269
Islamic Banking, Accounting And Finance International Conference–
The 9
thiBAF 2020
Theoretical and practical underpinnings of equity-based financing and credit risk in Islamic bank Farihana Shahari
Kulliyyah of Economics and Management Sciences, International Islamic University Malaysia (IIUM), 53100 Gombak, Kuala Lumpur
Tel: +603 6421 4673 E-mail: [email protected]
Md Saifur Rahman
College of Business, RMIT University, Melbourne, Australia [email protected]
Abstract
This study resolves the gap between Profit and Loss Sharing (PLS) theory and the practice of Islamic banks in financing customers. Two-step GMM estimation technique is used in the investigation. The empirical finding of the study is consistent with the PLS theory of reducing credit risk and inconsistent with the practice of Islamic banks. Equity-based financing has been found to reduce credit risk, while bank-specific determinants such as credit growth, profitability, and cost inefficiency influence the credit risk. Furthermore, institutional development is a macroeconomic indicator that reduces credit risk. This study offers policy implications for risk-minimised decisions in financing clients.
Keywords: Islamic Banks, equity-based financing instrument, Credit Risk, System GMM
1. Introduction
Equity-based financing requires Islamic banks to finance a client as a partner based on the Profit and Loss Sharing (PLS) theory. It does not allow charging a fixed rate and securitising a collateral asset; rather, it requires sharing the investment profit between the bank and client based on a pre-agreed ratio. Equity-based financing provides an uncertain return, while theoretically reducing the credit risk. As a partner, banks are required to provide technical support alongside financing to build the ground for a profitable business environment (Warde, 2000). Equity-based financing is based on the proposal of a potential investment project rather than financing randomly. The banks are also required to conduct fact-based forecasting, evaluation, and surveys to ensure a positive return to the project (Rinaldi and Sanchis-Arellano, 2006).
Islamic banks finance clients based on Profit and Loss Sharing (PLS) and non-PLS concepts. PLS is based on the idea of partnership, in which both banks and customers are engaged as business partners. The partnership is formed based on several contracts such as Mudarabah and Musharakah, which eliminate interest-based return and earn uncertain, and risk-adjusted returns (Shahari et al., 2015). Inversely, non-PLS financing instrument depends on contracts such as Murabaha, Salam, Istisna, Ijarah, and Tawarruk which generate fixed and guaranteed return (Aggarwal and Yousef, 2000; Siddiqi, 2006 and Abdul-Rahman et al., 2014). Theoretically, PLS-based financing reduces credit risk. It requires banks to engage in investment projects directly, along with partners (customers). As a partner, the Islamic bank invests effort to make a project successful since the return is uncertain (Warde, 2000). The nature of uncertain returns in PLS forces the banks to be selective in choosing a potential project. The banks evaluate, survey, and forecast the potential profitability of the project before offering finance (Rinaldi and Sanchis-
270 Arellano, 2006).
In practice, Islamic banks prefer debt-based financing instead of equity-based financing. More than 90% of the financing is provided based on debt-based financing, while less than 10% is disbursed based on equity-financing (Shahari et al., 2015). Debt-based financing is chosen to avoid uncertain income flow and liquidity-shortfall. It allows charging a fixed rate and securitising collateral assets to recover the financed amount in the case of default.
Therefore, Islamic banks use debt-based financing to reduce credit risk, but PLS theory ensures the reduction of credit risk by equity-based financing (Boumediene, 2011). This apparent gap between theory and practice is resolved empirically to decide which financing instrument should be used to reduce the credit risk.
Many studies examined the factors determining nonperforming loans, but none used an equity-based financing instrument to explore the credit risk as practised in the PLS theory of Islamic banks. Studies such as Misman (2011) used the ratio of bank-specific variables to investigate their impact on the credit risk of Malaysian Islamic banks.
The result shows that total equity and capital are negatively related to the nonperforming loan. Zaki et al. (2011) used macroeconomic and bank-specific variables to examine their impact on the credit risk of UAE Islamic and conventional banks. The findings indicate a positive association between the cost-income ratio and credit risk. It implies that higher banking costs lead to inefficiency in operational activities, and causes credit risk. Furthermore, Boumediene (2011) compared between Islamic and conventional banking based on default probability and distance- to-default. The finding reveals that the mean distance-to-default of conventional banks is lower than that of Islamic banks. The result indicates that Islamic banks manage credit risk well.
Similarly, Abedifar et al. (2013) used macroeconomic and bank-specific variables to investigate their effect on the credit risk of Islamic banks. The study finds that some of the determinants, such as loan growth, bank size, and the interest rate, have a significant effect on credit risk. Bank size is negatively correlated with the credit risk indicating that the larger banks’ capacity to reduce the credit risk is higher than that of smaller banks. Larger banks can better use economies of scale and diversification options in generating returns than smaller banks. Moreover, a positive relationship between the interest rate and credit risk implies that higher interest rate causes the credit risk. It occurs because a higher amount of payment burden associated with the interest causes the possibility of credit risk. This result is consistent with the study of Kabir et al. (2015) who investigated credit risk of 37 Islamic banks and 156 conventional banks by employing both macroeconomic and microeconomic effects. The study provides evidence of a significant relationship. It shows that inflation, GDP growth, cost efficiency, diversification, size, loan to asset ratio, and banks’ profitability have a significant effect on the credit risk.
In addition, Louhichi and Boujelbene (2016) examined the determinants of the credit risk of 30 Islamic banks in the Middle Eastern and North African (MENA) and Asian countries. By implementing the GMM technique, the study reveals that inflation and financial crises do not affect credit risk. The authors believe that Islamic banks are immunised from the harmful effects of economic changes. Moreover, bank-specific variables such as loan loss provision, credit growth, bank size, and cost efficiency play a significant role in affecting credit risk. Credit risk is also negatively affected by GDP growth. It implies that GDP growth contributes to reducing credit risk. The finding is consistent with that of Salas and Saurina (2002), Louzis et al. (2012), and Castro (2013).
The literature does not consider equity-based financing instrument, which is supported by PLS theory in exploring the credit risk of Islamic banks. Rather, most studies discuss some macroeconomic and bank-specific variables. This study focuses on Islamic financing via PLS theory and equity-based financing. This study offers significant policy implication as it pinpoints the financing outcomes by considering equity and non-equity-based financing.
The remainder of the study is arranged in several sections. Section two focuses on the data and variable,s followed by measurement and hypotheses development in section three. Section four discusses the estimation technique, while empirical results are discussed in section five. Directions for future study are suggested in section six, followed by the conclusion along with policy recommendations.
271 2. Data and Variables
Credit risk is the dependent variable, equity-based financing, and development of financial institutions are the focus- independent variables, while bank-specific and macroeconomic determinants are control-independent variables. The bank-specific factor consists of several variables such as banks’ cost inefficiency (EFF), capital ratio, credit growth (CG), banks’ profitability (PROFIT) and banks’ size (SIZE), lending rate (LR) and deposit rate while macroeconomic variables are GDP growth (GDPG), institutional development index (IDI) and exchange volatility (EXCH).
The data for macroeconomic variables are collected from Datastream, while Bankscope is used to collect bank- specific variables. Initially, 101 Islamic banks from 16 countries, namely Malaysia (16 banks), UAE (10 banks), Bangladesh (5 banks), Indonesia (11 banks), Bahrain (17 banks), Lebanon (2 banks), Pakistan (9 banks), Saudi Arabia (4 banks), Egypt (3 banks), Kuwait (9 banks), Turkey (4 banks), Qatar (4 banks), Jordan (2), Sudan (3), Yemen (1) and South Africa (1) are selected based on the availability of data. The data of the equity-based financing instrument is not provided by Bankscope and hence selected banks’ annual reports are utilised in this regard. The annual reports are collected through several sources such as downloading from the banks’ websites and by requesting the requisite information from the banks’ managers. The data of the PLS financing instrument is available for 40 out of 101 banks in 12 countries. It has been collected over the maximum available periods which are between the years 2005 and 2012.
3. Measurement and Hypotheses Development
The dependent variable, credit risk, is measured by the ratio of nonperforming loans to total loans as it implies a healthy position of the banks’ income (Rahman and Shahimi, 2010; Louhichi and Boujelbene, 2016). A higher ratio implies a higher degree of credit risk. The study considers value-at-risk (VaR) as a dependent variable to reinvestigate the credit risk for the robustness checking following Ismal (2010). It assesses the risk level by using statistical and simulation models designed to capture the volatility of portfolio assets. It can be presented in the following form:
[ ] V
w w w X X
X =
3 2 1 3 2
1
(1)
where,
V is the volatility of financing in the portfolio.
W is the weight of the different groups of financing in the portfolio.
X is derived from the equation below:
X
1= w
1( ) ασ
1 2+ w
2[ ( ασ2) ( ) ρ2,1ασ1 ] + w3[ ( ασ3) ( ) ρ3,1ασ1 ]
X
2= w
1[ ( ασ1) ( ρ1,2 ασ2) ] + w2( ασ2)2+ w3[ ( ασ3) ( ρ3,2 ασ2) ]
(2)
X
3= w
1[ ( ) ( ασ1 ρ1,3 ασ3) ] + w2[ ( ασ2) ( ρ2,3 ασ3) ] + w3( ασ3)2
where,
α
is the confidence interval factor which is considered at 95% based on the study of Felsenstein (1985)σ
is the standard deviation of the type of financingρ
is the correlation of coefficient between two types of financing272
The VaR is computed through the square root of V-value that shows an overall position of credit risk. The summary of the measurements and hypothesis is provided in Table 1.
Table 1: Summary of variables, definition, and hypotheses Bank specific variables
Variable Definition Hypotheses
Equity-financing
Instrument Negative
Lending Rate Positive
Cost Inefficiency Positive
Credit Growth Positive
Capital Ratio Negative
Profitability Negative
Size Negative
Macroeconomic Variables
GDP Growth Negative
Exchange Rate
Volatility Negative
Financial
Development Index Negative
Note: The ratio is presented in percentage
The equity-based financing instrument is the focused independent variable. The ratio of asset-based financing to debt-based financing is used to measure the variable. To avoid the probable size effect, the ratio of equity-based financing instrument is scaled by standardising the equity-based ratio. The standardised value is calculated by dividing the differentiated value of equity-based ratio and its mean by its standard deviation,
).
Usmani (2002) and Siddiqi (2006) state that equity-based financing enables the banks to reduce credit risk. They explain that the reduction of credit risk is contributed by the theoretical enforcement in Islamic financing. The financier takes part in the operational activities in utilising the financed amount. The cooperation strengthens the ability to generate more revenue leading to the reduction of credit risk (Abdul-Rahman et al., 2014). Due to the theoretical support, a negative relationship is hypothesised between equity-based financing instrument and credit
273 risk.
The bank-specific factors are used as controlled variables. Lending rate (LR) is a bank-specific variable that is assumed to have a positive relationship with credit risk following the studies of Louzis et al. (2012) and Abedifar et al. (2013). Clients face additional payment burdens when the lending rate increases. Cost inefficiency is another bank-specific variable that assumes a positive relationship with the credit risk following the studies of Louzis et al.
(2012), and Louhichi and Boujelbene (2016). It includes the operational cost, where higher operational cost indicates the inefficiency. Credit risk increases significantly when the degree of inefficiency increases.
Credit growth (CG) is also used to determine the credit risk following the study of Salas and Saurina (2002) who suggest a positive relation. The study suggests that a higher degree of credit growth leads to a higher credit supply which causes credit risk. Besides, Flannery (1989) and Gennotte and Pyle (1991) found that capital ratio has a negative relationship with credit risk. Given the above studies and their finding, we hypothesise a negative relationship between capital ratio and credit risk. Profitability (PROFIT) is also used to determine credit risk. A profitable bank indicates that it efficiently manages operational activities. Following the study of Louzis et al.
(2012), this study hypothesises a negative relationship between profitability and credit risk as efficiency in financial and operational activities reduces the credit risk. According to Abedifar et al. (2013) and Kabir et al. (2015), the size of a bank affects the credit risk, where larger banks have a better capacity to reduce the credit risk due to the economies of scale. Therefore, this study hypothesises a negative relationship between the size of bank and credit risk.
Macroeconomic variables; GDP growth and exchange rate volatility are also considered control variables to determine the credit risk. GDP growth is an important determinant of credit risk (Castro, 2013; Louhichi and Boujelbene, 2016). When growth is high, it implies economic expansion, whereby people have better earnings and business opportunities. It means clients are earning more, enabling them to repay the financing. Higher repayment of bank-financing means the reduction of credit risk. Therefore, a negative relationship is expected between credit risk and GDP growth. The foreign exchange rate is another macroeconomic variable that is used to determine credit risk following. Castro (2013) suggests a negative relationship between exchange rate volatility and credit risk because an increase in exchange helps to recover the financed amount form foreign customers. Finally, the Institutional Development Index (IDI) is used to determine the credit risk of Islamic banks following the study of Ang and McKibbin (2007). Its relationship is hypothesised as being negative with credit risk because the clients’ earning capacity improves during the expansion of intuitional development.
4. Estimation Technique
The two-step system GMM model proposed by Arellano and Bover (1995) and Blundell and Bond (1998) is used in the estimation. It can be formulated as follows:
where y is the dependent variable, is the lagged variable while implies a set of explanatory variables.
The error term has two elements;
η
indicates the unobserved country-specific effect while vi,t indicates the idiosyncratic shocks. Furthermore,i = 1 , 2 ,... N
implies the countries; j =1,2,3,………M implies the banks and t= 2,3,…..T implies the time period. The error term εi,t =ηi+vi,tcontains the standard error structure, where 0
) ( ) ( )
( i =E vi,t =E ivi,t =
Eη η for both individual and time.
Presumably, the transient error is serially uncorrelated, for
i = 1 , 2 ,... N
ands ≠ t
where the initial conditions274 1
y
i are predetermined and E(yitvi,t)=0 fori = 1 , 2 ,... N
andt = ,1 2 ,... T
. It indicates) 2 )(
1 ( 5 .
0 − −
= T T
m
moment restrictions, where,E ( y
i,t−s∆ v
it) = 0
fort = 3 ,... T and s > 2
which is rearranged asE(Z,∆vj)=0. The instrument variable, Zi is the (T-2) x m matrix shown as follows:
=
−2 , 1
2 1 1
...
. ...
0 0 0
. .
. . ...
. . .
0 ...
0 ...
0
0 ...
0 ...
0 0
T i i
i i i
y y
y y y
Zi
(4)where
∆ v
i is the( T − 2 )
vector( ∆ v
i3,∆ v
i4,,..., ∆ v
iT)
'. The system GMM technique maintains these moment restrictions which use lagged value dated (t-2) and instrument variables. This technique maintains the consistency in the estimator ofλ
where N ∞ and fixed T. The data used in this research is “short T” panel data as the number of T is smaller than the number of N. Therefore, it is the most suitable technique that can be used for the micro panel data. The specified model is formulated as follows:(With
α < ,1 i = 1 ,..., N
andt = 1 ,..., T
(5)where CR is the credit risk of Islamic banks, EFI is the equity-based financing instrument and IDI is the institutional development index. GDPG is the GDP growth, LR is the lending rate, EXCH is the exchange rate volatility, EFF is the cost inefficiency, CG is the credit growth, PROFIT is the profitability of Islamic banks, CAP is the capital ratio, SIZE is the size of the Islamic banks and
ε
it is the random error. For a reliable estimation, Sargan test and Hansen test are used for the validity of instrumental variables, while Arellano and Bond test is used to see the existence of autocorrelation in the model.5. The empirical results and discussion
The result in Table 2 indicates that the estimated coefficient of the equity-based financing instrument is significantly negative, thereby supporting the expected hypothesis. It implies that an increase in equity-based financing reduces the credit risk. This can be explained using the unique concept of equity-based financing. For example, Mudarabah, which is used to finance the purchase of real assets such as vehicles. Clients use the vehicles for renting purpose to earn a regular income that enables paying off financial obligations leading to a reduction in credit risk. The cooperation, additional effort and technical strategy of the bank in equity-based financing contribute to the reduction of credit risk.
Table 2: The Estimated results on Credit Risk GMM on Credit Risk
Variables GMM 1
Constant 20.709 (0.000)***
Lagged Credit Risk, CR (-1) 0.822 (0.000)***
Equity-based Financing Instrument -0.529 (0.000)***
Institutional Development -1.368 (0.000)***
275
Capital Ratio 0.547(0.100)
Cost Inefficiency 0.013 (0.000)***
Profitability -0.612 (0.000)***
Size - 2.003 (0.000)***
Lending Rate 0.051 (0.001)***
Credit Growth -1.302 (0.000)***
Exchange risk -0.0003 (0.000)***
GDP Growth 0.13 (0.137)
Number of Banks 40
Number of Observations 252
Sargan test 34.794 (0.116)
1st order Auto ( ) -2.383 (0.017)**
2nd order Auto ( ) -0.362 (0.717)
Note: Values in the bracket are p-values, where ***, ** indicates a significant level at 1%, and 5% respectively Bank-specific determinants such as profitability and bank size, reduce credit risk. Size is an important element for determining credit risk. Larger banks can overcome short-term financing risk by opting for risky investments which provide higher returns. Any unexpected negative market shock is adjusted in larger banks. Most importantly, larger banks can use economies of scale and portfolio diversification to minimise the financing risk, thereby supporting the Markowitz modern portfolio theory (Markowitz, 1991). Profitability resulting from effective operations, efficient management, and proper resource allocation reduces credit risk. This finding indicates that profitable banks take the steps necessary to minimise credit risk. This is consistent with Kabir et al. (2015) and Louhichi and Boujelbene (2016), who state that profitable banks have effective policies that help reduce credit risk.
Furthermore, the estimated coefficient of foreign exchange rate volatility is negative, indicating a reduction in credit risk. The increase in exchange rate depreciates the domestic currency, which enables the recovery of foreign financing leading and reduces credit risk. Inversely, cost inefficiency, which increases the operational expenditures, causes credit risk, thereby implying a positive relationship. This finding explains that higher cost inefficiency reduces profit margins which increases the payment failure and causes credit risk. Moreover, the lending rate, which is the tool for financing, is used to determine credit risk. The result shows a positive relationship indicating that higher lending increases the credit risk. A higher lending rate increases the cost of funding, making it difficult to settle outstanding debt leading to credit risk (Louzis et al., 2012).
In addition, institutional development is examined for its role in reducing credit risk. The study found that institutional development maintains a negative relationship with credit risk indicating the reduction of the credit risk of Islamic banks. It implies that development in financial institutions and financial markets, and improvement in financial systems enable clients to generate higher income which is used to pay bank debts thereby reducing the credit risk (Mollah and Zaman, 2015). Finally, the capital ratio, which is used to determine the credit risk is irresponsive to changes in credit risk despite being hypothesised as having a negative relationship. The lack of responsiveness can be explained by its irrelevance with the business cycle, as indicated by Flannery (1989) and Gennotte and Pyle (1991). Credit risk is related to the factors that cause customers’ income generation, not a capital ratio.
Table 3: Estimated results as robustness checking
Variables GMM 2 Fixed Effect with Robust
Constant 0.030 (0.120) 0.315 (0.000)***
Lagged VaR 0.898 (0.000)***
276
PLS-based Financing Instrument -0.005 (0.063)* -0.011 (0.048)**
Institutional Development -0.006 (0.000)*** 0.004 (0.148)
Credit Growth -0.002 (0.000) -0.002 (0.729)
Cost-Inefficiency -0.0006 (0.000)** 0.000005 (0.871)
Profitability 0.0001 (0.528) 0.002 (0.079)**
Size -0.001 (0.605) -0.022 (0.000)***
GDP Growth -0.001 (0.000)*** 0.00001 (0.949)
Exchange Rate Volatility -0.0003 (0.000)*** 0.012 (0.729)
Capital Ratio 0.006 (0.278) -0.004 (0.380)
Lending Rate 0.0005 (0.000)*** 0.0005 (0.046)**
Number of Banks 40 40
Number of Observations 252 252
Sargan test 29.700 (0.280) R-sq = 0.387
1st order Auto ( ) -2.434 (0.014)** F-value = 17.40 (0.000)
2nd order Auto ( ) -0.975 (0.330)
Note: Values in the bracket are p-values, where ***, **, * indicates significant level at 1%, 5% and 10%
respectively
6. Robustness Check
Value-at-Risk (VaR) is used as a proxy of credit risk to check the robustness of the previous results. The significant and negative coefficient in Table 3 confirms the reduction of credit risk, which is consistent with earlier findings.
The findings of institutional development are also consistent. Additional robustness is checked by employing a fixed effect model which is tested as the most suitable technique based on selection procedures among Pooled OLS, fixed effect, and random effect. The estimated coefficient of equity-based financing is negatively significant, confirming that equity-based financing reduces the credit risk of Islamic banks.
7. Conclusion and Policy Recommendation
This study examines whether equity-based financing reduces the credit risk of the Islamic bank. The finding of GMM estimation shows that equity-based financing reduces the credit risk of both full-fledged Islamic banks and Islamic banking-window supporting the theory of PLS. Institutional development also contributes to reducing credit risk. The result is consistent with the alternative estimations.
The study strongly recommends that Islamic banks finance customers based on equity-based financing that relies on the PLS theory complying with the true practice of Islamic financing. We suggest policymakers apply equity-based financing that reduces the credit risk and improves income diversity while improving socio-economic wellbeing and establishing social justice. Furthermore, future studies are suggested to carry out a comparative investigation of credit risk between Islamic and conventional banks.
Acknowledgments
This research is financially supported by the IIUM Research Management Centre (Grant KENMS-RG19-006-0006).
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