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Determinants of Bank Risk-Taking: A Comparative Analysis between Islamic and Conventional Banks

Salihu Liman Mairafi
By Salihu Liman Mairafi
7 months ago
Determinants of Bank Risk-Taking: A Comparative Analysis between Islamic and Conventional Banks

Islamic banking

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  1. 6th Annual ECoFI Symposium 2019 31 October 2019 , HIG Hotel, Langkawi, Kedah, Malaysia st Determinants of Bank Risk-Taking: A Comparative Analysis between Islamic and Conventional Banks Salihu Liman Mairafia, Sallahuddin Hassanb, Shamsul Bahrain Mohamed-Arshadc a Department of Banking & Finance Nasarawa State University, Keffi, bSchool of Economics, Finance & Banking Universiti Utara Malaysia, Abstract Even though several majors have been implemented by the regulators to ensure stability in the banking sector, the incessant failure of banks and their poor performance in the aftermath of the 2007-2008 global financial crisis have generate concerns. As a result, this study compares the bank-specific determinants of bank risk-taking between the Shariah- compliant banks and their conventional counterpart. Using a panel regression on data from GCC countries during the period 2006-2015, the findings disclose that capital, liquidity, size, and asset quality significantly affect conventional banks’ risk-taking while for the Islamic banks, assets quality and bank size are the only variable that shows significant relationship. Hence, we recommend further studies to consider other factors such as the macroeconomic variables in comparing their effects on bank risk-taking behaviour of the dual banking systems to provide additional insights to various stakeholders for an informed decision and policy making. Keywords: determinants, Islamic banks, conventional banks, risk-taking 1. INTRODUCTION Over the years, different policies have been implemented by the banking regulators to mitigate bank high risk-taking behaviour. For example, the Basel Committee on Banking Supervision (BCBS) has recommended standards such as the Basel I, Basel II, and Basel III. The Basel I (the capital accord), introduced in 1988 recommended among other things, the review of banks’ capital requirements as a benchmark for assessing the soundness of banks; the Basel II came up with recommendations on capital requirements, market discipline, and supervisory review in June 2004; and the Basel III introduced new standards on liquidity and additional standards on capital among other things (BCBS, 2004, 2008, 2013). However, it is argued that the Basel requirements often resulted to innovation and liberalisation in the banking sector that subsequently resulted to crises as a result of high risk-taking. For instance, Alsharif, Md Nassir, Kamarudin, and Zariyawati (2016) and Pedro (2013) maintain that the systemic banking crisis which causes huge losses in Asia and Eastern Europe was followed by the introduction of the Basel I. Similarly, the 2007-2008 global financial crisis (GFC) was said to have started few years after the implementation of Basel II accord due to innovations and high risk-taking. In the same way, Sanusi (2011) stated that banks were affected by the 2007-2008 GFC because of their excessive lending in risky sectors as a result of high liquidity acquired during the 2004-2005 recapitalisation and consolidation to comply with the Basel II requirements. Meanwhile, the 2007-2008 GFC was reported to have been triggered by the excessive risk-taking following the high liquidity inflow into the United State of America (USA) banks following the implementation of the Basel II (Acharya & Naqvi, 2012; Cornett, McNutt, Strahan, & Tehranian, 2011; Demirgüç-Kunt & Huizinga, 2010; Khan, Scheule, & Wu, 2017). Consequently, the BCBS has introduced the Basel III in the aftermath of the 2007-2008 GFC to ensure stability and sustainability in the banking sector by regulating the banks’ high risk-taking behaviour. The Basel III introduced additional standard on capital and new liquidity standards to intensify regulation, especially, on the banks’ high risk-taking behaviour and ensure the long-term sustainability of the banking sector. Basel III requires the banks 84
  2. to meet two different minimum liquidity ratios namely ; (1) liquidity coverage ratio (LCR) and (2) net stable funding ratio (NSFR). The LCR requires banks to hold adequate liquid assets to cover thirty days of cash outflows during a crisis period whereas the NSFR requires banks to finance their medium and long-term loans with stable funds that are not likely to run during the crisis period. In response to the Basel III liquidity standard, banks have to increase the amount of the liquid assets and cash they hold to hedge against liquidity risk (DeYoung & Jang, 2016; Khan et al., 2017). The new Basel III standards have received heightened attention across the globe, especially the liquidity standards which are new to the Basel requirements. The liquidity standards required banks to hold more high -quality liquid assets (HQLA) such as government bonds, financial assets such as securities issued or guaranteed by specific multilateral development banks or sovereign entities, and publicly traded common stock and investment-grade corporate debt securities of nonfinancial sector corporations (BCBS, 2013). The new Basel III standards are considered as a catalyst to improve the banks’ ability to enhance their riskreturn profile, control bank risk-taking, and the capability to absorb shocks, especially, during the crisis period (Bonner, 2016; Drehmann & Nikolaou, 2013; King, 2013). However, the magnitude of the new Basel III standards could have different effects on the two type of banking systems (Islamic and conventional banking) due to difference in their business models. While the Islamic banking operates based on the profit and loss sharing, the conventional banking system is an interest bearing. The differences result to different risks among the two type of banking systems which arises from the governance, nature of contracts, and liquidity (Mokni, Rajhi, & Rachdi, 2016). As a result, (Mairafi, Hassan, & Mohamed-Arshad, 2018) indicated the need to consider the unique feature of banking systems that influences their high risk-taking behaviour. Also, the new liquidity standards which emphasised on the need to maintain HQLA required a bank to efficiently allocate and manage it portfolio in a functional and well -developed bond markets, as well as the adequate financial instruments. Consequently, such market and instruments have been the major challenges faced by the Islamic banks (Albaity, Mallek, & Noman, 2019; Ibrahim & Rizvi, 2018). As a result, the Shariahcompliant banks are vulnerable to different classes of risks (Abedifar, Molyneux, & Tarazi, 2013). Therefore, the purpose of this paper is to identify the determinants of bank risk-taking between the Islamic and conventional banks within the context of the CAMEL. Identifying the determinants of bank risk-taking of the Islamic and conventional counterpart will contribute to extant literature on bank risk-taking. Similarly, the paper will contribute to the ongoing regulations on curtailing the bank high risk-taking behaviour. The remaining sections of this paper are as follows; Section 2 review relevant literature; Section 3 describes methodology; Section 4 present and discuss findings. Lastly, Section 5 concludes the study. 2. LITERATURE REVIEW This section review studies related to determinants of bank risk-taking and the differences in stability between the Shariah-compliant and the conventional counterparts. Many studies have attempted to assess the determinants of bank risk-taking due to their financial intermediary role. According to financial intermediation theory, banks risk-taking is influenced by their activities as financial intermediaries which expose them to risks from other sectors of the economy (Berger & Bouwman, 2009; Diamond, 1984; Mourouzidou-Damtsa et al., 2017). Theory asserts that banks create liquidity by making funds available for customers withdrawal needs and transform risk by renting out short term deposits to finance loan-term loans while earning an interest spread (Berger & Bouwman, 2009, 2017; Kashyap et al., 2002; Rajan, 1994). Thus, in pursue of high spread, banks tend to embark on a risky behaviour by lending out more, particularly to risky sectors and by compromising standards. Nevertheless, this behaviour can be restrained in banks with high capital adequacy, assets quality, management efficiency, consistent stream of income and high-quality liquid assets as these specific variables cushion the effects of risk-taking. Consequently, risk-taking is expected to be more influenced by the bank-specific determinants. Early studies that compares the risk-taking behaviour between the Islamic and conventional counterpart though limited but have cut across different determinants. However, most of these studies reported mixed outcomes in relation to the solvency and stability of the dual banking systems. A study by Cihak and Hesse (2010) is among the early studies that have examine the important of Islamic banks in financial stability. In their study, they used Z-score to proxy for insolvency risk by comparing the stability between Islamic and conventional banks across 18 countries during the period 1993 to 2004. They found that capital and size are the main determinants of stability among the tow banking systems. They showed that small Islamic banks are more stable than the conventional counterpart, large Islamic banks are financially less strong than large conventional banks, and large Islamic banks are riskier than the 85
  3. smaller ones . Another study by Gamaginta and Rokhim (2011) who also used the Z-score to assess the stability of 71conventional and 12 Islamic banks in Indonesia over the period 2004 and 2009 revealed that conventional banks are more stable than the Islamic banks. However, during the 2007-2008 GFC the stability of the dual banking was almost the same. Also, disclosed that small Islamic and conventional banks have the same level of stability. Furthermore, comparative studies have observed the two banking systems in terms of profitability, efficiency, capitalisation, assets quality and liquidity and report mixed findings, especially, during and after the 2007-2008 GFC. For example, studies by Parashar and Venkatesh (2010), Hussein (2010), and Beck et al. (2013) concluded lower liquidity, capitalization, and intermediations to conventional compared to the Islamic banks during the GFC period. Nonetheless, Alqahthani, et al. (2016) argued that generalisation of these findings that Islamic banks perform better than conventional counterpart is temporal and limited to countries affected by the GFC. They further found that Islamic banks in the GCC countries performed worse than conventional banks in terms of capitalisation and profitability, and liquidity some years after the split of the crisis to the real economic sector of the region. Accordingly, the growth of the banking institutions is associated with the performance increases concerning liquidity, profitability, and asset quality (Hassan & Aliyu, 2018). Meanwhile, Mirza et al. (2015) analysed data from Pakistan and established that Islamic banks have improved asset quality and stability compared to the conventional banks. Conversely, Sun et al. (2014) disclosed that low asset quality and high liabilities are more related to the Islamic banks than the conventional. In addition, their sample has shown that the two types of banks-Islamic and conventional revealed lower volatility in their growth. Meanwhile, it was reported that at the early stage of the 2007-2008 GFC, IB performed relatively better in terms of liquidity, capitalisation, profitability, and assets quality (Alqahtani et al., 2016; Beck, et al., 2013; Bourkhis & Nabi, 2013; Khediri et al., 2015; Olson & Zoubi, 2008). In view of these studies, there is the need to further assess their principal drivers of stability such as bank-specific determinants and the risk-taking to ascertain the main cause of the inconsistencies on their activities. In this study, we attempt to assess this question by analysing the influence of bank determinants on the stability of banks from five countries. 3. METHODOLOGY AND DATA This section contains a description of the methodology and data used. It consists of model specification along with the definition of dependent and independent variables as well as the estimation method used. The data sub-section consists of the description of the data used for all variables and the sample period and size. Data We utilised financial data of listed Islamic and conventional banks available in the Bankscope database as at December 2016. The data consist of 31 Islamic and 38 conventional banks from five countries. We consider countries with complete data on both Islamic and conventional banks from 2006-2015. This give us a total of 69 banks and develop a balanced panel data with 690-year observations. Model Specification We used a panel data analysis to examines the relationship between 31 Islamic banks and 38 conventional banks risktaking and their internal characteristics from five GCC countries. The use of panel data allows us to control for heterogeneity among the banks and the countries. This is because of the different characteristics of countries. Thus, a static panel estimator is applied because of distinct attributes of each bank. Previous studies that have used panel data includes studies such as (Demirgüç-Kunt & Huizinga, 2010; Laeven & Levine, 2009; Mairafi et al., 2019) among others. SDROAAit =  0 + 1CAPTit +  2 ASSTit + 3 PROTit +  4 EFFYit + 5 LIQTit +  6 SIZEit +  it Where i and t gauges for bank and year respectively, SDROAE and SDROAA are proxies for bank risk-taking, CAPT is the capital adequacy, ASST represent the assets quality, PROT stand for profitability, EFFY is the bank efficiency LIQT represent bank liquidity and SIZE is the bank size.  0 , 1 , …….  6 are the slope coefficient and  it stand for error term. 86
  4. Measurement of Variables Risk-Taking Variables We measure bank risk-taking as the volatility of returns , that is the level of changeability about the size of discrepancy in the bank returns. It is the extent of risk associated with the banks return. Accordingly, it is proxied by the standard of return on assets SDROAA. The proxy shows the risks confronted by banks, and the higher the standard deviations, the more the uncertainty of bank future returns. Similarly, the higher the risks and the more the likelihood of the banks’ instability. Prior studies by Barry, Lepetit, and Tarazi (2011), Laeven and Levine (2009), Lepetit, Nys, Rous, and Tarazi (2008) are among those that have used SDROAA to gauge the bank risk-taking. They argued that SDROAA give a true financial condition of banks. Following studies such as (Beck, De Jonghe, & Schepens, 2013; Karim, Alhabshi, Kassim, & Haron, 2019; Mairafi et al., 2019) data for three-consecutive-year rolling window are used in computing SDROAE and SDROAA. For instance, SDROAE and SDROAA for 2015 are computed by using data from 2013-2015 and so on. Bank-Specific Variables Capital: Bank capital is regarded as the buffer, a financial worth of banks provided to cushion the effect of malfunction that may arise in the cause of business. Capital protects banks from financial fragility that is measures as total equity to total assets. Higher capital ratio provides more protection to banks. Berger and Bouwman (2013) Ghosh (2014), Pellegrina (2012) are some of the studies that used this ratio to proxy for bank capital. Assets quality: is the extent of credit risks associated with the loan portfolios. It is measured by the net loans to total assets following studies such as Abduh and Idrees (2013), Arena (2008), Fiordelisi et al. (2011), and Hazzi and AlKilani (2013). Management efficiency: this measure the extent of expenses incurred on the management during the period. Following studies such as Beck et al. (2013) and Pappas et al. (2016). management efficiency is proxy by the cost to income. Profitability: Is the bank earnings during a period which ensures constant and stable revenue for banks. Profitability is proxy by the ratio of net income to total assets (ROA). Extant studies that have used this ratio includes (Athanasoglu, Brissimis, & Delis, 2008; Dahir et al., 2017; Jeon & Ryoo, 2013). Liquidity: It is considered as the ability of banks to aptly meet their customers’ needs which is determined by funding liquidity. Thus, it is measured by the total deposits divides by total assets. Among the studies that used the ratio as proxy for bank liquidity are (Dahir et al., 2017; El-Massah, Bacheer, & Sayed, 2019; Khan et al., 2017). Size: Is the degree of the banks’ assets. Following the previous studies such as by Ahmad and Ariff (2004) Berger and Bouwman (2013) and Laeven and Levine (2009) bank size is measured by the natural logarithm of the total assets. Table 1: Variables and Measurements Variables Dependent Variables Measurement SDROAE Net income / total equity SDROAA Net income / total assets Source Barry et al. (2011), Laeven & Levine (2009), Lepetit et al. (2008) and Mairafi et al. (2019). Mairafi et al. (2019) and Mairafi (2019). Indpendent Variables CAPT Equity to total assets Berger & Bouwman (2013), Ghosh (2014), and Pellegrina (2012) ASST Total loans/ total assets Abduh & Idrees (2013), Arena (2008), Fiordelisi et al. (2011), and Hazzi & AlKilani (2013). 87
  5. EFFY cost to income PROT Net income to total assets LIQY Total deposits to total assets SIZE Natural log of total assets Beck et al . (2013), Mairafi et al. (2019), and Pappas et al. (2016). Athanasoglu et al. (2008), Dahir et al. (2017) and Jeon & Ryoo (2013). Dahir et al. (2017), El-Massah et al. (2019) and Khan et al. (2017) Ahmad & Ariff (2004), Berger and Bouwman (2013), and Laeven & Levine (2009) 4. EMPIRICAL RESULTS Descriptive Statistics Table 2 show the summary statistics of the variables with common statistical data analysis such as the mean, the standard deviation, and the minimum and maximum for Islamic and conventional banks. Based on the table, the mean value of SDROAA Islamic bank is higher than the mean value of conventional bank. Similarly, the mean values of CAPT, EFFY, and LIQY for Islamic banks are higher than those conventional counterparts. On the other hand, the mean values of ASSTQ, PROFY and SIZE for conventional banks are higher than those of Islamic banks. Similarly, The Standard deviation of SDROAA for Islamic banks is higher than the standard deviation of conventional banks. Also, the standard deviation of CAPT, ASSTQ, EFFY, PROFY, and LIQY for Islamic banks are much more than the conventional banks. Whereas the standard deviation of SIZE for conventional banks is higher than the Islamic banks. Table 2: Statistical Description of Variables Islamic Banks Std. Dev Max Variable Mean SDROAA CAPT ASSTQ 3.43 35.33 46.28 5.06 29.54 28.26 EFFY PROFY 84.17 1.24 151.81 7.75 Conventional Banks Std. Dev Max Min Mean Min 24.97 99.78 99.32 0.01 6.34 0.00 0.54 14.02 57.64 0.76 4.25 12.22 5.88 29.85 82.01 0.01 0.77 16.28 1052.00 35.10 0.00 -45.31 33.97 1.92 9.91 1.20 96.40 8.24 11.64 -7.17 LIQY 59.40 28.18 93.91 0.02 0.78 0.07 0.94 0.51 SIZE 6.37 0.84 7.93 4.08 16.58 1.09 18.81 13.83 Note: ROAA is the standard deviation of average assets; CAPT is equity to total assets; ASSTQ is total loans to total assets; EFFY is cost to income; PROFY is net income to total assets; LIQY is deposits to total assets which indicates funding liquidity; SIZE is natural log of total assts. Estimation Results The objective of this paper is to examines the determinants of Islamic and conventional banks risk-taking. The study applied a static regression model namely, POLS, FEM, and REM to choose the appropriate method among the three methods, the following steps have been adopted. Firstly, a Breuch-Pagan, Langrangian Multiplier (LM) test has been conducted to select either POLS or REM. The results of LM test is presented in Table 3. The results show that the REM is more appropriate than the POLS as the calculated χ2 is greater than the tabulated χ2 and the p-value result is 0.000. This implies that there exist a bank-specific effects in the data. Secondly, we conduct a Hausman specification test to further choose between the REM and FEM to ensure an efficient and consistent estimate. The results are also presented in Table 3. The results of the Hausman test rejects the null hypothesis (Ho) which indicates that FEM is more fit for consistent estimation. 88
  6. Secondly , we conduct a Hausman specification test to further choose between the REM and FEM to ensure an efficient and consistent estimate. The results are also presented in Table 4. The results of the Hausman test rejects the null hypothesis (Ho) which indicates that FEM is more fit for consistent estimation. Thirdly, we carried out a diagnostic to check for heteroskedasticity and autocorrelation before the onward reporting of estimation results. The results of the tests are also presented in Table 3. The results rejected the Ho as the prob (χ 2) reveal as 0.000. The result indicates a presence of heteroskedascity in the model. Table 3: Tests Results Items Langarangian chibar2 (01) 20.220 Prob > χ2 χ2 (06) Hausman Test Heteroskedascity 0.013 16.080 0.000 0.000 χ2 (10) Autocorrelation 49.350 F (1, 9) Prob > F Decision 0.381 0.553 REM FEM Yes No However, the results of autocorrelation test have revealed no first order autocorrelation as the probability value is more than 0.05. It suggests that the data have no first order autocorrelation, and thus, the Ho accepted. Hence, following Asteriou and Hall (2011) the FEM robust is used to correct both problems. Finally, the results are presented in Table 4. The estimation results in Table 4 reveal that the coefficients of ASSTQ and SIZE for Islamic bank are statistically significant at 10 percent and one percent, respectively. The results indicate that ASSTQ and SIZE significantly affect SDROAA. The results show negative relationships which suggests that a percentage decrease in the banks’ assets quality increases the banks probability of insolvency by 0.61 percent as a result of their high risktaking behaviour. Also, a percentage decrease in banks’ size increases their risk-taking behaviour by 1.54 percent. These results are consistent with the “too big to fail” theory and findings of the previous studies such as Alam and Tang (2012). However, the coefficient of CAPT, PROFY, EFFY, and LIQY disclose an insignificant statistically relationship with the Islamic bank risk-taking behaviour at all levels. Table 4: Estimation Results Conventional Banks Islamic Banks Coefficient z-value CAPT ASSTQ 0.007 -0.008 1.230 -1.940 EFFY PROFY 0.001 -0.004 LIQY SIZE 0.008 -0.855 SDROAA p-value Coefficient z-value p-value 0.250 0.085* -0.038 0.013 -1.960 2.670 0.050** 0.008*** 1.400 -0.130 0.195 0.896 -0.013 -0.125 -1.550 -1.610 0.122 0.108 1.690 -4.720 0.126 0.001*** -3.597 -0.286 -3.810 -4.970 0.000*** 0.000*** 6.734 4.600 0.000 _CONS 5.143 3.310 0.009 Note: ***, **, * indicates 1%, 5% & 10% significant level On the other hand, the coefficients of CAPT, ASSTQ, LIQY, and SIZE for conventional banks are statistically significant at five percent and one percent, respectively. The results suggest that CAPT, ASSTQ, LIQY, and SIZE have significant influence on SDROAA. The results disclose negative relationship for CAPT, LIQY, and SIZE while for ASSTQ, the results reveal a positive relationship. This suggests that decrease in capital, liquidity, and size are the key 89
  7. determinants of conventional banks risk-taking that significantly influences the probability of solvency and stability of the conventional banks in the GGC countries during the period 2006-2015 . These results are consistent with the findings of Dahir et al. (2017), Demirguc-Kunt and Huizinga (2010), Khan et al. (2017), and Mairafi et al. (2019) who reported similar findings on capital, liquidity, size, and assets quality. Conversely, the coefficients of EFFY and PROFY reveal an insignificant relationship with SDRAA for the conventional banks. 5. CONCLUSION In this paper, we examine the determinants of banks risk-taking during the period 2006-2015 by comparing Islamic and conventional banks in the GCC. The empirical findings reveal that the assets quality and bank size are the key determinants of the Shariah-compliant bank risk-taking whereas capital, assets quality, liquidity, and size are the key determinant of conventional banks. 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