Download The Financial Performance of Islamic vs. Conventional Banks: An

Faculty of Business, Economics and Political Science
Bachelor's Dissertation/ Senior Year Project
Module Title: Research Methods 3.
The Financial Performance of Islamic vs.
Conventional Banks: An Empirical Study on
The GCC & MENA Region
Submitted by:
Manar Mahmood Al-Gazzar (113035)
Under the supervision of :
Date: 17/6/2014
Dr Dalia El-Mosallamy
Date: 09/01/2014
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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 oneway 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.
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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
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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
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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
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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
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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
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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
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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 macroeconomic factors such as GDP growth rate and annual inflation rate are also used in the
model to determine their significance on bank profitability.
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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.
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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.
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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
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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 creditworthiness 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
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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
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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 profitloss 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
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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
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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
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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 riskhedging 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
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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.
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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.
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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
21
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.
22
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.
23
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, wellfunctioning 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.
24
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.
25
 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.
26
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,
27
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.
28
Table 1: Sample of banks used in the study
Countries
Kuwait
UAE
Qatar
Egypt
Oman
Lebanon
Jordan
Israel
Saudi Arabia
Bahrain
Conventional Banks
National Bank of Kuwait S.A.K.
Burgan Bank SAK
Kuwait Projects Company Holding K.S.C.
Gulf Bank KSC (The)
Commercial Bank of Kuwait SAK (The)
Al Ahli Bank of Kuwait (KSC)
Emirates NBD PJSC
Commercial Bank of Dubai P.S.C.
First Gulf Bank
Abu Dhabi Commercial Bank
Mashreqbank PSC
Union National Bank
National Bank of Abu Dhabi
Qatar National Bank
Commercial Bank of Qatar (The) QSC
Doha Bank
Al Khalij Commercial Bank
Ahli Bank QSC
Commercial International Bank (Egypt)
QNB Al Ahli
Bank of Alexandria
Bank Muscat SAOG
Bank Audi SAL
Arab Bank Plc
Housing Bank for Trade & Finance (The)
Bank Hapoalim BM
Mizrahi Tefahot Bank Ltd.
Samba Financial Group
Saudi British Bank (The)
Banque Saudi Fransi
Arab National Bank
Saudi Investment Bank (The)
Saudi Hollandi Bank
Bank Al-Jazira
Ahli United Bank BSC
Islamic Banks
Boubyan Bank KSC
Kuwait Finance House
Dubai Islamic Bank PJSC
Abu Dhabi Islamic Bank
Qatar Islamic Bank SAQ
Qatar International Islamic Bank
Masraf Al Rayan (Q.S.C.)
Al Rajhi Bank
Alinma Bank
Bank AlBilad
Please refer to Appendix 1 to view the precise details of each bank's total asset and
market capitalisation compositions
29
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
Independent Variables
Dependent Variables
Bank specific Variables:





Capital Adequacy
Asset Quality
Management Quality
Earnings
Liquidity
Bank Performance
Indicators
ROA
ROE
Macroeconomic Variables


NIM
GDP Growth Rate
Inflation Rate
Moderating Variable
Islamic Vs. Conventional Bank
30
3.2.4 Model Specification
The following regression models will be used to test for the determinants of profitability
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
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.
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)+
β6(GDP* M)+ β7(INF* M)+ ε
α = Intercept
NIM= α1 + β1(CA* M)+ β2(AQ* M)+ β3(MQ* M) + β4(ER* M)+ β5(LM* M)+
β6(GDP* M)+ β7(INF* M)+ ε
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.
31
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"
32
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
33
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).
34
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
ETAR*
LLR
LDR
COSR
NLTA
ROA
ROE
NIM
GDP
INF
Minimum
224
224
224
224
224
224
224
224
179
179
.054
.000
.293
-4.652
.064
-.054
-.584
-.022
-.071
-.242
ETAR- Equity to Assets- To measure Capital Adequacy
LLR- Loan Loss Reserves/ Gross Loans- To measure Asset Quality
LDR- Loans to Deposits- To measure Management Quality
COSR- Cost to Income Ratio- To measure Earnings Quality
NLTA- Net Loans to Total Assets- To measure Liquidity
Maximum
.902
.138
13.480
2.488
.807
.040
.255
.055
.167
.213
Mean
.13959
.03794
.90584
.50290
.58785
.01528
.11481
.02119
.04952
.04852
Std. Deviation
.074404
.023069
.977391
.423617
.108916
.009520
.074530
.014042
.054150
.132086
ROA- Return on Assets- To measure Profitability
ROE- Return on Equity- To measure Profitability
NIM- Net Interest Margin- To measure Profitability
GDP- Gross Domestic Product Growth Rate- Macroeconomic Factor
INF- Annual Inflation Rate- Macroeconomic Factor
35
Skewness
6.174
1.155
10.860
-7.564
-1.001
-1.686
-3.843
-1.060
-.130
-.929
.163
.163
.163
.163
.163
.163
.163
.163
.182
.182
Table 4: Descriptive Statistics- Islamic Banks
N
ETAR
LLR
LDR
COSR
NLTA
ROA
ROE
NIM
GDP
INF
Minimum
49
49
49
49
49
49
49
49
39
39
.072
.000
.591
-4.652
.064
-.054
-.584
.000
-.071
-.242
ETAR- Equity to Assets- To measure Capital Adequacy
LLR- Loan Loss Reserves/ Gross Loans- To measure Asset Quality
LDR- Loans to Deposits- To measure Management Quality
COSR- Cost to Income Ratio- To measure Earnings Quality
NLTA- Net Loans to Total Assets- To measure Liquidity
Maximum
.902
.075
13.480
2.488
.736
.040
.235
.055
.167
.213
Mean
.18176
.03103
1.27602
.41552
.59532
.01584
.09510
.02855
.06151
.04903
Std. Deviation
.137145
.023747
2.035724
.829495
.100544
.015557
.121364
.010403
.061263
.143723
Skewness
3.773
.270
5.200
-4.584
-3.118
-1.805
-3.721
-.397
-.390
-.885
.340
.340
.340
.340
.340
.340
.340
.340
.378
.378
ROA- Return on Assets- To measure Profitability
ROE- Return on Equity- To measure Profitability
NIM- Net Interest Margin- To measure Profitability
GDP- Gross Domestic Product Growth Rate- Macroeconomic Factor
INF- Annual Inflation Rate- Macroeconomic Factor
Table 5: Descriptive Statistics- Conventional Banks
N
ETAR
LLR
LDR
COSR
NLTA
ROA
ROE
NIM
GDP
INF
Minimum
175
175
175
175
175
175
175
175
140
140
.054
.008
.293
.245
.255
-.006
-.069
-.022
-.071
-.242
ETAR- Equity to Assets- To measure Capital Adequacy
LLR- Loan Loss Reserves/ Gross Loans- To measure Asset Quality
LDR- Loans to Deposits- To measure Management Quality
COSR- Cost to Income Ratio- To measure Earnings Quality
NLTA- Net Loans to Total Assets- To measure Liquidity
Maximum
.276
.138
1.375
1.270
.807
.029
.255
.050
.167
.213
Mean
.12778
.03988
.80220
.52737
.58576
.01513
.12033
.01913
.04618
.04838
Std. Deviation
.035566
.022567
.178049
.193453
.111330
.007019
.053998
.014260
.051743
.129208
Skewness
.528
1.533
-.092
1.270
-.579
-.402
-.430
-1.086
-.098
-.956
.184
.184
.184
.184
.184
.184
.184
.184
.205
.205
ROA- Return on Assets- To measure Profitability
ROE- Return on Equity- To measure Profitability
NIM- Net Interest Margin- To measure Profitability
GDP- Gross Domestic Product Growth Rate- Macroeconomic Factor
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.
36
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
37
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
38
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
39
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
Profitability
CONVENTIONAL
BANKS
ISLAMIC
BANKS
COMMENTS
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
18.18%
Islamic banks are dominating in Capital
Adequacy
3.10%
Islamic banks are dominating in Asset
Quality
Capital Adequacy
ETAR
12.78%
Asset Quality
LLR
Management
Quality
LDR
3.99%
Islamic banks are dominating in Management
Quality
80.22%
127.60%
41.55%
Islamic banks are dominating in Earnings
Quality
59.53%
Conventional banks are dominating in
Liquidity
Earnings
COSR
52.74%
Liquidity
NLTA
58.58%
40
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
Capital Adequacy
Hypothesis
Decision
Comment
H1: Islamic banks have better SUPPORTED If sig. < 0.05 - Reject
capital adequacy measures
H0 -> Significant
than conventional banks.
Differences
Asset Quality
H2:
Islamic banks have SUPPORTED If sig. < 0.05 - Reject
better asset quality measures
H0 -> Significant
than conventional banks
Differences
Management
Quality
H3: Islamic banks are better SUPPORTED If sig. < 0.05 - Reject
than conventional banks in
H0 -> Significant
management quality.
Differences
Earnings
H4: Islamic banks
higher
earnings
conventional banks.
have
than
REJECTED
If sig. > 0.05 - Do not
reject H0 -> No
Significant Differences
Liquidity
H5: Islamic banks manage
their
liquidity
more
efficiently than conventional
banks.
REJECTED
If sig. > 0.05 - Do not
reject H0 -> No
Significant Differences
 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.
41
 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.
42
Table 8: One-way ANOVA Table
Sum of Squares
ETAR
LLR
LDR
COSR
NLTA
ROA
ROE
NIM
Between Groups
df
Mean Square
.112
1
.112
Within Groups
1.123
222
.005
Total
1.235
223
Between Groups
.003
1
.003
Within Groups
.116
222
.001
Total
.119
223
8.594
1
8.594
Within Groups
204.436
222
.921
Total
213.031
223
.479
1
.479
Within Groups
39.539
222
.178
Total
40.018
223
.004
1
.004
Within Groups
2.642
222
.012
Total
2.645
223
Between Groups
.000
1
.000
Within Groups
.020
222
.000
Total
.020
223
Between Groups
.024
1
.024
Within Groups
1.214
222
.005
Total
1.239
223
Between Groups
.003
1
.003
Within Groups
.041
222
.000
Total
.044
223
Between Groups
Between Groups
Between Groups
43
F
Sig.
22.059
.000
5.748
.017
9.333
.003
2.689
.102
.294
.588
.212
.646
4.453
.036
18.562
.000
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
44
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
ETAR
1
LLR
-.274
LDR
COSR
NLTA
**
0.046
0
**
1
**
**
.256
INF
-0.034
**
ROE
-.134
NIM
.249
*
**
COSR
-0.101
-.421
**
0.075
-.347
**
-.226
**
.154
*
NLTA
**
0.046
0
-.281
-0.026
0.105
-0.101
-0.026
0.105
-.281
.240
LDR
-.274
GDP
ROA
LLR
1
-.146
.191
*
**
-.146
*
.191
**
ROA
.240
ROE
**
-.347
**
-.213
**
-.134
-.226
-.257
.249
.256
**
.154
*
-.421
**
**
-0.091
0.043
.255
-0.091
1
-0.056
0.013
**
0.019
-0.126
.445
0.069
0.019
-0.057
0.106
-.213
**
0.043
-.257
**
**
.255
-0.059
-0.056
1
0.013
.856
**
0.053
.289
**
45
GDP
**
1
-0.089
0.028
NIM
*
0.075
0.028
-0.089
0.069
-0.059
0.019
0.019
0.053
-0.126
-0.057
1
.245
.296
.148
.149
*
0.025
.856
**
.157
*
-0.034
**
**
1
INF
**
*
.289
**
.157
*
1
**
.245
**
1
.445
**
0.106
.296
**
.149
.148
*
0.025
*
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.
46
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.
(Constant)
ETAR
LLR
LDR
COSR
NLTA
GDP
INF
ROA
0.0138
0.0066*
0.1055
0.1482**
-0.2557
0.0008*
-0.2106
0.0018*
0.0727
0.2643**
0.0371
0.5983**
0.2766
0.0004*
0.0761
0.2628**
ROE
0.1536
0.0001*
0.1055
0.0006*
-0.2557
0.00001*
-0.2106
0.0014*
0.0727
0.0000*
0.0371
0.8139**
0.2766
0.01852*
0.0761
0.0546**
30.54%
27.69%
0.0087
10.738
0.00
1.0508
32.29%
29.52%
0.0676
11.648
0.00
1.0454
R2
ADJUSTED R2
SSE
F-test
P-value
DURBIN WATSON
NIM
0.0138
0.0972**
4.5619
0.0000*
4.3018
0.0000*
-0.0304
0.9758**
-0.8836
0.3781**
2.5599
0.0113*
2.8933
0.0043*
-0.4779
0.6333**
17.78%
14.42%
0.0132
5.283
0.00
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
47
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).
48
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
(Constant)
ETAR*M
LLR*M
LDR*M
ROE
NIM
0.0152
0.1204
0.0192
0.0000*
0.0000*
-0.0609
-0.3174
0.0000*
0.1806
0.4999**
0.0003*
-0.2131
-0.2196
0.0634**
0.0475*
-0.1292
-0.1547
0.0824**
0.0315*
0.0728**
0.1650
0.1951**
-0.0319
0.6992**
0.1135
0.2083
0.4128
0.0026*
0.0000*
-0.0962
-0.0326
0.5502**
0.8338**
0.4702
0.2628
0.0000*
0.0127*
-0.0627
0.0510
0.6759**
-0.0179
0.3803**
0.4594**
0.8214**
R2
26.77%
31.80%
9.43%
ADJUSTED R2
24.28%
29.48%
6.35%
SSE
F-test
0.0084
10.7577
0.0634
13.7228
0.0138
3.0649
P-value
0.00
0.00
0.00
DURBIN WATSON
0.9009
0.9058
0.2300
COSR*M
NLTA*M
GDP*M
INF*M
0.1365**
-0.0676
0.7059**
0.0504
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
49
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 R2 ) of the
regression models decreased in magnitude as a result of the moderating effect of bank type.
50
Table 12: Coefficients of determination before and after moderation
Pure Regression Model
Model Fit
Moderated Regression Model
ROA
ROE
NIM
ROA
ROE
NIM
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%
R
2
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
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
Moderates the relationship
between earnings 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
51
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.
52
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
53
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 macroeconomic 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.
54
Table 14: Summary of Comparative Analysis of IBs and CBs. (Objective 1)
PERFORMANCE
MEASURES
Profitability
CONVENTIONAL
BANKS
ISLAMIC
BANKS
COMMENTS
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
18.18%
Islamic banks are dominating in Capital
Adequacy
3.10%
Islamic banks are dominating in Asset
Quality
Capital Adequacy
12.78%
ETAR
Asset Quality
3.99%
LLR
Islamic banks are dominating in Management
Quality
Management
Quality
LDR
80.22%
127.60%
41.55%
Islamic banks are dominating in Earnings
Quality
59.53%
Conventional banks are dominating in
Liquidity
Earnings
52.74%
COSR
Liquidity
58.58%
NLTA
Table 15: Summary of One-way Anova Test (Objective 2)
Performance
Measure
Capital Adequacy
Hypothesis
Decision
Comment
H1: Islamic banks have better SUPPORTED If sig. < 0.05 - Reject
capital adequacy measures
H0 -> Significant
than conventional banks.
Differences
Asset Quality
H2:
Islamic banks have SUPPORTED If sig. < 0.05 - Reject
better asset quality measures
H0 -> Significant
than conventional banks
Differences
Management
Quality
H3: Islamic banks are better SUPPORTED If sig. < 0.05 - Reject
than conventional banks in
H0 -> Significant
management quality.
Differences
Earnings
H4: Islamic banks
higher
earnings
conventional banks.
55
have
than
REJECTED
If sig. > 0.05 - Do not
reject H0 -> No
Significant Differences
Liquidity
H5: Islamic banks manage
their
liquidity
more
efficiently than conventional
banks.
REJECTED
If sig. > 0.05 - Do not
reject H0 -> No
Significant Differences
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
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
Moderates the relationship
between earnings 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
56
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
57
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
58
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Appendix 1: Details of banks included in the sample.
Total
Assets
mil USD
121,837
Listed
QATAR
World
rank by
assets
195
Listed
Market
Capitalisation
th USD
37,658,516
ISRAEL
217
109,549
Listed
7,457,296
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
623
30,597
Listed
3,859,181
UNITED ARAB EMIRATES
662
28,090
Listed
5,563,158
22
Kuwait Projects Company Holding
K.S.C.
Abu Dhabi Islamic Bank - Public Joint
Stock Co.
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
33
Commercial International Bank (Egypt)
S.A.E.
Bank Al-Jazira
EGYPT
1007
16,384
Listed
4,762,487
SAUDI ARABIA
1024
15,994
Listed
3,440,000
Bank Name
Country Name
1
Qatar National Bank
2
Bank Hapoalim BM
3
21
34
65
7,240,001
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.
66
Appendix 2: Correlation Coefficient of variables
(Multicollinerity Test)- SPSS
Correlations
ETAR
ETAR
LLR
1
.274
N
224
Pearson
Correlation
Sig. (2tailed)
N
N
N
N
NIM
0.498
1
224
224
**
1
ROA
**
*
.134
.249
0
0
224
224
224
0.026
0.105
0.101
0.694
0.119
-
224
224
224
0.046
-0.03
1
-.146
0.498
0.694
224
224
0
.281
**
.240
N
-0.03
0.045
0
0.001
0.653
224
224
179
179
-
-
.226
0.131
0
0.001
224
224
224
.191
**
-
.213
**
**
*
0.075
0.021
0
0.319
224
179
179
**
0.028
0.089
0.069
-
.257
-
**
.154
.421
0
0.678
0.236
0.356
224
224
224
224
224
224
179
179
0.105
*
.146
1
0.091
0.04
**
0.059
0.019
0.019
1
0.119
0.029
0.175
0.52
0
0.376
0.796
0.801
224
224
224
224
224
224
224
224
179
179
**
-0.091
1
-0.06
0.013
0.053
0.126
-0.06
0.41
0.846
0.428
0.092
0.448
224
224
179
179
**
0.106
-
**
-0.1
0
0.131
0.004
0.175
224
224
224
224
224
224
1
.191
**
.213
**
0.043
0.056
0
0
0.001
0.518
0.406
224
224
224
224
224
**
0.013
**
-.134
N
**
0
-
.347
Pearson
Correlation
Sig. (2tailed)
.256
0.004
Pearson
Correlation
Sig. (2tailed)
*
INF
**
**
.347
GDP
0.029
.240
NIM
ROE
224
Pearson
Correlation
Sig. (2tailed)
ROE
0
0
.281
ROA
0
Pearson
Correlation
Sig. (2tailed)
NLTA
0.046
NLTA
Pearson
Correlation
Sig. (2tailed)
COSR
**
-
.274
LDR
COSR
-
Sig. (2tailed)
LLR
LDR
Pearson
Correlation
*
-
-
.226
**
-
.257
**
.255
.255
.856
**
.289
**
.445
0
0
0
0.158
224
224
224
179
179
**
1
.157
.856
0.045
0.001
0
0
0.846
0
224
224
224
224
224
224
*
0.028
-0.059
0.053
*
**
.149
0.019
0
0.047
224
224
179
179
*
1
.148
*
0.025
.296
*
Pearson
Correlation
.249
**
.154
67
.289
**
.157
Sig. (2tailed)
N
GDP
0
0.021
0.678
0.376
0.428
0
0.019
224
224
224
224
224
224
224
**
-
0.089
0.019
0.126
0.001
0
0.236
0.796
0.092
179
179
179
179
-0.03
0.075
0.069
0.653
0.319
0.356
179
179
179
Pearson
Correlation
.256
Sig. (2tailed)
N
INF
**
.421
N
224
179
179
*
1
.148
0
0
0.047
179
179
179
179
0.019
0.057
0.11
.149
*
0.025
.245
0.801
0.448
0.16
0.047
0.74
0.001
Pearson
Correlation
Sig. (2tailed)
0.74
**
.445
**
0.047
.296
.245
**
0.001
179
179
**
1
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).
68
Appendix 3: One-way ANOVA Results
Sum of Squares
ETAR
LLR
LDR
COSR
NLTA
ROA
ROE
NIM
Between Groups
df
Mean Square
.112
1
.112
Within Groups
1.123
222
.005
Total
1.235
223
Between Groups
.003
1
.003
Within Groups
.116
222
.001
Total
.119
223
8.594
1
8.594
Within Groups
204.436
222
.921
Total
213.031
223
.479
1
.479
Within Groups
39.539
222
.178
Total
40.018
223
.004
1
.004
Within Groups
2.642
222
.012
Total
2.645
223
Between Groups
.000
1
.000
Within Groups
.020
222
.000
Total
.020
223
Between Groups
.024
1
.024
Within Groups
1.214
222
.005
Total
1.239
223
Between Groups
.003
1
.003
Within Groups
.041
222
.000
Total
.044
223
Between Groups
Between Groups
Between Groups
69
F
Sig.
22.059
.000
5.748
.017
9.333
.003
2.689
.102
.294
.588
.212
.646
4.453
.036
18.562
.000
Appendix 4: Regression Analysis Output Results
Model 1: ROA
Variables Entered/Removed
Model
1
b
Variables Entered Variables Removed
Method
INF, COSR, ETAR,
LDR, LLR, NLTA,
. Enter
a
GDP
a. All requested variables entered.
b. Dependent Variable: ROA
b
Model Summary
Model
R
1
R Square
.553
a
Adjusted R
Std. Error of the
Square
Estimate
.305
.277
Durbin-Watson
.008705
1.051
a. Predictors: (Constant), INF, COSR, ETAR, LDR, LLR, NLTA, GDP
b. Dependent Variable: ROA
b
ANOVA
Model
1
Sum of Squares
df
Mean Square
Regression
.006
7
.001
Residual
.013
171
.000
Total
.019
178
a. Predictors: (Constant), INF, COSR, ETAR, LDR, LLR, NLTA, GDP
b. Dependent Variable: ROA
70
F
10.739
Sig.
.000
a
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
Coefficients
Std. Error
Beta
(Constant)
.014
.005
ETAR
.013
.009
LLR
-.114
LDR
t
Sig.
2.749
.007
.106
1.452
.148
.033
-.256
-3.398
.001
-.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;
71
Tests of Normality
a
Kolmogorov-Smirnov
Statistic
Standardized Residual
.068
df
Shapiro-Wilk
Sig.
179
.040
a. Lilliefors Significance Correction
72
Statistic
.934
df
Sig.
179
.000
Model 2: ROE
Variables Entered/Removed
Model
1
Variables
Variables
Entered
Removed
b
Method
INF, COSR,
ETAR, LDR,
. Enter
LLR, NLTA,
a
GDP
a. All requested variables entered.
b. Dependent Variable: ROE
b
Model Summary
Model
R
1
.568
R Square
a
Adjusted R
Std. Error of the
Square
Estimate
.323
.295
Durbin-Watson
.067592
1.045
a. Predictors: (Constant), INF, COSR, ETAR, LDR, LLR, NLTA, GDP
b. Dependent Variable: ROE
b
ANOVA
Model
1
Sum of Squares
df
Mean Square
Regression
.373
7
.053
Residual
.781
171
.005
1.154
178
Total
a. Predictors: (Constant), INF, COSR, ETAR, LDR, LLR, NLTA, GDP
b. Dependent Variable: ROE
73
F
11.649
Sig.
.000
a
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Coefficients
Std. Error
.326
.035
ETAR
-.423
.115
LLR
-.721
LDR
Beta
t
Sig.
9.321
.000
-.275
-3.681
.000
.172
-.284
-4.186
.000
-.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
74
Tests of Normality
a
Kolmogorov-Smirnov
Statistic
Standardized Residual
df
.060
Shapiro-Wilk
Sig.
179
.200
a. Lilliefors Significance Correction
*. This is a lower bound of the true significance.
75
Statistic
*
.942
df
Sig.
179
.000
Model 3: NIM
Variables Entered/Removed
Model
1
Variables
Variables
Entered
Removed
b
Method
INF, COSR,
ETAR, LDR,
. Enter
LLR, NLTA,
a
GDP
a. All requested variables entered.
b. Dependent Variable: NIM
b
Model Summary
Model
R
1
.422
R Square
a
Adjusted R
Std. Error of the
Square
Estimate
.178
.144
Durbin-Watson
.013213
.433
a. Predictors: (Constant), INF, COSR, ETAR, LDR, LLR, NLTA, GDP
b. Dependent Variable: NIM
b
ANOVA
Model
1
Sum of Squares
df
Mean Square
Regression
.006
7
.001
Residual
.030
171
.000
Total
.036
178
a. Predictors: (Constant), INF, COSR, ETAR, LDR, LLR, NLTA, GDP
b. Dependent Variable: NIM
76
F
5.284
Sig.
.000
a
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Coefficients
Std. Error
Beta
-.013
.008
ETAR
.063
.014
LLR
.219
LDR
t
Sig.
-1.667
.097
.361
4.562
.000
.051
.352
4.302
.000
-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
77
Tests of Normality
a
Kolmogorov-Smirnov
Statistic
Standardized Residual
.176
df
Shapiro-Wilk
Sig.
179
.000
a. Lilliefors Significance Correction
78
Statistic
.900
df
Sig.
179
.000
Model 4: ROA (Moderated)
Variables Entered/Removed
Model
1
Variables
Variables
Entered
Removed
b
Method
INF_M,
ETAR_M,
COSR_M,
. Enter
LDR_M, LLR_M,
GDP_M,
a
NLTA_M
a. All requested variables entered.
b. Dependent Variable: ROA
b
Model Summary
Model
R
1
.517
R Square
a
Adjusted R
Std. Error of the
Square
Estimate
.268
.243
Durbin-Watson
.008382
.900
a. Predictors: (Constant), INF_M, ETAR_M, COSR_M, LDR_M, LLR_M, GDP_M,
NLTA_M
b. Dependent Variable: ROA
b
ANOVA
Model
1
Sum of Squares
df
Mean Square
Regression
.005
7
.001
Residual
.014
206
.000
Total
.020
213
F
Sig.
10.758
a. Predictors: (Constant), INF_M, ETAR_M, COSR_M, LDR_M, LLR_M, GDP_M, NLTA_M
b. Dependent Variable: ROA
79
.000
a
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Coefficients
Std. Error
Beta
.015
.001
ETAR_M
-.006
.009
LLR_M
-.130
LDR_M
t
Sig.
23.933
.000
-.061
-.676
.500
.070
-.213
-1.866
.063
-.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
80
Tests of Normality
a
Kolmogorov-Smirnov
Statistic
Standardized Residual
df
.054
Shapiro-Wilk
Sig.
214
.200
a. Lilliefors Significance Correction
*. This is a lower bound of the true significance.
81
Statistic
*
.956
df
Sig.
214
.000
MODEL 5- ROE (moderated)
Variables Entered/Removed
Model
1
Variables
Variables
Entered
Removed
b
Method
INF_M,
ETAR_M,
COSR_M,
. Enter
LDR_M, LLR_M,
GDP_M,
a
NLTA_M
a. All requested variables entered.
b. Dependent Variable: ROE
b
Model Summary
Model
R
1
.564
R Square
a
Adjusted R
Std. Error of the
Square
Estimate
.318
.295
Durbin-Watson
.063355
.906
a. Predictors: (Constant), INF_M, ETAR_M, COSR_M, LDR_M, LLR_M, GDP_M,
NLTA_M
b. Dependent Variable: ROE
b
ANOVA
Model
1
Sum of Squares
df
Mean Square
Regression
.386
7
.055
Residual
.827
206
.004
1.212
213
Total
F
Sig.
13.723
a. Predictors: (Constant), INF_M, ETAR_M, COSR_M, LDR_M, LLR_M, GDP_M, NLTA_M
b. Dependent Variable: ROE
82
.000
a
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Coefficients
Std. Error
Beta
.120
.005
-.246
.067
LLR_M
-1.049
LDR_M
t
Sig.
25.152
.000
-.317
-3.652
.000
.526
-.220
-1.994
.048
-.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
ETAR_M
a. Dependent Variable: ROE
83
Tests of Normality
a
Kolmogorov-Smirnov
Statistic
Standardized Residual
.064
df
Shapiro-Wilk
Sig.
214
.034
a. Lilliefors Significance Correction
84
Statistic
.962
df
Sig.
214
.000
MODEL 6- NIM (moderated)
Variables Entered/Removed
Model
1
Variables
Variables
Entered
Removed
b
Method
INF_M,
ETAR_M,
COSR_M,
. Enter
LDR_M, LLR_M,
GDP_M,
a
NLTA_M
a. All requested variables entered.
b. Dependent Variable: NIM
b
Model Summary
Model
R
1
.307
R Square
a
Adjusted R
Std. Error of the
Square
Estimate
.094
.064
Durbin-Watson
.013819
.230
a. Predictors: (Constant), INF_M, ETAR_M, COSR_M, LDR_M, LLR_M, GDP_M,
NLTA_M
b. Dependent Variable: NIM
b
ANOVA
Model
1
Sum of Squares
df
Mean Square
Regression
.004
7
.001
Residual
.039
206
.000
Total
.043
213
F
Sig.
3.065
a. Predictors: (Constant), INF_M, ETAR_M, COSR_M, LDR_M, LLR_M, GDP_M, NLTA_M
b. Dependent Variable: NIM
Coefficients
a
85
.004
a
Standardized
Unstandardized Coefficients
Model
1
B
Coefficients
Std. Error
Beta
(Constant)
.019
.001
ETAR_M
.026
.015
LLR_M
.149
LDR_M
t
Sig.
18.385
.000
.181
1.803
.073
.115
.165
1.300
.195
.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
86
Tests of Normality
a
Kolmogorov-Smirnov
Statistic
Standardized Residual
.197
df
Shapiro-Wilk
Sig.
214
.000
a. Lilliefors Significance Correction
87
Statistic
.893
df
Sig.
214
.000