INTRODUCTION:

Historically, banks main activities were to receive deposits from the customers and lend money to prospective borrowers. This process or the cycle is called as intermediation. While this can still be treated as one of the core activities of banks, in the modern-day, banks also provide other service-related payments and ancillary service to customers from which banks earn non-Interest Income (Isshaq and Appiah-Gyamerah, 2019). Further, the number of banks in existence have increased year on year, making the industry extremely competitive.

Hence banks are always finding alternative ways of income generation, which will result in non-interest income generation.

This study analyzes the effect of non-interest income on banks profitability for GCC region. Banks in GCC region generate non-Interest income from fees and commission, fees for remittance services, commission from advisory services, Issuance of Guarantees and Letters of Credits, ATM fees, Income from Foreign exchange transactions. These additional non-interest income generators require new investments into resources/assets or enhancement to existing assets, which will undoubtedly add additional costs to banks (Isshaq and Appiah-Gyamerah, 2019).

Hence the income generated through non-interest income should outperform the cost associated with it for a bank to improve profitability. This study aims to analyze whether the non-interest income has an effect on the profitability for GCC regions banks. There are hardly any studies conducted to test the relationship between non-Interest income to the profitability of banks in the GCC region as a whole; thus, this study aims to fill the gap by selecting a sample of banks from the GCC region.

RESEARCH AIMS AND QUESTIONS:

Over the years, the number of banks in the region have increased significantly. Hence the competition across these banks have increased, thereby reducing the wallet share as well as income per bank. Traditionally banks have earned income from lending to customers by earning interest income; however, due to competition in the banking industry, banks are now looking at alternative ways of income generation such as non-interest income. This study covers the effects of non-interest income on the top 23 listed banks in the GCC region. Further, the competition and reducing wallet share per banks is also one of the key reasons for mergers and acquisitions to create better synergies. There were 3 bank mergers in the most recent years and many more expected in the GCC region in the coming years.

The research questions for this study is, does the non-interest income for banks affect their overall profitability measures such as ROA (Return on Asset), ROE (Return on Equity). Accordingly, below are the research hypotheses.

LITERATURE REVIEW:

Profit is the income or revenue generated after deducting all costs and expenses incurred by a company in a particular year or a given period. This is also called net income /revenue in accounting terms. Further Adedeji and Adedeji, (2018) state that profitability is the ability of a company to generate profits year on year, and profitability is a relative measurement. Profits made by companies will undoubtedly contribute to the economy by way of additional income tax to governments, job creation, attract new investors, higher profits will result in increasing salaries of employees, innovations, generation of wealth for shareholders thereby enhancing standards of living for a nation.

Similarly, banks seek to improve profitability levels to optimum levels year on year for financial development by way of a stronger capital base to withstand any future unforeseen events, for a sustainable banking sector and future expansion. Further, based on previous literature by (Adedeji and Adedeji, 2018) , banks profitability is imperative both at the micro and macro levels of banking for sustainable banking, payout to shareholders and stability of the overall economy.

Another study was conducted in the year 2017 by Mostak Ahamed (2017) on the Indian banking sector. Unlike the previous author, this research sample covers approx. 95% of total assets in the Indian banking sector from 1998-2014, hence we can say that the sample used in this study is a true representation of the Indian Banking Sector. The idea of the author was to identify whether non-interest income increases the profitability of Indian Banks. Test results indicated that a higher share of non-interest income would generate higher profits as well as better risk-adjusted profits, particularly when banks are inclined towards trading activities. None of the macroeconomic factors is considered in this study, as well.

The banking sector in China was extremely competitive during recent years, hence, the author's key idea was to test the relationship between non-interest income and banks profitability (Sun et al., 2017). In this regard, the author identified 16 publicly listed banks and data were obtained for the period 2007-2013 to test the relationship between non-interest income and profitability. Threshold Effect Model was used with panel data, and the results revealed that non-interest income is negatively correlated to the profitability of banks and further identified that when non-interest income reaches higher thresholds, profitability improves.

There are a few studies conducted on the Turkish Banking sector. Olalere et al. (2017) tested the relationship of non-interest income and profitability. In this regard, they selected a balanced panel data set of 10 commercial banks observed over the period 2002-2010.

Further authors have rightly included internal determinants as well as external determinants for empirical review with 11 independent variables. Most of the research work conducted by other authors have not included external or macroeconomic determinants for analysis. The test results suggest that banks could improve profitability through increasing the size of the banks, increase in non-interest income and decreasing non-Performing loan ratio. The study was conducted for the period 2002-2010, and hence, the real effect of the global financial crisis happened in 2009 could not have been captured accurately in this study.

ROE and ROA are the two most important ratios in measuring banks profitability. ROE measures how well the bank utilizes its shareholder's Equity to generate profits, while ROA measures how efficiently the bank’s assets are utilized to generate profit. Generally, the ROA of a bank is not more than 3%. Previous Literature has supported ROE and ROA as a dependent variable of banks (Deyoung and Deyoung, 2003; Craigwell and Maxwell, 2005; Athanasoglou et al., 2006; Lepetit et al., 2008; Alper and Anbar, 2011; Karakaya and Er, 2012; Ismail et al., 2015; Al-Tarawneh et al., 2016; Sun et al., 2017; Mostak Ahamed, 2017; Cetin, 2018; Yüksel et al., 2018) have used ROE and /or ROA as performance measures.

DATA:

The top banks were selected based on the total asset size for the period 2012 to 2017. The data can be categorized under internal bank-related data and overall macroeconomic data. The data for bank-related variables such as Tier 1 Capital Ratio, Total Equity Growth, Deposit Growth Ratio, Non-Interest Income to Total Revenue, Real Estate Loans to Total Loans, Foreign Loans to Total Loans, Dividend Payout Ratio was collected using Annual Reports of Banks and from Capital IQ database. Altogether, seven bank-related variables have been used in the study. Further two macroeconomic variables, namely GDP Growth Rate and Inflation Rate, have been obtained from World Bank. The only dummy variable, which is whether the bank is owned by a sovereign or private, was collected from data provided in Capital IQ and respective Banks websites.

Equation 1 for ROA

ROA=α +b1 NPL +b2 T1 +b3 TE +b4 DG +b5 NII +b6 RE +b7 FL +b8 DP + b9 GDP + b10 INF + b11 GOV + m (1)

Equation 2 for ROE

ROE=α +b1 NPL +b2 T1 +b3 TE +b4 DG +b5 NII +b6 RE +b7 FL +b8 DP + b9 GDP + b10 INF + b11 GOV + m(2)

Thirteen variables are used in the study and at the end of this section, all the variables are tabulated with expected signs for profitability. Table 1 represents the summary statistics for all the variables used in this study.

NPL/TL (Non-performing loans to Total Loans):

Non-Performing Loans represents the total Non-Performing Loans for a particular bank or financial institution (Das and Dutta, 2014). Normally it comprises of Non-Accrual Loans, Restructured Loans, and Loans 90 Days Past Due and Accruing Interest. As per the banking industry norms, higher the NPL/TL lower the profitability.

Further same has been evident in previous studies conducted by (Kirui, 2014), (Patwary and Tasneem, 2019) and NPL ratio is negatively correlated to the profitability.

Tier 1 Capital Ratio:

Tier 1 capital is the main capital of a financial institution, and from regulators (Central Bank) point of view tier- 1 capital demonstrate the strength of a financial institution. Tier 1 capital comprises of common stock and retained earnings of a bank. Higher the Tier 1 capital ratio banks can withstand losses incurred due to non-performing loans. As per the Basel 3 norms, the minimum Tier 1 capital ratio requirement is 10.5%, accordingly the minimum value reported in the data set is 10.8% by Bank Dhofar SAOG in Oman. Higher the capital maintained by a bank, the profitability is lower due to the high cost of capital (Osborne et al., 2012). Hence, we presume that the relationship between the Tier 1 Capital Ratio is also inversely related to profitability.

Total Equity Growth:

The Total Equity is defined as the total of Preferred Equity, Common Equity and Minority Interest. Equity is usually grown each year either by way of retaining profits or introduction of additional capital. The reason for negative growth is certain banks is mainly on account of a reduction in fair value reserves and foreign currency translation reserves.

Total Deposit Growth:

Deposits are the primary source of funding for a bank and their decision to grow deposits depend on the liquidity position of that particular bank in a given period. If the bank is on low levels of liquidity, management will drive growth in deposits while it works vice versa for a high level of liquidity. It is noted that Deposits are negatively correlated to the profitability of banks in Palestine (Abu, 2018). Hence, we believe that growth in deposits too negatively correlated to profitability.

Non-Interest Income to Total Revenue:

Non-interest income is generated mainly from fees and commission earned from activities such as Credit Cards, Issuance of Guarantees/Letter of Credits, Annual Fees and Account Maintenance Fees. As per the Basel norms, the risk weighted asset requirement for products related to non-interest income such as Letter of Credits and Guarantees are comparatively lower than loans. Hence the pricing or the rate applicable on non-interest income products is lower than the loan rates. Non-Interest Income to Total Revenue ratio indicates what percentage of income is generated from non-Interest income for a particular financial institution.

Real Estate Loans to Total Loans:

Real Estate lending is described as the loans/lending secured by any real estate assets. These loans include Commercial mortgage loans, Construction Loans and Residential Mortgage Loans. The percentage for each financial institution depends on the risk appetite of the management of each bank or the other regulatory requirements applicable in respective countries.

Foreign Loans to Total Loans:

Foreign Loans represent loans given by the Bank to customers outside the country where it is situated directly or through its foreign branches and subsidiaries and this ratio too depends on the risk appetite of respective banks for foreign lending. Banks mainly choose to lend in foreign countries for risk diversification purpose as well as to maximize profitability. Further, in some of the smaller economies like Bahrain and Kuwait, where the lending to local customer base is saturated, banks prefer to go foreign to maximize profitability and better utilize the assets.

Dividend Payout Ratio:

Dividend payout ratio refers to the percentage of profit distributed to its Common/Preferential shareholders in a particular year. Based on previous literature, we understand that Dividend Payout Ratio is negatively correlated to the profitability, and a similar study was conducted by (Khan et al., 2015) showed same results.

GDP Growth:

GDP growth measures how fast the economy of a country is growing year on year. If the GDP growth is negative, it is known that the economy is in a recession. Some of the previous studies conducted on the topic suggest that GDP growth positively correlated to the profitability of banks,(Kosmidou, 2008) while other studies suggest negative correlation (Floros, 1966). Hence, we expect that the GDP growth will have a significant impact on the profitability; however, at this point, we are not sure whether it is positive or negative.

Rate of Inflation:

Inflation is the measurement of general rise or decline in prices of a basket of selected goods for a particular period, and this is expressed in percentage terms, and if the rate of inflation is positive, then the goods can be bought for a particular currency will be lower. Based on the previous literature by (Athanasoglou et al., 2006) and (Adedeji and Adedeji, 2018), we understand that inflation is having a positive relationship to the profitability, while (Yao et al., 2018) revealed that the relationship is negative, and we anticipate similar results from this research as well.

Government/Sovereign owned Banks:

Given that respective countries governments own most of the large banks in the region, our idea is to assess whether there is any correlation between these banks and profitability. The criteria for government-owned is minimum 50% ownership by government/sovereign or sovereign fund. Previous research conducted by (Farazi et al., 2013a) and (Cornett et al., 2010) suggest that government on owned banks generate less profitability than private banks, thus shows a negative correlation. Based on the above and previous literature below are the anticipated sign for each independent variable used in this study.

Correlation matrix reports no high correlation among variables or no signal of multicollinearity problem in the regression models used. Furthermore, graphical and formal tests show the absence of heteroscedasticity.

Table 2: Correlation Matrix

	NPL	T1	TE	DG	NII	RE	FL	DP	GDP	INF	GOV
NPL	1.0
T1	-0.03	1.0
TE	0.17	-0.05	1.0
DG	0.12	-0.25	0.18	1.0
NII	0.22	-0.01	-0.05	0.08	1.0
RE	0.14	-0.06	-0.19	-0.06	-0.21	1.0
FL	0.03	0.004	-0.04	-0.03	-0.03	0.50	1.0
DP	0.008	-0.09	-0.17	-0.01	-0.11	0.25	0.06	1.0
GDP	0.23	-0.26	0.25	0.26	-0.01	0.05	0.07	-0.14	1.0
INF	-0.12	-0.16	-0.06	0.16	0.13	0.10	0.15	0.09	0.30	1.0
GOV	0.19	0.20	-0.08	0.10	-0.24	0.30	0.17	-0.09	0.01	0.05	1.0

Empirical Findings and Discussions:

The panel data under analysis has a sample size of total 138 observations (6 years data for 23 listed banks). This study uses Pooled OLS, Random-effects model and Fixed effects model to examine the effect of Non-Interest Income on the profitability of 23 largest banks in the GCC region over the period 2012-2017.

Table 3: Regression results for ROE

VARIABLES	ROE	ROE	ROE
	Pooled	FE	RE
NPLTL	-0.623***	-0.950***	-0.787***
	(0.188)	(0.242)	(0.204)
Tier1capitalRatio	0.0972	-0.208	-0.124
	(0.13)	(0.183)	(0.154)
TotalEquityGrowth	0.0467	-0.0124	-0.00631
	(0.0427)	(0.0307)	(0.0301)
DepositGrowth	0.0725***	0.0434**	0.0447**
	(0.0268)	(0.0192)	(0.0188)
NonIntIncomeTotRevenue	0.00437	0.058	0.0565
	(0.0442)	(0.0543)	(0.0481)
RELoansTotLoans	-0.160***	-0.218**	-0.163***
	(0.0352)	(0.107)	(0.0561)
ForeignLoansTotLoans	-0.0149	-0.0834	-0.0327
	(0.0334)	(0.0671)	(0.0478)
DividendpayoutRatio	-0.0148	-0.0414***	-0.0359***
	(0.0141)	(0.0106)	(0.0104)
GDPGrowth	0.397**	0.350***	0.376***
	(0.173)	(0.128)	(0.122)
Inflation	0.0913	-0.0539	-0.0166
	(0.248)	(0.173)	(0.17)
GovtOwned	3.584***		4.432***
	(0.867)		(1.443)
Constant	13.08***	22.19***	17.55***
	(2.808)	(4.2)	(3.376)
Observations	91	91	91
R-squared	0.537	0.608	0.487
Number of Bank		17	17
Standard errors in parentheses
* p<0.01, p<0.05, *p<0.1

Table 4: Regression results for ROA

VARIABLES	ROA	ROA	ROA
	Pooled	FE	RE
NPLTL	-0.0791***	-0.195***	-0.122***
	(0.0256)	(0.0389)	(0.0312)
Tier1capitalRatio	0.0836***	0.0199	0.0570**
	(0.0176)	(0.0294)	(0.0236)
TotalEquityGrowth	-0.00238	-0.00608	-0.00582
	(0.00581)	(0.00494)	(0.00517)
DepositGrowth	0.00738**	0.00600*	0.00546*
	(0.00365)	(0.00308)	(0.00325)
NonIntIncomeTotRevenue	0.00224	0.000711	0.00607
	(0.00601)	(0.00874)	(0.00755)
RELoansTotLoans	-0.0132***	-0.0228	-0.012
	(0.00479)	(0.0173)	(0.00739)
ForeignLoansTotLoans	-0.00943**	-0.0272**	-0.0113*
	(0.00454)	(0.0108)	(0.00677)
DividendpayoutRatio	-0.000221	-0.00461***	-0.00262
	(0.00192)	(0.00171)	(0.00179)
GDPGrowth	0.0655***	0.0496**	0.0631***
	(0.0235)	(0.0206)	(0.0208)
Inflation	0.0258	-0.00257	0.00676
	(0.0337)	(0.0278)	(0.0294)
GovtOwned	0.168		0.286
	(0.118)		(0.185)
Constant	0.658*	2.694***	1.263**
	(0.382)	(0.676)	(0.508)
Observations	91	91	91
R-squared	0.469	0.53
Number of Bank		17	17
Standard errors in parentheses
* p<0.01, p<0.05, *p<0.1

The effect of Non-Interest Income on Banks ROE for GCC region:

The effect of non-Interest income on banks profitability has been estimated and the best-suited model for the data is discussed. Firstly, Breusch and Pagan Lagrangian Multiplier test for Random effects have been used for selecting the appropriate model between Pooled OLS and random/fixed-effects model. After that Hausman Test is used for selection between Fixed effects and Random-effects model. Table 3 and 4 represent the test results for pooled OLS, Random effects and Fixed effects model.

Further to above, the Breusch-Pagan test and White test re-affirm that data set is Homoscedastic. (Kumar and Kishore, 2019). Below is the summary of test results of null hypothesis for constant variance or homoscedastic data.

Table 5: Breusch-Pagan and White Test Results

Breusch-Pagan Test

(ROE)

White Test

(ROE)

Breusch-Pagan Test

(ROA)

White Test

(ROA)

Chi2

0.02

84.89

1.00

86.35

Prob>chi2

0.8972

0.22

0.3178

0.1955

NPLTL, Real Estate, GDP growth, government owned and Dividend Payout Ratio show a significant relation with ROE. All the 1% significant variables show a negative relationship to ROE except for GDP growth and government owned shows a positive relationship.

Further Deposit growth shows a significant positive relationship at a significance level of 5%. Out of 11 independent variables used in the study, 6 shows significant in explaining variation in ROE. Further, the number of significant variables at 1% significance level is far higher than other models. Hence assumption can be made, that this model is the most suitable model. Nevertheless, from the below tests (Breusch and Pagan Lagrangian/ Hausman Test), it can be easily identified, which model is the most suitable and appropriate model.

Bank specific determinants:

There are nine bank-related variables used in this study and out of which six variables are significant (1%,5% or 10%) in explaining variation in ROE. As per test results, NPLTL is highly significant at a significance level of 1%, and a standard error of 0.204. The relationship between NPLTL and ROE is negative and is in line with the norms in the banking industry (i.e., higher the loans losses, lower the profitability) as well as in line with our expected results. According to the regression output, 1 unit increase in NPLTL will result in a reduction of ROE by 0.787 units.

Similar results have been reported for studies conducted by (Kirui, 2014) and (Patwary and Tasneem, 2019) that NPL ratio is negatively correlated to the profitability. Apart from the banking norms, one of the other reasons for similar test results could be that the period selected in (Kirui, 2014) and (Patwary and Tasneem, 2019) are almost similar to that of this study.

Real Estate loans are another highly significant variable with a significance level of 1%. However, the relationship between ROE and Real Estate loan is negative meaning that every unit of increase in Real Estate loans would result in a decrease in ROE by 0.163 units. There were not many studies conducted previously to test this relationship in the GCC region; hence the anticipated sign was left undecided. Nevertheless, a study was conducted for Kenyan banks, and the relationship was significant and positive. The reasons for the negative relationship in this study could be that the mortgage loan segment, which falls under Real Estate loans are incredibly competitive and the rates offered by the banks are extremely low due to competition.

The dividend payout ratio is also highly significant at a significance level of 1% and having a negative relationship with ROE. As per regression output, each additional unit of dividend payment would result in a drop in ROE by 0.0359 units. Same is in line with economic theory as well as in line with our expectations. (More dividend paid to shareholders will result in lower levels of profitability). Same has also been confirmed by the study conducted by (Khan et al., 2015). Further, the research conducted by (Ahmed, 2015) has also confirmed the same test results (negative relationship) for the UAE economy.

The relationship between government-owned banks and ROE has been checked in this study, and it is also one of the highly significant variables at a significance level of 1%. Further, the relationship is positive, meaning that ROE increases when the ownership of the bank is government or sovereign related fund. Nevertheless, there are previous studies conducted (Farazi et al., 2013b) in this regard and found a negative relationship sighting reasons as operating inefficiencies, higher costs due to larger staffing and large non-performing assets.

The said study was conducted for the wider MENA region where the corruption rates and operating inefficiencies are high in some of the African countries, which could have been the reason for a negative relationship. (García-Herrero et al., 2009) and (Iannotta et al., 2007) are the other studies which show a negative relationship between ROE and government ownership in the Chinese market and European market respectively.

Contrasting to this, the GCC region government-owned banks are regularly in search of ways for improvement in operating efficiencies by way of mergers and acquisitions, (Emirates Banks and NBD bank, FGB/NBAD, ADCB/UNB/Al Hilal) and improvement in technologies. Thus, the relationship between ROE and government owned is positive. Further, the cost of funds in government owned banks is lower than the other banks as most of the government-related deposits are parked in these government owned banks.

Deposit growth is a variable significant at a significance level of 5% and having a positive relationship with ROE and is in line with previous studies conducted by (Fah and Hassani, 2014). As per regression result, an increase in deposit growth by one unit will result in ROE to increase by 0.0447 units. In contrary to this, the study conducted by (Abu, 2018) shows that Deposit growth is negatively impacting the ROE in the Palestinian market. Banks in the GCC region use sophisticated technology to achieve efficiencies such as differentiating net new money from transfers of existing funds.

Traditionally banks and their systems could not distinguish between new deposits and the existing deposits, and thus banks paid higher promotional interest rates to customers who move funds from account to account to avail the promotional rates offered for new funds. However, with the adoption in technology and new systems in GCC region, the banks now can trace new vs existing funds easily thereby offering lower rates for existing funds while promotional rates are offered for new money. Hence increasing overall profitability and positive relationship between ROE and deposit growth. However, Palestine is still an underdeveloped country and hence use of sophisticated systems in the banking system is minimal. This could be a reason for the negative relationship between ROE and deposit growth.

The primary purpose of this study is to understand the relationship between non-interest income and profitability (ROE). Based on the regression output, the relationship between the non-interest income and ROE is positive; nonetheless, the relationship is not statistically significant to conclude that non-interest income has an effect on ROE for the sample selected. One of the critical reasons for a non-significant relationship between non-interest income to ROE is that the banks in the region support the development of large government/quasi-government related projects by granting term debts, thereby earning a significant level of interest income.

Hence the Non-interest income generated is offset by the interest income earned by these banks. Further, to grow non-interest income related business, additional resource/asset allocation is required and thus incurring of additional costs. Hence the income generated through non-interest income should outperform the cost associated with-it significantly for a bank to improve profitability levels.

Macroeconomic determinants:

Apart from the Bank specific variables, there are two other macroeconomic variables selected for this study, namely GDP growth and Inflation. While there is no significant relationship shown from the Rate of Inflation, the GDP growth rate has shown a significant positive relationship at a significance level of 1%. As per regression results, each unit of increase in GDP growth will result in improving the ROE by 0.376 units and results are in line with the study conducted by (Kosmidou, 2008) and economic theory (Combey and Togbenou, 2017).

However, there is a study conducted for Chinese banks which gives contradictory results, i.e. GDP growth is negatively related to ROE. (Floros, 1966)This negative relationship is partially supporting the view that high economic growth improves the business environment and reduced entry barriers to banks. This consequently increased competition and thereby dampening the bank’s profitability.

The effect of Non-Interest Income on Banks ROA for GCC region:

To test the relationship between non-Interest income and ROA, initially three different models have been used and thereafter the best suited model has been selected to explain the regression output. Below are the regression outputs for Pooled OLS, Random effects and Fixed effect models.

Following LM and Hausman tests, the most suitable model is fixed effects model, and detailed analysis of output data under the method is provided below. Out of the nine bank-specific variables selected, four are reported as significant. NPLTL and ROA are shown a negative relationship and significant at 1% significance level. Increase in NPLTL by a single unit will result in a reduction of ROA by 0.195 and same is in line with economic theory as explained earlier. The test results are in line with the study conducted by (Kirui, 2014) for Kenyan Banking industry.

Deposit growth is statistically significant at a 10% significance level, and the relationship is positive, meaning that each unit increase in deposit would result in increasing ROA by 0.006 units. Test results are in line with previous studies conducted by (Fah and Hassani, 2014) and contrary to the study conducted by (Abu, 2018) shows that deposit growth is negatively impacting the profitability in the Palestinian market. Further, a study conducted for Omani Banks showed mixed results; however, the study was only limited to five banks in Oman, and hence it is not appropriate to make inferences from this study.

Foreign loans showed a statistically significant relationship at a significance level of 5%; nevertheless, the relationship was negative. Foreign lending is considered risky in GCC market due to non-payment and, bad loans, in turn, result in a reduction of profitability. In the case of ROE also the relationship between foreign loans were negative, nevertheless the relationship was not significant.

Dividend payout ratio is significant at 1% significance level and is in line with the economic theory, i.e., higher the dividend paid to shareholders profitability reduces. Further, it is in line with our expected results. As expected, the GDP growth rate has shown a significant positive relationship at a significance level of 5%. As per regression results, each unit of increase in GDP growth will result in improving the ROE by 0.496 units and results are in line with the study conducted by (Gul1 et al., 2011) and economic theory (Combey and Togbenou, 2017) . However, the study conducted in the Turkish Banking industry does not show any significant relationship between ROA and GDP growth.

Finally, even though our primary objective of this study was to find a relationship between non-Interest income and profitability, test results do not indicate any relationship between non-Interest income and ROA.

CONCLUSION AND RECOMMENDATION:

The main idea of conducting this research was to identify the relationship between non-interest income and profitability of banks in the GCC region. Several inferences arise from this research. Firstly, the NPLTL ratio has shown statistically significant for both ROE and ROA with a negative relationship. This is one of the critical issues faced by senior management teams in the region. With the current economic downturn in the region, the loan loss provisions are increasing year on year. According to GCC Bank outlook, the loan loss provision has increased by 4.9% as of 31.12.2018 in the GCC region. Hence going forward, banks should review the lending policies and conduct a thorough analysis of customers before lending, thereby minimizing the loan loss provision to increase profitability.

Secondly, Deposit growth ratio for both ROE and ROA shows significant, and the relationship is positive, meaning when the banks raise deposits, the profitability levels would go up. Nevertheless, to maintain this trend, banks should be mindful when intermediation. Accordingly, banks should ideally raise deposits cheaper and lend in sectors where the return is high with low credit risk.

Due to competition in the market for real estate loans as well as due to higher risk associated with Real Estate lending, the ROE significantly drops when the Real Estate loan book is increasing. Hence banks should maintain a balance between Real Estate loans vs other loans and also this could be a reason why regulators across GCC have put a cap on real estate lending. For example, in the UAE, banks can only lend real estate loans only up to 20% of the deposit book.

One of the key findings, which is vital for regulators as well as for senior management, is the fact that the ROE significantly increases when the ownership structure of the bank is government or government related. Given that the regulators in the region are positively looking at consolidation of the banking industry by mergers and acquisitions, this output would help them in making decisions in future.

GDP is positively affecting both ROE and ROA, and regulators and governments can take this as an example when deciding monitory and fiscal policy for each country. Finally, in contrast to some of the other studies conducted in different regions, there is no statistically significant relationship between non-interest income and profitability of banks in the GCC region. Nevertheless, worthwhile noting that the sample selected for this study are all large banks and in case the same test is conducted for a sample of smaller banks we may be able to find some statistically significant relationship between Non-Interest Income and profitability.

LIMITATIONS AND FUTURE STUDY:

This study is conducted for the top 23 listed banks in the GCC region. However, there are more than 200 banks in the region which includes, local, regional as well as International Banks like HSBC, Citi and Standard Chartered. If a broader study, including all the banks as the sample, were conducted for the same period, test results would have been different. However, the collection of data for all the banks was a difficult task as financials and details for some of the banks are not available freely. GCC region is in an economic downturn since the year 2015, and the data collected for this study includes this period as well, and hence the test results would have an impact (bias) from this downturn. Going forward the same study can be conducted with a larger sample of banks to obtain more accurate results for a larger sample.

REFERENCES:

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Received on 02.07.2023 Modified on 13.01.2024

Asian Journal of Management. 2024;15(2):117-126.

DOI: 10.52711/2321-5763.2024.00020

Variable	Obs	Mean (%)	Std. Dev	Min	Max
ROE	138	13.28	3.38	2.7	22.5
ROA	138	1.85	0.48	0.4	3.2
NPL/TL	136	2.91	2.28	0.6	14.2
Tier 1 Cap Ratio	129	15.93	2.11	10.8	22.2
Equity Growth	138	9.51	7.16	-2.2	47.6
Deposit Growth	138	7.60	10.10	-21	52
NII/Total Revenue	138	29.56	7.90	2.41	53.91
Real Estate Loans/ Total Loans	121	16.41	10.23	1.50	39.90
Foreign Loans/ Total Loans	109	13.16	19.37	0.2	82.6
Dividend Payout ratio	136	45.19	19.71	14.3	195.7
GDP Growth	138	3.27	2.09	-3.48	9.33
Inflation	138	2.02	1.17	-0.83	4.06
Government owned banks	138	Dummy	Dummy	0	1