Exploring the bearing of liquidity risk in the Middle East and North Africa (MENA) banks

Abstract

The paper examines how liquidity risk affects the Middle East and North Africa (MENA) bank profitability. Banks need profitability to survive, but liquidity risk measures long-term company health. Through Refinitiv Eikon, quantitative data was collected over 11 years from 2012 to 2022 for 71 MENA banks to support the theoretical study. Return on Equity (ROE), a profitability indicator, is the dependent variable, whereas liquidity risk is the independent variable and controlling for size, loan quality, inflation, gross domestic product, income diversification, operational efficiency, capital adequacy, and growth. This study estimates the impact of liquidity risk on MENA bank profitability using OLS and panel regression (fixed and random effects). Several results were found, such as that bank size, operational efficiency, and non-performing loans negatively affect profitability, suggesting that large banks have higher operating costs and may weaken profitability in MENA. Besides, additional non-performing loans increase the bank’s costs and thus diminish profitability. Also, if the bank has no control over the operational expenses, then this will lead to reduce profitability. Liquidity risk, capital adequacy, income diversification, and growth have a positive significant impact on ROE implying that banks with higher growth opportunities, better capital adequacy ratio, more income sources, and liquidity risk will result in higher profitability as explained by the risk-reward theory. The results are robust and this has been confirmed by applying the Generalized Method of Moments (GMM).

Impact statement

This study aims to investigate the influence of liquidity risk on the profitability of banks in the Middle East and North Africa (MENA) region over the period of 2012 to 2022 for a total of 71 banks. The analysis employs Ordinary Least Squares (OLS) and panel regression techniques, including fixed and random effects models. The results are robust and this has been verified by implementing the Generalized Method of Moments (GMM). Multiple findings indicate that factors such as bank size, operational efficiency, and non-performing loans have a negative impact on profitability. Besides, Liquidity risk, income diversification, growth, capital adequacy and gross domestic product have positive impact on profitability. This study provides valuable insights into the complex relationship between liquidity risk and profitability in the banking sector of the Middle East and North Africa (MENA) region. The study's conclusions not only contribute to academic knowledge but also have practical consequences for banking professionals, regulators, and investors, highlighting its broad influence across various sectors.

Keywords:

• Liquidity risk
• banks
• return on equity
• MENA region
• panel regression

REVIEWING EDITOR:

• David McMillan, University of Stirling, United Kingdom

SUBJECTS:

• Economics
• Finance
• Business, Management and Accounting

JEL:

• G15
• G20
• G33

1. Introduction

In countries of the Middle East and North Africa (MENA), banks are the main providers of financial services, as the economies have bank-based financial systems (Uzunkaya, 2012). Therefore, having vibrant banks is crucial for the MENA region as ill-performing banks or a failing banking sector impact the economy adversely. Furthermore, sound banks are considered to support the economy in unfavorable conditions and crises (Rdaydeh et al., 2017). Technological innovation, globalization, and a competitive market environment challenge the profitability of banks globally and in the MENA region. However, little is known about the factors that contribute to vibrant and profitable banks in this region. Our paper fills this research gap by studying bank and country-specific impact factors on banks’ profitability using a sample of 71 MENA banks for the years 2012 to 2022.

Liquidity risk can be defined as an arising risk when a bank is unable to meet its obligations at the time of due in the absence of incurring undesirable losses (Ismail & Ahmed, 2023). Therefore, this risk can have an adverse impact on the financial institutions’ capital and earnings. The bank’s management should ensure that there are sufficient funds available to fulfill future requests from lenders and borrowers at reasonable rates. Similarly, Hacini et al. (2021) defined liquidity risk as the possibility for an institution to lose money if it is unable to pay its bills on time or fund asset growth when it becomes necessary without incurring unacceptably high costs or losses. Another definition of liquidity risk is the danger of being unable to quickly and affordably liquidate a position.

The influence of the liquidity situation on the management of banks and other economic units has always been attractive and compelling. In the literature over the years, there seems to have been an endless debate on the functions, significance, and factors that affect liquidity risk. The goal of liquidity management is to ensure that an asset may be rapidly and reliably converted back into funds (cash or income) anytime the asset holder desires (Khalaf & Alajlani, 2021; Saleh et al., 2020). A liquid financial institution is when it has enough cash and liquid assets on hand, in addition to the capability to quickly raise funds through additional sources aiming to satisfy its financial commitments and respect deadlines for payments (Ouma, 2015). Furthermore, this empirical paper investigates the impact of liquidity risk on banks’ profitability in the MENA region.

This empirical paper is structured in five sections. Section 2 provides a summary of selected previous studies and Section 3 explains the methodology and the model development. Section 4 highlights the results and discussion. Section 5 shows the conclusion of this paper.

2. Literature review

Bourke (1989) examined the drivers of return on assets and found that institutions with superior liquidity generate better earnings. Banks typically have excessive profitability if they have excessive liquidity, according to Kosmidou’s (2008) observation. Moreover, Rahman et al. (2015) also researched liquidity risk and profits by analyzing the results of a selected sample of 25 Bangladeshi financial institutions between the years of 2003 and 2006. The findings indicate a link between both liquidity risk and bank profitability, indicating that institutions require greater liquidity to run more effectively. Liquidity, according to Islam & Nishiyama (2016), contributes to the profitability of banks but does not significantly do so. Chen et al. (2018) used panel data from 12 developed economies from 1994 till 2006 to identify factors driving Liquidity risk as well as its relationship to financial institutions profitability. The findings demonstrated a basic and adverse relationship between liquidity risk and profitability as projected through the funding gap. As predicted by ROA and ROE, a better financing gap (greater liquidity) lowers financial institution revenue. These facts lead to a conclusion stating that the profitability of financial institutions is significantly impacted by liquidity risk.

The impact of liquidity risk on the profitability of financial institutions, as determined by the return on equity in the Eurozone region, was discussed by Toutou and Xiaodong in 2011. Regression analysis was used to analyze secondary data that was gathered from financial reports for 12 banks in the Eurozone region between the years 2005 and 2010. The findings showed that liquidity risk has a favorable effect on ROE in the Eurozone region. Similar findings were made by Ruziqa (2013), who used secondary data gathered from 23 traditional banks between 2007 and 2011 to examine such a relationship in Indonesia and found that there was a positive significant relationship.

Liquidity was highlighted as one of the elements affecting profitability by Berríos (2013) in research aiming to identify the determinants of profitability in commercial financial institutions in Kenya. The research involved 35 financial institutions with the data for 5 years. The study estimated the factors influencing commercial banks’ profitability using descriptive statistics and multiple regression analysis. The study concluded that liquid assets considerably influence the profitability of commercial banks, particularly during the time of political chaos following elections. The results also showed that both the insider holdings in addition to the tenure of the CEO impact negatively the bank’s performance.

Rahman et al. (2015) examined the relationship between liquidity risk and Bangladeshi financial institutions’ profitability as determined by return on equity. The outcome was determined through the analysis of secondary data gathered from 6 banks out of which 3 banks were Islamic institutions and 3 other conventional commercial banks between 2007 and 2011. The results of the study revealed that the bank’s regulatory capital, size, liquidity, and loan intensity are positively influencing its profitability. Besides, other variables such as the bank’s cost efficiency and off-balance sheet activities in addition to the inflation rate are negatively influencing its profitability. Similarly, Saeed discovered this by analyzing 27 commercial Malaysian financial institutions using secondary data from the years 2005 to 2013 and the final results confirmed the positive impact of the bank’s liquidity on its profitability.

Ouma (2015) investigated the effect of liquidity risk on profitability. It was found out that if liquidity issues are left unchecked, they can negatively affect a financial organization’s capital, profitability, and, in extreme cases, even cause the institution to fail. In addition, a financial institution having liquidity issues might additionally experience problems in satisfying the demands of customers, although this liquidity risk can be avoided by keeping appropriate cash on hand, increasing the deposit base, and improving the commercial banks’ profitability. A financial institution’s dependency on the financial markets would be less just with enough cash holdings; thus, minimizing the cost of overnight borrowing. Such issues must be directly sermonized and instantaneous measures need to be considered to evade the outcomes of illiquidity.

To have a better understanding of liquidity risk and ROA, Salim & Bilal (2016) also investigated this connection in the Middle Eastern country Oman. The data was gathered from the annual reports of 4 Omani financial institutions for the period 2010-2014. Using the multiple regression analysis, the authors proved that the loan-to-total assets ratio and the liquid assets-to-total deposits are significantly influencing the bank’s ROA. Besides, this study failed to identify a significant relationship between the bank’s liquidity position and its net interest margin.

Malik et al. (2016) additionally discovered a mere relationship between liquidity and profitability in the private sector banks of Pakistan. The dataset was collected from 22 private Pakistani banks for the period 2009-2013. The study utilized the ordinary least square regression to prove statistically that the liquidity position of the Pakistani banks influences the bank’s return on assets. Consequently, the same study failed to identify a significant relation between the bank’s liquidity and its return on equity. Lastly, the authors advised the banks to formulate their future strategies in a way to properly manage their liquidity position.

Additionally, Chowdhury et al. (2018) sought to clarify the relationship between liquidity risk and ROE in light of the fact that Bangladesh’s banking sector was experiencing a liquidity crisis. Six Islamic banks’ information was gathered from 2012 to 2016. The findings revealed a non-significant link between liquidity measures and ROE. Berrios (2013), however, did not discover a similar relationship when examining information gathered from 5 Islamic banks between 2001 and 2011 in Bangladesh. This could be due to the fact that research periods in the two cases were different in terms of the number of years. The period 2010-2014 also demonstrated the essential positive influence of liquidity risk on the return on equity, in Oman by using secondary information accumulated from four commercial banks by Salim & Bilal (2016).

Addou & Bensghir (2021) article investigated the primary factors influencing the liquidity risk faced in the United Arab Emirates (UAE) by Islamic financial institutions. The research focused on 4 Islamic entities in the UAE using annual data extracted from their annual reports. The researchers used six variables such as size, return on equity, non-performing loans, return on assets, and capital adequacy ratio for data analysis using linear regression analysis. The results of the model demonstrate that both the return on assets and non-performing loans have a negative effect on the Emirati banks’ liquidity risk.

Abbas et al. (2021) examined how funding liquidity risk affects banks’ propensity to take risks is the driving force behind this research. They used data from US commercial banks between 2002 and 2018 and the two-stage system GMM method to examine the hypotheses. Their research shows that US commercial banks are more willing to take risks when they have access to liquid finance. In addition, there is less oversight of bank managers’ risky behavior and fewer funding shortages for banks with bigger deposits.

Widyarti et al. (2022) conducted research on the impact of non-performing loans, return on assets, return on equity, and bank size on the liquidity risk of Indonesian banks. The study encompassed 40 state-owned and private banks with a dataset extracted for the period 2016-2020. The authors utilized the OLS regression to prove that the bank’s return on assets and return on equity are positively influencing the bank’s liquidity risk in Indonesia. Subsequently, the results also revealed that the non-performing loans along with the bank size do not impact the liquidity risk.

A study by Saif-Alyousfi (2022) involving 2,446 banks aimed to identify the determinants of bank profitability in 47 Asian countries. The author compiled 41,582 observations to create a dataset for the period 1995-2017. Using the Generalized Methods of Moments (GMM) estimation technique, the results of the research proved that the liquidity risk, capital adequacy ratio, loan-to-total assets, bank size, GDP growth, and inflation rate are positively associated with the bank’s return on assets. Besides, other variables such as cost-to-income, non-performing loans, and loan loss provisions are negatively impacting bank profitability.

Using a dynamic GMM panel approach, Shoaib Ali et al. (2022) propose to examine the effect of banking-sector concentration on the liquidity creation of banks in GCC nations from 2012 to 2018. The findings point to a decrease in liquidity creation by banks throughout the GCC nations as a result of greater banking rivalry. The results of the study corroborate the ‘financial fragility hypothesis’, which states that when market competition is fierce, banks will cut back on lending.

Another study by Ismail & Ahmed (2023) created a panel dataset for the period 2016-2021 to examine the impact of liquidity risk, credit risk, and operational risk on the financial stability of Jordanian banks. The scholar selected all conventional commercial banks listed in the Amman Stock Exchange and utilized the panel regression technique to prove that both credit risk and COVID-19 have negatively affected the bank’s stability. The study also showed that liquidity risk, operational risk, and bank size do not have any significant influence over the Jordanian’s bank stability.

Rubbaniy et al. (2023) found empirical evidence that the Business Cycle (BC) has a nonlinear effect on liquidity generation using a panel smooth transition regression framework using US bank holding company quarterly data from 1993Q1 to 2020Q1, as well as a new proxy of the business cycle index. They discover that the BC has a positive and statistically significant nonlinear impact on liquidity generation, lending credence to the idea that liquidity creation is pro-cyclical and leading to an improvement in liquidity creation estimation when compared to earlier research.

3. Methodology

3.1. Sample used

In this research paper, the main market focus will be the MENA region’s banking sector with data extracted from the Refinitiv Eikon platform for the period 2012-2022. This paper comprised data for 11 countries, that is 7 countries from the Middle East and 4 other countries from North Africa, as this will help in better understanding the different financial environments. As stated in Table 1, the population and the final sample where any bank with missing data for more than 3 years was excluded from our sample.

Table 1. Sample of the study.

Country	Population	Final Sample	Country	Population	Final Sample
Panel A: Middle East			Panel C: Full Sample
Bahrain	9	4	Bahrain	9	4
Jordan	14	8	Egypt	10	6
Kuwait	11	7	Jordan	14	8
Oman	7	4	Kuwait	11	7
Qatar	9	6	Libya	14	8
Saudi Arabia	10	7	Morocco	6	4
UAE	18	9	Oman	7	4
Panel B: North Africa			Qatar	9	6
Egypt	10	6	Saudi Arabia	10	7
Libya	14	8	Tunisia	11	8
Morocco	6	4	UAE	18	9
Tunisia	11	8	Total	119	71

Source: Authors’ Analysis.

The selection of banks is based on the availability of data aiming to achieve reliable and accurate results. In addition, before excluding the bank, any missing data were searched for in the annual reports of the reflective banks or the stock market exchange related to that specific market.

3.2. Model development

3.2.1. Dependent variable: return on equity

Return on equity is a ratio calculated as net income divided by average shareholders’ equity (Noraini, 2012; Seissian et al., 2018). According to Farhi & Hacini (2021), the return on equity is considered a crucial financial metric that shows if the bank is properly utilizing its resources. Another study by Berrani & Hacini (2021) pinpointed the importance of the return on equity in showing how much the bank is earning from its total equity. Different conclusions were reached after looking into the impact of liquidity risk on ROE in several studies. Syafi’i & Rusliati (2016) and Mwangi (2014) discovered a significant affirmative effect of liquidity risk on ROE while others such as Hacini et al. (2021) discovered a significant negative relationship. Though, other studies similar to Badawi (2017) have discovered a non-significant relationship between the bank’s liquidity risk and its ROE.

3.2.2. Independent variables

3.2.2.1. Liquidity risk

Liquidity, as defined by the Basel Committee on Banking Supervision in 2008, is a bank’s capacity to finance asset growth and pay commitments when they become due without triggering impermissible losses (Ojo, 2010). According to the committee, a financial institution becomes vulnerable to liquidity risk when it fulfills the fundamental function of maturing short-term deposits into long-term loans, both on an institution-specific level and in a way that impacts the market as a whole. A bank can fulfill its irregular cash flow obligations which are impacted by external events and the activities of other agents by effectively managing its liquidity risk (Badawi, 2017).

Through the use of a panel data set of commercial banks from industrialized economies, Chung et al. studied the causes of liquidity risk in their study Bank Liquidity Risk and Performance. It was discovered that dependency on outside finance and liquid assets are the main contributors to liquidity risk. Due to the increased cost of funds, liquidity risk reduces bank profitability but boosts net interest margin. The findings demonstrated that, in a financial system based on markets, liquidity risk has a negative relationship with bank ROA and ROE. Other studies such as Hacini et al. (2021) confirmed that liquidity risk has a significant negative impact on the bank’s profitability. Hence, we hypothesize:

H₁: The liquidity risk has a negative impact on the profitability of banks.

3.2.2.2. Size

The Size of the bank is calculated using the natural logarithm of total assets (Awad et al., 2022; Khalaf et al., 2023a). This measure for the bank size is generally used as a measure of economies of scale in the banking industry according to Widyarti et al. (2022). Large financial institutions typically have the potential to raise capital at a lower cost and display higher profitability. Bank profitability is positively correlated with large capitalization. According to Mester (2010), the profitability and size of the bank are immensely related. Enlarging the bank size will enlarge the profitability of financial institutions by allowing them to recognize the economic scale. An illustration, enlarging size lets financial institutions increase fixed expenses over a greater asset base, thereby decreasing common expenses. Enlarging financial institutions’ asset size can also bring down risk by branching out operations across regions and sectors. Another study by Aladwan (2015) including Jordanian banks concluded that bank size has a significant effect on profitability. The study proved that small and medium-sized banks have a better financial performance implying a higher profitability in comparison to larger banks; thus, concluding a negative relation between profitability and size. Other studies by Yuen et al. (2022) and Phan et al. (2020) found that the size factor has a significant positive relationship with profitability. Given the mixed results in the literature, we hypothesize:

H₂: The size has a positive impact on the profitability of banks.

3.2.2.3. Loan quality

Loans offered by banks not paid off in due time go on to become non-performing loans in the banking sector. This causes unfavorable impacts on the banking sector, specifically in terms of profitability. The impact spreads onto related banks, central government budgets, and various other sectors (Koten, 2021). Lending money to people is one of the main functions of commercial banks, and their main revenue streams. Alternatively, loans are considered among the assets that will provide a high yield to the bank. According to Abreu and Mendes, it can be understood undeniably that the more loans commercial banks provide to the public, the more monetary value is created in the economy. However, banks must be cautious when expanding their loan portfolios as doing so exposes them to default and liquidity risks that negatively impact their capacity to generate income and survive. A study by Husni (2011) shows that the interest obtained on bank loans is a significant driver of profitability and has a positive association with the profitability of financial institutions. This was also discussed in research by Vong and Chan who demonstrated the relationship between profitability and loan quality suggesting to use the bank’s non-performing loans as a gauge of loan quality. Another study by Alnabulsi et al. (2022), reveals that bank profitability is inversely related to non-performing loans in the MENA region. This implies that banks with less non-performing loans report more profitability figures unlike the results acquired in North African Countries. Other studies by Yuen et al. (2022), Widyarti et al. (2022), and Badawi (2017) revealed that the bank’s non-performing loans level does not impact statistically its profitability. Still, a study by Syafi’i & Rusliati (2016) in Indonesia confirmed a significant negative association between the bank’s non-performing loans and its return on assets. Thus, we hypothesize:

H₃: The loan quality has a negative impact on the profitability of banks.

3.2.2.4. Growth

According to Fama & French (1992), the growth of a firm is estimated based on the anticipated figures of profits and cash flows. Firms with higher growth rates are anticipated to have higher profitability; and thus, higher returns for investors (Hasanudin, 2023). The price-to-book ratio measures how market participants value the equity of the firms in relation to its book value. The price-to-book is estimated by dividing the market price of the firm’s share by its book value per share (Doblas et al., 2020; La Torre et al., 2021). Various studies have examined the relationship between the firm’s price-to-book and its profitability. For instance, Fama & French (2007), Block (1995), and Fama & French (1992) considered that low price-to-book firms can beat high price-to-book firms since this reflects underpricing and high potential for future growth; thus, increasing profits and cash flows. Similar studies by Jensen et al. (1997) and Asness et al. (2013) proved that high price-to-book firms have also a future growth potential which can be achieved via innovation and new projects.

H₄: The growth has a positive impact on the profitability of banks.

3.2.2.5. Income diversification

The relationship between income diversity and return on equity (ROE) in banks is a significant area of focus in finance (Abu Khalaf et al., 2024). It investigates how banks might maximize their income streams to improve profitability and shareholder value. Income diversification involves banks broadening their income streams beyond conventional interest-based operations, such as loans, to encompass non-interest revenue sources such as fees, commissions, trading, and investment income. Diversifying revenue sources mostly reduces risk. Banks can reduce the instability linked to a single revenue stream by depending on various sources of income. During times of low-interest rates, interest revenue from loans may decrease, while income from fees and commissions may stay steady or rise. Reducing this risk can result in a more stable and potentially increased Return on Equity (ROE) for the bank, as it becomes less vulnerable to changes in any individual revenue stream. This study will use the ratio of non-interest income to total income as the measure of income diversification. This measure offers a thorough assessment of the significance of non-interest revenue within a bank’s total income profile.

H₅: The income diversification has a positive impact on the profitability of banks.

3.2.2.6. Operational efficiency

Organizations must sustain operational efficiency in their activities. Bank profitability is influenced by the operational efficiency of the bank, which is assessed by the total operating expenditure divided by total assets, mostly interest expense (Kundu & Banerjee, 2022). Bank operational efficiency involves managing interest expense to a minimum through asset and liabilities management (Altaf et al., 2022). The bank management consistently works to optimize bank operations, particularly by minimizing interest payments paid to clients, in order to enhance bank profitability (Boamah et al., 2022). The focus on interest expenses is not intended for banks to minimize the interest paid, as the interest rates on deposits offered to clients by banks must be competitive (Hidayat et al., 2021). It can increase interest expenses by using bank services to acquire funds from customers for daily transactions. Increasing the number of service facilities available to clients can lead to more cash collected, in addition to savings accounts and customer deposits.

H₆: The operational efficiency has a negative impact on the profitability of banks.

3.2.2.7. Capital adequacy (CapAd)

The ratio represents the proportion of a bank’s capital in relation to its weighted assets (Baldwin et al., 2019). A higher Capital Adequacy Ratio (CAR) may indicate a lower risk profile because the bank is allocating a greater part of its deposits to loans. This has the potential to increase liquidity and improve the financial performance of financial institutions (Abu Khalaf et al., 2024). According to Ajayi et al. (2019), a high Capital Adequacy Ratio (CAR) shows a bank’s greater ability to meet its financial commitments and depositor demands, as it represents a bigger safety buffer (Baldwin et al., 2019). A high Capital Adequacy Ratio (CAR) among banks in a given market suggests a strong and stable financial system there (Almazari et al., 2022).

H₇: The capital adequacy has a positive impact on the profitability of banks.

3.2.2.8. Gross domestic product (GDP)

Many researchers included the GDP as one of the macroeconomic variables to control for the relationship between liquidity risk and banks profitability, for example, Golubeva et al. (2019) and Nguyen et al. In addition, GDP is an essential indicator of a country’s economic health since it represents the worth of all products and services generated within its boundaries. Businesses tend to thrive as GDP grows, resulting in increased economic activity and increasing demand for financial services such as loans and investments (Klein & Weill, 2022). This rise in economic activity helps banks to increase their lending behavior. In contrast, during economic contractions, GDP tends to contract, affecting both firms and individuals. More specifically, banks may experience difficulties in such periods as loan defaults might rise and this, as a result, shall affect the profitability of banks (Lopez et al., 2020).

H₈: This paper expects that there is a positive relation between GDP and profitability.

3.2.2.9. Inflation (INF)

Several empirical papers investigated the impact of inflation on the profitability of banks. According to Jeevitha et al., inflation and bank profitability have a complex relationship in many ways. They argued that the general rise in prices can affect Banks’ bottom lines. In other words, banks tend to find ways and benefit from inflation through more borrowing, lending, and investments. Conversely, banks may face challenges due to high or unanticipated inflation which diminishes the buying power of money and impacts the value of assets (Tan & Floros, 2012). In addition, interest rates could be impacted by inflation uncertainty, which makes it challenging for banks to set reasonable prices for their financial products.

H₉: This paper expects that there is a positive relationship between inflation and banks profitability.

3.3. Model

Based on the previous section, the following model has been developed to investigate the impact of liquidity risk on banks’ profitability in the MENA region. $${\mathit{\text{ROE}}}_{i,t}={\beta }_0+{\beta }_1{\mathit{\text{LIQR}}}_{i,t}+{\beta }_2{\mathit{\text{Size}}}_{i,t}+{\beta }_3{\mathit{\text{Loan\hspace{0.5em}Quality}}}_{i,t}+{\beta }_4{\mathit{\text{Growth}}}_{i,t}+{\beta }_{5}I\mathit{\text{ncDi}}{v}_{i,t}+{\beta }_{6}O\mathit{\text{pEf}}{f}_{i,t}+{\beta }_{7}C\mathit{\text{apA}}{d}_{i,t}+{\beta }_8{\mathit{\text{GDP}}}_{i,t}+{\beta }_9{\mathit{\text{INF}}}_{i,t}+{\epsilon }_{i,t}$$

Where:

ROE is return on equity which is a measure of the profitability of banks

LIQR is Liquidity risk and is measured by Net loans-to-Total asset (TA)

Size is measured by the natural logarithm of Total Assets (LA)

Loan Quality is measured by the natural logarithm of non-performing loans (NPL)

Growth is measured by the price-to-book value per share

IncDiv is income diversification and measured by the ratio of non-interest income to total income

OpEff is operating efficiency and measured by the total operating expenditure divided by total assets

CapAd is capital adequacy and is measured by the bank’s capital in relation to its weighted assets

GDP is the Gross Domestic Product provided by the World Bank

INF is inflation as measured by the World Bank

ε is the error term

Table 2 provides the research model variables and the measures used by several researchers in the field.

Table 2. Research variables and measurements.

Types of variables	Variables	Notation	Measurements	References
Dependent	Profitability	ROE	Net Income divided by the Average Equity	Seissian et al., 2018
Independent	Liquidity Risk	LIQR	Net loans-to-Total Assets	Saif-Alyousfi, 2022
Control Variables	Size	SIZE	Ln (Total Assets)	Khalaf et al., 2023b; Khalaf, 2022a,2022b; Awad et al., 2022
	Loan Quality	NPL	Ln (Non-Performing Loans)	Widyarti et al., 2022; Badawi, 2017
	Growth	Growth	Price to book value per share	La Torre et al., 2021
	Income Diversification	IncDiv	The ratio of non-interest income to total income	Minh and Tranh 2020
	Operational efficiency	OpEff	Total operating expenditure divided by total assets	Linawati et al. 2023
	Capital Adequacy	CapAd	The ratio of the bank’s capital in relation to its risk-weighted assets	Abu Khalaf et al., 2024
	Gross Domestic Product	GDP	GDP growth (annual %)	Damayanthi & Gunawardhana, 2022
	Inflation	INF	Consumer Prices (annual %)	Muhammed et al., 2023

Source: Authors’ Analysis.

4. Results and findings

4.1. Descriptive statistics

With reference to Table 3, the descriptive statistics show that the standard deviation of the Size variable is found to be 1.85. In addition, ROE is a popular measurement for banks profitability, the mean value is 0.127 with a standard deviation of 0.092 and minimum and maximum values of 0.01 and 0.81 respectively. Furthermore, the mean value of NPL is 9.54 with a standard deviation of 0.226 and minimum and maximum values of 6.89 and 12.58 respectively. Whereas, the mean value of LIQR is 0.52 with a standard deviation of 0.548 with minimum and maximum values of 0.06 and 0.85. The mean value of growth is 1.25 with a standard deviation of 0.524 and minimum and maximum values of 0.45 and 2.56 respectively. It can be also analyzed that the growth, size, and liquidity risk variables have relatively higher standard deviations which indicates a high variability in these variables. The results also show that the banks in North Africa have slightly better capital adequacy as compared to the Middle East with 14% and 13% respectively. A high standard deviation is also observed in the capitalization level of MENA banks ranging around 0.473. Likewise, the operational efficiency level of commercial banks in the Middle East and North Africa differs slightly and displays a mean of 0.17 and 0.19 respectively.

Table 3. Descriptive statistics.

Variables	Min	Max	Mean	Std. Dev	Min	Max	Mean	Std. Dev
Panel A	Middle East				Panel B	North Africa
Size	8.14	18.67	10.0	0.595	7.58	17.18	12.7	0.513
ROE	0.04	0.81	0.14	0.076	0.01	0.79	0.58	0.068
NPL	8.51	9.97	9.14	0.424	6.89	12.58	8.65	0.475
LIQR	0.07	0.76	0.56	0.157	0.06	0.85	0.48	0.146
Growth	0.54	2.04	1.19	0.479	0.45	2.56	1.05	0.432
CapAd	0.09	0.49	0.13	0.482	0.11	0.51	0.14	0.419
OpEff	0.06	0.24	0.17	0.173	0.04	0.22	0.19	0.125
IncDiv	0.04	0.49	0.07	0.593	0.02	0.41	0.05	0.528
GDP	−0.08	0.09	0.05	0.845	−0.29	0.32	0.12	1.025
INF	−0.02	0.05	0.01	0.058	0.03	0.29	0.05	0.098
Panel C	Full Sample				Observations
Size	7.58	18.67	12.87	1.845	781
ROE	0.01	0.81	0.127	0.092	781
NPL	6.89	12.58	9.54	0.226	781
LIQR	0.06	0.85	0.52	0.548	781
Growth	0.45	2.56	1.25	0.524	781
CapAd	0.09	0.51	0.12	0.473	781
OpEff	0.04	0.27	0.08	0.221	781
IncDiv	0.02	0.49	0.06	0.589	781
GDP	−0.29	0.32	0.14	1.125	781
INF	−0.02	0.29	0.06	0.125	781

Source: Authors’ Analysis.

4.2. Correlation analysis

Table 4 discloses the correlation coefficient of the different variables included in the model developed in the previous section. The findings show that there is a significant positive correlation (0.243; p-value < 0.05) between the bank’s liquidity risk and its return on equity. It can be analyzed that as liquidity risk increases then this will affect ROE positively implying that high risk results in higher profitability. Therefore, higher liquidity enables banks to invest excess liquidity in profitable projects. In addition, growth has a positive correlation with ROE with a value of 0.268; this implies that MENA banks, that are in the growth stage, should expect an increase in their profitability. Moreover, the results also reveal that both income diversification (0.162; p-value < 0.10) and capital adequacy ratio (0.121; p-value < 0.05) are positively correlated with the bank’s return on equity suggesting that banks with more diversified income and higher capital ratios tend to attain better profitability metrics.

Table 4. Correlation matrix.

Variables	SIZE	ROE	NPL	LIQR	Growth	CapAd	OpEff	IncDiv	GDP	INF
SIZE	1
ROE	− 0.048*	1
NPL	0.196*	− 0.036	1
LIQR	0.203**	0.243**	0.326**	1
Growth	0.461***	0.268**	0.063	−0.103**	1
CapAd	0.083*	0.121**	0.032*	−0.052*	0.051*	1
OpEff	0.151**	−0.082	0.018*	0.042	0.072**	0.063*	1
IncDiv	0.014**	0.162*	0.097*	−0.043*	0.043*	0.059**	0.042**	1
GDP	0.056	0.084	0.032	0.021	0.085**	0.038	0.038**	0.027	1
INF	0.018	0.067	0.041	0.016	0.054	0.049	0.053**	0.031	0.062*	1

Note. ***, **, and * show statistical significance at 1%, 5% and 10% respectively.

Source: Authors’ Analysis.

4.3. Regression results

The main aim of this empirical paper is to investigate the impact of liquidity risk on banks’ profitability in the MENA region. Table 5 provides the results for the OLS and panel regressions (fixed and random effect techniques). The significant result of the Hausman test favored the fixed effect technique results. The Variance inflation factor (VIF) has been reported to all variables and it is less than 2 which indicates that there is no multicollinearity problem faced in data. This comes in line with Khalaf et al. (2023a) who stated that if the VIF value is less than 5 then there is no multicollinearity problem in the regression mode.

Table 5. Regression results.

Variables	OLS		Fixed Effect		Random Effect		VIF
	Coefficient	Sig	Coefficient	Sig	Coefficient	Sig
(Constant)	0.175	0.372	0.392	0.193	0.593	0.105
Size	−0.048	0.027	−0.071	0.010	−0.084	0.043	1.327
NPL	−0.032	0.025	−0.041	0.000	−0.051	0.000	1.392
LIQR	0.042	0.050	0.062	0.000	0.053	0.018	1.749
Growth	0.103	0.000	0.083	0.000	0.093	0.014	1.531
IncDiv	0.152	0.000	0.121	0.000	0.082	0.000	1.329
OpEff	−0.054	0.052	−0.043	0.021	−0.063	0.042	1.620
CapAd	0.082	0.000	0.093	0.000	0.081	0.000	1.291
GDP	0.052	0.028	0.079	0.000	0.071	0.000	1.252
INF	0.019	0.182	0.052	0.193	0.060	0.159	1.439
Adjusted R^2	0.295		0.427		0.416
Observations	781		781		781
Hausman Test p-value	81.192 (0.000)
Durbin Watson	1.89

Source: Authors’ Analysis.

It can be seen in Table 5 that most of the explanatory variables have a significant impact on the dependent variable. The liquidity risk is measured by net loans to total assets. The result stated a significant positive coefficient for liquidity risk on profitability with a 1% significance level. This partially confirms the first hypothesis (H₁) that assumes a relationship between liquidity risk and profitability. According to Olagunju et al. (2012), Bourke (1989), and Kosmidou et al. results, liquidity risk affects positively profitability. In addition, Ren (2022) investigated the liquidity risk on profitability during the period of COVID-19. He concluded that the bank’s main activity is to maintain more liquidity during such a critical period and this increased bank returns. In other words, higher liquidity risk leads to higher profitability because all cash is invested in positive return projects. Then again, Gizaw et al. (2015) stated the higher the risk the higher the profitability. This result contradicts other research results, for example, Khalid et al. (2019) stated that there is no significant relation between liquidity risk and profitability measured by return on assets and return on equity. Also, Chung et al., and Muranaga and Ohsawa concluded a negative relationship as liquidity risk is the possibility for an institution to lose money if it is unable to pay its bills on time or fund asset growth when it becomes necessary without incurring unacceptably high costs or losses.

The size of the bank has a negative coefficient and statistically significant impact on return on equity at the 5% level. The result confirms the second hypothesis (H₂) and the impact of size on profitability. This result comes in line with Trabelsi who also concluded a negative impact on profitability. Furthermore, according to Aladwan (2015) and Ouma (2015), profitability tends to decrease if the asset size increases. Also, the results indicated a negative impact of size on profitability and they argued that large banks have high operational expenses and thus, less profitability. In addition, this comes in line with the argument provided by Flögel (2018) that smaller banks have easier access to soft information compared to larger banks. This in turn allows smaller banks to apply better screening and leads to better profitability and less default risk.

The loan quality measured by the natural logarithm of non-performing loans (NPL) affects the banks’ profitability negatively at the 1% level; and hence, this fully supports the third hypothesis (H₃). Our results contradict the ones of Sebayang, Nursiana (2017), and Gizaw et al. (2015) who reported a significant positive impact of loan quality on profitability. This implies that MENA bank managers tend to increase their loans with quality decisions, and in case of any non-performing loans, they will follow up on the collections or find alternative plans to get the borrower back on track. On the contrary, other researchers reported similar results and are in line with our results such as Alnabulsi et al. (2022) who confirmed that lower non-performing loans leads to an increase in banks’ profitability in the MENA region. Similarly, the investigation of Ekinci & Poyraz (2019) in the banking sector of Turkiye indicated that there is a negative relationship between NPL and profitability implying that an increase in non-performing loans will lower the Turkish banks’ profitability.

The fourth hypothesis (H₄) proposing a relationship between the bank’s growth and profitability is confirmed as per the regression results showing a positive coefficient for growth on profitability at the 5% significance level. According to a study by Felix (2012), the results conclude that the variation in the regulatory framework, low liquidity level, unstable political environment, and bad economy cause variation in the Earnings per share. The regression results support our findings and indicate that there is a positive relationship between growth and profitability. Likewise, our findings supported the results obtained by Saeed and Tahir who confirmed the connection between growth and profitability in Pakistan.

The regression results also confirm our fifth hypothesis (H₅) which considers that there is a positive relationship between income diversification and the bank’s profitability with a strong statistical significance. This implies that banks with more diversified sources of income tend to have higher and more stable profitability indicators. This supports the conclusions of Abu Khalaf et al. (2024) and Karakaya & Er (2012) who stated that bank diversified income contributes to better profitability. Likewise, Phan et al. (2022), Mbekomize & Mapharing (2017), and Meslier et al. (2014) obtained identical positive results between both variables.

The sixth hypothesis (H₆) was tested and confirmed in Table 5 proving that operational efficiency is statistically influencing the bank’s return on equity at the 5% level. This outcome suggests that any cost-efficiency strategy implemented by commercial banks will result in a higher return on equity. On the contrary, banks with high cost-to-income ratios struggle to improve their profitability indicators. Our findings were also supported by the studies of Ozili (2021), Serwadda (2018), and Petria et al. (2015) who identified an empirical indirect relationship between the bank’s costs and its profitability.

Moreover, the seventh hypothesis (H₇) about the relationship between the bank’s capital adequacy and its profitability is verified via the fixed effect regression results at the 1% significance level. Such association signifies that well-capitalized banks are demonstrating higher returns on their equity investments. Such commercial banks have an adequate capital buffer that can absorb potential losses as a result of any negative unanticipated transactions. In reverse, lower capitalization indicates higher leverage and consequently higher interest costs. These results were consistent with the empirical findings of Menicucci & Paolucci (2023), Isayas (2022), Petria et al. (2015), and Alhassan & Tetteh (2017) who proved that the capital adequacy of commercial banks has a positive impact on their profitability. On the contrary, previous research by Mbekomize & Mapharing (2017) an empirical negative association between the bank’s capitalization and its return on equity. Despite these important results, other scholars, such as Al‐Matari (2023) and Serwadda (2018) confirmed empirically that the bank’s capital does not impact its profitability level.

As for the eighth hypothesis (H₈) which expects a relationship between the GDP growth rate and profitability, the results prove that there is a statistical significance at the 1% significance level. This implies that the economic performance of the MENA countries contributes to the overall profitability of commercial banks. In other words, an economic downturn is anticipated to lower the demand for loans and deposits and consequently, the profitability of banks (Sufian & Chong, 2008). Our results support the findings of El Khoury et al. (2023) and Petria et al. (2015) who proved that GDP has a positive influence on banks’ profitability. Nevertheless, our results were refuted by Menicucci & Paolucci, (2023) in Italy who found an inverse relationship between GDP and profitability. A similar study by Buallay et al. (2020) including MENA banks contradicted our analysis revealing a negative relationship between the GDP growth rate and the banks’ return on assets.

The regression results revealed that there is no empirical evidence to support the ninth hypothesis (H₉) which considers that there is a positive relationship between inflation rates and bank profitability. Our analysis confirmed the findings of Petria et al. (2015) who failed to identify a significant relation between inflation rates and the return on assets of EU banks. However, a study by Derbali (2021) refuted our findings and proved that the inflation rate is positively impacting the profitability of Moroccan banks.

5. Robustness of results

Only those financial institutions that were open for business throughout the whole time frame covered by the research investigation are considered. To test the hypotheses, this empirical paper looked at panel data from 71 different banks over 11 years. Data analysis is done using the Ordinary Least Square (OLS) approach and fixed and random effect techniques. To check the robustness of results, GMM is a viable option for analyzing this data in an attempt to address the problem of data heterogeneity. This paper used a dynamic model to account for endogeneity, heteroscedasticity, correlation, and the tendency of profitability among banks to remain stable throughout time. It also aims to demonstrate a robustness test complying with Berger et al. and Luo et al. For more accurate outcomes, the researchers followed the advice of Arellano and Bond (Arellano & Bond, 1991) and utilized the generalized methods of moments (GMM) estimator, which guarantees effectiveness and reliability. Table 6 shows that the results of the GMM provided the same results obtained earlier and this is a piece of clear evidence that our results are robust.

Table 6. GMM results.

Variables	Coefficient	Sig
(Constant)	0.201	0.439
Size	−0.032	0.028
NPL	−0.051	0.000
LIQR	0.082	0.000
Growth	0.073	0.000
IncDiv	0.152	0.000
OpEff	−0.038	0.000
CapAd	0.072	0.000
GDP	0.065	0.000
INF	0.083	0.241
Adjusted R^2	0.410
Number of Observations	781
F-statistic	1163.553 (0.000)
Hansen Test	0.537
AR (1)	0.382
AR (2)	0.437

Source: Authors’ Analysis.

6. Conclusion

The motivation for this research paper is to empirically investigate the impact of liquidity risk on banks’ profitability in the MENA region given the little evidence found about this research topic in the MENA region. Liquidity risk is the possibility for an institution to lose money if it is unable to pay its bills on time, meet depositors’ demands whenever needed, or fund assets for growth when it becomes necessary without incurring unacceptably high costs or losses. The dependent and independent variables are the return on equity and liquidity risk respectively while the control variables are the size of the bank, loan quality, growth, income diversification, operational efficiency, capital adequacy, country’s GDP, and inflation rate. Checking the literature review, several researchers had different empirical findings regarding liquidity risk and banks’ profitability. Such mixed results were a motivation to examine the impact of liquidity risk on profitability in the MENA region.

This paper gathered data for 11 years from 2012-2022 for the banks in the MENA region using different sources such as the Refinitiv Eikon platform, annual reports of relevant banks, and the relevant stock market exchange. The sample encompassed 11 countries: 7 from the Middle East and 4 from North Africa. The selected countries from the Middle East were Qatar, Bahrain, Kuwait, Oman, Saudi Arabia, UAE, and Jordan; whereas the countries chosen from North Africa were Egypt, Tunisia, Libya, and Morocco. OLS and panel regression were applied to investigate the relation between liquidity risk and profitability of MENA banks. In addition, GMM has been applied and revealed that results hold and our results are robust. The results suggested that the bank size, operational efficiency, and non-performing loans have a negative significant impact on profitability, but liquidity risk, income diversification, capital adequacy, growth, and GDP have a positive significant impact on ROE in the MENA region. Results suggested that MENA banks control and maintain liquidity regularly and create a contingency plan in advance for any unforeseen event. The results of this empirical paper can assist managers in developing an efficient banking system to stay in the competitive market for the long run.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Open Access funding provided by the Qatar National Library. Open Access funding provided by the Qatar National Library. Open Access funding provided by the Qatar National Library. Open Access funding provided by the Qatar National Library.

Notes on contributors

Bashar Abu Khalaf

Dr. Bashar Abu Khalaf is the program coordinator of Banking & FinTech at The University of Doha for Science & Technology. His MBA (Finance, 2005) and PhD (Accountancy and Finance, 2013) degrees were granted from Coventry University and Heriot-Watt University respectively in The United Kingdom. He has been awarded a PhD Scholarship from the University of Jordan in 2006. Dr. Abu Khalaf is a distinguished financial educator with over 15 years of academic and practical experience. Throughout his successful career, he has distinguished himself as a committed educator, productive scholar, and experienced financial consultant. His expertise in a variety of educational settings has given him the unique ability to engage with students from various cultural backgrounds while tailoring his teaching style to their specific requirements. With an impressive portfolio of more than 30 research papers, his expertise covers a broad range of accounting & finance topics, reflecting a deep understanding of both theoretical and applied financial concepts. Dr. Abu Khalaf is a chartered Certified Financial Consultant (CCFC) offered by The Canadian Association of Financial Consultants since 2007.

Antoine B. Awad

Dr. Antoine Awad is the Chair of Accounting and Finance in the College of Business at the University of Doha for Science and Technology. Prior to that, Dr. Awad was the Chair of Finance and Accounting at the American University of Science and Technology where he taught undergraduate and post-graduate courses for several years. Dr. Awad earned his B.S. and M.Sc. from the American University of Science and Technology and his Ph.D. in Finance from Kedge Business School. He also holds the “Chartered Financial Analyst” (CFA) and “Certified Management Accountant” (CMA) designations. Dr. Awad has served as an adjunct professor at Saint Joseph University of Beirut (USJ), Beirut Arab University (BAU), and Global Business School (GBSB) in Barcelona. Dr. Awad has supervised many capstone projects, MSc theses, and Ph.D. dissertations. His research interests revolve around corporate governance, firm performance/value, and bank risk management. Besides academia, he held consultancy jobs for Asset Management firms in the field of equities and quantitative research in addition to corporate training and prep courses for CFA, CMA, and CPA candidates. Dr. Awad served as a board member in CFA Society Doha between 2021 and 2023 in addition to being an active member in CFA Society Lebanon where he served as a Chair of Education and University Relations between 2018 and 2021.