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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 1 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. 2 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 3 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 4 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 5 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 6 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 7 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 8 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. 9 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. 10 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. 11 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 12 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 13 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 14 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 15 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 16 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 17 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 18 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. 19 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. 20 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. 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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