The determinants of banking regulation in the MENA region

Ksiaa, Hanene; Gallali, Mohamed Imen Article The determinants of banking regulation in the MENA region The Journal of Entrepreneurial Finance (JEF) Provided in Cooperation with: The Academy of Entrepreneurial Finance (AEF), Los Angeles, CA, USA Suggested Citation: Ksiaa, Hanene; Gallali, Mohamed Imen (2022) : The determinants of banking regulation in the MENA region, The Journal of Entrepreneurial Finance (JEF), ISSN 2373-1761, Pepperdine University, Graziadio School of Business and Management and The Academy of Entrepreneurial Finance (AEF), Malibu, CA and Los Angeles, CA, Vol. 24, Iss. 2, pp. 1-22, https://doi.org/10.57229/2373-1761.1408 , https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8 This Version is available at: https://hdl.handle.net/10419/264425 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ The Journal of Entrepreneurial Finance The Journal of Entrepreneurial Finance Volume 24 Issue 2 Winter 2022, Issue 2 Article 8 5-2022 The determinants of banking regulation in the MENA region The determinants of banking regulation in the MENA region Hanene ksiaa Dr Universte Tunis el Manar, Faculte des Sciences Economiques et de Gestion Mohamed Imen Gallali Professor Universite Mannouba, Ecole Superieur de Commerce Follow this and additional works at: https://digitalcommons.pepperdine.edu/jef Part of the Administrative Law Commons, Business Law, Public Responsibility, and Ethics Commons, Finance and Financial Management Commons, Management Sciences and Quantitative Methods Commons, and the Organizational Behavior and Theory Commons Recommended Citation Recommended Citation ksiaa, Hanene Dr and Gallali, Mohamed Imen Professor (2022) "The determinants of banking regulation in the MENA region," The Journal of Entrepreneurial Finance : Vol. 24: Iss. 2, pp. 186-208. Available at: https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8 This Article is brought to you for free and open access by the Graziadio School of Business and Management at Pepperdine Digital Commons. It has been accepted for inclusion in The Journal of Entrepreneurial Finance by an authorized editor of Pepperdine Digital Commons. For more information, please contact bailey[email protected]. The determinants of banking regulation in the MENA region Hanene Ksiaa Institut Des Hautes Etudes de Sousse, Sousse, Tunisia [email protected] Mohamed Imen Gallali Ecole Superieur de Commerce de Tunis, 2010 Campus Univesitaire de la Manouba [email protected] Abstract: Theoretical foundations in banks' response to capital settlement suggest that the systems proposed by Basel are not sound. It is conceivable that regulators will consider alternative approaches to enhance the safety and soundness of the banking system. The regulation includes several decrees and ratios; the areas of interest encompassing the areas are subject to principal component analysis (PCA).The paper aims to present a regulatory framework based on balance sheet ratios, such as Capital requirements (equity ratio; Tier 1 ratio; Total Equity/Net Loans; Total Equity/Deposits); for liquidity needs (liquidity/deposits; liquidity/total assets; liquidity/deposits and loans, and net loans/total assets); for leverage requirements (total liabilities/total assets; total assets/equity; and total liabilities/equity); also banking restriction index; Official supervision index; Private surveillance index, finally global index of regulations and supervision. Besides, it performs a PCA analysis on a set of 13 financial ratios to exploit and compare the financial characteristics of 239 banks (175 Conventional and 64 Islamic commercial banks) in the MENA region over a 2004-2015 period. This gives the main indices EXIGCP, EXIGLIQ, LEVCP, and LEVP. Keywords : Banks; Regulation; Basel I, II, III; Principal Component Analysis; MENA JEL Classification: G18; G21; G28; C38 1 ksiaa and Gallali: The determinants of banking regulation in the MENA regionPublished by Pepperdine Digital Commons, 2022 1. Introduction All aspects of banking are directly or indirectly influenced by the availability of capital. This is one of the key factors to consider when assessing the safety and soundness of a particular bank. Indeed; capital absorbs losses and serves as the basis for maintaining the confidence of depositors; it is also the essential determinant of lending capacity. In the late 1980s, the Basel Committee on Banking and Supervision launched the first set of guidelines (Basel I) to harmonize banking regulation. It aimed to improve the stability of banking systems and to close the harmonization gap that had caused past financial crises. However, the Basel I accord was ineffective for the rapid development of financial innovation. As a result, in 2004, it published a framework under Basel II. This agreement is based on three pillars: the minimum capital requirement, prudential supervision, and market discipline. Implementing Basel II has been slow and difficult. However, the financial crisis of 2007-2008 showed that even Basel II was insufficient to avoid bank failures. For example, many banks noted by governments appeared to have sufficient minimum capital requirements shortly before the onset of the crisis (Demirguc-Kunt, Detragiache and Merrouche, 2013). This position led the Basel Committee on Banking and Supervision to implement another new banking regulatory framework and resulted in the Basel III directives. Even with the extreme instability of the financial system, it was noted that unlike conventional banks, Islamic financial service institutions were not affected by the crisis. This will increase the biggest challenge for banks, without compromising the returns they need to integrate a higher level of risk management tools. With Basel III regulations, it is imperative to know which Islamic or conventional banking systems are best equipped to withstand any future financial crisis. This sparked new thinking about the classic Western financial system. These reflections led to new avenues of research on the role of Islamic financial institutions and explaining how and why Islamic banks survived the crisis. Previous research has analyzed the performance, efficiency, and risk of this system by comparing it to conventional banks. The aim was to identify the major differences between the two systems to understand which system is the most reliable in particular circumstances. However, no empirical study has been conducted to examine the impact of banking regulations on the stability of Islamic banks. This study intends to fill this gap in the literature. This paper concentrated on contributing to the existing literature by reviewing the main regulatory, legal, and institutional environment, theories relevant to the MENA Banks. The rest of the article is organized as follows: first section summarizes an overview of existing theories and empirical research related to capital and risk 2The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 8 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8 requirements, liquidity and risk requirements, leverage and risk requirements. Second section provides examples of the selection process, database, and experimental models. Third section presents the results of the statistical tests. Discussions of the results admit drawing conclusions and suggest ways for future research. 2. Review of the literature The study of the impact of capital requirements on the stability of banking systems has always been confusing. VanHosse (2007) argues that banking regulators are still looking for an appropriate method to calculate minimum capital requirements. Besides, Islamic banks do not share the same risk as conventional banks, their financing structure is very different and the Basel III agreement, based on the balance sheet of conventional banks, does not consider the particularities of the economic model Islamic bank (Bitar and Madiès, 2013). Unlike conventional banks, the funding structure of Islamic banks does not guarantee multiple types of accounts. Islamic banks finance the growth of their balance sheets through three sources of funding: Capital, demand deposits, and investment accounts [Turk-Ariss and Sarieddine (2007), Beck, Demirgüç-Kunt and Merrouche (2010), Saeed and Izzeldin (2014)]. These hold restricted and unrestricted investment accounts, which are not guaranteed by the bank because Investment Account Holders (IAH) is investors. Then, the profit and the initial capital invested by this category of depositors are linked to the success of the investment and, therefore, it does not require deposit insurance. Consequently, the operation of deposit insurance is not required for Islamic banks. According to the Islamic Financial Services Board, IFSB (2005a), the rate of return on PSIA depends on the level of competition between banks in a country. As a result, bank managers will experience “incentive misalignment” by engaging in risky investments leading to higher risk and lower levels of bank capitalization [IFSB (2010); Abedifar, Molyneux and Tarazi, (2013)]. The theory of financial intermediation defines a bank as an enterprise of liquidity creation and risk transformation [Berger and Bouwman (2009)]. Despite the importance of capital ratios in determining the stability and solvency of the banking sector, one outcome of recent financial crises is the recognition that liquidity is important for the stability of banks, and for equity. This was quickly reflected in the Basel III guidelines. This has been reflected in articles such as Berger and Bouwman (2012) and Horváth, Seidler and Weill (2012). For Islamic banks, liquidity management is one of the most important challenges for the development of the banking sector (Ray, 1995, Vogel and Hayes, 1998) and Abdullah (2010). Yilmaz (2011) defines the liquidity risk of Islamic banks as:”the ability of a bank to maintain sufficient funds to honor its commitments, which may be related to its ability to attract deposits or sell its assets”. 3 ksiaa and Gallali: The determinants of banking regulation in the MENA regionPublished by Pepperdine Digital Commons, 2022 Liquidity risk arises from insufficient maturities (Oldfield and Santomero, 1997) for the lack of short-term liquid Islamic investment tools (Harzi, 2012), excessive reliance on long-term debt like Mudaraba (Metwali, 1997), like Murabaha (Ariffin, 2012). As a result, a sudden and unexpected withdrawal can lead to cash or liquidity mismatches, making Islamic banks more vulnerable to risk than conventional banks. The authors argue that Basel III liquidity risk requirements will affect Islamic banks for several reasons. First, it cannot transfer the excess liquidity of Islamic bankingto conventional banks [Akhtar, Ali, and Sadaqat (2011)]. Second, access to liquidity in stressful situations is limited by the constraints imposed on borrowing and selling debt (Anas and Mounir, 2008; Beck, Demirgüç-Kunt and Merrouche, 2013) imposed by Sharia law. Third, Yilmaz (2011) expresses that Islamic banks operate in an underdeveloped Islamic money market (Sundarajan and Erico, 2002, Iqbal and Llewellyn, 2002, Čihák and Hesse, 2010) and cannot benefit from the Central Bank as a lender of last resort, making them more vulnerable to liquidity risk than traditional counterparties. However, Islamic banks do not have good liquidity management as the industry is still in its infancy. Therefore, requiring Islamic banks to apply Basel III could penalize them compared to their traditional counterparts. The subject of leverage has never been a priority in banking literature. The subprime mortgage crisis has shown that underestimating the importance of financial leverage on the stability of the banking system was a bad research policy. Papanikolaou and Wolff (2010) study the relationship between the debt ratio and the risk of American commercial banks “Too big to fail”. The authors claim that the use of leveraged commercial banks has led them to blatantly abuse the use of financial products. Their results indicate that the use of mass finance and modern financial instruments can lead to financial vulnerability and contribute to the fragility of the financial system. They also suggest that, on the one hand, commercial bank assets should be more focused on traditional lending than on derivatives and highly complex financial products. In contrast, the liability of commercial banks should rest more on traditional intermediation activities such as accepting deposits than on non-interest activities. Overall, the results support the ongoing debate on the need for stricter banking rules by requiring explicit and risk-free leverage. The Basel risk-weighting method is ineffective in dealing with complex financial products such as CDS (Credit Default Swap) contracts that allow banks to expand their leverage with no limits. They should consider the Basel III leverage ratio a key indicator, given the degree of the ineffectiveness of risk-based capital adequacy measures. According to Toumi, Viviani and Belkacem (2011), this ratio illustrates the frequency with which banks multiply their invested capital by attracting new resources. Pappas, Izzeldin and Fuertes (2012) argue that Islamic banks are less indebted than conventional banks. They 4The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 8 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8 further explain that they require these banks with asset-backed rather than debt-based financial products to comply with Shariah. As a result, working under such circumstances exerts pressure by forcing Islamic banking leverage on conventional banks by associating them closely with the real economy [Saeed and Izeldin (2014)]. To sum up, the literature almost agrees that conventional high-leverage banks are more exposed and less stable than low-leverage banks. On the other hand, the constraints of Sharia law on the influence of Islamic banks make them, on the one hand, less indebted, more stable and less risky than conventional banks, and a greater constraint on yields and interest margins. Yet Islamic banks can benefit from PLS and over-indebtedness smoothing policies, which can lead to insolvency. 3. Methodology: variables, data sources, and Analysis of the principal components The choice of the four aggregate indices constructed from the analysis of the principal component of 13 balance sheet ratios, selected from the BankScope data base; the Bank regulation and supervision database from the World Bank; Barth et al., 2001, 2003, 2004, 2006, 2008, 2012); and Balance Sheet of each bank in MENA 1 region. The components of variables are from, balance sheet ratios, such as capital requirements (ratio equity; Tier 1 ratio; equity net loans; equity/deposits); for liquidity needs (liquidity/deposits; liquidity/total assets; liquidity/deposits and loans, and net loans/total assets); for leverage requirements (total liabilities/total assets; total assets/equity; and total liabilities/equity); also banking restriction index; Official supervision index; Private surveillance index, finally the global index of regulation and surveillance. The intuition for the choice of PCA: the literature uses different accounting measures to examine the same financial phenomena; for example, studies show that capital requirements can be measured using the equity-to-assets ratio, the solvency ratio, or the Tier1 ratio. PCA is, essentially, robust for exploring a multidimensional data structure. The principle of this method is to provide an approximate representation of the cloud of a multitude of variables in a subspace of restricted size. The PCA helps to minimize the dimension of the different variables by creating a new platform of optimal components that correspond to the most important part of information. This procedure makes it possible to identify and feed regression models with certain components that represent as much information on the variables initially introduced. According to Canbas, Cabuk and Kilic (2005), Andreica (2013) and Bitar, Madiès and Taramasco (2015), principal component analysis is a procedure for 1 MENA Region : Algérie, Bahreïn, Egypte, Iran, Irak, Israël, Jordanie, Kuwait, Liban, Libye, Maroc, Oman, Palestine, Qatar, Arabie Saoudite, Syrie, Tunisie, Emirates Arabes Unis, Yemen 5 ksiaa and Gallali: The determinants of banking regulation in the MENA regionPublished by Pepperdine Digital Commons, 2022 understanding different data models, the correlated variables (which measure the same characteristics financial data) are examined to determine the most useful indicators of changes in the financial position of banking institutions. Thus, this technique is a way to highlight the similarities and differences by shrinking the original dataset and channeling a complex array of correlated variables into a few uncorrelated variables or factors called components. Before proceeding with PCA, it performs several tests to assess the validity of such a technique for analysis. First, it developed a Pearson correlation matrix to capture all potential subgroups of highly correlated variables. The literature shows that each category of financial indicators (eg capital, stability, liquidity, debt) can be measured by different financial ratios. These ratios are strongly correlated, which makes it possible to continue the PCA. Second, the work of Canbas, Cabuk, and Kilic (2005), Adeyeye, and Oloyede (2014), help to calculate Bartlett's test of sphericity to assess the adequacy of the financial data set before engaging in PCA. Adeyeye et al. (2015) also use principal component analysis with discriminant analysis to study the probability of failure of Nigerian banks. The authors point out that profitability, liquidity, credit risk and capital adequacy are good predictors of bank failure and that the PCA is important, but it is a complementary tool that can explore the financial structure. Badarau and Leveiuge (2011) assess the potential strength of the capitalization channel of banks in eight European countries using the PCA. The results show that Germany and Italy could be exposed to financial shocks through the banking channel, while other countries, such as France, are the least exposed to financial shocks. Issah and Antwi (2017) study the role of macroeconomic conditions and try to predict a firm’s basic performance represented by return on assets (ROA) and macroeconomic variables. The predictor variables used in the model’s construction were selected using PCA. The results indicate that macroeconomic conditions should consider when forecasting company performance. So, using this technique is a way to highlight the similarities and differences by reducing the initial dataset and channeling a complex array of correlated variables into a few uncorrelated variables or factors called components. The literature shows that the PCA is a powerful tool that adds value and concatenates an important set of financial measures into a few components that represent the key information necessary to compare several aspects of banking and financial soundness. 6The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 8 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8 Table 1 : Definitions of variables: Banking and financial indicators. Review of the literature, hypotheses tested and sources Indicators Literature for Islamic and conventional banks 1. Capital requirements a. The capital adequacy ratio RAC It’s the sum of Tier & Tier2 as a percentage of risk-weighted assets. According to the Basel Committee, banks must maintain a minimum capital adequacy ratio of 8%. Capital is positively (negatively) associated with the financial health and stability of banks. Stiroh (2004a); Canbas and al. (2005); Mercieca and al. (2007); Shih and al. (2007); Pasiouras (2008); Demirgüç-Kunt and Huizinga (2010); Chortareasa and al. (2012); Vasquez and Federico (2012); Barth and al. (2013); Beck et al. (2013); Berger and Bouwman (2013); Lee and Hsieh (2013); Pessarossi and Weill (2013); Anginer and Demirgüç-Kunt (2014); Imbierowicz and Rauch (2014); Rozman and al. (2014). b. Tier 1 ratio (RT1) Similar to the capital adequacy ratio, the Tier1 ratio. This measure of capital adequacy measures the level of Tier 1 divided by risk-weighted assets calculated according to the Basel rules. Banks must maintain Tier1 of at least 4.5% (Basel II) and 6% (Basel III). Capital is negatively (positively) associated with bank stability. Pettway (1976); Kahane (1977); Koehn and Santomero (1980); Kim and Santomero (1988); Berger and Di Patti (2006); Altunbas and al. (2007); Goddard and al. (2010); Abedifar and al. (2013). c. Total Equity / Net Loans (TCPN) d. Total Equity / Deposits (TCD) • Another bank capitalization ratio. It measures the amount of bank equity relative to bank deposits and short-term financing. • This ratio is the equity financing of a bank balance sheet as a percentage of its liabilities. It is seen as another way to look at the adequacy of bank capital. Peltzman (1970); Rime (2001); Ariff and Can (2008); Demirgüç-Kunt and Detragiache (2010). 2. Liquidity requirements a. liquidity/ deposits (LD) b. liquidity / total assets (LA) c. liquidity / deposits and loans (LDE) a. The ratio of liquidity to total deposits in loans. Like liquid assets to be deposited and the short-term financing ratio, this ratio also examines the amount of available cash for depositors and borrowers. b. The Total Liquid Assets / Assets ratio refers to assets that are readily convertible into cash at any time without any constraints. Liquidity is positively (negatively) associated with stability. Canbas and al. (2005); Shih and al. (2007); Srairi (2008); Čihák and Hesse (2010); Belans and Hassiki (2012); Pappas and al. (2012); Vasquez and Federico (2012); Beck and al. (2013); Rajhi (2013); Anginer and Demirgüç-Kunt (2014). d. Net Loans / Total Assets (PNA) Liquidity is negatively associated with the profitability or efficiency of banks. Ariff and Can (2008); Alam (2012). 3. Leverage requirements a. Total Liabilities / Total Assets (AP) The ratio of total liabilities to total assets measures the share of bank debt relative to bank assets. This ratio is also referred to as the debt ratio and considered a measure of bank risk. Leverage is positively associated with the efficiency or profitability of banks. Berger and Di Patti (2006); Srairi (2008); Männasoo and Mayes (2009); Belans and Hassiki (2012). b. Total Assets / Equity (AC) This is the ratio of equity to assets. This is the traditional measure of bank capital (leverage). 7 ksiaa and Gallali: The determinants of banking regulation in the MENA regionPublished by Pepperdine Digital Commons, 2022 liabilities/total assets; total assets/equity; and total liabilities/equity); also banking restriction index; Official supervision index; Private surveillance index, finally the global index of regulation and surveillance. Also, a PCA analysis is carried out on a set of 13 financial ratios to exploit and compare the financial characteristics of 239 banks (175 conventional commercial banks and 64 Islamic commercial banks) in the MENA region over a period 2004-2015. The application of the PCA technique to all previously processed data allows us to identify four principal components that together represent 90% of the total data dissemination. The PCA has identified four global indices that are considered being key drivers of banking regulation in the MENA region: capital requirement, liquidity requirement, financial leverage, and leverage on liabilities. This gives the main indices EXIGCP, EXIGLIQ, LEVCP, and LEVP. The results are interesting, the first index in EXIGCP includes information on the Tier1 ratio; this leads to the conclusion that for banks in the MENA region, it is appropriate to focus on the core of capital in order to maintain the bank's stability. Therefore, banks are encouraged to increase the Tier 1 element in their capital adequacy ratio instead of the Tier 2 element. The second EXIGLIQ index provides information on the relationship between cash and total debt deposits. Like liquid assets to be deposited and the short-term financing ratio, this ratio also examines the amount of cash not only for depositors but also for borrowers. The third LEVCP index highlights the information from the two ratios of total liabilities to total assets that measures the ratio of bank debt to bank assets and the ratio of capital to assets. Finally, LEVP is the traditional measure of bank capital (leverage). Several indicators of the quality of public administration and financial development have contributed to the role of institutional quality in decisions about the leverage of banks in the MENA region. It is also the first study to analyze the determinant of the structure of banking regulation in the MENA region. To refine analyzes, the overall results for MENA countries can be examined in more detail to consider the intrinsic peculiarities of each country. 14The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 8 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8 References Abdullah, D. V. 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(2011) Managing liquidity in the Islamic financial services industry, BIS central bankers’ speeches, Bank for International Settlements. https://www.bis.org/review/r110419k.pdf 17 ksiaa and Gallali: The determinants of banking regulation in the MENA regionPublished by Pepperdine Digital Commons, 2022 Appendixs: Table 2: Descriptive statistics Descriptive statistics NOT Minimum Maximum Average Standard deviation Year 2856 2004 2015 2009.50 3.453 RAC 2850 0 3 , 21 , 206 TIER1 2848 0 2 , 19 , 195 CPPN 2850 -1 10 , 65 1,373 CD 2850 -1 10 , 35 , 970 CPP 2850 0 9 ,30 , 816 LD 2849 0 9 , 50 , 696 THE 2850 0 2 , 31 , 209 LDE 2848 0 9 , 42 , 472 PNA 2850 0 8 , 47 , 344 PA 2850 0 2 , 84 , 165 AP 2850 0 1 , 10 , 112 Pc 2850 -1 104 , 13 1,950 PCP 2849 0 1 , 89 , 142 IG 2616 1 2 2.08 , 354 IO 2808 -2 2 , 72 1.349 Valid N (list) 2557 18The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 8 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8 First panel KMO index and Bartlett test Kaiser-Meyer-Olkin sampling precision measurement. , 774 Bartlett's sphericity test Approximate chi-square 9492,890 Dof 10 Meaning of Bartlett , 000 Total variance explained Component Initial eigenvalues Extraction Sums of squares of the factors selected Total % of variance cumulative% Total % of variance cumulative% 1 3.365 67.308 67.308 3.365 67.308 67.308 2 , 680 13,594 80.902 3 , 592 11.846 92.749 4 , 263 5.255 98.004 5 , 100 1,996 100,000 Extraction method: Principal component analysis. Component matrix a Component 1 RAC , 887 TIER1 , 896 CPPN , 693 CD , 760 CPP , 848 Extraction method: Principal component analysis. at. 1 extracted components. Quality of representation Initial Extraction RAC 1,000 , 786 TIER1 1,000 , 802 CPPN 1,000 , 481 CD 1,000 , 577 CPP 1,000 , 719 Extraction method: Principal component analysis. Matrix of the coefficients of the coordinates of the components Component 1 RAC , 263 TIER1 , 266 CPPN , 206 CD , 226 CPP , 252 Extraction method: Principal component analysis. Component scores. 19 ksiaa and Gallali: The determinants of banking regulation in the MENA regionPublished by Pepperdine Digital Commons, 2022 Second panel Component matrix a Component 1 LD , 861 THE , 824 LDE , 902 PNA -, 414 Extraction method: Principal component analysis. at. 1 extracted components. Quality of representation Initial Extraction LD 1,000 , 742 THE 1,000 , 679 LDE 1,000 , 814 PNA 1,000 , 171 Extraction method: Principal component analysis. KMO index and Bartlett test Kaiser-Meyer-Olkin sampling precision measurement. , 703 Bartlett's sphericity test Approximate chi-square 4143,277 dof 6 Meaning of Bartlett , 000 Total variance explained Component Initial eigenvalues Extraction Sums of squares of the factors selected Total % of variance cumulative% Total % of variance cumulative% 1 2.406 60,140 60,140 2.406 60,140 60,140 2 , 897 22,418 82.558 3 , 466 11.647 94.205 4 , 232 5.795 100,000 Extraction method: Principal component analysis. Matrix of the coefficients of the coordinates of the components Component 1 LD , 358 THE , 342 LDE , 375 PNA -, 172 Extraction method: Principal component analysis. Component scores. 20The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 8 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8 Third panel KMO index and Bartlett test Kaiser-Meyer-Olkin sampling precision measurement. , 500 Bartlett's sphericity test Approximate chi-square 27,793 dof 1 Meaning of Bartlett , 000 Total variance explained Component Initial eigenvalues Extraction Sums of squares of the factors selected Total % of variance cumulative% Total % of variance cumulative% 1 1.099 54.929 54.929 1.099 54.929 54.929 2 , 901 45,071 100,000 Extraction method: Principal component analysis. Quality of representation Initial Extraction AP 1,000 , 549 PCP 1,000 , 549 Extraction method: Principal component analysis. Total variance explained Component Initial eigenvalues Extraction Sums of squares of the factors selected Total % of variance cumulative% Total % of variance cumulative% 1 1.099 54.929 54.929 1.099 54.929 54.929 2 , 901 45,071 100,000 Extraction method: Principal component analysis. Matrix of the coefficients of the coordinates of the components Component 1 AP , 675 PCP , 675 Extraction method: Principal component analysis. Component scores. Component matrix a Component 1 AP , 741 PCP , 741 Extraction method: Principal component analysis. at. 1 extracted components. 21 ksiaa and Gallali: The determinants of banking regulation in the MENA regionPublished by Pepperdine Digital Commons, 2022 Fourth panel Quality of representation Initial Extraction PA 1,000 , 513 Pc 1,000 , 513 Extraction method: Principal component analysis. Total variance explained Component Initial eigenvalues Extraction Sums of squares of the factors selected Total % of variance cumulative% Total % of variance cumulative% 1 1.026 51.301 51.301 1.026 51.301 51.301 2 , 974 48,699 100,000 Extraction method: Principal component analysis. Component matrix a Component 1 PA , 716 Pc , 716 Extraction method: Principal component analysis. At. 1 extracted components. Matrix of the coefficients of the coordinates of the components Component 1 PA , 698 Pc , 698 Extraction method: Principal component analysis. Component scores. 22The Journal of Entrepreneurial Finance, Vol. 24, Iss. 2 [2022], Art. 8 https://digitalcommons.pepperdine.edu/jef/vol24/iss2/8