Bank Risk and Financial Development: Evidence Form Dual Banking Countries

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Bank Risk and Financial Development: Evidence Form Dual Banking Countries

Baharom Abdul Hamid, Wajahat Azmi & Mohsin Ali

To cite this article: Baharom Abdul Hamid, Wajahat Azmi & Mohsin Ali (2019): Bank Risk and Financial Development: Evidence Form Dual Banking Countries, Emerging Markets Finance and Trade, DOI: 10.1080/1540496X.2019.1669445

To link to this article: https://doi.org/10.1080/1540496X.2019.1669445

Published online: 09 Oct 2019.

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Bank Risk and Financial Development: Evidence Form Dual Banking Countries

Baharom Abdul Hamid¹, Wajahat Azmi², and Mohsin Ali²

1Research Management Centre, International Centre for Education in Islamic Finance (INCEIF), Kuala Lumpur, 59100, Malaysia;²Taylor's Business School, Taylor's University,Lakeside Campus, Subang Jaya, 47500, Selangor

ABSTRACT:This study examines the impact of financial development on bank risk-taking, measured as bank capitalization and bank income diversification. We observe the relationship using annual bank-level data from countries with dual-banking systems. The dataset spans from 2000 to 2014. Our results suggest that the impact of financial development on bank capitalization is heterogeneous across Islamic and conventional commercial banks. Moreover, the effect is different across listed and unlisted banks.

However, on average, the response of income diversification to financial development is similar across most specifications. Additionally, bank risk is found to be countercyclical, suggesting that bank risk increases in good times. On average, these results (countercyclical evidence) hold across bank types (Islamic and conventional) and ownership structure (listed and unlisted). However, these results are contingent on the size (small vs. large) factor. The results are robust to alternative proxies of financial development.

KEY WORDS: bank capitalization, dual-banking system, financial development, income diversification, Islamic banks

JEL: G21, E22, O16

The literature on bank performance and efficiency is voluminous, but it has failed to address the impact of financial development on bank risk-taking, especially in countries with a dual-banking system. An examination of the relationship between financial development and bank risk is important for several reasons. First, financial market development is expected to increase risk-management practices via improved regulations, such as a higher capital adequacy ratio or higher capitalization. Second, the regulations can also restrict the banks from venturing into nontraditional activities,¹as recent evidence suggests that income diversification is associated with income volatility and bank failure (DeYoung and Torna 2013) and hence can be used as a proxy for bank risk.

However, some of these reforms may increase bank risk-taking by reducing market discipline (Hadad et al. 2011). Due to the ambiguity about the potential impact of financial development on bank risk-taking, we revisit the debate in countries with a dual-banking system. Based on these arguments, this paper explores whether financial development plays any role in influencing the risk- taking behavior of banks in a dual-banking system. More importantly, this paper examines whether the impact of financial development varies across Islamic and conventional banks. The division of banks into Islamic and conventional is important for the following reasons. Theoretically, Islamic banks are different from conventional banks because of their risk-sharing nature and other unique contracts. Structurally, Islamic banks are also very different from conventional banks. For instance, recent evidence suggests that Islamic banks are better capitalized (Beck, Demirgüç-Kunt, and Merrouche 2013), exhibit lower risk aversion (Ashraf, Ramady, and Albinali 2016), have a lower

Address correspondence to Baharom Abdul Hamid, International Centre for Education in Islamic Finance (INCEIF), Kuala Lumpur 59100. E-mail:baharom@inceif.org

ISSN: 1540-496X print/1558-0938 online

DOI: https://doi.org/10.1080/1540496X.2019.1669445

probability of a bank run (Farooq and Zaheer2015), and make lending decisions that are not cyclical in nature (Ibrahim2016).

We use banking-sector development and stock market development as proxies for financial development. Bank risk is measured by bank capitalization (Rime2001) and bank income diversi- fication (DeYoung and Torna 2013). Higher capitalization is associated with bank stability, as they have more capital to cushion a crisis or bank run. In contrast, income diversification is expected to increase bank risk. The literature closest to ours is Vithessonthi (2014a, 2014b). However, we improve and add to the literature in multiple ways. For example, this paper uses a fairly large sample of banks in dual-banking countries. We also make a distinction between Islamic and conventional banks because of the structural differences between them (see Beck, Demirgüç-Kunt, and Merrouche 2013), and these structural differences can lead to a heterogeneous response from Islamic banks to the level of financial development that has not been tested before. Moreover, we split the sample based on the ownership structure (listed vs. unlisted). This distinction is crucial, as the listed banks have at least three differences from their unlisted counterparts. Listed banks have to comply with additional listing regulations in addition to banking regulations. Second, the listed banks are different in terms of their risk-taking behavior (Falkenheim and Pennacchi 2003). Third, listed banks are in a better position to withstand risk, as they have more avenues for raising funds other than deposits.

Moreover, little is known about the characteristics of listed Islamic banks, so we add to the overall banking literature on these banks.

We also test whether bank risk is countercyclical or moves with the business cycle. To the best of our knowledge, the literature has ignored this aspect in previous studies. The relationship is important, especially for regulatory authorities, as it demonstrates the behavior of banks in terms of change in their risk level in good times and bad. Moreover, in dual-banking countries, the authorities would be informed as to whether the risk changes are similar in Islamic and conventional banks.

Our findings can be summarized as follows. On average, the effect of financial development is evident only in bank capitalization and banking sector development. Second, the effect varies not only across listed and unlisted banks but also across Islamic and conventional banks. This confirms our intuition that the bank type (Islamic and conventional) and ownership structure (listed vs.

unlisted) matter. More precisely, Islamic banks are more responsive to banking sector development than their conventional counterparts, as they improve their capitalization more in response to changes in financial development, especially banking-sector development. At the same time, we found that listed banks are indifferent to changes in financial development, whether in banking or the stock market. This also confirms the findings of Falkenheim and Pennacchi (2003). Finally, we found bank risk to be countercyclical. In other words, risk at both types of banks increases in good times.

However, the results vary across large and small banks.

The paper makes a number of contributions. First, we examine the effect of financial development on bank risk in countries with dual-banking economies, the literature on which has largely concen- trated on developed countries and Southeast Asian countries. The advent of Islamic banking and its massive growth beyond the Muslim-majority countries indicates its success and longevity. The performance of Islamic banking during the global financial crisis also showed its mettle, and since then its advocates have proposed it as an alternative to the current financial system.

Second, we add to the literature on Islamic banking and finance and extend the related work of Alqahtani and Mayes (2018), who examine the comparative stability of Islamic banks vis-à-vis conventional banks. Moreover, most of these dual-banking economies are in Muslim-majority countries, which have been the focus of much less academic research on various issues. These countries have huge disparities, as they comprise some of the richest as well as the poorest countries in the world. Moreover, the economy in these countries has been growing and has immense potential to grow further because of vast human capital and cost efficiency (Dewandaru et al.2014). This paper

also extends the related work of Dewandaru et al. (2014), which explores the factors that have impeded stock market development among these countries.

Finally, another important contribution of the paper is that it explores the risk-taking behavior of listed banks. This topic is under-researched, especially in the context of Islamic banks. As listed banks are quite different from their unlisted counterparts, we shed some light on their responses to financial sector development.

Brief Literature Review and Hypotheses

The positive effect of the financial sector on growth has long been emphasized in works by Rajan and Zingales (1998), Levine (1997), and others. Despite its significance, relatively little research has been conducted to clarify the effect of financial development on bank risk (Vithessonthi and Tongurai 2016). This issue is important, as financial sector development can have positive as well as negative effects. The previous literature argues that financial sector development through financial reforms can reduce bank risk (Espenlaub et al. 2012; Williams and Nguyen 2005). For instance, financial liberalization reforms in Southeast Asia after the Asian crisis (1997) improve bank efficiency (Williams and Nguyen2005), whereas the findings of Espenlaub et al. (2012) indicate that moral hazard between banks and connected firms weakened after the liberalization of the banking sector.

Some of these reforms include better capitalization and restrictions on nontraditional banking activities. However, reforms directed at strengthening the banking sector can fail if they weaken market discipline (Hadad et al. 2011). The lack of clarity on the possible effect of financial development inspires us to examine the association between bank risk and financial development in a group of countries about which we know very little, that is, Muslim-majority countries (Dewandaru et al.2014). We measure bank risk by bank capitalization and revenue diversification and hypothesize that higher (lower) capitalization implies low (high) risk, whereas higher (lower) revenue diversification implies high (low) risk. Financial sector development is measured by the ratio of credit to the private sector to GDP (banking sector development) and the ratio of market capitalization to GDP (stock market development). We explain the choice of our variables in more detail in the data and methodology section.

This leads to our first hypothesis:

H1: Financial sector development and bank risk are linked.

The advent and rise of Islamic banks over the past decade or so can be regarded as a financial disruption, as it expanded beyond Muslim-majority countries. Total Islamic banking assets grew at a rapid pace and totaling USD 1.49 trillion at the end of 2016 (Islamic Financial Services Industry Stability Report—IFSB 2017). The growing significance of Islamic banking has led to much research exploring its performance, stability, efficiency, and capitalization vis-à-vis its conventional counter- parts. Much of this research shows the uniqueness of Islamic banks due to their risk-sharing arrangements and the prohibition on investing in toxic assets. In particular, Islamic banks were better capitalized (Abedifar, Molyneux, and Tarazi 2013), less cost efficient, more liquid (Khediri, Charfeddine, and Youssef 2015), and more stable (Čihák and Hesse 2010) and have lower credit and insolvency risk (Safiullah and Shamsuddin2018). These findings clearly indicate the differences between Islamic and conventional banks that may have varying effects on the linkage between financial development and bank risk.

This leads to our second hypothesis:

H2: The link between financial sector development and bank risk depends on the bank type.

Moreover, listed and unlisted banks have fundamental differences, which we expect to affect financial development and bank risk in a heterogeneous manner. For instance, listed banks are required to comply with listing requirements, in addition to banking regulations. This means that

a listed bank has to comply with regulations from both bank and capital market regulators. Moreover, listed banks are also different in terms of their risk-taking behavior. Listed banks have more latitude in raising funds during economic crunch times than unlisted banks, which rely primarily on deposits.

These two kinds of banks in terms of ownership can affect the linkage between financial development and bank risk differently.

This leads to our final hypotheses:

H3: The link between financial sector development and bank risk depends on the ownership type.

Data and Methodology

Our data come from the Bureau van Dijk’s Bankscope²for a period of 15 years, from 2000 to 2014.

Our dataset consists of 499 banks, of which 143 are Islamic and 356 are conventional. As we focus on dual-banking countries, we omit Iran and Sudan from the sample. All the countries in our sample are members of the Organization of Islamic Cooperation (OIC): Bahrain, Bangladesh, Brunei, Egypt, Indonesia, Iraq, Jordan, Kuwait, Lebanon, Malaysia, Maldives, Mauritania, Pakistan, Palestine, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, Turkey, United Arab Emirates, and Yemen. Appendix A lists the number of banks in each of these countries.

The objective is to examine the impact of financial development on bank risk-taking. The proxy for bank risk is bank capitalization, measured as the capital adequacy ratio (CAR), and revenue diversification, measured as the ratio of non-interest income to operating income (NIIOI).³ Bank capitalization has been used as a measure of risk in studies such as Vithessonthi (2014a, 2014b), Schaeck and Cihak (2012), and Calmès and Théoret (2013). Similarly, revenue diversification has been used as a proxy for bank risk by Vithessonthi (2014a,2014b), Stiroh (2006), and Calmès and Théoret (2013).

As mentioned earlier, the choice of bank capitalization and revenue diversification is based on previous theoretical and empirical work by Diamond and Rajan (2000), DeYoung and Torna (2013), and Rime (2001). Theoretically, optional pricing models have shown that bank capital has a stabilizing effect (Rime 2001). The framework demonstrates that, in an unregulated market, banks take excessive risk to maximize shareholder value at the expense of depositors’ insurance (see, e.g. Keeley and Furlong1990). The capital requirements can decrease the incentives for bank risk-taking by forcing them to absorb a majority of these losses through shareholder capital in form of minimum capital requirements. In particular, bank capital requirements are associated with less risk- taking and hence less potential for bank default overall. In other words, higher capitalization is associated with bank stability, as they mean that a bank has more capital to cushion a crisis or a bank run. Similarly, Diamond and Rajan (2000) demonstrate in their theoretical model that although an increase in bank capital limits the banks’ability to create liquidity, it enables them to survive more often and avoid financial distress.

The aftermath of the global financial crisis led many papers to explore its root causes. Many bankers and commentators blamed it on deregulation of the banking sector in the form of the Gramm- Leach-Bliley Act of 1999, which allowed banks to venture into nontraditional activities, such as venture capital, investment banking, insurance underwriting, security brokerage, and asset securitiza- tion. Most of those who blame this act for the crisis argue that the system was not ready to take on the challenges of a deregulated market (DeYoung and Torna2013). The empirical work of DeYoung and Torna (2013) shed light on the fact that nontraditional activities had a significant effect on the probability of bank default. Their results were robust to the inclusion of other factors in bank failure based on the extant literature. In other related work, scholars have argued that the increase in the share of nontraditional income tends to increase systemic bank risk (De Jonghe 2010). The main channel through which nontraditional income has a negative effect is the increase in the volatility of bank profits and hence in overall bank risk (Bikker and Metzemakers2005). Moreover, other related

works also indicate that the permission to conduct off-balance-sheet activities has led to an increase in banks’ earnings volatility (Calmès and Théoret 2010,2013). In contrast to bank capitalization, income diversification is expected to increase bank risk.

In line with the literature, we proxy financial development (FD) by two different measures. First, in line with Becerra, Cavallo, and Scartascini (2012), we employed banking-sector development (the ratio of domestic credit to the private sector to GDP [CREDIT]). Second, following Chinn and Ito (2006), Gimet and Lagoarde-Segot (2011), and Becerra, Cavallo, and Scartascini (2012), we used stock market development (stock market capitalization to GDP [MKTCAP]).

A developed financial market not only drives economic performance but also provides banks with growth opportunity by increasing the credit demand from the private sector. A bank that provides more credit is required to simultaneously increase its capitalization to meet the reg- ulatory requirements. In other words, a country with a developed financial market is expected to have better risk-management practices in place, one of which is higher capitalization. Along similar lines, stock market development, measured as the ratio of market capitalization to GDP, indicates more listed firms and hence more funding opportunities for banks. A priori, we expect financial market development to increase bank capitalization. So, an overall increase in credit to the private sector to GDP and market capitalization to GDP is expected to increase bank capitalization. Stock market development is empirically shown to be associated with economic development (Gimet and Lagoarde-Segot2012). At the same time, financial market development is expected to provide more opportunities for nontraditional activities and hence encourages more nontraditional activities relative to income from traditional sources, that is, interest income.

Similarly, stock market development implies more opportunities for nontraditional activities, such as trading. A priori, we expect financial market development to increase nontraditional activities. In other words, financial market development is expected to increase bank capitaliza- tion and nontraditional income.

Based on earlier works on bank capitalization and income diversification (Ahmad et al.2008;

Distinguin et al.2013; Laeven et al. 2014; Laeven, Ratnovski, and Tong2016; Rime2001), we also add a few bank-level controls, including size, measured as the log of total assets (LTA), return on assets (ROA), liquidity (CASHDEP), and the log of loan-loss reserves to total assets (LLRTA).

Bank size and profitable banks are expected to have a significant effect on bank capitalization and income diversification. Large banks are less capitalized (the“too big to fail”hypothesis) and engage more in nontraditional activities (Gennaioli, Shleifer, and Vishny 2013). First, large banks benefit from economies of scale, which allow them to achieve better diversification and to operate with lower capital. Being large also facilitates engaging in more market-based banking activities (Laeven et al.

2014). It has also been argued that markets for large and small banks are segmented. Large banks are expected to have a comparative advantage in nontraditional banking activities, as they require significant fixed costs. Venturing into more nontraditional activities triggers more leverage and unstable funding. Hence, large banks are expected to be riskier (less capitalized and more nontradi- tional activities). In other words, large banks are associated with more risk. At the same time, profitable and liquid banks are also expected to be better capitalized, as they can afford to put more capital aside than less profitable banks can. In particular, based on the charter value hypothesis, profitable and liquid banks provide shareholders with adequate income to raise additional equity to protect them against financial distress (Ahmad et al.2008). More importantly, increasing bank capital through retained earnings gives these banks an opportunity to signal positive value about their future prospects in an environment of information asymmetry (Rime 2001). Similarly, to diversify their portfolios, more profitable and liquid banks can also venture into nontraditional banking. Therefore, profitable and liquid banks are expected to have better capitalization and are more involved in nontraditional banking.

A bank’s loan-loss provision reflects the potential for a bank to recover loan repayment. A bank with high loss provisions is forced to raise its capitalization to provide an additional buffer. These banks are closely monitored by regulators to maintain high capitalization. These banks are also less involved in nontraditional banking because of regulatory pressure, as these activities further increase the volatility of profitability. In sum, the ratio of loan-loss provision to total assets is expected to increase (decrease) capitalization (nontraditional banking).

We also included macroeconomic variables to control for different macroeconomic conditions. We used GDP growth (GDPG), inflation (INF), and trade openness, measured as a ratio of exports plus imports to GDP (OPEN). We take the log of all the macroeconomic factors except GDP growth and inflation. The macroeconomic variables are extracted from World Bank’s database.

We run the following specifications to achieve our objective:

CARi;j;t¼f CARi;j;t1;FDj;t;Xj;t;ZIjt

(1)

NIIOIi;j;t¼f NIIOIi;j;t1;FDj;t;Xj;t;ZIjt

(2)

FD represents the financial development proxies. X and Z are macroeconomic and bank-specific variables respectively.

To account for risk persistence, we also include a lagged dependent variable as an explanatory variable. The inclusion of a lagged dependent variable triggers an endogeneity issue. In other words, traditional panel estimators cannot handle models with lagged dependent variables because of nonzero correlation between individual fixed effects and lagged dependent variables (Ibrahim 2016). In order to account for endogeneity, we used the panel system generalized method of moments (GMM), suggested by Arellano and Bover (1995) and Blundell and Bond (1998). In system GMM, we instrument the level regression with lagged first-difference variables, whereas the first-difference regression is instrumented with lagged-level variables (Ibrahim2016). System GMM is superior, as it overcomes the issue of weak instruments associated with first-difference GMM. We use two-step system GMM and employ the Windmeijer (2005) correction procedure to handle the downward bias of two-step standard errors.

Findings and Analysis

We report our descriptive statistics inTable 1. The average (median) bank-capital adequacy ratio is 22.30% (16.46%), while the average (median) share of non-interest/financing income to operating income (NIIOI) is 25.39% (31.26%). Average (median) stock market development, measured as the ratio of stock market capitalization to GDP, is 57.28% (45.85%). In terms of bank size, average (median) total assets in thousand USD are $6,422,832 ($13.8 million). The high standard-deviation value indicates substantial variation in the distribution of bank size. NIIOI also has a high standard deviation, indicating high variability in non-interest/financing income across the banks in our sample, which is understandable because our banks are in different countries and have different structures. The capital adequacy ratio, however, does not have high variability. Market capitalization, private credit, and the money supply do not have a high dispersion across the countries in our sample.

The significant lag-dependent variables in all the specifications affirm the choice of a dynamic model. The insignificant AR(2) coefficients and the high p-values of the Sargan test pass the diagnostic assessment. Specifically, the p-values of AR (2) and the Sargan test confirm that the errors are autocorrelated in the second order, and the overidentifying restrictions are correct. We start our discussion on the basis of our full sample empirical results reported in Table 2.

Bank Risk and Financial Development (Full Sample)

As reported inTable 2, the impact of banking-sector development (the ratio of domestic credit to the private sector to GDP) on bank capitalization is significantly positive, whereas its effect on bank diversification is significantly negative. But the effect of stock market development on both measures of bank risk is insignificant. In particular, banking-sector development lowers bank risk, whereas stock market develop- ment does not have any effect on it. These results are different from those in Vithessonthi (2014b) but in line with our expectations. However, the positive relationship makes sense, as banks operating in a country with a developed banking sector are expected to be well capitalized and less involved in nontraditional banking. Banking sector development is expected to increase risk-management practices at banks in a financially developed country, mainly through improved regulations. These regulations include an improved capital adequacy ratio and restrictions on nontraditional banking. The increased monitoring and close scrutiny of the banks’nontraditional activities, especially after the global financial crisis, may have resulted in a negative association between banking-sector development and nontraditional activities.

The results imply that countries with a developed banking sector are in a better position to face a crisis.

These results are in line with Vithessonthi (2014a). Surprisingly, stock market development has no effect on bank capitalization and revenue diversification. The results could be due to our sample. For instance, the stock market is generally underdeveloped in member countries of the OIC and hence may not offer banks in these countries adequate opportunities for more nontraditional activities, such as trading.

The interesting part of our results is that Islamic banks are more sensitive to the level of financial development. For instance, the positive and significant interaction of the Islamic dummy variable and banking sector development affects capitalization more at Islamic banks than at their conventional counterparts, though stock market development effects are not different in the case of Islamic banks’ capitalization. This finding suggests that although Islamic banks are already well capitalized (Alqahtani, Mayes, and Brown2016; Beck, Demirgüç-Kunt, and Merrouche2013), they respond more to improve- ments in banking sector development. Interestingly, the response of the Islamic banks’revenue diversi- fication strategy is positively affected by stock market development. As stock market development is

Table 1. Descriptive statistics.

Variable Mean Median Standard Deviation

CAR 22.30 16.46 25.14

NIIOI 25.39 31.26 475.58

CREDIT 3.49 3.54 0.83

CASHDEP 34.54 26.72 31.45

ROA 1.37 1.35 3.85

LTA 14.19 14.19 1.83

MKTCAP 57.28 45.85 39.96

M2GDP 61.07464 51.00 32.07734

OPEN 4.26 4.17 0.57

LLRTA 2.99 1.64 5.90

GDPG 5.00 5.24 4.72

INF 7.50 5.64 8.64

This table presents descriptive statistics for our sample of banks during the period 2000–2014. In this table, CAR = capital adequacy ratio, NIIOI = non-interest /financing income to total operating income, CREDIT = ratio of domestic credit to private sector to GDP, CASHDEP = ratio of cash to total deposits of banks, ROA = return on assets, TA = total assets, MKTCAP = ratio of stock market capitalization to GDP, M2GDP = ratio of money and quasi money (M2) to GDP, LLRTA = loan loss reserve to total assets, GDPG = GDP growth, and

INF = inflation.

measured by market capitalization, the findings indicate that the size of the stock market is an important determinant of income diversification at Islamic banks. Thus, we conclude that the increase in market capitalization leads to greater risk at Islamic banks. Hence, larger stock markets are riskier for Islamic banks in terms of diversification. One plausible reason for this result is that Islamic banks are engaging

Table 2. Bank risk and financial development.

(1) (2) (3) (4)

CAR CAR NIIOI NIIOI

L.CAR 0.308*** 0.306***

(20.37) (20.30)

L.NIIOI −0.025*** −0.011*

(−3.63) (−1.89)

ROA 0.471*** 0.479*** 0.216 0.453

(6.89) (6.98) (1.19) (0.97)

CASHDEP 0.162*** 0.160*** 0.626 0.764

(12.66) (12.51) (0.96) (1.17)

LTA −0.675*** −0.869*** 0.014*** 0.815***

(−10.62) (−10.93) (5.81) (5.75)

LCREDIT 0.302*** 0.473*** −0.104*** −0.116**

(5.18) (4.63) (−2.64) (−1.97)

GDPG −0.212*** −0.218*** 0.112 0.797

(−3.00) (−2.82) (0.03) (0.18)

INF −0.041 −0.049 1.980 2.221

(−0.71) (−0.85) (0.80) (0.89)

LMKTCAP 0.818 0.745 0.157 −0.168

(1.51) (1.30) (1.49) (−0.65)

LOPEN −0.742*** −0.121*** 0.015** 0.515

(−5.75) (−5.98) (2.00) (1.47)

LLRTA −0.0410 −0.0538 0.117 0.608

(−0.66) (−0.86) (1.03) (1.52)

ISLAMICD −2.730 −3.189** 4.691 −1.687

(−0.60) (−2.53) (0.16) (−1.63)

ISLAMIC_GDP 0.051 −0.456

(0.28) (−0.43)

ISLAMIC_CREDIT 0.508** −0.118

(2.16) (−0.82)

ISLAMIC_MCAP 1.384 0.168***

(0.76) (5.30)

_cons 0.861*** 1.057*** −0.976*** −0.700***

(9.80) (10.14) (−4.61) (−4.00)

N 1852 1852 2245 2245

N_g 271 271 318 318

ar1p 0.001 0.111 0.111 0.001

ar2p 0.957 0.104 0.938 0.996

Sarganp 0.52 0.51 0.63 0.68

tstatistics are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

This table presents the results obtained by using GMM for our sample of banks during the period 2000–2014.

(Refer to previous table for variable definitions if they repeat). ISLAMICD is a dummy variable for Islamic banks and has a value of 1 when the bank is Islamic and 0 when the bank is conventional. ISLAMIC_GDP,

ISLAMIC_CREDIT, ISLAMIC_MCAP are interaction terms obtained by interacting Islamic dummy with GDP, CREDIT, and MKTCAP respectively.

in more nontraditional activities, and in that sense the stock market provides better investment oppor- tunities for banks to diversify their income and earn nontraditional income through stock trading. These findings present some implications for regulators and Islamic bank managers. The regulators may come up with guidelines for Islamic banks to limit their investment in stock markets. Managers may attempt to strike a balance in terms of their risks and returns from investing in the stock market.

Control Variables

Profitability and liquidity seem to be the strong driving forces behind bank capitalization. In other words, more profitable and more liquid banks are better capitalized. This relationship is under- standable, as profitability and liquidity provide banks with the flexibility to improve their capitaliza- tion. These findings are consistent with Rime (2001) and Vithessonthi (2014b). However, the impact of profitability and liquidity on revenue diversification is insignificant. Additionally, assets have a negative effect on capitalization and a positive effect on revenue diversification. This indicates that bigger banks are more risky and “too big to fail”—this is consistent with Laeven, Ratnovski, and Tong (2016). Finally, except for trade openness and GDP growth, which are negatively related to bank capitalization, inflation and the ratio of loan-loss reserves to total assets are insignificant.

Moreover, the effect of GDP growth on revenue diversification is insignificant. The negative effect of GDP growth suggests that bank capitalization is countercyclical. The results are not surprising, as banks become riskier in good times, possibly either through more credit expansion or poor credit quality (Vithessonthi2014b). This is also in line with Bikker and Metzemakers (2005), who argue that bank risk is significantly associated with business cycles. Not surprisingly, the interaction of the Islamic dummy variable and GDP growth is insignificant. The insignificant interaction term between GDP growth and the Islamic dummy variable indicate that bank capitalization of both types of banks responds in a similar way. In other words, our results indicate that bank capitalization is counter- cyclical at both Islamic and conventional banks. This also shows that banks in OIC member countries can help to overcome bad times, which is an interesting implication for the regulators.

Bank Risk and Financial Development (Listed Vs. Unlisted)

As mentioned above, listed banks are different from unlisted banks, especially in terms of their risk- taking; therefore we split the sample into listed and unlisted banks. Separate empirical results for listed and unlisted banks are reported inTable 3.

The impact of banking sector development on bank risk is insignificant (except for one specifica- tion) in the case of listed banks, whereas it is positive in the case of unlisted banks. Similarly, in the case of stock market development, the effect is only evident in unlisted banks. This implies that listed banks do not respond to financial sector development by adjusting their risk. However, in the case of listed Islamic banks, they respond to banking sector development by increasing their capitalization.

This implies differentiation in approach from Islamic banks, which tend to show more confidence in the development of the financial sector. However, regarding GDP growth, the results are similar across listed and unlisted banks. The findings indicate that the bank capitalization is countercyclical at both types of banks (listed/unlisted and Islamic/conventional). Alternatively, it suggests that banks become riskier in good times, regardless of whether they are listed or unlisted. It also means that banks are ready to take risks in bad times and to expand. In case of interactions between banking and stock market development with an Islamic dummy, listed Islamic banks respond by raising their capitalization, whereas the interaction terms are insignificant for unlisted banks.

Regarding bank-specific variables, the impact of profitability, liquidity, size, openness, and infla- tion is similar to the full sample results at both types of banks. However, in the case of LLRTA, the impact is negative for listed banks but insignificant for unlisted banks. On average, our results reveal that listed banks are different from unlisted banks, as the responses are heterogeneous and vary across

Table3.Bankriskandfinancialdevelopment—listedvs.unlisted. ListedBanksUnlistedBanks (1)(2)(3)(4)(5)(6)(7)(8) CARCARNIIOINIIOICARCARNIIOINIIOI L.CAR0.272***0.262***0.283***0.282*** (13.77)(13.09)(13.57)(13.57) L.NIIOI−0.050***−0.026***−0.064**−0.067** (−3.91)(−5.47)(−2.08)(−2.06) ROA0.495***0.512***8.1066.0450.486***0.492***−2.399***−2.340*** (9.13)(9.37)(1.27)(0.94)(3.32)(3.36)(−7.24)(−7.06) CASHDEP0.0763***0.0716***2.0532.2290.196***0.196***0.197***0.177*** (5.11)(4.79)(1.28)(1.39)(10.34)(10.30)(4.16)(3.71) LTA−1.936***−2.123***2.434***2.379***−6.304***−6.540***6.167***5.746** (−4.82)(−5.24)(5.53)(5.44)(−9.00)(−9.22)(2.77)(2.55) LCREDIT1.4050.988−2.193−1.5218.196***7.420***−1.158**−1.583*** (1.61)(1.11)(−1.02)(−1.37)(4.10)(3.57)(−2.18)(−2.66) GDPG−0.159***−0.132**2.4342.766−0.124*−0.190**0.7411.010 (−2.62)(−2.04)(0.34)(0.36)(−1.92)(−2.33)(0.12)(1.14) INF0.0170.0113.7234.1090.2080.205−1.182−1.198 (0.41)(0.28)(0.92)(1.02)(1.21)(1.20)(−0.21)(−0.24) LMKTCAP−0.243−0.3300.579−0.8640.533**2.340*4.3977.473 (−0.55)(−0.72)(1.02)(−0.80)(2.13)(1.81)(1.39)(1.19) LOPEN−7.029***−7.692***9.757−3.102−1.450***−1.511***2.002**2.008** (−4.49)(−4.86)(0.52)(−0.17)(−5.49)(−5.68)(2.20)(2.21) LLRTA−0.0643−0.0753*6.481*5.846*0.5290.4890.1560.257 (−1.49)(−1.74)(1.94)(1.77)(0.53)(0.33)(0.24)(0.40) ISLAMICD7.811**3.419***2.869−0.162−2.144−5.3951.1953.772 (2.44)(2.81)(0.07)(−0.97)(−0.77)(−1.09)(0.98)(0.60) ISLAMIC_GDP−0.178−3.5700.277−1.155 (−0.93)(−0.19)(0.91)(−1.24) ISLAMIC_CREDIT7.858***−271.46.14419.72 (3.05)(−0.95)(1.51)(1.52) ISLAMIC_MCAP2.3815.794***1.803−18.98 (1.31)(4.53)(0.61)(−0.14)

_cons0.447***0.242***−0.833***−0.151***1.261***1.360***−1.255**−1.199** (7.37)(8.04)(−3.74)(−2.97)(8.20)(8.43)(−2.43)(−2.27) N9669661202120288688610431043 N_g142142168168129129150150 ar1p0.1590.1490.6080.9670.1410.0010.0130.114 ar2p0.9900.9540.9090.9770.2390.2630.8670.816 Sarganp0.3200.1180.2530.2710.4240.6240.5110.110 tstatisticsarereportedinparentheses.***,**,and*denotesignificanceatthe1%,5%,and10%levels,respectively.ThistablepresentstheresultsobtainedfromusingGMM foroursampleofbanksduringtheperiod2000–2014.SeeTable2forvariabledefinitions.

both banks. The response of unlisted banks is similar to the overall results. This implies that regulators may not have separate guidelines for listed and unlisted banks.

Robustness

To add credence to our main results we conducted robustness tests by splitting the sample based on size. We have also used different proxies of financial development and found similar results. The discussion on robustness results follows.

Size Effect

In this section, we limit our discussion to the variables of interest (financial development and GDP growth). It could be that big banks do not strictly follow regulatory requirements. For instance, Gennaioli, Shleifer, and Vishny (2013) showed that large banks are less capitalized (“too big to fail” hypothesis) and engage more in nontraditional activities. Moreover, regulatory authorities are also accused of being lax when it comes to big banks. As mentioned above, it is easier for big banks to achieve more revenue diversification, as it requires significant fixed costs.

In other words, size can be an important differentiator between banks with high/low capitalization and those with high/low revenue diversification. Moreover, it has also been argued that markets are segmented between big and small banks, and if that is the case, it is important to split the sample based on the mean size of banks. Therefore, to test whether the results are driven by the size factor, we split the sample based on the mean of total assets and report our results inTable 4.

The results are heterogeneous across big and small banks. The big banks seem to be unaffected by financial development (by both measures) across both bank-risk measures. In the case of Islamic banks, only the effect of banking-sector development is found to bring more improvement in bank capitalization, and that is also true of large banks. At small Islamic banks, the effect of financial development on both bank-risk measures is not different from that of conventional banks. In other words, large Islamic banks gain more in response to banking-sector development as it further raises their bank capitalization. Furthermore, the impact of GDP growth indicates that bank risk is countercyclical across both risk measures at big banks. However, the evidence of countercycli- cality is present in bank capitalization only at small banks. This is in line with the full sample results. These findings are similar for small Islamic banks. However, capitalization improves big Islamic banks in good times, suggesting that these banks respond by raising their capitalization (in good economic times), hence the relationship is procyclical in nature.

Additional Robustness

As an additional robustness test, we also explore other measures of financial development, such as M2/GDP and liquid stock trading/GDP. These are alternative proxies for banking-sector and stock market development that have been used extensively in the literature. The split is based on the sample mean. On average, the results are similar to the main results.⁴We also split the sample by region.

Overall, the results are similar to the main results (Table 5). The main reason that no difference is seen in the results is that all the countries in our sample are developing, with bank-based economies.

Based on these characteristics, we infer that, on average, the level of financial development in the region is similar; hence the impact on bank risk does not differ.

Conclusion

The goal of this paper is to explore the impact of the financial sector on bank capitalization and revenue diversification. More importantly, we explored whether the impact is heterogeneous or

Table4.Bankriskandfinancialdevelopment—sizeeffect. BigBanksSmallBanks (1)(2)(3)(4)(5)(6)(7)(8) CARCARNIIOINIIOICARCARNIIOINIIOI L.CAR0.542***0.540***0.294***0.293*** (22.73)(22.67)(15.88)(15.85) L.NIIOI0.268***0.270***−0.208**−0.589* (8.11)(8.20)(−2.43)(−1.88) ROA0.1740.2244.285***4.317***0.481***0.486***3.7763.056 (0.92)(1.17)(6.25)(6.28)(5.79)(5.83)(0.85)(0.69) CASHDEP0.066***0.067***0.02360.01500.170***0.168***0.7520.928 (4.36)(4.41)(0.35)(0.22)(10.47)(10.32)(0.89)(1.09) LTA−0.485−0.469−1.245−1.158−5.472***−5.672***1.793***1.763*** (−1.13)(−1.09)(−0.54)(−0.50)(−8.63)(−8.80)(5.13)(5.07) LCREDIT0.6990.5122.3372.2317.143***6.633***−2.329**−1.684 (1.02)(0.75)(0.63)(0.60)(4.44)(3.95)(−2.38)(−1.64) GDPG−0.145***−0.165***1.136***1.310***−0.332***−0.338**−1.190−0.534 (−3.75)(−4.01)(5.61)(6.05)(−2.74)(−2.45)(−0.18)(−0.07) INF0.144***0.138***−0.142−0.122−0.0874−0.09393.5504.357 (3.54)(3.40)(−0.87)(−0.75)(−1.03)(−1.11)(0.91)(1.11) LMKTCAP0.0620.076−4.133**−4.672**1.450*1.420*5.230−2.483 (0.15)(0.18)(−2.02)(−2.22)(1.89)(1.73)(1.35)(−0.59) LOPEN−5.230***−5.174***4.9085.508−8.856***−9.483***2.3981.500 (−4.67)(−4.62)(0.85)(0.95)(−3.46)(−3.64)(1.59)(0.99) LLRTA0.1420.1470.5820.696−0.0543−0.0664.6184.003 (1.02)(1.05)(0.94)(1.12)(−0.71)(−0.87)(1.57)(1.37) ISLAMICD6.245***−2.724*−2.190−1.316−1.019−2.497−6.774−1.994 (2.64)(−1.68)(−0.98)(−1.56)(−0.19)(−1.57)(−0.19)(−1.35) ISLAMIC_GDP0.213*−1.181**0.030−6.226 (1.75)(−1.99)(0.10)(−0.39) ISLAMIC_CREDIT6.876**1.5084.643−1.676 (2.43)(0.99)(1.39)(−0.85) ISLAMIC_MCAP0.69811.001.0714.523*** (0.34)(1.13)(0.44)(4.39) (Continued)

Table4.Bankriskandfinancialdevelopment—sizeeffect.(Continued) BigBanksSmallBanks (1)(2)(3)(4)(5)(6)(7)(8) CARCARNIIOINIIOICARCARNIIOINIIOI _cons3.275***3.288***1.6751.4379.346***1.052***−8.964***−2.761*** (3.43)(3.45)(0.34)(0.29)(6.70)(6.88)(−3.77)(−3.15) N6796797427421173117315031503 N_g106106109109213213265265 ar1p0.1060.1030.06130.04720.0120.0220.0090.010 ar2p0.4710.6610.0890.0910.1810.1900.0430.078 Sarganp0.7600.1740.0220.0210.3290.4310.0180.061 tstatisticsarereportedinparentheses.***,**,and*denotesignificanceatthe1%,5%,and10%levels,respectively.ThistablepresentstheresultsobtainedbyusingGMMfor oursampleofbanksduringtheperiod2000–2014.SeeTable2forvariabledefinitions.

Table5.Bankriskandfinancialdevelopment—regionalresults. (1)(2)(3)(4)(5)(6)(7)(8) CARCARCARCARNIIOINIIOINIIOINIIOI EastAsiaSouthAsiaGCCMiddleEastEastAsiaSouthAsiaGCCMiddleEast L.CAR0.299***0.299***0.318***0.349*** (17.31)(17.35)(9.25)(8.91) L.NIIOI−0.088***−0.134***0.001**−0.003** (−3.50)(−4.80)(2.12)(−2.36) ROA0.366***0.381***0.529**0.375*−0.294**−0.277−2.151−5.124 (3.31)(3.39)(2.51)(1.75)(−2.46)(−0.31)(−0.12)(−0.33) CASHDEP0.184***0.182***0.150***0.135***0.144***0.137***3.5803.508 (13.64)(13.42)(5.70)(5.09)(3.99)(3.77)(0.55)(0.59) LTA−4.064***−4.261***1.949*1.520−4.789***−4.269***4.244***4.179*** (−9.03)(−9.34)(1.95)(1.51)(−3.28)(−2.84)(3.71)(3.43) LCREDIT4.204***3.884***4.619*6.489**−9.036***−6.515***−1.198***−5.831** (4.11)(3.71)(1.87)(2.55)(−7.81)(−11.25)(−4.16)(−2.41) GDPG−0.205***−0.214***0.557***0.650***−0.641−0.819−0.1511.263 (−2.89)(−2.75)(3.44)(3.61)(−1.41)(−1.64)(−0.18)(0.22) INF−0.0903−0.0969−0.449***−0.477***0.091−0.0111.2692.652 (−1.49)(−1.60)(−4.05)(−4.29)(0.04)(−0.05)(0.56)(1.25) LMKTCAP0.6270.508−0.3360.3762.5394.0355.392−1.851 (1.14)(0.88)(−0.30)(0.31)(0.96)(1.45)(1.57)(−0.55) LOPEN−6.925***−7.475***−9.312**−9.169**2.248***2.182***−2.609−1.004 (−4.05)(−4.33)-(2.37)(−2.33)(4.41)(4.22)(−0.26)(−1.08) LLRTA0.03240.0192−0.347−0.352−0.703***−0.6533.9982.421 (0.52)(0.30)(−0.80)(−0.86)(−2.60)(−0.40)(1.18)(0.79) ISLAMICCD−0.942−2.948**1.726*−3.897−0.156−2.149−3.7851.993 (−0.21)(−2.37)(1.78)(−1.50)(−0.02)(−0.14)(−0.64)(1.43) ISLAMIC_GDP0.00170.05780.423−0.000−0.481***0.2811.208−0.232 (0.49)(0.31)(1.05)(−0.75)(−13.68)(0.91)(1.00)(−1.68) ISLAMIC_CREDIT1.931***4.931*7.173***0.038***−0.4910.0732.821−0.317 (9.19)(1.96)(3.19)(4.37)(−0.63)(0.01)(0.88)(−0.71) ISLAMIC_MCAP0.00911.789−4.190−6.081−0.000***−7.691***−2.3445.810*** (Continued)

Table5.Bankriskandfinancialdevelopment—regionalresults.(Continued) (1)(2)(3)(4)(5)(6)(7)(8) CARCARCARCARNIIOINIIOINIIOINIIOI EastAsiaSouthAsiaGCCMiddleEastEastAsiaSouthAsiaGCCMiddleEast (0.47)(0.99)(−1.33)(−0.57)(-s5.01)(−4.47)(−1.52)(5.71) _cons8.190***8.914***−2.701−1.5611.343***1.276***−11.038**−6.423 (8.06)(8.45)(−1.21)(−0.69)(4.53)(4.24)(−2.46)(−1.51) ar1p0.1200.3200.0120.2240.1470.8000.0340.001 ar2p0.8990.9700.9940.9750.4960.2210.8830.258 Sarganp0.1140.9150.6390.2310.1870.2650.8570.132 tstatisticsarereportedinparentheses.***,**,and*denotesignificanceatthe1%,5%,and10%levels,respectively.ThistablepresentstheresultsobtainedusingGMMforour sampleofbanksduringtheperiod2000–2014.SeeTable2forvariabledefinitions.