The Financial Performance of Islamic vs. Conventional Banks: An

1

Date: 09/01/2014

Date: 17/6/2014

Faculty of Business, Economics and Political Science

Bachelor's Dissertation/ Senior Year Project

Module Title: Research Methods 3.

Submitted by:

Manar Mahmood Al-Gazzar (113035)

Under the supervision of :

Dr Dalia El-Mosallamy

Supervised by:

The Financial Performance of Islamic vs.

Conventional Banks: An Empirical Study on

The GCC & MENA Region

2

Abstract

Due to the significance of the banking sector in the stability and welfare of any economy; it

is imperative to constantly monitor and evaluate its performance. In the recent decades, a new

prototype of banking, Islamic Banking, was introduced and was capable of achieving

widespread and accelerating growth of total assets and market share on a global basis,

including non-Muslim countries. Numerous empirical studies endeavor to measure the

financial performance of the dissimilar banks in an attempt to gain more insights into Islamic

banking model and the chronic reason behind its rapid success.

Consequently, the purpose of this study is to compare the financial performance of Islamic

vs. Conventional banks in the MENA & GCC region over the period 2009-2013, using a

sample of the top 45 listed banks. Descriptive statistics will be used based on the CAMEL

framework's bank-specific performance measures in addition to external macroeconomic

variables. The differences in performance will be tested for statistical significance using one-

way ANOVA tests. Furthermore, using regression analysis. the study will attempt to examine

the major determinants of profitability of banks in the region, and evaluate whether or not the

moderating role of bank type has a significant impact on bank performance.

The empirical findings of the study, revealed that Islamic banks outperformed conventional

banks in terms of capital adequacy, asset quality, management quality and earnings quality,

however they had a weaker liquidity position in comparison to conventional banks.

Additionally, significant statistical differences were found to exist between Islamic and

conventional banks in capital adequacy, management quality and asset quality. Finally, the

significant determinants of bank profitability are capital adequacy, asset quality, management

quality and GDP rate. Nonetheless, the moderating role of bank type does have a significant

impact on bank performance in the MENA & GCC region.

3

Table of Contents

Introduction…........... .................................................. 6

Background of the Study ...................................................... 6

Research Aim ....................................................................... 9

Dissertation Structure ......................................................... 10

Literature Review ..................................................... 11

Islamic Finance & Banking ................................................ 11

Challenges facing Islamic Banks ....................................... 16

Previous Studies Done ...................................................... 20

Importance of Study Sample ............................................. 24

Aims & Methodology ................................................ 25

Research Aims .................................................................. 25

Research Methodology ..................................................... 27

Data Analysis ............................................................. 35

Descriptive Statistics............................................ .............. 35

One-way ANOVA Tests ........................................... .......... 40

Correlation Analysis............................................ ............... 44

Regression Analysis........................................... ................ 46

Conclusion ................................................................. 53

Reference List.............................................................59

Appendix.....................................................................65

4

List of tables and figures Fig 1: Islamic Sharia'a Illustration.….... ....................... 7

Fig 2: Schematic Diagram showing relationship.........25

Table 1: Sample of banks selected.…. ........................ 24

Table 2: Financial ratios used .…. .............................. 27

Table 3: Descriptive Statistics- All banks.…. ............. 30

Table 4: Descriptive Statistics- Islamic Banks .…. ..... 31

Table 5: Descriptive Statistics- Conventional Banks...31

Table 6: Comparative analysis of IBs and CBs.…. ..... 35

Table 7: Summary of ANOVA tests and hypothesis .. 36

Table 8: One-way ANOVA table.…. .......................... 38

Table 9: Correlation Coefficient Analysis.…. ............ 40

Table 10: Pure Regression Model.…. ......................... 42

Table 11: Moderated Regression Model.…. ............... 44

Table 12: Coefficients of Determination after

moderation....................................................................46

Table 13: Variable Coefficients after Moderation.…. 46

Table 14: Summary of objective one.… ..................... 50

Table 15: Summary of objective two.…. .................... 50

Table 16: Summary of objective three.…. .................. 50

5

List of Abbreviations;

IBs; Islamic Banks

CBs; Conventional Banks

AAOFI; Accounting Auditing Organization for Islamic Financial Institutions

IFSB; International Financial Services Board

ETAR; Equity to Total Assets ratio, used to measure capital adequacy

LLR: Loan Loss Reserves ratio, used to measure asset quality

LDR: Loan to Deposit ratio, used to measure management quality

COSR: Cost to income ratio, used to measure earnings

NLTA: Net Loans to Total assets, used to measure liquidity

GDP: Gross Domestic Product growth rate

INF: Annual Inflation rate

ROA: Return on Assets used to measure profitability

ROE: Return on Equity used to measure profitability

NIM: Net Interest Margin used to measure profitability

6

CHAPTER 1: INTRODUCTION

1.1 Background of Study

A country’s economic growth, among several other factors, is based on its financial sector’s

performance, with the banking sector being the most prominent. Siraj and Pillai (2012) assert

that the stability and growth of any economy to a great extent depends on the stability of its

banking sector. It functions as an intermediary linking surplus and deficit units, facilitates

funds for productive purpose and thereby contributes to economic development. Rose (2012)

claims that although banks are identified by the discernible functions they perform such as;

cash management, insurance, brokerage, credit and payment functions; they are above all else

considered as financial intermediaries managing transactions between different parties.

Hudgins and Rose (2013) claim that in the recent years, banks have experienced vibrant and

extensive changes which are rapidly reshaping and revolutionizing the banking industry.

These key trends include government deregulation, service proliferation, geographic

expansion, an increasingly interest-sensitive mix of funds and many others. One of the most

enormous transformations in the field was the initiation of a new prototype of banking,

Islamic Banking, which has gained the attention of both Islamic and non-Islamic economies

worldwide. Today Islamic banks are operating in all areas of the globe, as a practical and

feasible alternative system to the conventional banking system. Srairi (2009) asserts that

although it was originally developed to satisfy the requirements of Muslims, at present

Islamic banking has currently achieved worldwide acceptance and is documented as one of

the greatest rising areas in finance and banking as stated in the Global Finance Report (2012).

The first Islamic bank launched abiding to Islamic Sharia'a principles was Mit Ghamar in

Egypt which commenced in 1963 but closed down in 1967. However, prior to the initiative

7

proposed by the Organization of the Islamic Conference (OIC) and the accruement of

theoretical interest and knowledge in Islamic Finance; the Islamic Development Bank and

Dubai Islamic Bank were both scrupulously established in 1975 as cited in Merchant (2013).

Despite the fact that the majority of Islamic banks were established within the Middle East

Muslim countries, many banks in developed countries have started to value the enormous

demand for financial products of Islamic banks. Rashwan (2012) and Al-Mazari (2013) assert

that the Islamic Banking industry has been witnessing an accelerating increase with over 614

Islamic banks operating in more 75 countries world-wide. Furthermore, it is worth noting that

conventional banks such as HSBC, Citibank and UBS are currently incorporating Islamic

products in their overall banking services due to the evident success of Islamic Banking as

asserted by Siddiqi (2008).

According to the World Islamic Banking Competitiveness report published by Ernst and

Young (2012), "Islamic banking assets with commercial banks globally grew to $1.3 trillion

in 2011, suggesting an average annual growth of 19% over past four years. The Islamic

banking growth story continues to be positive, growing 50% faster than the overall banking

sector." Furthermore, according to the Global Islamic Finance Report (2012),"Islamic finance

is expected to account for 50% of all banking assets within next 10 years in Islamic counties"

Pizzi (2013) also mentions that London has recently announced the establishment of a new

British Islamic Market Index and the first Islamic Bond (sukuk) issued by a non-Muslim

country, as its not content with being "the leading capital of Islamic Finance in the West but

wants to also start competing with powerhouses in the Muslim world". These recent events

raise plenty of questions as to whether Islamic Finance and Banking is really successful and

efficient as opposed to the conventional banking systems. A few studies have previously been

done in an attempt to examine the diverse products and practices used by the two different

8

banking systems; to investigate what precisely distinguishes Islamic banks from conventional

banks and determinants of the chronic reason behind their successful financial performance.

Recent studies carried out by Khamis and Senhadji, (2010), Hassan and Dridi (2010),

Rashwan (2012), and Merchant (2012) to empirically contrast performance of Islamic banks

(IBs) and Conventional banks (CBs) pre and post the global financial crisis argue that

performance of Islamic banks during the 2008 financial crisis was more efficient than their

counterpart conventional banks; as their mechanism complying with Islamic Sharia'a proved

better resilience to negative profitability and speculation that tremendously affected

conventional banks. Consequently, this led to the phenomenal widespread of Islamic Banking

in an attempt to stabilize financial systems and restore investors' confidence in the banking

industry as affirmed by Jusufovic (2009).

Due to the banking sector's significant role in the wellbeing of any economy, it is vital to

constantly monitor and evaluate banks' performance; to ensure that the financial sector is

strong and efficient. Sayed and Hayes (2012) assert that the continuous assessment of bank

performance is fundamental in order to protect the banking operations against its inherent

risks or poor management that can threaten the entire financial system of any country.

Furthermore, Jamali, Shar and Ali (2012) assert that bank performance is a very important

subject to all the banks' stakeholders including customers, investors and the general public.

Consequently, numerous studies have been undertaken on financial institutions to determine

their impact on the efficiency of economic growth and also discover the determinants of

successful bank performance. There are various techniques and financial performance

indicators used by researchers to evaluate the determinants of successful bank performance,

including internal bank-specific factors (such as liquidity and asset quality) and external

macroeconomic variables (such as GDP growth rate and annual inflation). The Basel

Committee on Banking Supervision proposed the CAMEL framework in 1988 to be used for

9

managerial and financial assessment, to provide a comprehensive evaluation of financial

organizations and help in ranking the performance of banks as cited in Awan (2009) and

Akhtar (2010). The CAMEL model has previously been used by researches in foreign

countries to contrast the performance of banks and identify the determinants of profitability.

However, little efforts have been done to introduce this model to the Arab countries, with

only a few banks adopting it to measure their performance. Hence it is not a formal method of

bank evaluation recommended by the Central Bank as is done in several other countries.

1.2. Research Aim

The purpose of this research is to critically examine the determinants of financial

performance in Islamic and conventional banks in the MENA & GCC region during the

period 2009-2013 following the financial crisis. A comparative study of the top 45 banks in

the region was selected for our sample, with 10 Islamic banks and 35 conventional banks

covering ten different countries (Egypt, Jordan, Saudi Arabia, Qatar, Kuwait, Oman, Israel,

Lebanon, Bahrain and United Arab Emirates). Three stages of analysis were performed in

this study. First, descriptive statistics were computed to understand the differences in

characteristics of the two types of banks. Next, to determine whether these differences were

significant, one-way ANOVA tests were carried out on each variable. Finally, regression

analysis was carried out to analyze the effect of the variables on bank profitability. The study

employs the CAMEL framework to measure and compare the financial performance of

Islamic and conventional banks in order to detect whether there any significant differences in

the performance indicators of the two banking systems in terms of; capital adequacy, asset

quality, management quality, earnings and liquidity. In addition to that, external macro-

economic factors such as GDP growth rate and annual inflation rate are also used in the

model to determine their significance on bank profitability.

10

1.3 The Dissertation Structure

The dissertation is divided into five subsections and elaborated below;

Chapter One: A brief introduction about the topic and the subject of the research and its

significant importance.

Chapter Two: A thorough review on the Islamic banking model and what differentiates it

from conventional banking systems; in addition to the current challenges impeding its

performance. Furthermore, a review on the previous studies undertaken to highlight the

comparative performance and Islamic and conventional banks and the determinants of their

profitability and financial performance is presented.

Chapter Three: The aims and methodology of the research will be identified in addition to

identifying the sample size, data collection and analysis methods.

Chapter Four: An extensive analysis of the empirical results of the study is presented with

respect to the literature review to identify whether the findings are consistent or opposing to

previous studies.

Chapter Five: Finally the key findings of the research are presented along with their

theoretical implications; while highlighting the limitations faced and suggesting some

recommendations for future further research.

11

CHAPTER 2: LITERATURE REVIEW

The purpose of this section is to present a comprehensive review on the Islamic banking

model and what differentiates it from conventional banking systems; in addition to the

current challenges impeding its performance. Furthermore, a review on the previous studies

undertaken to highlight the comparative performance and Islamic and conventional banks and

the determinants of their profitability and financial performance.

2.1 Islamic Finance and Banking 2.1.1 Islamic Law (Sharia'a) Kettel (2011) asserts that in the uttermost belief of all Muslims, Islam is the religion

revealed by Allah to his last messenger Prophet Mohammed (Peace Be Upon Him) to earth. It

is a complete religion comprising all aspects of human life in this world and hereafter world.

Islam is alleged as comprising of three broad concepts:

Aqidah: which concerns all forms of faith and belief by a Muslim in Allah and his will,

from the fundamental faith in His being to the ordinary beliefs in His commands.

Sharia'a: which concerns all forms of practical actions by Muslims manifesting their

faith and belief, including man-to-man activities (Muamalat); which comprise all

mankind activities (political, economic and social).

Akhlaq: concerns behaviour, attitude and work ethics, within which Muslims perform

their practical day-to-day activities.

Sharia'a or Islamic Law , at times referred to Islamic Jurisprudence, is the instigating

foundation of Islamic Banking. As illustrated in the table below, a significant portion of

Muamalat is the conduct of economic activities which constitute banking and financial

services that form the founding principles of Islamic Banking.

12

As shown in the table(Kettell,2011)

Fig 1: Islamic Sharia'a

Sharia'a principles are pertaining from various sources namely; Quran which is the primary

source of Sharia'a that was revealed exclusively to Prophet Mohammed through divine and

manifest revelation, Sunnah which are the normative practices that Muslims follow in

accordance to the Prophet's sayings or behaviour. Furthermore, Vogel and Hayes (1998)

assert that the secondary sources of Sharia'a which are derived from the legal injunctions of

Quran and Sunnah include; Ijma which is the consensus of opinion and agreement on various

Islamic matters taken by qualified Islamic scholars, Qiyas which refers to analogical

deduction to deriving logical conclusions on various matters and finally Ijtihad which is the

use of one's reasoning to arrive at applied solutions of new problems not expressly regulated

before in the primary sources of Sharia'a.

2.1.2 Inception of Islamic Banking

Islamic Finance is a dynamic execution of Sharia'a (Islamic Law), consequently Islamic

financial institutions base their objectives and operations on focal Sharia'a principles. There

are several key principles of Islamic Banking, with the central tenet being prohibition of

interest (Riba) as revealed in Quran (Al-Baqarah,2:275) "Allah has permitted trade and has

forbidden riba". Geelani (2005) assets that Riba refers to any predetermined payment above

13

the actual amount of the loan principal; this is contrary to conventional banks that charge

fixed interest rates on both deposits and loans. Uncertainty and speculation (gharar) are also

forbidden, since any transaction the bank enters should have well-known outcomes that all

contracting parties must have perfect knowledge of as cited in Kahf and Khan (2007).

The profit and loss sharing scheme is considered extremely vital in Islamic Banking,

Mashayekhi et al (2007) maintain that Islam encourages Muslims to invest their money and

become partners in a business instead of becoming creditors. Consequently " the depositor,

the bank and the borrower all share the risks and rewards of financing a business venture" as

elaborated by Chapra and Ahmed (2012). Kettel (2011) also declares that Islamic banks

promote risk sharing between providers of funds (investors) and users of funds

(entrepreneurs), while their counterpart conventional banks assure the investor a

predetermined rate of interest and pass all the risk to the entrepreneur. According to Sharia'a,

this kind of unjust risk distribution is prohibited. Under the PLS scheme, Islamic banks

consider granting loans based on the soundness or profitability of the project and competence

of the entrepreneur, in contrast to conventional banks that merely consider the credit-

worthiness of the borrower. Therefore, the eventual outcome of PLS should be ethical

investments that are channelled to productive ventures benefiting the whole community and

leading to economic prosperity and development.

All financial transactions an Islamic bank undertakes should be asset-backed; meaning that

"making money out of money" is prohibited. Khan and Bhatti (2008), Khamis et al (2010)

and Al-Janabi (2012) all confirm that money in Islam is considered a medium of exchange

that represents the purchasing power of individuals and has no value on itself. Hence, it only

becomes capital generating when it is invested in a productive business. This is divergent

14

with conventional banking systems that regularly use tools such as; currency derivatives,

future and forward contracts that involve non-asset backed transactions.

A very important distinction between the two divergent banking systems is that conventional

banks are secular in their orientation, while Islamic banks follow and abide by Sharia'a

principles in all their transactions. Kettel (2011) argues that in Islamic banks only Sharia'a

approved contracts are to be accepted, any activity considered haram (prohibited in Islam)

cannot be financed. To ensure that all financial transactions are in conformity to Sharia'a

law, a Sharia'a Supervisory Board (SSB) is mandatory in all Islamic financial institutions.

This supervisory board examines all the bank's contracts, dealings and transactions to

guarantee and certify that the banking activities are halal (permissible) and that Sharia'a

principles are being implemented accordingly as cited in Lewis (2005).

2.1.3 Modes and Instruments of Islamic Banks

The following section describes Sharia'a- compliant Islamic banking modes of financing;

2.1.3.1 Murabaha (cost-plus) refers to a sales contract, whereby the Islamic bank (IB) sells a

specific asset to a customer at a pre-agreed profit mark-up on the original cost. Kettel (2011)

mentions that the actual sale of a real asset is a necessary condition for the contract to comply

with Sharia'a principles. Al-Tiby (2012) also asserts that Murabaha is one of the most

primarily used instruments by Islamic banks and constitutes over 70% of their assets.

2.1.3.2 Salam is a forward sale, where the IB pays in advance for buying specified assets at a

predetermined price, quality and quantity specifications, which the seller agrees to supply on

a future date. Siddiqi (2008) declares that it is used for products that can be traded on

secondary markets such as agriculture or mineral products.

2.1.3.3 Ijarah (leasing) is an agreement made by an IB to purchase an asset and lease it to a

customer for an agreed period of time against fixed rental charges. The bank must retain the

15

risk and liabilities of asset ownership including maintenance. Ijarah wa iqtina, offers the

lessee an option to own the asset at the end of the lease period as stated by Kahf et al (2007).

2.1.3.4 Istisna'a is an agreement to sell a non-existent asset to a customer, which is to be

produced for future delivery at pre-determined prices and quality specifications. These

contracts are used for financing manufacturing and construction as cited in Geelani(2005).

2.1.3.5Takaful is a Sharia'a compliant system of insurance in which the participants donate

part of their contribution to pay claims for damages suffered by some of the participants.

Chapra (2012), Hassan (2010) and Kettel (2011) emphasize that the bank's role is restricted to

managing the insurance operations and investing the insurance contributions.

2.1.3.6 Mudarabah instruments are the cornerstone of Islamic Banking based on the profit-

loss sharing principle. Iqbal and Mirakhor (2011) indicate that it is a contract between two

parties; an Islamic bank as an investor (Rabul Mall) who provides a second party, the

entrepreneur (Mudarib) with financial resources to finance a particular project. Ikha et al

(2011) assert that profits are shared between the parties in a portion agreed in advance, while

the losses are the sole liability of the IB because the Mudarib (entrepreneur) sacrifices only

his/her efforts and expected share in profits.

2.1.3.7 Musharka refers to an equity participation contract because the bank is not the sole

provider of funds. Consequently, as affirmed by Geelani (2005) two or more partners

contribute to the joint capital of an investment, hence profits and losses are shared strictly in

accordance to the respective capital contributions written within the terms of the contract.

Chong and Liu (2009) through their investigation on IBs in Malaysia claimed that Islamic

banking practised today, deviates largely from the theoretical PLS paradigm of Musharaka

and Mudaraba. They discovered that adoption of the PLS paradigm has been much slower on

the asset side (0.5%) than on the liability side (70%), and IBs generally prefer investing in

non-PLS modes of financing. Furthermore, they conclude that contrary to expectation of

16

interest-free and equity-like theory of the PLS paradigm, IBs are closely pledged to the

deposit rate setting of conventional banking and their investment rates are positively related

to those of conventional deposits rates.

2.2. Challenges faced by Islamic banks

Islamic banking is still highly nascent as compared with conventional banking, and this is an

immense factor contributing to the range of challenges IBs are currently facing;

2.2.1 Lack of standardized regulatory frameworks

Khalid and Amjad (2012) assert that due to the novelty of Islamic banking systems, their

legal and regulatory frameworks are still quite complex and un-standardized. Therefore, they

tend to follow varied accounting and other practices with no universally recognized

standards. Some of them follow International Accounting Standards (IAS), others adhere to

standards issued by Accounting Auditing Organization for Islamic Financial Institutions

(AAOIFI), while some adopt accounting standards prevalent in their local markets. This issue

results in perplexity due to the heterogeneity in accounting practices and disclosure of Islamic

banks as asserted by Sultan (2006). However, it should be noted that AAOIFI and Islamic

Financial Services Board (IFSB) have been working to develop universal accounting and

auditing practices for Islamic banks. AAOIFI has developed more than 63 accounting

standards for the guidance of and adoption by 130 member institutions, representing 30

countries as stated in IFSB (2012).

2.2.2. Unsatisfactory record for innovation

Furthermore, Al-Janabi (2006), Al-Ajmi (2012) and Siddiqi (2012) all argue that Islamic

banks have a very unsatisfactory record for R&D and innovation, which has lately led to a

mounting pressure on them to develop genuinely Islamic and productive products that differ

substantially from conventional practise. Haron and Ahmad (2010), amongst others, have

provided empirical evidence that Islamic banks use conventional profitability theories in

17

determining returns on their products. Additionally, Khan and Bhatti (2008) confirm that they

use the London Inter-bank Offered Rate (LIBOR) market interest rates, discounting tables

and time value of money techniques to fix PLS ratios and returns on their murabaha and

other investments. Islamic banks should take very seriously the challenge of coming up with

a full array of genuinely distinctive, innovative and competitive products.

2.2.3. Shortage of Sharia'a experts and human capital resources

Khamis et al (2010) emphasize that there is still an acute shortage of skilled human resources

in Islamic banks and inadequate training is given to staff on how to incorporate fundamental

Sharia'a- complaint Islamic banking principles. Most importantly, there is an evident scarcity

of competent Sharia'a experts in the Islamic banking industry, with a small group of experts

serving on several Sharia'a boards of Islamic banks worldwide. Mathews (2008) and Tett

(2009) declared that Sharia'a experts earn as much as $88,500 per year per bank and in some

cases, charge up to $500,000 for advice on large capital market transactions. On the other

hand, Sharia'a scholars at small Islamic banks have little insight into the complexities of

present-day financial markets. Nevertheless, Islamic banks are urged to build up a strong base

of research &training to develop a corps of Sharia'a experts with high moral and professional

integrity. They should also establish a central Sharia'a board and an external audit committee

to provide a truly independent scrutiny of the their adherence to Sharia'a principles.

2.2.4. Risk Management Challenges

Moreover, Al-Tiby (2012) asserts that IBs face another crucial challenge in improving their

risk management strategies and corporate governance because of their adhere to Sharia'a

principles. They are currently exposed to all types of risk including those faced by CBs and

those unique to IBs. Ikha and Abdullah (2011) declare that the assets and liability structures

of IBs have unique risk characteristics as a result of the Islamic banking model evolving into

pure Islamic modes and instruments as previously discussed. Rehman et al (2011) and

18

Merchant (2012) further elaborate that "on the liability side of Islamic banks, saving and

investment deposits take the form of profit sharing investment accounts and demand deposits

take the nature of qard hasan (interest-free loans) that are returned fully on demand. While

on the asset side, banks use murabaha, bai-muajjal, istisnaa, salam and ijara and PLS modes

of financing (musharaka and mudaraba). These instruments on the asset side, using the

profit-sharing principle to reward depositors, are a unique feature of Islamic banks". Hence,

while the conventional banks guarantee the capital and rate of return, the Islamic banking

system, based on the principle of PLS, cannot, by definition, guarantee any fixed rate of

return on deposits. In some cases the capital is not guaranteed either, because if there is a loss

it has to be deducted from the capital. As a result, Hassan (2009) confirms that IBs face not

only the regular risks encountered by conventional banks but they also face other risks as a

result of their unique asset classes and liability structures.

Olson and Zoubi (2011) argues that without an efficient capital market to operate within,

Islamic banking will not continue to grow meaningfully. In addition to the many specific

risks inherent to Islamic banks, there are a number of more general factors that make Islamic

banking riskier than conventional banking. To begin with, Islamic banks have fewer risk-

hedging instruments and techniques available, since they are prohibited from using

derivatives such as; options, futures and forwards that are regularly used by conventional

banks to effectively hedge risks; given that these tools are based on interest and speculation

which are non-compliant to Sharia'a principles as asserted by Shaista and Umadevi (2013)

Consequently, as confirmed by Khan and Bhatti (2008) IBs are haunted by the chronic

problem of excess liquidity, because they carry about "40 percent of surplus cash & other

liquid assets in comparison to CBs due to the serious dearth of long-term Sharia'a-compliant

investment tools and avenues." In a study by Iqbal (2012) on the perceptions of Islamic

banking customers, he emphasises that "depositors of Islamic banking have three equal

19

intentions when becoming clients and interacting with banks, (i) religious (to help Islamic

project financing); (ii) profit (to look for the highest return) and; (iii) transaction purposes (to

take money whenever needed)." Nonetheless, these conditions invite liquidity problems,

especially coming from some of Islamic banking depositors who behave conventionally

(considering level of interest, expecting regular and competitive deposit returns).

Subsequently, it is vital that Islamic banks take appropriate actions to further educate people

on Sharia'a banking concept and practices and redirect their portfolio management to match

with the bank’s liquidity and financing management.

The preceding discussion makes it quite evident that Islamic banking is not a negligible or

merely temporary phenomenon. Islamic banks are here to stay and there are signs that they

will continue to grow and expand. The Islamic banks present some innovative ideas which

could add more variety to the existing financial network. Consequently, it is essential that IBs

resolve all their inherent challenges and come up with practical and feasible solutions to solve

any obstacles they face in response to the rapidly changing regulatory and operating

environment brought about by globalization and heightened competition.

20

2.3 Previous Studies Done

Bank performance can be measured by using both qualitative and quantitative techniques.

Numerous studies have been done on the different determinants of bank performance

measured in term of; profitability, growth, efficiency, liquidity, credit risk performance, and

solvency. Additionally, several variables and statistical techniques have been used for

analysis and results are drawn from them aiming at performance evaluation. Profitability,

however is the ultimate goal of any bank, so there has been a widespread interest by several

scholars and researches to use it as a foremost bank performance indicator. Prior studies on

bank performance and profitability have tackled the impact of attributes such as firm-specific

and macroeconomic variables as significant determinants of bank profitability. Based on

extensive comparative studies between the performance of Islamic and conventional banks,

there is a general agreement in literature that Islamic banks are superior to conventional

banks in terms of their performance as concluded by Samad and Hassan (2004) and Safiullah

(2010). On the other hand, there are several other studies indicating no significant differences

in the performance of the divergent banking systems, while others claim that CBs are still

superior to IBs in terms of their performance.

In a study on Malaysian banks by Guru and Shanmugam (2010) to determine why some

banks more successful than others and to what extent the profitability performance disparities

are due to variations in management internal factors rather than environmental external

factors. The study concluded that efficiency in expenses-management is one of the most

significant determinants of bank profitability; consequently banks can improve their

profitability by focusing attention on proper cost control and operating efficiency. Similar

results were reported by Safiullah (2010) in Bangladesh. showing that operational efficiency

is a significant determinant of profitability, and that conventional banks were doing better

than Islamic banks based on productivity and operational efficiency.

21

Hassan et al (2010) attempted to examine the performance of Islamic banks (IBs) and

conventional banks (CBs) during the recent global crisis by looking at the impact of the crisis

on profitability, credit and asset growth, and external ratings. They concluded that IBs have

been affected differently than CBs. Factors related to IBs‘ business model helped limit the

adverse impact on profitability in 2008, while weaknesses in risk management practices in

some IBs led to a larger decline in profitability in 2009 compared to CBs. IBs' credit and

asset growth performed better than did that of CBs in 2008–09, contributing to financial and

economic stability. Conversely, Kassim and Abdulle (2012) conducted a similar comparative

analysis on the impact of the 2008 crisis in Malaysia and documented two main findings,

"there was no major difference in profitability and credit risk among the two types of the

banking institutions due to the financial crisis; and IBs banks were holding more of the liquid

assets than their conventional counterparts, thus are less exposed to the liquidity risks due to

the financial crisis". Hence, this might be a driving factor behind Islamic banks' rapid success

in the global financial crisis as compared with conventional banks.

Manarvi & Muhammad (2011) and Momeneen & Jaffar (2011) compared the performance of

the Islamic and the conventional banks in Pakistan using the CAMEL test. They both

concluded that the Islamic banks are better in processing adequate capital and present a

better liquidity position of IBs as compared to CBs in Pakistan, however CBs pioneered in

management quality and earning ability, while asset quality for both streams of banking was

almost the same. These findings are also consistent with the results of Ika and Abdullah

(2011) that concluded that IBs in Indonesia are more liquid than CBs and have better

liquidity management practices.

Akhtar, Ali & Sadaqat (2011) did a comparative analysis of Islamic and conventional banks

by focusing on the importance of firm size, networking capital, return on equity, capital

22

adequacy and return on asset with liquidity risk management. The results indicated that bank

size and networking capital to net assets have positive but insignificant relationships with

liquidity risk. Whereas the capital adequacy in CBs and return on asset in IBs has a positive

and significant relationship with liquidity risk.

In a study by Javaid, Anwar and Zaman (2011) to discover the main determinants of the

profitability of banks in Pakistan using internal factors only (the impact of assets, loans,

equity, and deposits on profitability). The empirical results showed that these variables have a

strong influence on the profitability. However, they concluded that higher total assets may

not necessarily lead to higher profits due to diseconomies of scales and that higher loans

contribute towards higher profitability however their impact is not significant. Respectively,

Ali, Akhtar and Ahmed (2012), also studied the determinants of profitability of banks in

Pakistan however using both internal and macroeconomic variables. The study documented a

significant effect of capital adequacy ratio, credit risk, asset management, GDP and consumer

price index with profitability when measure with return on assets (ROA) and significant

relation of operating efficiency, asset management and GDP with profitability when

measured with return on equity (ROE).

Rahman, Farzand, Kurshed and Zafar (2012) further evaluated the effect of banks-specific

and macroeconomic variables on the profitability of IBs and CBs in Bahrain; using liquidity,

capital adequacy and expenses management as internal factors and ownership, firm size and

external economic conditions as external determinants. The study concluded that variables

used in the model have strong effect on the profitability and higher total assets may not be

positively related to higher profit, because as the assets of the bank increase there may be

inefficiency in the bank management. However, profitability and the size (total asset) of the

financial institution was found to have positive relationship, in addition to efficient expense

management and the macroeconomic factor, inflation rate.

23

Shaista and Umadevi (2013) attempted to analyze the differences in bank characteristics of

Islamic and conventional banks in Malaysia, in terms of profitability, capital adequacy,

liquidity, operational efficiency and asset quality, corporate governance issues and economic

conditions. The findings of the study revealed that the return on average assets, bank size and

board size values of conventional banks was higher compared to Islamic banks. The other

variables- operational efficiency, asset quality, liquidity, capital adequacy and board

independence- were higher for Islamic banks. Significant differences between the two bank

types were found for all the variables, except for profitability and board independence. All

variables except for liquidity, board characteristics and type of bank, were found to be highly

significant in affecting profitability. However, contrasting results were found for the

independent t-tests and regression analysis. These findings are consistent to those of

Almazari (2014) on Saudi and Jordanian banks as the study also reported that capital

adequacy, asset quality and liquidity have significant impact on profitability.

Faizulayev (2011) also carried a comparative study between IBs and CBs in several countries

using the CAMEL framework. By utilizing regression analysis to evaluate impact of

profitability determinants and ANOVA tests to measure the significance he concluded that

CB are different than IBs in terms of capital adequacy, asset quality, earnings quality, liquidity

quality and management quality and IBs are less liquid than CBs because they are dealing mostly

with long term investment. Furthermore, he indicated that the moderating effect of bank type

had a significant impact on bank performance. Conversely, in a study done by Ongore et al

(2013) to study the moderating effect on the ownership structure on bank performance in

Kenya, they concluded that the moderating role of ownership identify was insignificant on

the profitability of banks and hence does not affect performance.

24

3.4 Importance of Research Sample

During the last two decades the banking sector in the Middle East and North Africa (MENA)

region has experienced major transformations in its operating environment. A sound, well-

functioning banking system is essential in providing for sustained growth and development in

this politically and economically important part of the world. While the efficiency of the

banking sectors in North America and Europe has been analyzed rather thoroughly, less is

known about the determinants of cost efficiency and bank profitability in developing

countries. Said (2013) asserts that “the number of cross-country comparative studies is still

limited and that most of these studies focus upon Europe". The MENA region is important

for a number of reasons. It represents a bridge between Europe and Asia; it is a fast growing

region in terms of both population and wealth, and its banking sector is relatively young with

most banks only being established in the 1970s or later. The MENA region includes the

rapidly expanding, oil rich countries of the Gulf Cooperation Council (GCC) as well as the

Arab countries of the Near East and North Africa. The world's largest Islamic banks are

located in the MENA region and its mix of CBs & IBs permits a comparison of efficiency

and profitability by type of bank.

Consequently, the purpose of this research is to perform a comparative study between the

financial performance of conventional and Islamic banks in the MENA & GCC region to

identify which banking sector is currently performing relatively well in comparision to ther

other. The variables to be measured include the CAMEL framework bank specific variables;

capital adequacy, asset quality, management quality, earnings and liquidity; in addition to

external factors such as GDP and inflation rate as done in previous studies.

25

CHAPTER 3: AIMS AND METHODOLOGY

The purpose of this section is to define the research aims and objectives of the study, in

addition to the hypotheses to be tested. Furthermore, the research methodology appropriate to

the research's objectives is analysed, along with the data collection & analysis techniques.

3.1. Research Aims

3.1.1. Research Aims

The purpose of this research is to empirically examine the determinants of financial

performance in conventional and Islamic banks in the MENA & GCC region using both

bank-specific (internal factors) and macroeconomic (external factors). Furthermore, the

study will attempt to discover whether any significant differences exist between the two

divergent banking systems in terms of capital adequacy, asset quality, management

quality, earnings quality and liquidity.

3.1.2. Research Objectives

To empirically compare the performance of Islamic and conventional banks in the region

using several indicators such as Capital Adequacy, Asset Quality, Management Quality,

Earnings and Liquidity.

To test for differences between the performance of Islamic vs. Conventional banks in

terms of Capital Adequacy, Asset Quality, Management Quality, Earnings and Liquidity.

To critically examine the various determinants of profitability in Islamic and

Conventional banks including the bank-specific variables and macroeconomic variables

to conclude which variables have a significant impact on profitability. Furthermore, the

moderating role of bank type is tested to evaluate its significance on profitability.

26

3.1.3. Hypotheses:

H1: Islamic banks have better capital adequacy measures than conventional banks.

H2: Islamic banks have better asset quality measures than conventional banks

H3: Islamic banks are better than conventional banks in management quality.

H4: Islamic banks have higher earnings than conventional banks.

H5: Islamic banks manage their liquidity more efficiently than conventional banks.

27

3.2. Research Methodology

In the next section, the data collection and research design techniques are scrutinized to

justify why they were appropriate for this particular study. The sample selection process is

also described in addition to the statistical data analytical tools used by the study. Finally, the

model specifications including the dependent and independent variables are presented.

3.2.1. Data Collection

The data for all banks in the sample was compiled from Bankscope database, with a few

individual banks' data compiled from the annual reports from their respective websites. The

collated secondary data derived from the bank's financial statements was transformed into

percentages and ratios so that comparison can be made between the different types of banks.

Financial management theories provide various indices for measuring a bank's performance,

with the most significant being financial ratio analysis. Financial ratios have been used quite

commonly and extensively in previous studies done by Samad (2004), Javaid et al (2011)

Ikha et (2011) and Momeneen et al (2012).

Sirari (2009) asserts that "financial performance analysis is the process of scientifically

making a critical and comparative evaluation of profitability and the financial health of banks

through the applications of the techniques of financial statement ratio analysis". There are

several ratios used by banks to measure their financial performance and reveal their true

financial position, however this study utilizes the standardised CAMEL framework which

helps in identifying the relative strengths and weaknesses of banks and provide

recommendations for improving future performance.

3.2.2 Research Design

3.2.2. 1 Sample Size

Bank level data was collected using Bankscope database, which provides a standardized

measure in financial statement presentations abiding to the IFRS. In determining the sample,

28

the researcher set the database to select the top 50 listed banks in the MENA and GCC

region. The rationale behind selecting only listed banks is that the financial data related to the

publicly traded institutions are more accurate due to their adherence to more restricted rules

in terms of capital, practice, governance and disclosure as previously done by Rashwan

(2012). To classify whether a bank is commercial or Islamic, we used the Bankscope

classification of bank specialisation as a starting point. Bankscope defines Islamic banks as

those that are members of the International Association of Islamic Banks. However, to gain

more accuracy and reliability we cross-checked the Bankscope classification with the

information available from the Global Banking and Finance Review databases for the

relevant countries and the information available on the respective banks’ websites.

The banks selected in our sample include the top ranked banks according to total assets and

market capitalisation (USD) as previously done by Loghod (2006), Momeneen (2012)

Merchant (2012), Siraj & Pillali (2012) and Azam & Siddiqoui, S. (2012) The following

represent the standard criteria used for sample selection of banks;

1) The bank's total assets are over USD 5 billion.

2) The bank is listed on the stock market & has market capitalisation of over USD 2 billion.

3) The bank has a complete data set with annual financial statements from 2009- 2013.

Consequently, according to the above criteria our sample process rendered 45 banks in total

with 35 conventional banks and 10 Islamic banks covering ten, countries (Saudi Arabia,

United Arab Emirates, Kuwait, Oman, Qatar, Bahrain, Jordan, Israel Lebanon and Egypt) as

shown below. Hence, the total number of observations in the study were 224, with 175 for

CBs and 49 for IBs. It is worth noting that thirty eight of the banks selected in our sample

were ranked in the GCC's top 50 banks in the Gulf Business Report (2013).

Note: Five banks were excluded from the sample due to incomplete data sets.

29

Table 1: Sample of banks used in the study

Countries Conventional Banks Islamic Banks

Kuwait National Bank of Kuwait S.A.K. Boubyan Bank KSC

Burgan Bank SAK Kuwait Finance House

Kuwait Projects Company Holding K.S.C.

Gulf Bank KSC (The)

Commercial Bank of Kuwait SAK (The)

Al Ahli Bank of Kuwait (KSC)

UAE Emirates NBD PJSC Dubai Islamic Bank PJSC

Commercial Bank of Dubai P.S.C. Abu Dhabi Islamic Bank

First Gulf Bank

Abu Dhabi Commercial Bank

Mashreqbank PSC

Union National Bank

National Bank of Abu Dhabi

Qatar Qatar National Bank Qatar Islamic Bank SAQ

Commercial Bank of Qatar (The) QSC Qatar International Islamic Bank

Doha Bank Masraf Al Rayan (Q.S.C.)

Al Khalij Commercial Bank

Ahli Bank QSC

Egypt Commercial International Bank (Egypt)

QNB Al Ahli

Bank of Alexandria

Oman Bank Muscat SAOG

Lebanon Bank Audi SAL

Jordan Arab Bank Plc

Housing Bank for Trade & Finance (The)

Israel Bank Hapoalim BM

Mizrahi Tefahot Bank Ltd.

Saudi Arabia Samba Financial Group Al Rajhi Bank

Saudi British Bank (The) Alinma Bank

Banque Saudi Fransi Bank AlBilad

Arab National Bank

Saudi Investment Bank (The)

Saudi Hollandi Bank

Bank Al-Jazira

Bahrain Ahli United Bank BSC

Please refer to Appendix 1 to view the precise details of each bank's total asset and

market capitalisation compositions

30

3.2.3. Data Analysis Techniques.

Descriptive Statistics (including mean, standard deviation, minimum and maximum) are

used to compare and analyse the performance of Islamic and conventional banks.

One-way ANOVA to test for any differences between the financial performance of

Islamic and conventional banks using the CAMEL model variables.

Multiple linear regression model used to determine the significance of the each

determinant (explanatory variables) in affecting the profitability of banks (dependent

variable). The moderating effect of different banking systems was evaluated by using

bank type as a dummy variable.

Figure 2: Schematic Diagram showing the relationship between variables

Bank specific Variables:

Capital Adequacy

Asset Quality

Management Quality

Earnings

Liquidity

Bank Performance

Indicators

ROA

ROE

NIM Macroeconomic Variables

GDP Growth Rate

Inflation Rate

Moderating Variable

Islamic Vs. Conventional Bank

Independent Variables Dependent Variables

31

3.2.4 Model Specification

The following regression models will be used to test for the determinants of profitability

Where;

α = Intercept

CA =Capital Adequacy of bank i at time t

AQ = Asset Quality of bank i at time t

MQ = Management Quality of Bank i at time t

ER= Earnings of Bank i at time t

LM =Liquidity Ratio of Bank i at time t

β1-β7= Coefficients parameters

GDP= Gross Domestic Product (GDP) at time t

INF = Average Annual Inflation Rate at time t

ε = Error term where i is cross sectional and t time identifier

The following is an extended model to estimate the moderating effect of bank type.

α = Intercept

M = Bank Type (1=Islamic and 0=Conventional)

3.2.4.1. Model Assumptions:

The following diagnostic tests were carried out to ensure that the data suits the basic

assumptions of classical linear regression model:

1) Normality; Several normality tests were used to test for normal distribution of the model

residuals; including Kolmogorov-Smirnov Test (since the sample size exceeds 200

observations), Normal P-P Plot of Residuals and the histogram of standardized residuals.

Kindly refer to appendix 4 for further illustrations.

ROE= α1 + β1(CA)+ β2(AQ)+ β3(MQ)+ β4(ER)+ β5(LM)+ β6(GDP)+ β7(INF)+ ε

ROA= α1 + β1(CA)+ β2(AQ)+ β3(MQ)+ β4(ER)+ β5(LM)+ β6(GDP)+ β7(INF)+ ε

NIM= α1 + β1(CA)+ β2(AQ)+ β3(MQ)+ β4(ER)+ β5(LM)+ β6(GDP)+ β7(INF)+ ε

ROE= α1 + β1(CA* M)+ β2(AQ* M)+ β3(MEQ* M) + β4(ER* M)+ β5(LM* M)+

β6(GDP* M)+ β7(INF* M)+ ε

ROA= α1 + β1(CA* M)+ β2(AQ* M)+ β3(MQ* M) + β4(ER* M)+ β5(LM* M)+


NIM= α1 + β1(CA* M)+ β2(AQ* M)+ β3(MQ* M) + β4(ER* M)+ β5(LM* M)+


32

2) Muliticollinearity: The existence of strong correlation between the independent variables

was tested using Pearson's Correlation Coefficient as shown in Appendix 2.

The following table presents the exact ratios that are used to represent the variables.

Table 2: Measurements used to present the explanatory variables.

Variable Measurement

Return on Assets Net income/ Total Assets (ROA)

Return on Equity Net income/ Total Equity (ROE)

Net Interest Margin Net interest income/Total Assets (NIM)

Capital Adequacy Total Equity/Total assets (ETAR)

Asset Quality Loan Loss Reserves/ Total Loans (LLR)

Management Quality Loans/Deposits (LDR)

Earnings Quality Total expenses/Total revenue (COSR)

Liquidity Net loans/Total Assets (NLTA)

Gross Domestic Product Annual Gross Domestic Product (GDP)

Inflation rate Annual average inflation (INF)

3.2.4. 2 Dependent Variables

ROA: Return on Assets is an indicator of managerial efficiency; as it measures

management's capability of converting the bank's assets into net earnings. In other words, it

measures the ability and efficiency of banks to generate profit by using its assets, as asserted

by Madvari et al (2012).

ROE: Return on Equity is used to measure profitability generated from the amount of capital

that shareholders invested. It measures the rate of return flowing to the bank's shareholders,

because it approximates the net benefit that the shareholders have received from investing

their capital in the bank as stated in Momeneen et al (2012).

NIM: Net interest margin is used to measure the difference between interest income earned

by lending or any other investment and interest expenses that have been paid to depositors, all

relative to total assets. This ratio indicates whether or not the bank made wise decision in

terms of loan investment. Furthermore, Rose (2012) asserts that it "measures how large a

spread between interest revenues and interest costs management has been able to achieve by

close control over the bank's assets and the pursuit if the cheapest sources of funds"

33

3.2.4. 2. Independent Variables

Capital Adequacy: Capital adequacy measures the financial strength and viability of the

banks in terms of capital over assets like investments and loans. It can assist the bank

management in understanding the shock captivating capability of the bank during times of

risk. In our study, capital adequacy will be measured by using the Equity to total assets ratio

(ETAR) as previously done Javaid et al (2011) and Merchant (2012). This parameter

measures the proportion of total assets being financed by shareholders and the ability to

withstand to any unexpected loses and bankruptcy. Samad (2004) asserts that a high ETAR

will aid the bank in providing a strong cushion to increase its credit undertakings, lower the

unanticipated risks and supports the organization in charming asset losses. This implies that

as the amount of the equity to back the assets of banks depresses, the bankruptcy risk of the

bank intensifies. Also, Akhtar et al (2011) state that constant lowering of ETAR hints to

invitation of risk in the banks. Hence, we assume this ratio to be as higher as possible.

Asset Quality: The loans constitute the greater proportion of assets in balance sheets of any

bank, hence the quality of loans or asset of any banks is very significant for investors or

depositors because they are the main source of generating profit for banks. Asset Quality will

be measured in this study by Loan Loss Reserves over Total Loans (LLR) which is used as an

indicator to evaluate the value of loans and the creditworthiness of the banks. This parameter

helps the bank in understanding the amount of funds that have been reserved by the banks in

event of bad and doubtful loans. Since this ratio delivers an image of the sum of the provision

that have been kept aside for bad and doubtful loans, banks should focus and ensure that they

uphold low provision for bad loans. Merchant (2012) asserts that banks that maintain high

provision for bad loans should be concerned as this will signal towards future losses. Hence,

in our study we will assume this ratio to be as low as possible.

Management Quality: This measure of performance will shed light on the superiority of the

management. The duty of the management is to safeguard that the banks operation runs in a

34

smooth and decent manner. Faizulayev (2011) states that management quality measures how

efficiently and productively the bank manages to get more deposits from trustworthy and

financially strong depositors and reduce of the defaults of borrowers by giving the loans to

creditworthy customers. Total loans over total deposits (LDR) indicates the percentage of

bank's loans funded through deposits. The higher the ratio, the more effective and superior

bank management is, however this also invites liquidity problems since the majority of the

customers' deposits are tied up in loans.

Earnings Quality: To measure the efficiency and earnings quality of a bank we should

assess the bank's ability to control costs and increase productivity, ultimately achieving

higher profits. In our study the cost to income ratio (COSR) will be utilized to measure the

earnings quality and efficiency. COSR can be extensively defined as the cost incurred by the

organization to generate a dollar of income. Hence, in our study we expect the COSR to be

low, because the lower the ratio, the more profit will be generated by bank.

Liquidity: This parameter of performance is very crucial for all banks, because it aids in

assessing the risk of unforeseen circumstances which can may lead to insolvency and

bankruptcy. To assess the liquidity of the banks, the net loan to total assets (NLTA) will be

used, which measures the amount assets that have been engaged in loans. Hence, in our

study, we expect this ratio to be as lower as possible.

GDP: This presents the Gross Domestic product growth rate of the country that the bank

resides in. INF: is the average annual inflation rate of the particular country. Both

macroeconomic variables are expected to have a positive impact on profitability as

confirmed by Sufian et al (2009) and Wasiuzzaman et al (2010). The data of macroeconomic

variables was retrieved from the World Bank Database (2014).

35

CHAPTER 4: DATA ANALYSIS

This chapter will present an extensive analysis of the empirical results retrieved from testing

the three objectives depicted in the previous section. First, the comparative analysis between

the banking systems will be presented by using descriptive statistics. Second, to determine

whether these differences are significant one-way ANOVA tests are examined. Finally, the

results of the regression model are scrutinized to analyse the determinants of profitability.

4.1 Descriptive Statistics.

In order to compare the differences in financial performance of Islamic vs. Conventional

banks the following descriptive statistics we computed.

Table 3: Descriptive Statistics- All Banks

N Minimum Maximum Mean Std. Deviation Skewness

ETAR* 224 .054 .902 .13959 .074404 6.174 .163

LLR 224 .000 .138 .03794 .023069 1.155 .163

LDR 224 .293 13.480 .90584 .977391 10.860 .163

COSR 224 -4.652 2.488 .50290 .423617 -7.564 .163

NLTA 224 .064 .807 .58785 .108916 -1.001 .163

ROA 224 -.054 .040 .01528 .009520 -1.686 .163

ROE 224 -.584 .255 .11481 .074530 -3.843 .163

NIM 224 -.022 .055 .02119 .014042 -1.060 .163

GDP 179 -.071 .167 .04952 .054150 -.130 .182

INF 179 -.242 .213 .04852 .132086 -.929 .182 ETAR- Equity to Assets- To measure Capital Adequacy ROA- Return on Assets- To measure Profitability LLR- Loan Loss Reserves/ Gross Loans- To measure Asset Quality ROE- Return on Equity- To measure Profitability LDR- Loans to Deposits- To measure Management Quality NIM- Net Interest Margin- To measure Profitability COSR- Cost to Income Ratio- To measure Earnings Quality GDP- Gross Domestic Product Growth Rate- Macroeconomic Factor NLTA- Net Loans to Total Assets- To measure Liquidity INF- Annual Inflation Rate- Macroeconomic Factor

36

Table 4: Descriptive Statistics- Islamic Banks


ETAR 49 .072 .902 .18176 .137145 3.773 .340

LLR 49 .000 .075 .03103 .023747 .270 .340

LDR 49 .591 13.480 1.27602 2.035724 5.200 .340

COSR 49 -4.652 2.488 .41552 .829495 -4.584 .340

NLTA 49 .064 .736 .59532 .100544 -3.118 .340

ROA 49 -.054 .040 .01584 .015557 -1.805 .340

ROE 49 -.584 .235 .09510 .121364 -3.721 .340

NIM 49 .000 .055 .02855 .010403 -.397 .340

GDP 39 -.071 .167 .06151 .061263 -.390 .378

INF 39 -.242 .213 .04903 .143723 -.885 .378 ETAR- Equity to Assets- To measure Capital Adequacy ROA- Return on Assets- To measure Profitability LLR- Loan Loss Reserves/ Gross Loans- To measure Asset Quality ROE- Return on Equity- To measure Profitability LDR- Loans to Deposits- To measure Management Quality NIM- Net Interest Margin- To measure Profitability COSR- Cost to Income Ratio- To measure Earnings Quality GDP- Gross Domestic Product Growth Rate- Macroeconomic Factor NLTA- Net Loans to Total Assets- To measure Liquidity INF- Annual Inflation Rate- Macroeconomic Factor

Table 5: Descriptive Statistics- Conventional Banks


ETAR 175 .054 .276 .12778 .035566 .528 .184

LLR 175 .008 .138 .03988 .022567 1.533 .184

LDR 175 .293 1.375 .80220 .178049 -.092 .184

COSR 175 .245 1.270 .52737 .193453 1.270 .184

NLTA 175 .255 .807 .58576 .111330 -.579 .184

ROA 175 -.006 .029 .01513 .007019 -.402 .184

ROE 175 -.069 .255 .12033 .053998 -.430 .184

NIM 175 -.022 .050 .01913 .014260 -1.086 .184

GDP 140 -.071 .167 .04618 .051743 -.098 .205

INF 140 -.242 .213 .04838 .129208 -.956 .205

ETAR- Equity to Assets- To measure Capital Adequacy ROA- Return on Assets- To measure Profitability LLR- Loan Loss Reserves/ Gross Loans- To measure Asset Quality ROE- Return on Equity- To measure Profitability LDR- Loans to Deposits- To measure Management Quality NIM- Net Interest Margin- To measure Profitability COSR- Cost to Income Ratio- To measure Earnings Quality GDP- Gross Domestic Product Growth Rate- Macroeconomic Factor NLTA- Net Loans to Total Assets- To measure Liquidity INF- Annual Inflation Rate- Macroeconomic Factor

ROA, ROE and NIM are the financial measures that depict the profitability of Islamic and

conventional banks. ROA of IBs is 1.58% which is higher than that of CBs at 1.51%,

indicating that managerial efficiency in IBs is higher since their assets are capable of

generating higher returns than CBs. This shows that IBs are more profitable, and is

inconsistent with the findings of Momeneen et al (2012) which asserted that CBs are more

profitable than IBs in Pakistan.

37

The ROE of IBs is 9.51%, which is lower than CBs of 12.78%, hence elaborating that

conventional banks are more efficient in generating profits from every unit of shareholders

equity/bank capital, and hence are more profitable using this particular measure. The NIM of

IBs (2.85%) is higher than CBs (1.91%), which may indicate that IBs have been more

profitable in terms of finding least costly funding options and effective in making wise loan

decisions as confirmed by Madvari (2012).

IBs are dominating in capital adequacy, since they have higher ETAR than CBs (IBs 18%

and CBs 13%). This may signify that IBs are more capable of withstanding any unexpected

losses and unforeseen events, because as Samad (2004) asserts a high ETAR will aid the bank

in providing a strong cushion to increase its credit undertakings, lower the unanticipated risks

and supports the organization in charming asset losses. Consequently, as also confirmed by

the findings of Rahman et al (2012), Islamic banks are stronger in responding to balance

sheet shocks, such as liabilities payments; operational and credit risks; or any other losses.

IBs are also dominating in Asset Quality, since they have a lower LLR than CBs (IBs; 3.10%

and CBs 3.99%). This indicates that they have fewer loan loss reserves as a proportion to

38

their gross loans, which relatively means that IBs have more credible and superior asset

quality in relation to CBs. This is consistent with findings of Momeneen et al (2012) as they

assert that banks maintaining high provisions for bad loans should be concerned as this will

signal towards future losses.

Furthermore, IBs are dominating in Management Quality, since they have a higher LDR than

CBs (IBs; 127.6% and CBs; 80.2%). Total loans over total deposits (LDR) reveals the

percentage of bank loans funded through deposits; the higher the ratio, the more effective

and superior bank management is in acquiring more deposit from trustworthy and financially

strong depositors. This is consistent with the findings of Faizulayev (2011) that asserted that

IBs are superior in LDR too, but opposing to Jaffar et al (2011) and Rozzani et al (2012)

In the earnings quality, IBs are still dominating, since they have a lower COSR than CBs

(IBs; 41.5% and CBs 52.7%). The lower cost to income ratio indicates that IBs use lower

costs to generate a dollar of income. Hence, they are more capable of controlling their costs

39

and increasing productively which ultimately results in higher profitability as also confirmed

by Faizulayev (2011), Rozzani et al (2012) and Momeneen et al (2012).

Finally, CBs are dominating in Liquidity, since they have a lower NLTA than IBs (IBs;

59.5% and CBs 58.5%). The lower net loans to total assets ratio in CBs indicates that they are

more liquid, because they have fewer assets engaged in loans. Iqbal et al (2011) and

Merchant (2012) found that the NLTA should be as low as possible, because a high NLTA

means that bank is highly engaged in lending and this may have adverse effects as the bank

might face huge risk of defaulters. The findings are inconsistent with those of Momeneen et

al (2011) and Manvari (2011).

In conclusion therefore, these results support the findings of Javaid et al (2012) and Madvari

et al (2012) who find that Islamic banks are better in maintaining Capital Adequacy and

Asset Quality, but are contrary to the study of Jaffar et al (2011). However, CBs are shown

to have better liquidity positions than CBs, which is in contrary to the findings of Rozzani et

40

al (2013) and Haron et al (2012) and the general belief that IBs have excess liquidity.

Finally, IBs were better management quality which is contradicting with the findings of

Jaffar et (2011) and Rozzani et al (2013) that suggested that "there is lack of management

ability in regards to Islamic banks, which are more focused on growth and expansion

strategies rather than profit-oriented strategies"

The summary of the study's empirical findings are summarized in the table below.

Table 6: Summary of Comparative Analysis of IBs and CBs

PERFORMANCE

MEASURES

CONVENTIONAL

BANKS

ISLAMIC

BANKS

COMMENTS

Profitability

ROA 1.51% 1.58% Islamic banks are dominating in ROA

ROE 12.03% 9.51% Conventional banks are dominating in ROE

NIM 1.91% 2.85% Islamic banks are dominating in NIM

Capital Adequacy Islamic banks are dominating in Capital

Adequacy ETAR 12.78% 18.18%

Asset Quality Islamic banks are dominating in Asset

Quality LLR 3.99% 3.10%

Management

Quality

Islamic banks are dominating in Management

Quality

LDR 80.22% 127.60%

Earnings Islamic banks are dominating in Earnings

Quality COSR 52.74% 41.55%

Liquidity Conventional banks are dominating in

Liquidity NLTA 58.58% 59.53%

41

4.2 One-Way ANOVA;

In order to test the set of hypotheses stated in the previous chapter, one-way ANOVA is used

to evaluate whether there are significant statistical differences in the performance of Islamic

banks and conventional banks based on the CAMEL model measures.

H0 : There are no significant differences between Islamic and Conventional Banks

H1 : There are significant differences between Islamic and Conventional Banks

The general rule is that;

If sig. < 0.05 - Reject H0 If sig. > 0.05 - Do not reject H0

Table 7: Summary of One-way Anova Test

Performance

Measure

Hypothesis Decision Comment

Capital Adequacy H1: Islamic banks have better

capital adequacy measures

than conventional banks.

SUPPORTED If sig. < 0.05 - Reject

H0 -> Significant

Differences

Asset Quality H2: Islamic banks have

better asset quality measures

than conventional banks


H0 -> Significant

Differences

Management

Quality

H3: Islamic banks are better

than conventional banks in

management quality.


H0 -> Significant

Differences

Earnings H4: Islamic banks have

higher earnings than

conventional banks.

REJECTED If sig. > 0.05 - Do not

reject H0 -> No

Significant Differences

Liquidity H5: Islamic banks manage

their liquidity more

efficiently than conventional

banks.


reject H0 -> No


Capital Adequacy: Since the p-value .000<0.05, we reject the null hypothesis and hence

we can infer that based on the analysis there are significant statistical differences between

Islamic and Conventional banks in capital adequacy which is in contrary to the findings

of Kamaruddin and Mohd (2013) in Malaysia.

42

Asset Quality: Since the p-value .017<0.05, we reject the null hypothesis and hence we

can infer that based on the analysis there are significant statistical differences between

Islamic and Conventional banks in asset quality, which is also inconsistent with the

findings of Kamaruddin et al (2013) in Malaysia.

Management Quality: Since the p-value .003<0.05, we reject the null hypothesis and

hence we can infer that based on the analysis there are significant statistical differences

between Islamic and Conventional banks in management quality which is also

inconsistent with the findings of Kamaruddin et al (2013) in Malaysia.

Earnings Quality: Since the p-value .102>0.05, we reject the null hypothesis and hence

we can infer that based on the analysis there are no significant statistical differences

between Islamic and Conventional banks in earnings.

Liquidity: Since the p-value .588>0.05, we reject the null hypothesis and hence we can

infer that based on the analysis there are no significant statistical differences between

Islamic and Conventional banks in liquidity. This result is consistent with findings of

Samad (2004) and Kamaruddin et al (2013) which find that there is a significant

difference in the means of the liquidity ratios between IBs and CBs.

43

Table 8: One-way ANOVA Table

Sum of Squares df Mean Square F Sig.

ETAR Between Groups .112 1 .112 22.059 .000

Within Groups 1.123 222 .005

Total 1.235 223

LLR Between Groups .003 1 .003 5.748 .017

Within Groups .116 222 .001

Total .119 223

LDR Between Groups 8.594 1 8.594 9.333 .003


Total 213.031 223

COSR Between Groups .479 1 .479 2.689 .102


Total 40.018 223

NLTA Between Groups .004 1 .004 .294 .588


Total 2.645 223

ROA Between Groups .000 1 .000 .212 .646


Total .020 223

ROE Between Groups .024 1 .024 4.453 .036


Total 1.239 223

NIM Between Groups .003 1 .003 18.562 .000


Total .044 223

44

4.3 Correlation Analysis;

This section presents the explanatory variables of the study and their relationship with bank

performance as expressed by the 3 independent variables ROA, ROE and NIM. The Pearson's

correlation coefficient demonstrates the magnitude and direction of the relationships; whether

they are strong or weak and positive or negative. Another purpose of correlation is to test for

the multicollinearity problem, in other words whether independent variables are highly

correlated with each other or not. Since most of the independent variables have a correlation

of less 0.4, then this signals a weak relationship between each independent variable and hence

indicates absence of significant correlation between all independent variables which helps us

separate effects of the individual explanatory variables on the regression model

Capital adequacy is positively related to ROA and NIM, but inversely related to ROE. This is

consistent with the findings of Sheikh (2010), Mehta (2012) and Ongore et al (2013), because

as the ETAR increases the bank will have a stronger cushion to absorb any losses or credit

undertakings. And the negative correlation with ROE is in line with the argument that "higher

capital ratios encourage banks to invest in safer assets, such T-bills and T-bonds which may

affect bank performance as asserted by Mehta (2012).

Asset Quality is negatively correlated ROE and ROA, because as the loan loss reserves of the

bank relative to total loans increase, the bank's profitability is at stake. The negative

correlation between ROA & ROE is very strong due to the fact that loans constitute the

largest share of assets in banks that are used to generate income for shareholder and hence

can strongly affect profitability in a negative way if the LLR increase.

Management quality (LDR) also has an inverse relationship with ROE & ROA, while a

positive relationship with NIM. As the percentage of loans given out from customer's

deposits increases, the firm is at a high risk of insolvency or bankruptcy, which negatively

45

impacts profitability. However, the positive relationship between LDR and NIM, is due to the

fact that higher loans generate higher interest income to the bank and hence a higher NIM.

Management quality reveals how effectively and productively bank managers are funding

loans through deposits and attracting more financially strong depositors; therefore as LDR

increases, profitability is expected to increase as confirmed by Jaivid et al(2011) and

Momeneen et al (2012). However, our empirical results offer opposing findings, because

LDR has a negative relationship with profitability, this may possibly be due to the liquidity

problem that arises when most of the customer's deposits are tied up in loans.

Earnings quality (COSR) however has a positive relationship with ROA and ROE, but a

negative relationship with NIM. Liquidity (NLTA) has a negative correlation with ROA,

because as the amount of assets being engaged in loans increases, liquidity decreases and this

negatively affects bank performance. however the relationship is very weak and is not

statistically significant (since p-value is <0.05). Finally the macroeconomic external variable

GDP has a very strong positive relationship with all 3 performance measures, meaning as the

economy is growing bank performance is expected to increase. Inflation also has positive

significant (p-value<0.05) as opposing to Ali et al (2012) and Rehman et al (2012).

correlation with all 3 profitability indicators, however the relationship is not statistically

Table 9: Correlation Coefficient between variables

ETAR LLR LDR COSR NLTA ROA ROE NIM GDP INF

ETAR 1 -.274** 0.046 0 -.281

** .240

** -.134

* .249

** .256

** -0.034

LLR -.274** 1 -0.026 0.105 -0.101 -.347

** -.226

** .154

* -.421

** 0.075

LDR 0.046 -0.026 1 -.146* .191

** -.213

** -.257

** 0.028 -0.089 0.069

COSR 0 0.105 -.146* 1 -0.091 0.043 .255

** -0.059 0.019 0.019

NLTA -.281** -0.101 .191

** -0.091 1 -0.056 0.013 0.053 -0.126 -0.057

GDP .256** -.421

** -0.089 0.019 -0.126 .445

** .296

** .148

* 1 .245

**

INF -0.034 0.075 0.069 0.019 -0.057 0.106 .149* 0.025 .245

** 1

ROA .240** -.347

** -.213

** 0.043 -0.056 1 .856

** .289

** .445

** 0.106

ROE -.134* -.226

** -.257

** .255

** 0.013 .856

** 1 .157

* .296

** .149

*

NIM .249** .154

* 0.028 -0.059 0.053 .289

** .157

* 1 .148

* 0.025

46

4.3. Regression Analysis

This section will present the output of the regression analysis, to explain how any change in

the independent or explanatory variables (internal CAMEL factors and external

macroeconomic factors) will affect the determinants of profitability (ROA, ROE and NIM).

Six regression models were estimated as previously done by Faizulayev (2011) and Ongore

et al (2013). In the pure regression model all internal and external factors are taken into

consideration and a regression is run on all banks in the sample. However, in the second type

of regression model, the moderating role of bank type on the performance of banks is

accounted for to evaluate whether the differences in banking systems will have an altered

impact on profitability. As previously explained, several diagnostic tests were run on all 6

regression models to ensure that the data suits the assumptions of linear regression models.

Based on the normality tests (the data follows a normal distribution); multicollinerality test

(no significant relationships exist between the independent variables; since correlation

coefficients are <0.4)

Kindly refer to the appendix 4 to view all the regression output tables and results, in

addition to the model diagnostic tests.

47

4.3.1. Pure Regression Model.

The following regression results show the impact of bank-specific and macro-economic

variables on the performance of banks in the MENA & GCC region.

Table 10: Regression output of bank-specific and macroeconomic variables.

ROA ROE NIM

(Constant) 0.0138 0.1536 0.0138

0.0066* 0.0001* 0.0972**

ETAR 0.1055 0.1055 4.5619

0.1482** 0.0006* 0.0000*

LLR -0.2557 -0.2557 4.3018

0.0008* 0.00001* 0.0000*

LDR -0.2106 -0.2106 -0.0304

0.0018* 0.0014* 0.9758**

COSR 0.0727 0.0727 -0.8836

0.2643** 0.0000* 0.3781**

NLTA 0.0371 0.0371 2.5599

0.5983** 0.8139** 0.0113*

GDP 0.2766 0.2766 2.8933

0.0004* 0.01852* 0.0043*

INF 0.0761 0.0761 -0.4779

0.2628** 0.0546** 0.6333**

R2 30.54% 32.29% 17.78%

ADJUSTED R2 27.69% 29.52% 14.42%

SSE 0.0087 0.0676 0.0132

F-test 10.738 11.648 5.283

P-value 0.00 0.00 0.00

DURBIN WATSON 1.0508 1.0454 0.4333 Note: The figures in Italics are the p-values of the coefficients

* Statistically significant at 5%

** Statistically not significant

ETAR- Equity to Assets- To measure Capital Adequacy ROA- Return on Assets- To measure Profitability

LLR- Loan Loss Reserves/ Gross Loans- To measure Asset Quality ROE- Return on Equity- To measure Profitability

LDR- Loans to Deposits- To measure Management Quality NIM- Net Interest Margin- To measure Profitability

COSR- Cost to Income Ratio- To measure Earnings Quality GDP- Gross Domestic Product Growth Rate- Macroeconomic Factor

NLTA- Net Loans to Total Assets- To measure Liquidity INF- Annual Inflation Rate- Macroeconomic Factor

As clearly illustrated in the table above, several bank-specific internal factors significantly

impact the profitability of banks as expressed by ROA, ROE and NIM at 95% confidence

48

level (since their p-values are> 0.05). Capital adequacy, asset quality and management

quality are all internal factors with significant statistical impact on profitability as also

concluded by Javaid et al (2011), Aktar et al (2011) and Rehman et al (2012). As capital

adequacy increases, profitability of banks is expected to increase, while as the loan loss

reserves of banks decrease (asset quality), profitability is expected to increase. Furthermore,

although we expected management quality (measured by Loan to Deposit ratio, LDR) to be

positively related to profitability as asserted by Faizulayev (2011), the results depicted above

provide contradicting results. LDR is negatively related to performance, because, as LDR

increases, profitability is expected to decrease (as measured by ROE & ROA), however, as

LDR increases, profitability ( as measured by NIM) is expected to increase due to the extra

interest income received from lending more loans.

Alternatively, other internal factors such as earnings quality and liquidity which are

supposedly positively related with bank performance exerted no effect on the profitability

indicators ROE, ROA and NIM of banks in the MENA & GCC over the period 2009-2013,

because they are statistically insignificant. These findings are similar to those reported by

Faizulayev (2011) and Ongore et al (2013).

The impact of macro-economic variables on bank performance was also evaluated, and

according to the results above, GDP has a strong positive relationship with bank's

performance as measured by ROA, ROE and NIM and is statistically significant. On the other

hand, although INF was expected to have a positive relationship with profitability, the impact

is not significant. These results are inconsistent with Ongore et al (2013) who studied the

performance of Kenyan banks and concluded that macroeconomic variables have

insignificant impact on profitability. It might be the case that banks operating in the GCC &

MENA region are closely related to their economy's stability and growth, which is consent to

similar studies by Sufian et al (2009) and Wasiuzzaman et al (2010).

49

4.3.2. Moderated Regression Model.

In order to evaluate the moderating effect of bank type on the performance of banks in the

MENA & GCC region from 2009-2013, another regression model analysis was performed to

assess whether the differences in bank type had a significant impact on performance.

Table 11: Regression Output as Moderated by Bank Type

ROA ROE NIM

(Constant) 0.0152 0.1204 0.0192

0.0000* 0.0000* 0.0000*

ETAR*M -0.0609 -0.3174 0.1806

0.4999** 0.0003* 0.0728**

LLR*M -0.2131 -0.2196 0.1650

0.0634** 0.0475* 0.1951**

LDR*M -0.1292 -0.1547 -0.0319

0.0824** 0.0315* 0.6992**

COSR*M 0.2083 0.4128 0.1135

0.0026* 0.0000* 0.1365**

NLTA*M -0.0962 -0.0326 -0.0676

0.5502** 0.8338** 0.7059**

GDP*M 0.4702 0.2628 0.0504

0.0000* 0.0127* 0.6759**

INF*M -0.0627 0.0510 -0.0179

0.3803** 0.4594** 0.8214**

R2 26.77% 31.80% 9.43%

ADJUSTED R2 24.28% 29.48% 6.35%

SSE 0.0084 0.0634 0.0138

F-test 10.7577 13.7228 3.0649

P-value 0.00 0.00 0.00

DURBIN WATSON 0.9009 0.9058 0.2300

Note: The figures in Italics are p-values

* Statistically significant at 5%

** Statistically not significant

M: Moderating Factor, Bank Type (1= Islamic Banks 0= Conventional Banks)

ETAR- Equity to Assets- To measure Capital Adequacy ROA- Return on Assets- To measure Profitability

LLR- Loan Loss Reserves/ Gross Loans- To measure Asset Quality ROE- Return on Equity- To measure Profitability

LDR- Loans to Deposits- To measure Management Quality NIM- Net Interest Margin- To measure Profitability

COSR- Cost to Income Ratio- To measure Earnings Quality GDP- Gross Domestic Product Growth Rate- Macroeconomic Factor

NLTA- Net Loans to Total Assets- To measure Liquidity INF- Annual Inflation Rate- Macroeconomic Factor

50

As observed from the table above, the moderating role of bank type is relatively strong. This

means that there were significant differences on the coefficients of variables after being

moderated by the bank type.

The results of the majority of bank-specific internal factors; capital adequacy, asset quality,

management quality and earnings changed significantly after being moderated by bank type.

Capital adequacy had a positive significant relationship with ROE and ROA in the pure

regression model, while after being moderated it now has a negative insignificant relationship

with ROA and ROE. Asset Quality, management quality also had significant effects on

profitability, however after being moderated they became insignificant determinants of bank

performance. On the other hand, earnings quality which was considered to have an

insignificant impact on profitability, now has a significant impact after being moderated by

bank type.

However, the type of bank didn't moderate the relationship between bank performance and

the macro-economic explanatory variables; GDP and inflation. Due to the fact that there are

significant differences in their coefficients and significance level (GDP still have a strong

positive and significant impact on profitability, while inflation has a negative insignificant

impact on profitability). Additionally, the negative relationship and relative insignificance of

liquidity on profitability also remained the same.

Moreover, as indicated below, the coefficient determinants (R2 and adjusted R

2 ) of the

regression models decreased in magnitude as a result of the moderating effect of bank type.

51

Table 12: Coefficients of determination before and after moderation

Pure Regression Model Moderated Regression Model

Model Fit ROA ROE NIM ROA ROE NIM

R2 30.54% 32.29% 17.78% 26.77% 31.80% 9.43%

ADJUSTED R2 27.69% 29.52% 14.42% 24.28% 29.48% 6.35%

% Change -12.32% -0.11% -55.92%

Although the decrease in of Adjusted R2

of ROE was low, the percentage decrease on ROA

and NIM was quite high. Hence elaborating that bank type does have a significant effect on

the profitability and financial performance of banks in the MENA & GCC region as

elaborated by the coefficient of determinations of both categories of regression models.

Table 13: Summary of variable coefficients before and after moderation

Performance

Measure

Pure Regression

Model

Moderated

Regression Model

Comment

CAPITAL

ADEQUACY Significant Insignificant Moderates the relationship

between capital adequacy &

bank performance

ASSET QUALITY Significant Insignificant Moderates the relationship

between asset quality & bank

performance

MANAGEMENT

QUALITY Significant Insignificant Moderates the relationship

between management quality &

bank performance

EARNINGS

QUALITY Insignificant Significant Moderates the relationship

between earnings bank

performance

LIQUIDITY Insignificant Insignificant No moderating effect on bank

performance

GDP Significant Significant No moderating effect on bank

performance

INF Insignificant Insignificant No moderating effect on bank

performance

The results of this study are opposing to the findings of Athanasologou et al (2005) on the

performance of Greek banks and Ongore et al (2013) in Kenya, who concluded that

ownership identity, did not moderate the relationship between bank performance and its

52

determinants in Greece or Kenya, and hence the ownership status appeared to be insignificant

in affecting the profitability of banks. The reason behind these differences, might be the

different sample of countries used by the study.

53

CHAPTER 5: CONCLUSION

This chapter draws together the main findings of the empirical results of the research study,

including the implications of the findings, the limitations of the study and recommendations

for future research.

5.1. Key Aims and Findings

As previously stated, a country’s economic growth, among several other factors, is based on

its financial sector’s performance, especially the financial institutions working in that

country; with the banking sector being the most prominent. Due to the banking sector's

significant role in the wellbeing and stability of any economy, it is imperative to constantly

monitor and evaluate banks' performance to guarantee that the economy's financial sector is

operating efficiently. Consequently, the purpose of this research was to evaluate the

performance of banks in the MENA & GCC region over the period 2009-2013 using the

CAMEL model approach. The precise objectives of the study were; firstly to compare the

performance of Islamic vs. conventional banks using; capital adequacy, asset quality,

management quality, earnings and liquidity as performance determinants; secondly to test for

any significant differences in the performance between IBs and CBs; and finally the

determine the determinants of profitability using both bank-specific and macroeconomic

variables, while moderating for the effect of bank type on performance.

The findings of the first objective were achieved through descriptive statistics, and it was

concluded that Islamic banks dominate conventional banks in capital adequacy, asset quality,

management quality and earnings, while they are weaker in liquidity management. To test

whether the differences in performance were significant, one-way ANOVA tests were done,

and we found statistically significant differences in the performance of Islamic and

54

conventional banks in capital adequacy, asset quality and management quality, while no

significant differences existed in earnings and liquidity management of both banks. To test

for the relationship between profitability (independent variable) and bank-specific and macro-

economic variables (explanatory variables), the Pearson's correlation coefficient was used

and results indicated strong positive relationships between capital adequacy, earnings quality,

liquidity, GDP and inflation with profitability. While negative relationships between poor

asset quality and management quality and profitability. Finally, the results of the regression

analysis revealed that the most significant bank-specific internal determinants of bank

performance in the MENA & GCC region over the period 2009-2013, were capital adequacy,

asset quality and management quality, while the significant macroeconomic variables were

GDP growth rate and annual inflation rate. However, after considering the moderating role of

bank type in the regression model, we discovered significant differences in the coefficient of

the parameters and their significance levels changing rapidly. The empirical results of our

study at times were consistent to those of previous literature studies, but at times, however

contradicting results were discovered.

55

Table 14: Summary of Comparative Analysis of IBs and CBs. (Objective 1)

PERFORMANCE

MEASURES

CONVENTIONAL

BANKS

ISLAMIC

BANKS

COMMENTS

Profitability

ROA 1.51% 1.58% Islamic banks are dominating in ROA

ROE 12.03% 9.51% Conventional banks are dominating in ROE

NIM 1.91% 2.85% Islamic banks are dominating in NIM

Capital Adequacy Islamic banks are dominating in Capital

Adequacy ETAR 12.78% 18.18%

Asset Quality Islamic banks are dominating in Asset

Quality LLR 3.99% 3.10%

Management

Quality

Islamic banks are dominating in Management

Quality

LDR 80.22% 127.60%

Earnings Islamic banks are dominating in Earnings

Quality COSR 52.74% 41.55%

Liquidity Conventional banks are dominating in

Liquidity NLTA 58.58% 59.53%

Table 15: Summary of One-way Anova Test (Objective 2)

Performance

Measure

Hypothesis Decision Comment

Capital Adequacy H1: Islamic banks have better

capital adequacy measures

than conventional banks.


H0 -> Significant

Differences

Asset Quality H2: Islamic banks have

better asset quality measures

than conventional banks


H0 -> Significant

Differences

Management

Quality

H3: Islamic banks are better

than conventional banks in

management quality.


H0 -> Significant

Differences

Earnings H4: Islamic banks have

higher earnings than

conventional banks.


reject H0 -> No


56

Liquidity H5: Islamic banks manage

their liquidity more

efficiently than conventional

banks.


reject H0 -> No


Table 16: Summary of regression analysis before and after moderation (Objective 3)

Performance

Measure

Pure Regression

Model

Moderated

Regression Model

Comment

CAPITAL

ADEQUACY Significant Insignificant Moderates the relationship

between capital adequacy &

bank performance

ASSET QUALITY Significant Insignificant Moderates the relationship

between asset quality & bank

performance

MANAGEMENT

QUALITY Significant Insignificant Moderates the relationship

between management quality &

bank performance

EARNINGS

QUALITY Insignificant Significant Moderates the relationship

between earnings bank

performance

LIQUIDITY Insignificant Insignificant No moderating effect on bank

performance

GDP Significant Significant No moderating effect on bank

performance

INF Insignificant Insignificant No moderating effect on bank

performance

5.2. Implications of theory

It is worth noting, that the empirical tests for liquidity management indicate that CBs

outperform IBs, and this is largely inconsistent to the typical conviction that IBs are haunted

by the chronic problem of excess liquidity, since they carry surplus cash and other assets in

comparison to CBs. The study utilised the net loans to total assets ratio (NLTA) to measure

liquidity and discovered that IBs have a higher ratio which makes them relatively illiquid and

makes CBs superior to them. The reason behind this finding, might be due to differences in

ratios used to measure liquidity or due to the different sample selection. Nevertheless, it is

57

imperative that additional studies should be undertaken to further examine this issue. It is

usually the case, that capital adequacy is positively related to profitability and is a significant

determinant of bank performance, because as the equity to total assets ratio increases, the

bank's capital is more capable of absorb any unforeseen losses or financial shocks the bank

faces as supported by several scholars. Also asset quality as measured loan loss reserves are

expected to have a negative relationship with profitability, because poor asset quality in terms

of higher LLR has a drastic impact on profitability. Furthermore, earnings quality as

measured by cost to income ratio (COSR) is expected to lower profitability as the COSR

increases, as this indicates bank inefficiency. Management quality is measured by the loans to

deposits ratio and reveals how effectively and productively bank managers are funding loans

through deposits through attracting more trustworthy and financially strong depositors; as

LDR increases, profitability is expected to increase. However, according to our study, LDR

has a negative relationship with profitability, which might be due to the liquidity problem that

arises when most of the customer's deposits are tied up in loans. The macroeconomic

variables are also both expected to have a positive relationship with profitability, however

our empirical findings illustrate that GDP has a significant impact on profitability, while

inflation rate's impact on profitability is insignificant.

5.3. Research Limitations

The main limiting factor in the study was the absence of complete financial statements for

some banks in the sample resulting in in-complete data sets. Furthermore, some contrasting

results were discovered throughout the study, for example the positive relationship of cost to

income ratio to profitability, which makes no sense given that costs should be minimal to

ensure that a bank maintains its profitability. This could be due to some factors not taken into

consideration in this study or there could simply be some discrepancies. Additionally, as

58

shown in the appendix, part of the data variables provided a non-normal distribution

(measured by the Kolmogorov-Smirnov Test as shown in the appendix). Moreover, in order

to generalise the conclusions of this empirical study, the sample size of the study should be

widened to include countries outside the MENA region such as Malaysia, Indonesia and

Pakistan which also have rapidly expanding Islamic banking sectors

5.4. Direction of Future Research

As previously discussed, Islamic banking is still highly nascent in comparison to

conventional banking and this is a fundamental reason behind the many challenges that are

currently impeding its mounting success. Several further studies, should therefore be

undertaken in order to provide scrupulous comparative analysis of the different banks'

determinants of profitability; in an attempt to solidify the Islamic banking model and

replicate only the successful practices and determinants of conventional banks' performance.

Conversely, since IBs face a crucial challenge in improving their risk management strategies,

corporate governance and other practices due to their adherence to Sharia'a; innovative and

precisely tailored solutions should be presented to resolve these challenges. It is evident that

Islamic banking is not a negligible or a temporary phenomenon, Islamic banks are here to

stay and there are evident signs that they will continue to grow and expand worldwide.

Consequently, it is imperative that IBs overcome any difficulties that are currently impeding

their performance and quickly adapt to the rapidly changing environment. Nevertheless, this

will be achieved by continuous empirical studies that can provide new insights into the

Islamic banking model and present effective recommendations that IBs can adopt as an

endeavour to improve their performance. We therefore, recommend further studies to be

undertaken in the risk management and corporate governance practices of Islamic banks!

THE END

59

REFERENCE LIST

The databases used for data collection;

Bank Scope Database (2014)

World Bank Database (2014)

Abdel-Hameed M. Bashir (2012). Determinants Of Profitability In Islamic Banks: Some

Evidence From Middle East, Islamic Economic Studies. Retrieved from Springer Database

Akhtar, M. F., Ali, K., & Sadaqat, S. (2011). Liquidity Risk Management: A comparative

study between Conventional and Islamic Banks of Pakistan. Interdisciplinary Journal of

Research in Business, 1(1), 35-44. Retrieved from Emerald

Akhter, W., Raza, A., Orangzab, & Akram, M. (2011). Efficiency and performance of

Islamic Banking: The case of Pakistan. Lahore: Far East Research Centre.

Akhtar, H., (2010). Technical Efficiency And Productivity Growth Of Saudi Banks: A Data

Envelopment Analysis Approach. Global Business Review 11:2.

Al-Ajmi J. & Hameeda, A.H. (2012).Risk management practices of conventional and Islamic

banks in Bahrain. The Journal of Risk Finance, 13 ( 3), 215-239.DOI:

10.1108/15265941211229244

Ali, K., Akhtar, M. F., & Ahmed, H. Z. (2011). Bank-Specific and Macroeconomic Indicators

of Profitability - Empirical Evidence from the Commercial Banks of Pakistan. International

Journal of Business and Social Science, 2(6), 235-242

Al-Janabi, M.A.M. (2012). Proactive risk management in emerging and Islamic Financial

markets. Banking and Finance International Conference (BFIC): Beirut.

Almazari, A,A. (2014). Impact of Internal Factors on Bank Profitability: Comparative Study

between Saudi Arabia and Jordan. Journal of Applied Finance & Banking, vol. 4, (1), 125-

140 ISSN:1792-6599 (online). Retrieved from Springer Database

Al-Tiby, M, A. (2011). Islamic Banking: How to Manage Risk And Improve Profitability.

Wiley Finance: USA.

Athanasoglou, P.P., Sophocles, N.B., Matthaios, D.D. (2005) Bank-specific, industry-specific

and macroeconomic determinants of bank profitability . Working paper, Bank of Greece.

1(1), 3-4.

Awan, A., (2009). Comparison Of Islamic And Conventional Banking In Pakistan.

Proceedings 2nd CBRC. Lahore: Pakistan

Azam, M., Siddiqoui, S. (2012) Domestic and Foreign Banks’ Profitability: Differences and

Their Determinants. International Journal of Economics and Financial Issues 2(1), 33-40.

60

banks. International Journal of Trade, Economics and Finance, vol. 3. (3) pp. 158-163.

Chapra, A,U. & Khan, T. (2012). Regulation and supervision of Islamic Banks. Jeddah: Saudi

Arabia: Islamic Development Bank: Islamic Research & Training Institute. Retrieved from:

Emerald Data base

Chong, B.S, Liu, M.H., (2008).Islamic banking: Interest-free or interest-based? Pacific-Basin

Finance Journal, 17 (2009) 125–144. Retrieved from Science Direct.

ElSeify, E. (2013). Comparative Analysis of Qatari Islamic Banks Performance versus

Conventional Banks Before, During and After the Financial Crisis. International Journal of

Business and Commerce, 3, (3) pp 11-41. ISSN: 2225-2436 Retrieved from Asian Society of

Business and Commerce Research.

Ernst & Young (2012). World Islamic Banking Competitiveness Report (2012-2013).

Growing Beyond DNA of Successful Transformation. Accessed at 18/11/2013. Retrieved

from

http://www.ey.com/Publication/vwLUAssets/The_World_Islamic_Banking_Competitiveness

_Report/$FILE/World%20Islamic%20Banking%20Competitiveness%20Report%202012-

13.pdf

Faizulayev, A. (2011). Comparative Analysis between Islamic Banking and Conventional

Banking Firms in terms of Profitability, 2006-2009. Jeddah: Eastern Mediterranean

University May 2011

Geelani, I. (2005). Islamic Finance: A synthesis. The Nottingham Business School.

Global Islamic Finance Report, GIFR (2012). CIMB ISLAMIC. Retrieved from

http://www.gifr.net/home_ifci.htm

Guru, B.K. & Shanmugam, B. (2004). Determinants of commercial bank profitability in

Malaysia. International Journal of Business. Retrieved from Emerald Database.

Haron, N. H & Abdul Rahman, R. (2012). Financial distress–Detection model for Islamic

banks. International Journal of Trade, Economics and Finance, vol. 3. (3) pp. 158-163.

Haron, S. and Ahmad, N. (2010), “The effect of conventional interest rate and rate of profit

on funds deposited with Islamic banking system in Malaysia”, International Journal of

Islamic Finance News, 3 (10/11), pp. 10-13.

Hasan, M. & Dridi, J. (2010). The effects of the Global Crisis on Islamic and Conventional

banks: A comparative study. International Monetary Fund.

Hassan, A. (2009). Risk management practices of Islamic banks of Brunei Darussalam. The

Journal of Risk Finance 10 (1) pp. 23-37. Retrieved from Emerald Database DOI:

10.1108/15265940910924472

http://www.ey.com/Publication/vwLUAssets/The_World_Islamic_Banking_Competitiveness_Report/$FILE/World%20Islamic%20Banking%20Competitiveness%20Report%202012-13.pdf



61

Hudgins, S.C & Rose, P.S (2013). Bank Management and Financial Services. 8ed. New

York: McGraw Hill International Editions

Ika, S. R., & Abdullah, N. (2011). A comparative study of Financial performance of Islamic

banks and conventional banks in Indonesia. International Journal of business and Social

sciences. Retrieved from Emerald Database.

International Financial Service Board (2012). Regulatory Standards, Retrieved from

http://www.ifsb.org/

Iqbal, A. (2012). Liquidity Risk Management: A Comparative Study between Conventional

and Islamic Banks of Pakistan. Global Journal of Management and Business Research, 12, 1

(5). Retrieved from Global Journals Inc. ISSN: 2249-4588

Iqbal, Z. and Mirakhor, A. (2011), An Introduction to Islamic Finance: Theory and Practice,

2nd ed. Singapore: Wiley Finance

Jaffar, M., & Manarvi, I., (2011). Performance comparison of Islamic and Conventional

banks in Pakistan. Global Journal of Management And Business Research 11: 1.

Javaid, S. Anwar, J., Zaman,K. Gafoor, A. (2011). Determinants of Bank Profitability in

Pakistan: Internal Factor Analysis. Mediterranean journal of social sciences, 2, (1). ISSN

2039-2117

Jusufović, J. (2009). Financial Crisis and Islamic Finance. Retrieved from

http://www.ius.edu.ba/mcan/GPROJECTS/Graduation%20thesis%20-

%20Jasmin%20Jusufovic.pdf

Kahf, M., & Khan, T. (2007). Principles of Islamic Finance. Jeddah: Islamic Research and

Training Institute- Islamic Development Bank.

Kassim, S,H., & Abdulle, M. Y. (2012). Impact of Global Financial Crisis on the

Performance of Islamic and Conventional Banks: Empirical Evidence from Malaysia.

Retrieved from Science Direct Database

Kettell, B. (2011). Introduction to Islamic Banking and Finance. Wiley Finance: United

Kingdom.

Khalid, S., & Amjad, S. (2012)Risk management practices in Islamic banks of Pakistan. The

Journal of Risk Finance. 13 (2),148-159. DOI 10.1108/15265941211203198

Khamis, M. & Senhadji, A. (2010). Impact of the global financial crisis on the Gulf

Cooperation Council countries and challenges ahead. Washington D.C: International

Monetary Fund Publication.

http://www.ius.edu.ba/mcan/GPROJECTS/Graduation%20thesis%20-%20Jasmin%20Jusufovic.pdf

http://www.ius.edu.ba/mcan/GPROJECTS/Graduation%20thesis%20-%20Jasmin%20Jusufovic.pdf

62

Khan, M., & Bhatti, I,. (2008). Islamic banking and finance: on its way to globalization.

Managerial Finance, 34 (10). pp. 708-725. DOI: 10.1108/0307435081089102. Retrieved

from Emerald Database.

Khan, M.K. & Usman, A. (2012). Evaluating the Financial Performance of Islamic and

Conventional Banks of Pakistan: A Comparative Analysis. International Journal of Business

and Social Science, 3 (7). Retrieved from Springer

Lewis. K, M,. (2005). Islamic Corporate Governance. International Association for Islamic

Economics: Review of Islamic Economics, 9 (1). pp 5-29

Loghod, H.A. (2006). Do Islamic Banks Perform Better than Conventional Banks? Evidence

from Gulf Cooperation Council countries

Matthews, O. (2008), “How the west came to run Islamic banks”, Newsweek International

Edition, 31 October.

Mehta, A. (2012). Financial Performance of UAE Banking Sector- A Comparison of before

and during Crisis Ratios. International Journal of Trade, Economics and Finance, 3, (5).

Merchant, I.P. (2012). Empirical Study of Islamic Banks Versus Conventional Banks of

GCC. Global Journal of Management and Business Research, 12 (20).

Momeneen, W., Tahir, A., Tariq, M., Hanif, M. (2012). Comparative Performance Study of

Conventional and Islamic Banking in Pakistan. International Research Journal of Finance

and Economics, 83. ISSN 1450-2887. Retrieved from EuroJournals Publishing, Inc

Muhammad, J., & Manarvi, I. (2011). Performance comparison of Islamic and conventional

banks in Pakistan. Global journal of Management and Business Research , 7.

Mukherjee, A., Nath, P. & Pal, M. N. (2002). “Performance Bench Marketing and Strategic

Homogeneity of Indian Banks. International Journal of Bank Marketing, 20, (3). pp. 122-139

Olson, D., Zoubi, T.A. (2011). Efficiency and bank profitability in MENA countries.

Emerging Markets Review,12 pp 94–110. Retrieved from Science Direct

Ongore, V.O. & Kusa, G.B. (2013). Determinants of Financial Performance of Commercial

Banks in Kenya. International Journal of Economics and Financial Issues, Vol. 3, No. 1,

2013, pp.237-252, ISSN: 2146-4138

Pizzi, M. (2013, October 29). Cameron: London can be a world capital for Islamic finance.

Aljazeera America. Accessed at 19/11/2012. Retrieved from

http://america.aljazeera.com/articles/2013/10/29/cameron-london-

canbethenextdubaiofislamicfinance.html

Rahman, A. (2010). The art of Islamic banking and Finance: Tools and techniques for

community-based banking. Wiley Finance: New Jersey.

http://america.aljazeera.com/articles/2013/10/29/cameron-london-canbethenextdubaiofislamicfinance.html

http://america.aljazeera.com/articles/2013/10/29/cameron-london-canbethenextdubaiofislamicfinance.html

63

Rahman, R.A., Tafri, F.H. and Omar, N. (2011), “Empirical evidence on the risk

management tools practised in Islamic and conventional banks”, Qualitative Research in

Financial Markets, 3 (2), pp. 86-104.

Rahman. S. Farzand, A,K. Kurshed, I. & Zafar, A. (2012). Parameters of conventional and

Islamic Bank’s profitability in Pakistan: Evaluation of Internal Factor. Research Journal of

Finance and Accounting, 3 (3). ISSN 2222-1697

Rashwan, M,H. (2012). How did listed Islamic and Traditional Banks Perform: pre and post

2008 Financial Crisis? Journal of Applied Finance and Banking, 2 (2). p. 149-175

Rose, P.S (2012). Commercial Bank Management. 4ed. New York: McGraw Hill

International Editions

Rozzani, N. & Rahman, R. (2013). Camels and Performance Evaluation of Banks in

Malaysia: Conventional Versus Islamic. Journal of Islamic Finance and Business Research, 2

(1). Issue. pp. 36 – 45

Said, A. (2013). Risks and Efficiency in the Islamic Banking Systems: The Case of Selected

Islamic Banks in MENA Region, 3 (1) pp.66-73. International Journal of Economics and

Financial Issues. ISSN: 2146-4138

Samad, A. (2004). Performance of Interest-free Islamic banks vis-à-vis Interest-based

Conventional Banks of Bahrain. IIUM Journal of Economics and Management, 12(2): 1-15.

Jamali, H., and Shar,A., Ali, M., (2010). Performance Evaluation Of Pre-Post

Nationalization OfBanking Sector In Pakistan: An Application Of Clsa-Stress Test.

International Journal OfBusiness And Management 5: 11.

Sheik, S.A., & Ali, A., (2009). Risk management in Islamic and conventional banking: a

differential analysis. Journal of Independent Studies and Research 7: 2.

Sheikh, M., Taseen, U., Haider, S.A., Naeem, M., (2010). Islamic Vs Conventional Banks in

Pakistan(A case study of Bahawalpur). Journal of Education Research.

Shaista, W. & Umadevi, N. G. (2013). Comparative study of the performance of Islamic and

conventional banks: The case of Malaysia. Humanomics, 29 (1), pp. 43-60. Retrieved from

Emerald Database. DOI 10.1108/08288661311299312

Siddiqi, N.M. (2008). The Comparative Advantages of Islamic Banking and Finance.

Massachusetts. Center for Middle Eastern Studies: Harvard University Forum on Islamic

Finance. p. 183-188.

Siraji, K.K., Sudarsana, P. (2012). Comparative Study on Performance of Islamic Banks and

Conventional Banks in GCC region. Journal of Applied Finance & Banking, 2, (3), pp 123-

161. International Scientific Press. ISSN: 1792-6580

Srairi, S, A. (2009). Cost and profit efficiency of conventional and Islamic banks in GCC

countries. J Prod Anal, 34:45–62. Retrieved from Springer Database

64

Sufian, F. and Habibbullah, M.S. (2009), “Bank specific and macroeconomic determinants of

bank profitability: empirical evidence from the China banking sector”. Frontiers of

Economics in China, Vol. 4 No. 2, pp. 274-91.

Sultan, S.A.B.M. (2006), “An overview of accounting stardards for Islamic financial

institutions”, Islamic Services, Vol. 1 No. 4, pp. 1-9.

Syed Ali, Hayes, M (2012). Islamic Banking in MENA region, Financial Flagship. Islamic

Research and Training Institute. World Bank Database

Tett, G. (2009), “Banks seek Islamic scholars versed in finance”, Financial Times, 19 May.

Usman, M., Akhtar, M, F. Ahmed, N. (2011). Risk Management Practices and Islamic Banks:

An Empirical Investigation from Pakistan. Interdisciplinary Journal of Research in Business,

1 (6), (pp.50-57)

Vogel, F. & Hayes, S. (1998). Islamic Law & Finance: Religjon, Risk and Return. Kluwer

International School. Massachatues- USA pp-90.

Wasiuzzaman, S. & Gunasegevan, U.N. (2013). Comparative study of the performance of

Islamic and conventional banks: The case of Malaysia. Humanomics, 29 (1) pp. 43-60

65

Appendix 1: Details of banks included in the sample.

Bank Name Country Name World rank by assets

Total Assets

mil USD

Listed Market Capitalisation

th USD 1 Qatar National Bank QATAR 195 121,837 Listed 37,658,516

2 Bank Hapoalim BM ISRAEL 217 109,549 Listed 7,457,296

3 Emirates NBD PJSC UNITED ARAB EMIRATES 243 93,141 Listed 15,057,823

4 National Bank of Abu Dhabi UNITED ARAB EMIRATES 251 88,512 Listed 18,699,419

5 Al Rajhi Bank SAUDI ARABIA 288 74,632 Listed 29,900,002

6 National Bank of Kuwait S.A.K. KUWAIT 329 65,888 Listed 16,545,647

7 Kuwait Finance House KUWAIT 377 57,173 Listed 12,619,624

8 Samba Financial Group SAUDI ARABIA 392 54,676 Listed 13,152,000

9 First Gulf Bank UNITED ARAB EMIRATES 403 53,106 Listed 18,371,680

10 Mizrahi Tefahot Bank Ltd. ISRAEL 489 51,747 Listed 3,050,475

11 Abu Dhabi Commercial Bank UNITED ARAB EMIRATES 434 49,869 Listed 12,493,913

12 Saudi British Bank (The) SAUDI ARABIA 460 47,281 Listed 16,888,890

13 Banque Saudi Fransi SAUDI ARABIA 477 45,348 Listed 10,076,786

14 Arab National Bank SAUDI ARABIA 557 36,783 Listed 7,733,334

15 Bank Audi SAL LEBANON 610 36,191 Listed 2,133,470

16 Arab Bank Plc JORDAN 577 34,561 Listed 7,056,227

17 Ahli United Bank BSC BAHRAIN 596 32,652 Listed 4,488,162

18 Commercial Bank of Qatar (The) QSC QATAR 618 31,075 Listed 5,685,828

19 Dubai Islamic Bank PJSC UNITED ARAB EMIRATES 619 30,848 Listed 6,793,239

20 Kuwait Projects Company Holding K.S.C.

KUWAIT 623 30,597 Listed 3,859,181

21 Abu Dhabi Islamic Bank - Public Joint Stock Co.

UNITED ARAB EMIRATES 662 28,090 Listed 5,563,158

22 Burgan Bank SAK KUWAIT 732 25,345 Listed 3,175,433

23 Mashreqbank PSC UNITED ARAB EMIRATES 749 24,412 Listed 5,616,716

24 Union National Bank UNITED ARAB EMIRATES 768 23,838 Listed 5,294,327

25 Bank Muscat SAOG OMAN 808 22,072 Listed 3,542,256

26 Saudi Investment Bank (The) SAUDI ARABIA 819 21,465 Listed 4,384,000

27 Saudi Hollandi Bank SAUDI ARABIA 820 21,458 Listed 5,143,824

28 Qatar Islamic Bank SAQ QATAR 828 21,251 Listed 5,173,782

29 Doha Bank QATAR 920 18,405 Listed 4,407,944

30 Masraf Al Rayan (Q.S.C.) QATAR 925 18,282 Listed 9,024,726

31 Gulf Bank KSC (The) KUWAIT 937 17,941 Listed 3,870,260

32 Alinma Bank SAUDI ARABIA 980 16,800 7,240,001

33 Commercial International Bank (Egypt) S.A.E.

EGYPT 1007 16,384 Listed 4,762,487

34 Bank Al-Jazira SAUDI ARABIA 1024 15,994 Listed 3,440,000

66

35 Commercial Bank of Kuwait SAK (The) KUWAIT 1111 13,920 Listed 3,731,855

36 Commercial Bank of Dubai P.S.C. UNITED ARAB EMIRATES 1215 12,111 Listed 3,724,259

37 QNB Al Ahli EGYPT 1232 11,704 Listed 2,436,988

38 Al Khalij Commercial Bank QATAR 1262 11,335 Listed 2,239,121

39 Al Ahli Bank of Kuwait (KSC) KUWAIT 1263 11,311 Listed 2,635,163

40 Housing Bank for Trade & Finance (The) JORDAN 1341 10,179 Listed 3,194,366

41 Bank AlBilad SAUDI ARABIA 1387 9,686 Listed 4,704,000

42 Qatar International Islamic Bank QATAR 1407 9,456 Listed 3,472,333

43 Boubyan Bank KSC KUWAIT 1596 7,765 Listed 3,692,078

44 Ahli Bank QSC QATAR 1667 7,192 Listed 2,359,964

45 Bank of Alexandria EGYPT 1865 5,894 Listed 2,016,735

Source: Bankscope Database.

67

Appendix 2: Correlation Coefficient of variables

(Multicollinerity Test)- SPSS

Correlations

ETAR LLR LDR COSR NLTA ROA ROE NIM GDP INF

ETAR Pearson Correlation

1 -

.274** 0.046 0

-.281

** .240

**

-.134

* .249

** .256

** -0.03

Sig. (2-tailed)

0 0.498 1 0 0 0.045 0 0.001 0.653

N 224 224 224 224 224 224 224 224 179 179

LLR Pearson Correlation

-.274

** 1

-0.026 0.105

-0.101

-.347

**

-.226

** .154

*

-.421

** 0.075

Sig. (2-tailed) 0

0.694 0.119 0.131 0 0.001 0.021 0 0.319

N 224 224 224 224 224 224 224 224 179 179

LDR Pearson Correlation

0.046 -0.03 1 -.146* .191

**

-.213

**

-.257

** 0.028

-0.089 0.069

Sig. (2-tailed) 0.498 0.694

0.029 0.004 0 0 0.678 0.236 0.356

N 224 224 224 224 224 224 224 224 179 179

COSR Pearson Correlation

0 0.105 -

.146* 1

-0.091 0.04 .255

**

-0.059 0.019 0.019

Sig. (2-tailed) 1 0.119 0.029

0.175 0.52 0 0.376 0.796 0.801

N 224 224 224 224 224 224 224 224 179 179

NLTA Pearson Correlation

-.281

** -0.1 .191

** -0.091 1 -0.06 0.013 0.053

-0.126 -0.06

Sig. (2-tailed) 0 0.131 0.004 0.175

0.41 0.846 0.428 0.092 0.448

N 224 224 224 224 224 224 224 224 179 179

ROA Pearson Correlation

.240**

-.347

**

-.213

** 0.043

-0.056 1 .856

** .289

** .445

** 0.106

Sig. (2-tailed) 0 0 0.001 0.518 0.406

0 0 0 0.158

N 224 224 224 224 224 224 224 224 179 179

ROE Pearson Correlation

-.134*

-.226

**

-.257

** .255

** 0.013 .856

** 1 .157

* .296

** .149

*

Sig. (2-tailed) 0.045 0.001 0 0 0.846 0

0.019 0 0.047

N 224 224 224 224 224 224 224 224 179 179

NIM Pearson Correlation

.249** .154

* 0.028 -0.059 0.053 .289

** .157

* 1 .148

* 0.025

68

Sig. (2-tailed) 0 0.021 0.678 0.376 0.428 0 0.019

0.047 0.74

N 224 224 224 224 224 224 224 224 179 179

GDP Pearson Correlation

.256**

-.421

**

-0.089 0.019

-0.126 .445

** .296

** .148

* 1 .245

**

Sig. (2-tailed) 0.001 0 0.236 0.796 0.092 0 0 0.047

0.001

N 179 179 179 179 179 179 179 179 179 179

INF Pearson Correlation

-0.03 0.075 0.069 0.019 -

0.057 0.11 .149* 0.025 .245

** 1

Sig. (2-tailed) 0.653 0.319 0.356 0.801 0.448 0.16 0.047 0.74 0.001

N 179 179 179 179 179 179 179 179 179 179

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

69

Appendix 3: One-way ANOVA Results

Sum of Squares df Mean Square F Sig.

ETAR Between Groups .112 1 .112 22.059 .000


Total 1.235 223

LLR Between Groups .003 1 .003 5.748 .017


Total .119 223

LDR Between Groups 8.594 1 8.594 9.333 .003


Total 213.031 223

COSR Between Groups .479 1 .479 2.689 .102


Total 40.018 223

NLTA Between Groups .004 1 .004 .294 .588


Total 2.645 223

ROA Between Groups .000 1 .000 .212 .646


Total .020 223

ROE Between Groups .024 1 .024 4.453 .036


Total 1.239 223

NIM Between Groups .003 1 .003 18.562 .000


Total .044 223

70

Appendix 4: Regression Analysis Output Results

Model 1: ROA

Variables Entered/Removedb

Model Variables Entered Variables Removed Method

1 INF, COSR, ETAR,

LDR, LLR, NLTA,

GDPa

. Enter

a. All requested variables entered.

b. Dependent Variable: ROA

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression .006 7 .001 10.739 .000a

Residual .013 171 .000

Total .019 178

a. Predictors: (Constant), INF, COSR, ETAR, LDR, LLR, NLTA, GDP


Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate Durbin-Watson

1 .553a .305 .277 .008705 1.051



71

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) .014 .005 2.749 .007

ETAR .013 .009 .106 1.452 .148

LLR -.114 .033 -.256 -3.398 .001

LDR -.002 .001 -.211 -3.160 .002

COSR .002 .001 .073 1.120 .264

NLTA .003 .006 .037 .528 .598

GDP .052 .014 .277 3.647 .000

INF .006 .005 .076 1.123 .263

a. Dependent Variable: ROA

Normality Tests;

72

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

Standardized Residual .068 179 .040 .934 179 .000

a. Lilliefors Significance Correction

73

Model 2: ROE


Model

Variables

Entered

Variables

Removed Method

1 INF, COSR,

ETAR, LDR,

LLR, NLTA,

GDPa

. Enter


b. Dependent Variable: ROE

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the


1 .568a .323 .295 .067592 1.045



ANOVAb


1 Regression .373 7 .053 11.649 .000a

Residual .781 171 .005

Total 1.154 178



74

Model


Standardized

Coefficients


1 (Constant) .326 .035 9.321 .000

ETAR -.423 .115 -.275 -3.681 .000

LLR -.721 .172 -.284 -4.186 .000

LDR -.095 .045 -.304 -2.119 .036

COSR -.144 .020 -.497 -7.214 .000

NLTA .044 .076 .088 .578 .565

GDP .091 .080 .083 1.135 .258

INF .014 .029 .033 .499 .619

a. Dependent Variable: ROE

Normality Tests

75

Tests of Normality



Standardized Residual .060 179 .200* .942 179 .000


*. This is a lower bound of the true significance.

76

Model 3: NIM


Model

Variables

Entered

Variables

Removed Method

1 INF, COSR,

ETAR, LDR,

LLR, NLTA,

GDPa

. Enter


b. Dependent Variable: NIM

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the


1 .422a .178 .144 .013213 .433



ANOVAb


1 Regression .006 7 .001 5.284 .000a

Residual .030 171 .000

Total .036 178



77

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) -.013 .008 -1.667 .097

ETAR .063 .014 .361 4.562 .000

LLR .219 .051 .352 4.302 .000

LDR -2.886E-5 .001 -.002 -.030 .976

COSR -.002 .002 -.062 -.884 .378

NLTA .025 .010 .196 2.560 .011

GDP .063 .022 .239 2.893 .004

INF -.004 .008 -.035 -.478 .633

a. Dependent Variable: NIM

78

Tests of Normality





79

Model 4: ROA (Moderated)


Model

Variables

Entered

Variables

Removed Method

1 INF_M,

ETAR_M,

COSR_M,

LDR_M, LLR_M,

GDP_M,

NLTA_Ma

. Enter



Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the


1 .517a .268 .243 .008382 .900

a. Predictors: (Constant), INF_M, ETAR_M, COSR_M, LDR_M, LLR_M, GDP_M,

NLTA_M


ANOVAb


1 Regression .005 7 .001 10.758 .000a

Residual .014 206 .000

Total .020 213

a. Predictors: (Constant), INF_M, ETAR_M, COSR_M, LDR_M, LLR_M, GDP_M, NLTA_M


80

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .015 .001 23.933 .000

ETAR_M -.006 .009 -.061 -.676 .500

LLR_M -.130 .070 -.213 -1.866 .063

LDR_M -.001 .001 -.129 -1.746 .082

COSR_M .005 .002 .208 3.051 .003

NLTA_M -.004 .007 -.096 -.598 .550

GDP_M .129 .030 .470 4.341 .000

INF_M -.009 .011 -.063 -.879 .380

a. Dependent Variable: ROA

Normality Tests

81

Tests of Normality



Standardized Residual .054 214 .200* .956 214 .000


*. This is a lower bound of the true significance.

82

MODEL 5- ROE (moderated)


Model

Variables

Entered

Variables

Removed Method

1 INF_M,

ETAR_M,

COSR_M,

LDR_M, LLR_M,

GDP_M,

NLTA_Ma

. Enter



Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the


1 .564a .318 .295 .063355 .906


NLTA_M


ANOVAb


1 Regression .386 7 .055 13.723 .000a

Residual .827 206 .004

Total 1.212 213



83

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .120 .005 25.152 .000

ETAR_M -.246 .067 -.317 -3.652 .000

LLR_M -1.049 .526 -.220 -1.994 .048

LDR_M -.011 .005 -.155 -2.166 .031

COSR_M .074 .012 .413 6.268 .000

NLTA_M -.011 .050 -.033 -.210 .834

GDP_M .564 .224 .263 2.514 .013

INF_M .061 .082 .051 .741 .459

a. Dependent Variable: ROE

84

Tests of Normality





85

MODEL 6- NIM (moderated)


Model

Variables

Entered

Variables

Removed Method

1 INF_M,

ETAR_M,

COSR_M,

LDR_M, LLR_M,

GDP_M,

NLTA_Ma

. Enter



Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of the


1 .307a .094 .064 .013819 .230


NLTA_M


ANOVAb


1 Regression .004 7 .001 3.065 .004a

Residual .039 206 .000

Total .043 213



Coefficientsa

86

Model


Standardized

Coefficients


1 (Constant) .019 .001 18.385 .000

ETAR_M .026 .015 .181 1.803 .073

LLR_M .149 .115 .165 1.300 .195

LDR_M .000 .001 -.032 -.387 .699

COSR_M .004 .003 .113 1.495 .137

NLTA_M -.004 .011 -.068 -.378 .706

GDP_M .020 .049 .050 .419 .676

INF_M -.004 .018 -.018 -.226 .821

a. Dependent Variable: NIM

Normality Tests

87

Tests of Normality





Documents

The Financial Performance of Islamic vs. Conventional Banks: An