831 0.104326 0.086874 0.108933 0.075591 0.083657 0.087512 0.151793 0.108473 0.067735 0.076716 0.089409 0.112295 0.146242 0.090602 0.075330 0.071478 0.094635 0.155973 0.108811 0.072643 0.063632 0.062870 0.161624

0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019

8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000

16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495

0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178.

PBL - 04 PBL - 05 PBL - 06 PBL - 07 PBL - 08 PBL - 09 PBL - 10 PBL - 11 PBL - 12 PBL - 13 SGGH - 03 SGGH - 04 SGGH - 05 SGGH - 06 SGGH - 07 SGGH - 08 SGGH - 09 SGGH - 10 SGGH - 11 SGGH - 12 SGGH - 13 STANBIC 03 STANBIC 04 STANBIC 05 STANBIC 06 STANBIC 07 STANBIC 08 STANBIC 09 STANBIC 10 STANBIC 11 STANBIC 12

0.024272 0.024364 0.013509 0.016271 0.016791 0.009782 0.010739 0.017227 0.015700 0.018743 0.042074 0.046876 0.034689 0.030112 0.029565 0.036324 0.038074 0.030683 0.029957 0.031364 0.031546

0.462025 0.411604 0.239153 0.332797 0.322363 0.148744 0.125685 0.191525 0.145453 0.155671 0.269376 0.305540 0.242578 0.203968 0.200181 0.242345 0.216541 0.172360 0.171379 0.188876 0.200069

0.656958 0.673775 0.751178 0.646987 0.732322 0.843416 0.806560 0.724397 0.742066 0.637532 0.535117 0.564997 0.648503 0.651412 0.636387 0.693530 0.633739 0.655511 0.684924 0.637894 0.599024

0.0187 0.0160 0.0094 0.0160 0.0040 0.0080 0.0630 0.0720 0.0710 0.0730 0.0720 0.0376 0.0098 0.0301 0.0240 0.0210 0.0140 0.0720 0.0700 0.0500 0.0550

0.337682 0.476803 0.577112 0.472191 0.558390 0.545999 0.554784 0.533416 0.629791 0.632653 0.357113 0.301395 0.425799 0.386901 0.508414 0.657379 0.513649 0.435552 0.409648 0.477627 0.608607

18.28426 18.43669 18.85642 19.30606 19.45379 19.64403 19.82307 20.12503 20.33045 20.53767 19.15773 19.31234 19.49039 19.71845 19.85065 19.89491 20.17282 20.34626 20.55019 20.80846 20.91929

0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006

0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1

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

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

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

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

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

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

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

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

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

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

79

0.055036 0.062761 0.052365 0.046679 0.056750 0.073212 0.095670 0.085718 0.126038 0.115817 0.156192 0.151046 0.136268 0.156678 0.139820 0.159517 0.188179 0.169474 0.179144 0.155947 0.159221

0.647358 0.435779 0.442310 0.704898 0.097594 0.286699 0.350319 0.354131 0.208364 0.248442 0.000000 0.250721 0.131226 0.323847 0.182313 0.068341 0.300438 0.274673 0.263273 0.372729 0.078042

0.132630 0.103417 0.079095 0.084758 0.092383 0.134500 0.101160 0.066513 0.069908 0.069280 0.068301 0.050238 0.046775 0.040344 0.029980 0.024203 0.031477 0.023055 0.024182 0.023518 0.027061

0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902

5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001

12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833

179. 180. 181. 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208.

STANBIC 13 SCB - 03 SCB - 04 SCB - 05 SCB - 06 SCB - 07 SCB - 08 SCB - 09 SCB - 10 SCB - 11 SCB - 12 SCB - 13 ROYAL - 13 UNIBANK 03 UNIBANK 04 UNIBANK 05 UNIBANK 06 UNIBANK 07 UNIBANK 08 UNIBANK 09 UNIBANK 10 UNIBANK 11 UNIBANK 12 UNIBANK 13 UBA - 05 UBA - 06 UBA - 07 UBA - 08 UBA - 09 UBA - 10

0.046219 0.046030 0.046807 0.048667 0.050190 0.043479 0.037005 0.048132 0.047009 0.042692 0.062492 0.077344 0.033445

0.367372 0.432602 0.452952 0.425554 0.422741 0.390913 0.373192 0.461751 0.406166 0.362500 0.501128 0.521133 0.085096

0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.195006

1 1 1 1 1 1 1 1 1 1 1 1 0

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

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

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

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

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

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

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

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

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

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

0.023682 -0.134032 -0.040266 -0.023860 -0.043761 0.002998 0.027473

0.212728 -0.673327 -0.255229 -0.269879 -0.945566 0.019846 0.137380

0.441270 0.491529 0.483073 0.460346 0.468042 0.515140 0.611092 0.458696 0.472129 0.429992 0.372193 0.308031 0.662896

0.710815 2.708378 1.105589 0.997542 1.528138 0.819211 0.602545

0.0280 -0.0085 0.0032 0.0487 -0.0058 0.0060 0.0040 0.0350 0.0540 0.0500 0.0380 0.0400 0.0000

0.0130 0.0600 0.0600 0.0600 0.0320 0.0860 0.0750

0.332867 0.368422 0.372167 0.420150 0.605022 0.555255 0.467375 0.290937 0.280087 0.302742 0.401390 0.378216 0.204857

0.635331 0.248034 0.453519 0.322739 0.188615 0.140874 0.217271

21.79856 19.76333 19.90180 20.05821 20.38220 20.51097 20.70810 21.06274 21.23482 21.40184 21.59485 21.81799 19.44349

20.98484 17.11241 17.80717 18.35746 19.08811 19.41245 19.81027

80

0.119173 0.215392 0.106403 0.000000 0.100667 0.173424 0.126075 -0.015707 0.113410 0.368922 0.109300 0.200420 0.090829 0.387873 0.113642 0.122316 0.117503 0.311323 0.117995 0.354476 0.130234 0.151729 0.162960 0.043956 0.393029 0.000000

0.116014 0.199060 0.137152 0.060296 0.039531 0.231740 0.178645

0.284848 0.000000 1.455264 0.667975 1.066397 0.329773 0.554366

0.043651 0.048941 0.041444 0.042866 0.047597 0.062684 0.053454 0.045796 0.063560 0.035280 0.033281 0.054572 0.069282

0.179093 0.025189 0.057896 0.095354 0.090863 0.073977 0.051769

0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.059902

0.059902 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937

7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 7.585001

7.585001 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593

11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 11.60833

11.60833 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757

0.195006 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177

0 1 1 1 1 1 1

209. 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235.

UBA - 11 UBA - 12 UBA - 13 UMB - 03 UMB - 04 UMB - 05 UMB - 06 UMB - 07 UMB - 08 UMB - 09 UMB - 10 UMB - 11 UMB - 12 UMB - 13 UTB - 09 UTB - 10 UTB - 11 UTB - 12 UTB - 13 ZEN - 06 ZEN - 07 ZEN - 08 ZEN - 09 ZEN - 10 ZEN - 11 ZEN - 12 ZEN - 13

0.045995 0.074364 0.079797 0.016488 0.042695 0.038729 0.035207 0.026606 0.050536 0.009912 0.006131 -0.029632 -0.025992 -0.067179 0.035490 0.027191 0.036628 0.024628 0.008399 -0.046038 0.000637 0.035102 0.026119 0.018042 0.034642 0.036853 0.051295

0.270327 0.433407 0.597415 0.162152 0.387480 0.326917 0.336835 0.280085 0.466386 0.101982 0.078142 -0.456909 -0.395397 -0.639374 0.337582 0.270016 0.426403 0.220717 0.075899 -0.415390 0.009759 0.412673 0.220276 0.136887 0.238516 0.242664 0.383371

0.458396 0.350219 0.273730 0.463728 0.387143 0.482436 0.474320 0.492494 0.379929 0.445428 0.505512 1.127119 1.001817 0.916399 0.481828 0.580614 0.600491 0.619149 0.698747 2.069347 0.923292 0.555875 0.513920 0.596742 0.591884 0.516376 0.405618

0.0740 0.0150 0.0390 0.0725 0.0518 0.0297 0.0279 0.0800 0.0540 0.0960 0.0170 0.0000 0.0000 0.0000 -0.0020 0.0850 0.0480 0.0470 0.0590 0.0094 0.0130 0.0090 0.0520 0.0730 0.1130 0.0890 0.0540

0.298966 0.394542 0.111202 0.369682 0.466488 0.596819 0.651448 0.623488 0.700745 0.478470 0.555893 0.303026 0.404961 0.452954 0.652512 0.610293 0.666652 0.688666 0.686244 0.216013 0.424613 0.354790 0.338687 0.412655 0.286591 0.343730 0.352376

20.16662 20.37401 21.16186 18.41390 18.75246 19.07011 19.63077 19.97240 19.90254 20.37141 20.50827 20.46689 20.55561 20.40648 12.26397 13.15509 20.38480 20.71008 21.01320 17.98582 18.86696 19.77640 20.13387 20.29868 20.36630 20.67143 21.37592

81

0.164192 0.302995 0.177585 0.059291 0.113552 0.519891 0.101685 0.000000 0.116246 0.352385 0.120084 0.316255 0.095641 0.569695 0.094532 0.441387 0.123180 -0.010563 0.080929 0.607724 0.076317 0.280413 0.052904 -0.082868 0.077480 0.167966 0.137097 -0.237052 0.105129 0.000000 0.098885 1.282777 0.085892 1.445669 0.130139 0.461653 0.096286 0.153524 0.110831 0.000000 0.046369 1.563589 0.100642 1.332851 0.131113 0.398345 0.132394 0.178612 0.157249 0.044653 0.147904 0.353352 0.126826 0.366100

0.034817 0.042908 0.079993 0.074933 0.052699 0.059892 0.058950 0.110270 0.096602 0.132739 0.124600 0.063134 0.073890 0.113848 0.222284 0.149030 0.183938 0.108100 0.135306 0.017946 0.077666 0.080067 0.109636 0.052215 0.066087 0.037314 0.052960

0.064397 0.062994 0.059902 0.116019 0.106536 0.106243 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902 0.075906 0.069937 0.064397 0.062994 0.059902 0.095964 0.089943 0.081494 0.075906 0.069937 0.064397 0.062994 0.059902

15.00889 8.785039 7.585001 5.200000 5.600000 5.900004 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001 3.991571 8.008593 15.00889 8.785039 7.585001 6.399912 6.459591 8.430638 3.991571 8.008593 15.00889 8.785039 7.585001

8.726837 9.160778 11.60833 26.67495 12.62457 15.11819 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833 19.25071 10.70757 8.726837 9.160778 11.60833 10.91517 10.73273 16.52214 19.25071 10.70757 8.726837 9.160778 11.60833

0.339950 0.251392 0.195006 0.232408 0.272752 0.194675 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006 0.247391 0.319177 0.339950 0.251392 0.195006 0.393405 0.368345 0.391782 0.247391 0.319177 0.339950 0.251392 0.195006

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1