Determinants of Financial Stability: A Dynamic Panel Data Analysis

doi:10.21203/rs.3.rs-4816711/v1

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Determinants of Financial Stability: A Dynamic Panel Data Analysis

https://doi.org/10.21203/rs.3.rs-4816711/v1

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Financial soundness is crucial for the health of the financial sector and economic stability. The study examines the degree of financial stability and its drivers in commercial banks using dynamic panel data analysis. Arellano & Bover and Blundell & Bond a two-step system GMM model were applied to test the hypothesis. The 'Z-score' was used as a proxy for financial stability. The results indicate that the previous year's financial stability, profitability, and liquidity positively influenced financial stability, while bank size, credit risk, and bank concentration have negative impacts. External factors including the GDP growth rate positively affected financial stability, whereas real interest rates have a negative impact on the financial stability of the banks. The findings of this study will have implications for banks, bank regulators, policymakers, and the government in fostering a stable banking environment through targeted policies and strategic management of financial metrics.

financial stability

dynamic Panel data

Commercial Banks

two-step system GMM

Commercial banks, play a vital role in any country's economic development by mobilizing savings and allocating credit to the production sector, as well as helping boost economic performance in terms of mobilizing or transferring funds among market role payers (Jahan et al.,2019). Banks are then seen as the lifeblood of the economy. If they fail to fulfill their responsibilities, the consequences for the entire economy might eventually become so severe that even the banking system would be subject to massive shocks (Sifrain, 2021). Following the Global Financial Crisis (GFC) of 2007–2009, policymakers have paid reasonable attention to financial soundness of banks as a top priority on the international agenda (Beck, 2008).

Bank stability and financial stability have neither been properly defined nor agreed in the extent literature (Kasri & Azzahra, 2020). Financial stability is indeed a crucial aspect of an economy, encompassing the smooth functioning of the financial intermediation process. This process involves various entities like households, businesses, and the government interacting through financial institutions such as banks, insurance companies, and microfinance institutions (Khan, 2011).

On the other hand, financial instability has been a major cause for bank failures in the world, leading to large economic losses that would take a decade or more to recover. Massive commercial bank failures had occurred during the worldwide financial crisis of 2008/2009 (Jahn and Kick, 2012). According to the International Monetary Fund (IMF), during the 2008/2009 financial crisis, commercial banks globally suffered extensive losses, estimated at over $2.7 trillion in asset write-downs. Major institutions such as Citigroup and Bank of America reported significant losses due to the collapse of mortgage-backed securities and other risky assets linked to the U.S. housing market. Government interventions, including the U.S. Troubled Asset Relief Program (TARP), injected trillions of dollars to stabilize financial markets and support affected banks, highlighting the crisis's widespread economic impact. The recent financial crisis in Greek has led to a deterioration of the Greek economy and many Greece banks have experienced financial insecurity through a series of crises in the period from the Great Financial Crisis in 2008 until 2016 by large scale deleveraging as banks reduced lending significantly with the credit portfolio shrinking every year. As a result, the share of loans in total assets of the banking sector came down from more than 60% (Ozturk and Sozdemir, 2017).

Unstable financial institutions can cause a banking crisis, which has far-reaching negative consequences for the economy by interrupting the free flow of capital, reducing lending and investment, and causing the overall economy (Hutchison & Noy, 2005). The financial crisis of 2007, which began in the United States, created a global recession, financial institutions wrote off losses worth billions of dollars, and the government also incurred a loss in subsidizing these financial institutions.

In Ethiopia, the banking industry is growing but the country remains with the lowest access to banking services in Africa. The banking industry is currently improving steadily, with almost all banks are reporting positive accounting earnings. According to the National Bank of Ethiopia (2021) report, commercial banks in Ethiopia made a total profit of more than 42 billion Ethiopian Birr in 2021. However, this does not guarantee the going concern of the banks, and it does not necessarily mean that all profitable banks are healthy enough to fulfill their short-term and long-term obligations. For a country like Ethiopia, where the financial sector is dominated by commercial banks, any failure in the sector adversely affects its economic growth. During the COVID-19 breakout, the National Bank of Ethiopia (NBE) subsidized 15-billion birr (about 450 million USD) liquidity for private commercial banks to enable them to provide debt relief and additional loans to their customers in need (Lelissa, 2020).

In relation, to the determinants of a bank’s financial stability, several studies have been conducted in different countries and at different levels of economies, on different sets of factors and methodologies. However, there are mixed findings on the degree and its drivers of banks' financial stability. For example, in developed countries (Pascual et al., 2015, and, Jahan et al., 2019. In emerging economies (Karim et al. 2016; Ali & Puah, 2018; Ozili, 2019; Koskei, 2020, and Pham et al., 2021). In developing and sub-Saharan African countries (Ozili, 2018) .

The current study addressed this literature gap in two folds (1) the positive performance of the banks does not grantee for financial healthiness because the profit figure may not be fully cashed and available to meet short terms obligations (2) We used firm specific factors and macro variables to examine and identify the potential drivers of financial stability. It helps to enhance understanding on the major drivers of financial stability, which is ignored or less investigated in Ethiopian Context.

The study contributed significantly to the literatures and theories by identifying key factors influencing the stability of banking sector, such as regulatory frameworks, macroeconomic conditions, and institutional arrangements by establishing causal relationships and introducing methodological advancements in measuring financial stability, the study offers crucial insights for policymakers in designing effective regulations and policies to mitigate systemic risks. The study's implications extend to fostering a more informed approach among stakeholders, including regulators, financial institutions, investors, and researchers, aiming to strengthen the overall financial stability of the banking sector.

Definition and Concepts of Bank Financial Stability

Many commercial banks have failed as a result of the recent global financial crisis, creating renewed interest in the banking business. The financial crisis also brought attention to the significance of having a stable financial system that can withstand financial and economic shocks. Financial and bank stability has always been a priority for all central banks since it has direct consequences on the banking industry's long-term stability. One of the most commonly debated topics in today's economic literature is bank financial stability. The importance of financial stability evaluations was initially acknowledged during the international financial crises of the late 1990s and was further bolstered by the financial and economic crisis of 2007 (Karim et al., 2019).

Financial stability is the only one of the three generally given central bank missions that do not yet have a single, quantifiable, operational definition that is widely agreed upon. In many research papers, the word financial stability and banking stability are used interchangeably. This is a result of an absence of consensus and a widely accepted model of financial stability as well as difficulty in measuring and defining financial stability (Karim et al., 2019). According to Gadanecz & Jayaram, (2009), unlike price stability, financial stability is not easy to define or measure given the interdependence and the complex interactions of different elements of the financial system among themselves and with the real economy. This view of the inter-relatedness of banking and financial stability is supported by Swamy, (2014), and stresses the importance of differentiating banking from financial stability. This inter-relatedness is also evidenced in the definition by the ECB (2007) as cited by (Gadanecz & Jayaram, 2009) that defines financial stability as a condition of a financial system, that consists of banks, markets, and market infrastructure, able to absorb the likelihood of financial shock and mitigate the shocks in the banking processes.

Financial stability is very much related to banking stability as well as other components of the financial system, a review of the works of literature revealed various definitions of financial stability. The common theme in the definition of financial stability are the ability to absorb or withstand (Beck, 2008). According to Schinasi (2004), financial stability is defined in terms of a bank’s ability “to facilitate and enhance economic processes, manage risks, and absorb shocks over time, along a continuum rather on over a static condition.” Whereas, Houben et al (2004) define financial stability in terms of its ability to help the economic system allocate resources, manage risks, and absorb shocks, again over a continuum, changeable over time and consistent with multiple combinations of its constituent elements.

Hypothesis Development

In this study, the following variables based empirical review was conducted to investigate the determinants of financial distress in Ethiopian insurance companies. The empirical reviews were made in the Global context, in African context, and in Ethiopian context, but for the sake of simplicity it was summarized as follows through variables based.

The previous year bank financial stability, measured by the previous year Z-score, is not a one-time achievement but a continuous outcome shaped by a bank's ongoing performance. Research indicates that a bank's stability in one year typically contributes positively to its stability in the following year, highlighting the cumulative nature of financial stability over time.(Pham et al., 2021) .According to Pascual et al, (2015) and Carretta et al., (2015) banks' financial stability shows persistence over time, suggesting that the stability levels of banks tend to be sustained across consecutive periods and ongoing nature of risk and stability in banking, where past performance in terms of risk and stability influences future outcomes. Edimealem, (2014) concluded that the previous year's banking system stability, was among the determinants that affected the banking system stability positively in Ethiopia. In this study, the previous year's bank stability will be measured by the previous year's Z-score value.

Bank profitability is vital for commercial banks as it allows them to accumulate reserves that serve as buffers against economic downturns, thus promoting financial stability. This importance has been underscored by regulatory authorities, particularly following the global financial crisis of 2008–2009, highlighting profitability's dual role in ensuring bank resilience and supporting overall financial stability(Tan & Anchor, 2016). Furthermore, financial system stability has a direct relationship with the determinants of bank profitability (Ali, 2015). Ghenimi et al., (2017) stated profitability has a positive and significant effect on banking stability as the most profitable banks are more creditworthy than the less profitable banks. In this study, Profitability is measured as the ratio of net income before interest and tax to total assets.

Previous year bank financial stability (previous year Z-score) has a positive and significant effect on the bank’s financial stability.

Profitability has a positive and significant effect on the bank’s financial stability.

Bank size is measured by the natural logarithm of the total assets of the bank. Large banks have more market power, which can enable them to increase profit and build up high capital buffers, thus making them less susceptible to liquidity or macroeconomic shocks. According to Adusei, (2015) bank Financial stability has been typically supported by bank size larger banks essentially enjoy economies of scale which in turn positively influences their profitability and ultimately financial stability. Pham et al., (2021) and Kiemo et al., (2019) Study showed a positive relationship between bank size and a bank’s financial stability. Bank Size is measured as natural Logarithms of total assets.

Funding risk is proxied as a deposit to total assets plus equity to total assets divided by the standard deviation of deposit to total assets. The relationship between bank funding risk and stability received a considerable amount of attention among the researchers (Adusei, 2015). Bank instability is mainly associated with a larger portion of non-deposit funding (Demirgüç-Kunt & Huizinga, 2010). Adusei, (2015) reported a positive relationship between bank stability and funding risk. Funding risk has a negative effect on bank profitability. The result implies that banks are aggressive to generate more customer deposits. This situation increases operational costs such as, increasing promotional activities, offering low and attractive interest rate and others. Thus, the bank faces lower profitability due to a decrease in interest income from investment and lending operations(Ali & Puah, 2018). Funding risk is calculated through the sum of the deposit-to-total asset (DEP/TA) ratio and the E/TA ratio, which is further divided by the standard deviation of the DEP/TA ratio.

Bank size has a positive and significant effect on a bank’s financial stability

Funding risk has a negative and significant effect on a bank’s financial stability

Loan to asset ratio is an indicator of credit risk, measured by the level of bank liquidity that indicates the percentage of the bank’s loans to total assets in a given year. The total loans-to-total assets ratio indicates the extent to which the bank is vulnerable to variations in the repayment attitudes of its borrowers. Thus, if a large number of borrowers or borrowers with huge loan amounts default, the bank’s insolvency risk increases (Ghenimi et al., 2017). Credit risk (CR), which is measured by loans- the to-assets ratio, indicates the vulnerability of a bank to variations in the attitudes of its borrowers toward repayment. This means that an increase in borrower defaults leads to closing the bank being insolvent (Adusei, 2015). Liquidity is another factor that determines the level of a bank financial stability and is proxied as a liquid asset to total assets. The higher this value is the higher the liquidity the bank has and hence the higher will be the banking system stability. Liquidity refers to the ability of the bank to fulfill its obligations, mainly to depositors. Berger & Bouwman, (2013) argued that high cash holding can decrease liquidity risk and help banks lower the likelihood of failure. Torna & Young, (2012) showed that banks with more liquid assets are less likely to fail. It is proxied as liquid assets divided by the total assets.

Credit risk has a significant negative effect on a bank’s financial stability

Liquidity has a significant positive and significant effect on a bank’s financial stability

The financial stability of the banking sector can be affected by banking concentration. Boyd et al, (2004) argue that concentrated banking systems with larger banks may enhance stability by increasing profitability and maintaining higher capital buffers. Ozili, (2018), and Ozili, (2019) suggested that when large banks in concentrated markets are well-regulated, financial stability could be improved. On the contrary, Boyd and De Nicoló, (2005) claimed that banks in more concentrated markets can charge higher loan interest rates which can increase the moral hazard problem to borrowers inducing them to invest in riskier projects, which may weaken the banking system stability when losses are realized. The relationship between financial system development and economic growth is crucial for governments aiming to enhance living standards and economic performance. Research indicates that countries with robust and stable financial systems typically experience faster economic growth. Two main hypotheses—supply-leading and demand-leading—debate whether financial stability drives economic growth or vice versa through market efficiency and expansion. Pham et al., (2021), and, Ali and Puah, (2018), showed that the financial stability of the banking system can be affected by macroeconomic variables and indicated that the growth of the real Gross Domestic Product (GDP) increases the stability of the private banks, state-owned banks and the stability of the whole banking system.

Bank concentration has a positive and significant effect on a bank’s financial stability

Gross domestic product has a positive and significant effect on a bank’s financial stability

Interest rates significantly impact the financial stability of commercial banks, especially during economic downturns where higher rates can slow loan growth and increase nonperforming loans, potentially leading to greater losses. High rates can improve bank margins but also heighten the risk of defaults, affecting overall stability ((Pascual et al., 2015); Koskei, 2020). Both borrowers and banks are affected: rising rates strain borrowers' ability to repay loans, while low rates reduce profitability from lending. Edimealem, (2014) evidenced real interest rates were found to affect the banking system stability in Ethiopia negatively.

Real interest rate has a negative and significant effect on a bank’s financial stability

Using a judgmental sampling method, sixteen private commercial banks were selected based on the availability of consistent data from 2016 to 2020. We obtained data from the National Bank of Ethiopia.

We applied the two-step system GMM dynamic panel estimators of Arellano & Bover (1995), and Blundell & Bond, (1998) for the estimation since the dynamic panel GMM is better than conventional estimators due to its ability to correct potential endogeneity, heteroscedasticity, and autocorrelation in panel data. Following prior studies such as Koskei, (2020), Ali and Puah, (2018), Zaidi et al., (2018), (Adusei, 2015) and Köhler, (2015) we measured financial stability using a Z-Score: The Z-score is calculated as follow:

$\:{Zsc’re}_{it}$ =α+$\:{\gamma\:Zscore}_{it-1}$+$\:{BX}_{it}$ +$\:{\epsilon\:}_{it}$. 1

Where$\:{\:Zscore}_{it}$ is the financial stability of the bank i at time t, with i = 1…. N and t = 1,…,T. α is a constant term. $\:{\gamma\:Zscore}_{it-1\:\:}$is the lagged dependent variable. $\:{X}_{it}$ is a set of explanatory variables. β is a vector of the coefficient to be estimated. $\:{\epsilon\:}_{it}$ are error terms that are assumed to be distributed identically and independently with mean 0 and constant variance, by expanding we specified the last model of this study as the following.

$\:{Zscore}_{it}={\alpha\:}+\beta\:1{Zscore}_{it-1}+{B2X2}_{it}+{\beta\:3X3}_{it}+{\beta\:4X4}_{it}+\:{\beta\:5X5}_{it}\:+{\beta\:6X6}_{it}+\:{\beta\:7X7}_{it}+{\beta\:8X8}_{it}+{\beta\:9X9}_{it}+{\epsilon\:}_{it}$ 2

Where $\:{Zscore}_{it}$= Financial Stability (the dependent variable which is measured by Z-score); $\:{Zscore}_{it-1}$=the lagged dependent variable, previous year Z-score; X2=Profitability (ROA) measured by Net Income before Interest and tax/Total Asset; X3=Bank size (BS) measured by Natural Logarithm of Total Asset, X4=Funding risk (FR) measured by deposit/Total assets plus equity/total assets divided by standard deviation of deposit/total assets.; X5=Credit risk (CR) measured by Total Loan/Total Assets; X6=Liquidity (LIQ) measured by Liquid Asset/Total Asset; X7=Bank concentration (BC) measured by Hirschman-Herfindahl Index (HHI); X8=Gross domestic product growth rate(GDP) measured by The annual real Growth rate of gross domestic product; X9=Annual average real interest rate(RITR) measured by Annual Real Average interest rate; α = Constant; β1-β9 coefficients of the independent variables; εit=Error term.

Description and Measurements of Variables

The study investigates the relationship between the financial stability of private commercial banks in Ethiopia, measured by Z-score, and three categories of variables: bank-specific, industry-specific, and macroeconomic factors. By analyzing these factors, the research aims to uncover their respective impacts on bank stability. It provides insights relevant to bank management strategies and regulatory policies in the Ethiopian banking sector.

Measuring Bank financial stability (Z-Score), the Dependent Variable

Bank financial stability was measured using the Z-score. A higher Z-score implies a low probability of failure, which means the bank is stable. In contrast,’ a lower Z-score suggests a higher risk of insolvency. The Z-score is the most commonly used in the empirical banking literature to estimate a bank’s financial stability. Consistent with past studies, in this study the researcher calculated bank financial stability by using a Z-score as a measure of bank solvency risk or bank stability (Ali & Puah,2018); (Adusei, 2015). The computation of bank financial stability by Z-score is as follows:

Z-Score=$\:\frac{\text{R}\text{e}\text{t}\text{u}\text{r}\text{n}\:\text{o}\text{n}\:\text{A}\text{s}\text{s}\text{e}\text{t}\text{s}\left(\text{R}\text{o}\text{a}\right)+\left(\text{E}\text{a}\right)\raisebox{1ex}{$\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{E}\text{q}\text{u}\text{i}\text{t}\text{y}$}\!\left/\:\!\raisebox{-1ex}{$\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{A}\text{s}\text{s}\text{e}\text{t}\text{s}\:$}\right.}{\text{S}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}\:\text{D}\text{e}\text{v}\text{i}\text{a}\text{t}\text{i}\text{o}\text{n}\left(\delta\:\right)\:\text{o}\text{f}\:\text{R}\text{e}\text{t}\text{u}\text{r}\text{n}\:\text{o}\text{n}\:\text{A}\text{s}\text{s}\text{e}\text{t}\text{s}\left({\delta\:}\text{r}\text{o}\text{a}\right)}\:\:\:\:\:\:$

Independent Variables (Bank Specific Variables)

Previous Year’s Bank Stability Financial (Previous Year Z-Score)

In its nature, a bank's stability is not a single stage of accomplishment and success, rather it is the continuous performance of the bank over time, which means the previous year's bank’s financial stability can affect the current year's bank's financial stability. In this study, the previous year's bank stability will be measured by the previous year's Z-score value (Pham et al., 2021);(Pascual et al, 2015) and (Carretta et al., 2015).

Profitability

In this study, profitability is defined as the ratio of net income before interest and tax to total assets, reflecting how effectively banks manage their assets to minimize non-performing assets and enhance stability (Pascual et al., 2015) The importance of profitability lies in its direct relationship with financial system stability; as banks become more profitable, they build reserves that act as buffers against adverse shocks (Ali, 2015). This resilience is crucial for maintaining overall financial stability, as profitable banks are better equipped to withstand economic uncertainties and contribute positively to the stability of the banking sector.

Bank Size (SIZE)

Bank size is measured by the natural logarithm of the total assets of the banks. Large banks have more market power which can enable them to increase profit and build up high capital buffers, thus making them less susceptible to liquidity or macroeconomic shocks. More assets like loans mean banks can generate more revenue and by charging relatively higher or competitive interest rates due to economies of scale, they will be able to increase their business value (Ali & Puah,2018). The large-sized bank has the advantage of providing a large menu of financial services to their customers and thereby mobilizes more funds which will lead to serving their customers and generating profit that can boost their financial stability.

Funding Risk

Bank funding risk (FR) is the fourth independent variable which is also computed as follows. This variable is newly introduced by (Adusei, 2015) in banking literature, who argued that funding risk is an important factor to analyze because bank activities are dependent on customer deposits. Funding risk has a negative effect on bank financial stability. The result implies that banks are aggressive in generating more customer deposits. This situation increases operational costs such as increasing promotional activities, offering low and attractive interest rates, and others. Thus, the bank faces lower profitability due to a decrease in interest income from investment and lending operations(Ali & Puah, 2018). In line with past studies, in this study, we compute funding risk as follows$\:$$\:\text{F}\text{u}\text{n}\text{d}\text{i}\text{n}\text{g}\:\text{R}\text{i}\text{s}\text{k}\left(\text{F}\text{R}\right)=\frac{\frac{\text{D}\text{E}\text{P}}{\text{T}\text{A}}+\text{E}/\text{T}\text{A}}{\delta\:DEP/TA}$

Credit Risk

Loan asset ratio is an indicator of credit risk, measured by the level of bank liquidity that indicates the percentage of the bank's loans to total assets in a given year. A higher loans-to-assets ratio indicates a bank has more loans issued or loans issued make up a large portion of total assets. A higher loans-to-total assets ratio indicates that the bank has more of its assets in loans which means that if there should be more borrower default, the bank is closer to insolvency. Indeed, the use of the loan-to-assets ratio as a measure of credit risk is not novel. Researchers such as (Adusei, 2015), and, Ali & Puah, (2018) have measured credit risk by loan-to-asset ratio.

Liquidity

Liquidity is another factor that determines the level of a bank financial stability and is proxied as a liquid asset to total assets. The higher this value is the higher the liquidity the bank has and hence the higher will be the banking system stability. Liquidity refers to the ability of the bank to fulfill its obligations, mainly to depositors. Berger & Bouwman, (2013) argued that high cash holding can decrease liquidity risk and help banks lower the likelihood of failure. Torna & Young, (2012) showed that banks with more liquid assets are less likely to fail. It is proxied as liquid assets divided by the total assets.

Bank Concentration/ Herfindahl Index

In this study, banking concentration, measured by the Hirschman-Herfindahl Index (HHI), serves as an indicator of market competition within the banking sector. The HHI calculates market concentration by squaring the market shares of all banks operating in the industry and summing these values. Market concentration, as reflected by HHI, influences competitive dynamics and pricing strategies among banks. Higher concentration levels may lead to reduced competition, potentially impacting the efficiency of intermediation processes. The study posits that competitive pressures resulting from lower market concentration could enhance bank financial stability by promoting efficient operations and strategic pricing strategies. Thus, understanding HHI helps assess how market structure affects the stability and competitiveness of the banking sector.. It is measured as the sum of the square of the market shares of all firms in industry j for year t, the market share of each bank is the ratio of the total asset (ta) of the i^th bank to the industry’s total asset (TA).

$$\:\text{H}\text{e}\text{r}\text{f}\text{i}\text{n}\text{d}\text{a}\text{h}\text{l}\:\text{H}\text{i}\text{r}\text{s}\text{c}\text{h}\text{m}\text{a}\text{n}\:\text{i}\text{n}\text{d}\text{e}\text{x}\:\left(\text{H}\text{H}\text{I}\right)=\sum\:_{i=1}^{n}\left({MS}_{it}^{2}\right)=\sum\:_{i=1}^{n}\left(\frac{Tait}{Ta}\right)2$$

The Herfindahl Hirschman Index (HHI) is normally used to compute the market concentration. The HHI ranges from 0 to 1 with higher values indicating high concentrated and less competitive banking industry (Boyd and De Nicoló, 2005).

Gross Domestic Product

Gross domestic product growth is the most important indicator that affects the banking system stability of a country. It is generally presumed that GDP growth will have a positive impact on the stability of the banking system. Deterioration in the growth of GDP will contribute to a downturn impact on bank loans and deposits which intern will have a negative impact on banking stability (Ali and Puah, 2018)The slowdown in output is one of the best indicators of the banking crisis and adverse shocks affecting the whole economy will increase the nonperforming loans of banks and cause a systematic banking crisis. A strong economic condition measured by GDP, as a motivating factor to banks, has a statistically significant impact on banks' stability (Pham et al., 2021).The yearly real Gross Domestic Product (GDP) growth rate was used in this study.

Real Interest Rate

Real interest Refers to the inflation-adjusted interest rate. It is expected that an increase in real interest rate will have a positive impact on bank returns, but will have a negative influence on the borrowers through increasing interest loans((Pascual et al., 2015). That means high real interest will discourage borrowers and decrease applicants for a loan. Besides, a rise in market interest rates, whose direct effect is an increase in bank returns for newly made or variable interest loans, nonetheless bears a danger of increased credit risk( Koskei, 2020).In this study, the average real interest rate was used for analysis.

Descriptive statistics of variables

To give an overall description of data employed in the model, descriptive statistics are used to determine the minimum, maximum, mean, and standard deviation as follows.

Table 4.1

Summary of Descriptive Statistics Descriptive Statistics
VARIABLE	OBS	MEAN	STD. DEV.	MIN	MAX
ZSCORE	80	2.951	.362	2.382	4.294
ROA	80	17.541	2.663	5.879	23.233
SIZE	80	2.26e + 10	1.78e + 10	1.29e + 09	8.93e + 10
FR	80	2.874	.453	2.345	4.425
CR	80	53.114	5.629	38.503	65.473
LIQ	80	19.389	5.21	10.717	33.111
BC	80	42.945	4.628	14.157	48.067
GDP	80	8.047	1.409	6.057	9.564
RITR	80	− .216	4.519	-7.3	5.25

The Table 4.1 reveals the financial stability of private commercial banks in Ethiopia between 2016 and 2020 based on the Z-score metric. The mean Z-score of 2.951 indicates that these banks generally maintained adequate equity to cover potential losses, supported by positive returns on assets and equity, as well as stable return patterns. Despite slight variation with a standard deviation of 0.362, the range from a minimum Z-score of 2.382 to a maximum of 4.294 suggests consistent but not significant differences in financial stability among the sampled banks. This period did not see any substantial losses that would threaten the banks' equity, attributed to their increasing profitability, sufficient capital adequacy, and stable returns. Overall, the findings underscore the resilience and sound financial health of private commercial banks in Ethiopia during the study period.

The Z-score is a crucial measure indicating a bank's ability to absorb returns variability with its capital, as described by (Köhler, 2015). A higher Z-score signifies greater financial stability and lower risk of insolvency. (Köhler, 2015) categorizes Z-scores: Z > 2.99 indicates no financial problems, 1.88 < Z < 2.99 suggests slight issues, and Z < 1.88 indicates serious financial problems or defaults. In the study of private banks in Ethiopia from 2016 to 2020, Z-scores ranged from 2.382 to 4.294. The mean Z-score of 2.951 suggests moderate financial stability overall, with a low likelihood of imminent financial instability among the sampled banks.

Commercial banks in Ethiopia, on average, maintained a moderate liquidity position with a LIQ ratio of 19.389%, ranging from 10.717–33.111%. The standard deviation of 5.21% indicates substantial variability in liquidity levels among these banks during the study period. This variability was the highest among the variables examined, reflecting significant differences in liquidity management among private commercial banks in Ethiopia.

The bank size (SIZE) variable exhibited significant dispersion, with a mean value of 22,600,000,000 and a large standard deviation of 17,800,000,000. The range extended from a minimum of 1,290,000,000 to a maximum of 89,300,000,000, indicating considerable variation in bank sizes among the sampled institutions. Larger banks have the advantage of offering a broader range of financial services and attracting more funds, which can lead to more efficient customer service through economies of scale derived from their size.

During the study period, Ethiopian private commercial banks, on average, allocated 53.114% of their total assets to loans and advances, reflecting their exposure to credit risk (CR). This proportion ranged from 38.503–65.473%, indicating varying strategies in credit allocation among banks. The standard deviation of 5.629% underscores the diversity in how banks manage their credit portfolios. Comparing these credit proportions directly across banks may be complex due to differing levels of stability and risk management practices.

The study's industry-specific factor, bank concentration (BC), measured by the Herfindahl–Hirschman Index (HHI), averaged 42.945% during the sample period. The HHI ranged from a minimum of 14.157% to a maximum of 48.067%, indicating moderate market concentration among selected banks. The standard deviation of 4.628% suggests some variability in market concentration levels within the industry. This index serves as a measure of firm size distribution and competition intensity among banks in the studied context. On the other side, the return on assets (ROA) indicates that the minimum return was 5.879% while the maximum is 23.233%. The mean value of return on asset(ROA) of private commercial banks was 17.541, which indicates that the private commercial banks were earning an average return of 17.541% on their asset during the sample period under study with a standard deviation of 2.663% Furthermore, the result of the descriptive statistics for funding risk(FR) measured by equity to total asset plus deposit to total asset divided to standard deviation of deposit over total assets shows the deposit mobilization of the banks was 2.874 on average, with a minimum of 2.345 and a maximum of 4.425 with a standard deviation of 0.453.

Two-Step System GMM Model Regression Result

The final model used in this study for testing the formulated hypothesis was a two-step system GMM due to the fact it is an efficient estimator in the presence of Autocorrelation and Heteroscedasticity.

Table 4.2

Two-Step System GMM Model Result
Dynamic panel-data estimation,	system	GMM
Group variable: id	Number of obs	=	64
Time variable : year	Number of groups	=	16
Number of instruments = 16	Obs per group: min	=	4
Wald chi2(9) = 2.47e + 06	avg	=	4.00
Prob > chi2 = 0.000	max	=	4

Zscore	Coef.		St.Err.	t-value		p-value	[95% Conf		Interval]	Sig
L.score	.371		.131	2.83		.005	.114		.628	***
Roa	3.455		1.306	2.64		.008	.895		6.016	***
Size	− .121		.048	-2.51		.012	− .215		− .026	**
Fr	.319		.236	1.36		.175	− .142		.781
Cr	-1.544		.497	-3.11		.002	-2.518		− .57	***
Liq	1.843		.864	2.13		.033	.151		3.536	**
Bc	− .004		.002	-1.88		.06	− .008		0	*
Gdp	.07		.022	3.24		.001	.028		.113	***
Ritr	− .042		.012	-3.66		0	− .065		− .02	***
Constant	1.266		.593	2.14		.033	.104		2.427	**
Mean dependent var		2.998			SD dependent var			0.361
Number of obs		64			Chi-square			2466638.752
* p < .01, p < .05, * p < .1

Arellano-Bond test for AR (1) in first differences: z = -1.56 Pr > z = 0.119
Arellano-Bond test for AR (2) in first differences: z = -1.17 Pr > z = 0.241
Sargan test of overid. restrictions: chi2(6) = 7.26 Prob > chi2 = 0.298
(Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(6) = 4.21 Prob > chi2 = 0.649
(Robust, but weakened by many instruments.)
Number of Instruments = 16
Number of Groups = 16
Source: Own Computation via Stata 14, 2024

The two-step system GMM estimation results demonstrate a significant positive relationship (β = 0.3712244, z = 2.8, p = 0.005 < 0.01) between the previous year's Z-score (PYFS), a measure of financial stability, and the current year's financial health of banks. This indicates that banks maintaining stability in one year tend to exhibit stronger financial health in the following year compared to less stable banks. The persistence of financial stability over time within banks underscores the influence of past stability levels on current financial health. This finding aligns with previous studies by (Pham et al., 2021), Pascual et al, (2015) and, Edimealem, (2014).

The coefficient of return on assets (ROA) as a profitability proxy is significant (β = 3.455147, z = 2.64, p = 0.008 < 0.01), indicating a positive relationship between ROA and the Z-score proxy for financial stability of private commercial banks during the study period. This suggests that as banks' return on assets increases, their financial stability, as indicated by the Z-score, also improves, making them less likely to face financial instability. This finding is consistent with studies such as Tan & Anchor, (2016), (Ali, 2015) ,Ghenimi et al., (2017),and Koskei, (2020) which similarly found a positive and significant link between profitability (ROA) and financial stability in various banking contexts. These studies argue that profitable banks are better equipped to maintain stability by accumulating reserves from profits, enhancing their resilience compared to less profitable counterparts.

The study's analysis, as noted in Tables 4.9, reveals that the coefficient of bank size (SIZE) is negative and statistically significant (β = -0.21206529, z = -2.51, p = 0.012 < 0.05), contrary to expectations. This suggests that, holding other variables constant, an increase in bank size by one percent in log of total assets leads to an average decrease of -0.21206529 in the Z-score, indicating reduced financial stability. This finding aligns with agency theory and Size Fragility theory, which predict a negative relationship between size and financial stability, but contradicts stewardship and size stability theories that anticipate a positive relationship. Similar results have been found in prior studies such as Adusei, (2015) ,Pham et al., (2021), Kiemo et al., (2019) ,Ozili, (2018) and, Edimealem, (2014), which also reported a negative and significant impact of size on banking stability, suggesting diseconomies of scale as banks grow beyond a certain size. Belete, (2013) additionally highlighted the significant costs Ethiopian banks incur to acquire fixed assets, potentially exacerbating instability in larger and more monopolized banks compared to smaller counterparts.

The study measured funding risk (FR) in Ethiopian private commercial banks using a ratio involving deposits, total assets, equity, and their standard deviation, aiming to understand its impact on financial stability. Contrary to expectations, the analysis found a statistically insignificant positive impact of funding risk on financial stability (β = 0.3194531, z = -1.36, p = 0.175 > 0.1). This result suggests that funding risk does not significantly influence bank stability, diverging from the initial hypothesis that predicted a negative relationship. Therefore, there is insufficient evidence to conclude that funding risk is a primary determinant of financial stability in Ethiopian private commercial banks based on this study's findings.

Table 4.13 in the study confirms a negative relationship between credit risk (CR) and the financial stability of Ethiopian commercial banks (β = -1.544111, z = -3.11, p = 0.002 < 0.01), aligning with the research hypothesis. The findings indicate that a one unit increase in credit risk results in an average decrease of -1.544111 in banks' financial stability. Higher credit risk, evidenced by increased loans to total assets, adversely impacts banks by escalating non-performing loans, reducing income, and indirectly undermining financial stability. This outcome resonates with previous research, including studies by Ghenimi et al., (2017), by Adusei, (2015), and Ali & Puah, (2018), all of which underscore the negative association between credit risk and bank stability.

The study reveals a positive and statistically significant association between liquidity ratio (LIQ), measured by liquid assets to total assets, and the Z-score of private commercial banks in Ethiopia (β = 1.843472, z = 2.13, p = 0.033 < 0.05). This indicates that higher liquidity ratios are correlated with greater financial stability among these banks, allowing them to effectively manage unexpected withdrawals or credit demands. This finding is consistent with prior research by Ghenimi et al., (2017) and Kiemo et al., (2019),which also observed that increased liquidity levels contribute positively to bank stability. They argue that banks with robust liquidity positions are less susceptible to financial shocks and better equipped to maintain their financial health by meeting maturing obligations promptly.

The study identifies a negative and statistically significant relationship (β = -0.0036984, z = -1.88, p = 0.060 < 0.1) between bank concentration (BC), measured by the Herfindahl–Hirschman Index (HHI), and bank financial stability in Ethiopia. This unexpected finding suggests that as market power among banks increases, financial stability tends to decrease, despite one commercial bank dominating a significant portion of the sector. This aligns with previous research by Boyd & De Nicoló, (2005), and Čihák & Hesse, (2010), which also highlight the negative implications of banking market concentration on stability due to higher loan interest rates, moral hazard issues, and potential "too-big-to-fail" risks.

The study explores the relationship between GDP growth rate and bank financial stability, finding a statistically significant positive impact (β = 0.0701874, z = 3.24, p = 0.001 < 0.01). This indicates that as GDP growth increases, bank stability also increases, likely due to heightened demand for credit and financial services in a growing economy. The findings support the Demand Following Hypothesis, which posits that economic growth drives demand for bank services, thereby enhancing their financial performance and stability. These results are consistent with earlier studies by Adusei, (2015) who reported that the economic growth has a positive impact on the bank financial stability.

The study indicates a significant negative impact of real interest rates (RITR) on the financial stability of private commercial banks in Ethiopia (β = -0.042395, z = -3.66, p = 0.000 < 0.01). A one percent increase in interest rates is associated with a decrease of approximately − 0.042395 units in bank stability, highlighting the sensitivity of bank stability to interest rate changes. This result is in line with the findings of Karim et al.,(2019), Koskei,(2020), and Edimealem, (2014). Pascual et al., (2015), concluded that weakening economic conditions with an indicator of increasing interest rates could increase non-performing loans and hence reduce bank stability.

The study provides a comprehensive analysis of the factors influencing financial stability in Ethiopian commercial banks. Firstly, it emphasizes the significant role of continuity in financial stability, highlighting that stability from previous periods positively affects current stability. This indicates a cumulative effect where sound financial performance over time enhances a bank's resilience to economic fluctuations. Secondly, the study underscores profitability as a critical determinant. It suggests that profitable banks are better equipped to invest in advanced credit risk management systems, technology upgrades, and skilled workforce, all of which contribute to enhancing financial stability. Profitability also enables banks to pursue profitable investments, thereby bolstering their overall stability.

However, the study identified credit risk as a substantial challenge. High credit risk levels lead to an increase in non-performing loans, which directly impacts a bank's income and profitability, thereby undermining its stability. Managing and mitigating credit risk emerges as a crucial area for maintaining stability. Furthermore, the study explores the negative effects of bank size and market concentration on financial stability. Larger banks and concentrated markets tend to charge higher interest rates on loans, reflecting their market power. This can compel borrowers to undertake riskier projects, increasing default risks and operational vulnerabilities, thereby reducing overall stability. Liquidity management is another critical factor discussed. Adequate liquidity allows banks to meet their financial obligations promptly and seize profitable investment opportunities. Insufficient liquidity, on the other hand, can lead to vulnerabilities during periods of financial stress, potentially affecting overall stability. From a macroeconomic perspective, the study finds that economic growth, as measured by GDP, positively influences bank stability. A growing economy encourages savings and investment, which are intermediated by the financial sector, thereby supporting stable banking operations.

Conversely, the study found a negative relationship between inflation-adjusted real interest rates and bank stability. Higher real interest rates may increase banks' income from interest, but they can also constrain borrowers' ability to repay loans, impacting asset quality and overall stability negatively. In conclusion, the study underscores the importance of coordinated efforts among banks, government authorities, and regulatory bodies like the National Bank of Ethiopia to manage these factors effectively. By addressing issues related to profitability, credit risk management, market structure, liquidity management, and macroeconomic conditions, stakeholders can promote sustained stability in the Ethiopian banking sector, crucial for economic growth and financial resilience.

This study was not an end by itself. The study recommends future research on the determinants of financial stability in Ethiopian commercial banks to expand beyond financial and quantitative variables by including additional bank-specific, industry-specific, and macroeconomic factors. It suggests incorporating operational stability determinants alongside financial ones, emphasizing the dynamic nature of the banking sector necessitating continuous investigation. Researchers are encouraged to use a mixed-methods approach integrating qualitative and quantitative data to provide a comprehensive understanding. This approach aims to uncover evolving factors influencing stability and ensure the banking sector's long-term resilience and improvement, highlighting the collaborative efforts needed between academic researchers and banking professionals.

AIB	Awash International Bank
BOA	Bank of Abyssinia
BC	Banking Concentration
CBE	Commercial Bank of Ethiopia
CVH	Charter Value Hypothesis
CS	Concentration–Stability
CF	Concentration–Fragility
CBO	Cooperative Bank of Oromia
CAMEL	Capital Adequacy, Asset Quality, Management Quality, Earnings Ability, and Liquidity
DFH	Demand Following Hypothesis
FEM	Fixed Effect Model
GLS	Generalize Least Square
GDP	Gross Domestic Product
GMM	Generalize Moment Method
HHI	Herfindahl–Hirschman Index
IMF	International Monetary Fund
MoFED	Ministry of Finance and Economic Development
NBE	National Bank of Ethiopia
NIB	Nib International Bank
OIB	Oromia International Bank
PYFS	Previous Year Financial Stability
POLS	Pooled Ordinary Least Square
REM	Random Effect Model
SLH	Supply Leading Hypothesis
TBTFH	The too Big to Fail Hypothesis
WB	World Bank

Funding Information:

The authors did not receive any funds for the research.

Author contributions

AT wrote the main manuscript text and OT and GD drafted the work, commented and substantively revised it from proposal write stage to the final stage of completion of the paper.

Data availability

All data generated or analyzed during this study are included in the manuscript.

Acknowledgments:

The authors would like to thank Obsa Teferi for his support and assistance with this research.

Ethics approval and consent to participate

The datasets generated and/or analyzed during the current study are obtained from each insurance private commercial banks and on the other hand, data on macro-economic factors, such as information and exchange rate, were collected from National bank reports and the Central Statistics Agency (CSA).

Conflict of Interest Statement:

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Consent for publication

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Determinants of Financial Stability: A Dynamic Panel Data Analysis

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Abstract

1. Introduction

2. Literature Review and Hypothesis Development

3. Materials and Methods

4. Results and Discussion

5. Conclusion

Abbreviations

Declarations

References

Status:

Version 1