Liquidity Risk Mediation in the Dynamics of Capital Structure and Financial Performance: Evidence from Jordanian Banks

Abstract

Maximising financial performance while maintaining adequate liquidity is a crucial and ongoing challenge for bank management, particularly in emerging markets. This study focuses on the relationship between capital structure and financial performance in Jordanian banks, with the mediating role of liquidity risk. Using panel data from 13 central Jordanian banks over the 2015–2022 period, we employ structural equation modelling (SEM) to analyse how capital structure ratios (equity-to-asset, debt-to-loan, and deposit-to-asset) influence financial performance metrics (return on assets and net income-to-expenditure ratio). Our findings reveal a significant positive association between capital structure and financial performance. However, liquidity risk fully mediates this effect. Capital structure primarily impacts performance by influencing a bank’s liquidity risk profile. Furthermore, the strength of this mediating effect is noteworthy—capital structure exhibits a statistically more robust association with liquidity risk than its direct impact on performance. This highlights the crucial role of managing liquidity risk within the complex dynamics of bank operations. This research makes a significant contribution to the existing literature by demonstrating the positive impact of capital structure on performance using the underlying mechanism through which this effect occurs. The insights of this research provide several implications for practice in the context of banking industries.

1. Introduction

Financial economics continues to scrutinise the intricate dynamics of banking sector performance. At the heart of this complex system lie three critical variables: capital structure, liquidity risk, and financial performance. These elements form a triad of interdependent factors that collectively shape banking institutions’ stability, efficiency, and profitability worldwide (Berger and Bouwman 2013; Phuong et al. 2015; Sasso 2016).

Capital structure, the mix of debt and equity used to finance a bank’s operations, is the foundation for banking activities. It determines a bank’s funding costs and influences its risk-bearing capacity and regulatory compliance (Banna and Alam 2021; Boshnak 2023; Gropp and Heider 2010). Capital structure remains a subject of ongoing debate, with competing theories, such as the trade-off theory and the pecking order theory, offering different perspectives on how banks should balance debt and equity to maximise value (Bataineh 2021; Myers 2001).

Inextricably linked to capital structure is liquidity risk, which refers to a bank’s ability to meet its short-term obligations without incurring unacceptable losses (Chen et al. 2021; Diamond and Rajan 2001). Liquidity risk management has gained prominence in the post-2008 financial crisis era, with regulators and bank managers recognising its critical role in maintaining financial stability (Basel III 2013). The relationship between capital structure and liquidity risk is complex and bidirectional. In contrast, higher capital levels can provide a buffer against liquidity shocks, and the need to maintain liquidity can also influence a bank’s capital structure decisions (Distinguin et al. 2013; Rout et al. 2021).

Financial performance, the third vertex of this triad, is the ultimate measure of a bank’s success in navigating the challenges posed by capital structure decisions and liquidity risk management. Commonly assessed through metrics such as return on assets (ROA), return on equity (ROE), and net income-to-expenditure ratio (NIER), financial performance reflects a bank’s ability to generate profits while managing risks and meeting regulatory requirements (Athanasoglou et al. 2008; Dey et al. 2023). The pursuit of solid financial performance must be balanced against prudent risk management and long-term stability, creating a complex optimisation problem for bank managers (Altunbas et al. 2007; Isanzu 2017).

The interrelationships among these three variables form a dynamic system central to understanding bank behaviour and performance. Capital structure decisions influence both liquidity risk and financial performance; liquidity risk management affects capital allocation and profitability; and financial performance outcomes feed back into capital structure and liquidity management strategies (Berger and Bouwman 2009). This complex web of interactions is further complicated by the unique characteristics of emerging markets, where institutional frameworks, regulatory environments, and economic conditions can differ significantly from those in developed economies (Khan et al. 2020; Naceur and Omran 2011).

In the context of emerging markets, the banking sector plays an even more crucial role in economic development, acting as the primary conduit for capital allocation and financial intermediation (Beck et al. 2010). The commercial banking sector, in particular, is a vital funding source for various economic sectors and development projects. However, banks in these markets face unique challenges in balancing profitability and risk management, and particularly in liquidity risk (Hunjra et al. 2020).

With its strategically essential and relatively stable banking sector, Jordan provides an ideal setting for examining these relationships in an emerging market context. The Jordanian banking sector has undergone significant reforms and modernisation efforts in recent years, making it a fascinating case study for investigating how capital structure, liquidity risk, and financial performance interact in a rapidly evolving financial landscape (Al-Shrari 2023; Al-Homaidi et al. 2019). By focusing on this market, we can gain valuable insights that may apply to other emerging economies grappling with similar challenges in balancing growth, profitability, and risk management.

Despite the growing body of research on these topics, a consensus on how capital structure influences financial performance in the banking sector remains elusive, particularly in emerging markets. This lack of agreement underscores the complexity of the relationships at play and highlights the need for further investigation. Moreover, the potential mediating role of liquidity risk in this relationship has been largely unexplored, presenting a significant gap in our understanding of bank financial dynamics (Le and Pham 2021).

This study addresses these gaps by investigating the complex relationships between capital structure, liquidity risk, and financial performance in Jordanian commercial banks. Our research examines the direct impact of capital structure on financial performance while also analysing the mediating role of liquidity risk in this relationship. By doing so, we aim to provide empirical evidence that can inform banking practices and regulatory policies in Jordan and similar emerging markets.

The significance of this research lies in its potential to enhance our understanding of how banks can optimise their capital structures to improve financial performance while effectively managing liquidity risk. By elucidating these relationships, we contribute to the ongoing debate on bank capital regulation and offer practical insights for bank managers and policymakers in emerging markets (Bataineh 2021; Neville and Lucey 2022). Furthermore, our study employs advanced statistical techniques, including structural equation modelling (SEM), to provide a more nuanced analysis of the complex interrelationships between these variables. This methodological approach allows us to capture both direct and indirect effects, offering a more comprehensive picture of the financial dynamics at play in the banking sector (Kline 2023).

Our research is particularly timely given the ongoing challenges faced by the global banking sector, including the aftermath of the COVID-19 pandemic and increasing regulatory pressures. Focusing on the Jordanian banking sector, we provide a case study that can offer valuable lessons for other emerging markets facing similar challenges. The insights gained from this study can help banks in these markets better navigate the complex interplay between capital structure decisions, liquidity risk management, and financial performance optimisation.

Based on the outcomes of 13 Jordanian banks from 2015 to 2022, this research reveals a significant positive association between capital structure and financial performance, and this relationship is fully mediated by liquidity risk. Capital structure primarily impacts performance by influencing a bank’s liquidity risk profile. This highlights the crucial role of managing liquidity risk within the complex dynamics of bank operations.

In the subsequent sections of this paper, we will delve into a comprehensive review of the relevant literature, develop our hypotheses based on existing theories and empirical evidence, describe our rigorous methodology, present our findings, and discuss their implications for both theory and practice in the field of banking and finance. This structured approach aims to contribute meaningful insights to the ongoing discourse on bank financial management and provide a solid foundation for future research in this critical area.

2. Literature Review and Hypothesis Development

2.1. Theoretical Framework

This study is grounded in two primary theories: the trade-off theory and the pecking order theory. These theories provide a solid foundation for understanding the relationships between capital structure, liquidity risk, and financial performance in the banking sector.

The trade-off theory, proposed by Kraus and Litzenberger (1973), suggests that firms balance the benefits of debt financing (such as tax shields) against the costs (such as financial distress and bankruptcy costs). In the context of banks, this theory implies that there is an optimal capital structure that maximises firm value by balancing the benefits of debt against its costs.

The pecking order theory, developed by Myers and Majluf (1984), posits that firms have a hierarchy of financing preferences. They prefer internal financing to external financing and debt to equity when external financing is required. This theory is particularly relevant to banks, as it helps explain their capital structure decisions about liquidity management and financial performance.

These theories provide a framework for understanding how banks make capital structure decisions and how these decisions impact their liquidity risk and financial performance.

2.2. Capital Structure and Financial Performance

Banks’ capital structure decisions directly impact profitability and are central to their financial strategy (Suryani and Nadhiroh 2020; Velnampy and Niresh 2012). Banks often balance debt and equity in their capital structure. Debt can be beneficial in optimising capital structure, reducing agency costs, and realising tax benefits (Al-Najjar and Taylor 2008). Therefore, the blend of debt and equity is critical in shaping a bank’s capital structure and maximising financial efficiency and overall performance.

The pecking order theory suggests that external funding should be prioritised through debt when internal funds are insufficient. This is because the tax deductions resulting from interest payments offer significant benefits (Brealey et al. 2008; Sudana 2015). Capital structure reinforces recapitalisation by fulfilling banks’ requirements by augmenting the minimum paid-up capital. It enables banks to enhance operational efficiency and effectiveness in customer interactions (Saleh and Abu Afifa 2020).

Studies have proposed theories predicting the impact of bank capital structure on financial performance, with varying outcomes. Lee and Hsieh (2013) analysed the relationship between banks’ capital structure and profitability across 42 Asian countries, finding that Middle Eastern banks had the most significant positive impact on financial performance. Iannotta et al. (2007) found a significant and positive association between capital and banks’ profitability, attributing this correlation to factors including superior bank management quality and lower bankruptcy costs.

However, this understanding of capital’s influence on profitability is not universally held. Naceur (2003) uncovered evidence suggesting capital may adversely impact bank profitability. Additional research conducted by Velnampy and Niresh (2012) further supports this alternate viewpoint, highlighting a negative correlation between bank capital structure and performance.

Recent research by Oanh et al. (2023) using a Bayesian approach has provided new insights into how capital structure affects bank performance. Their findings emphasise the complex nature of this relationship and suggest that other factors, including liquidity, may moderate the impact of capital structure on performance.

Considering these divergent findings, scholars such as Berger and Bouwman (2009) and Anarfo (2015) have called for further exploration into the relationship between bank capital structure and profitability. Thus, we propose the following hypothesis:

Hypothesis (H1).

Banks’ capital structures in the Jordanian banking sector significantly impact their financial performance.

2.3. Capital Structure and Liquidity Risk

Banks act as intermediaries to mitigate information asymmetry and reduce transaction costs between borrowers and lenders (Parvin et al. 2020). This function addresses market imperfections arising from divergent perceptions between borrowers and lenders, which can result in a moral hazard or adverse selection (Khaldi and Hamdouni 2018).

Diamond (1984) shows that banks generate liquidity to meet depositors’ withdrawal requirements. Banks transfer risk using riskless deposits to fund risky investment portfolios and earn profits through risk transfer (Mairafi et al. 2018). Banks may maintain sufficient liquidity to meet their immediate obligations.

According to Diamond and Rajan (2000), augmenting bank capital can reduce liquidity and facilitate banks’ maintaining financial stability, thereby avoiding potential financial difficulties. Acosta-Smith et al. (2020) propose that a bank’s capital influences liquidity risk via two pathways: capital’s role in absorbing losses and as a catalyst prompting banks to reconfigure their capital structure for more effective liquidity risk management.

Several empirical studies have shed light on the principal determinants of bank liquidity risk, confirming a positive relationship between liquidity risk and the financial performance of banks. These determinants include bank size, capital adequacy, and financial leverage (Akhtar et al. 2011; Al-Harbi 2017). However, offering a different perspective, studies by Burksaitiene and Draugele (2018) and Sumani and Roziq (2020) indicate that capital structure wields a significant influence over liquidity risk, independent of the traditionally recognised factors.

Boamah et al. (2023) further contribute to this discussion by examining the interplay between capital regulation, liquidity risk, and bank performance in emerging economies. Their findings underscore the importance of considering liquidity risk when studying the relationship between capital structure and bank performance.

Thus, we propose the following hypothesis:

Hypothesis (H2).

The capital structure of banks in the Jordanian banking sector significantly impacts liquidity risk.

2.4. Liquidity Risk and Financial Performance

Banks face significant liquidity risk (Arif and Nauman Anees 2012). Such factors negatively impact banking performance and deter existing and potential clients from engaging with the bank (Saleh and Abu Afifa 2020). Liquidity risk emerges from insufficient requisite liquidity to meet short-term debts and unpredictable expenses (Dey et al. 2023).

Bourke (1989) studied banks’ profitability and causes, revealing that banks with better liquidity generate higher profits Rahman and Saeed (2015) established a positive correlation between bank performance and liquidity risk, indicating that banks must increase their liquidity to enhance their efficacy. Chen et al. (2018) investigated the drivers of liquidity risk and how that risk relates to bank profitability. The findings demonstrated that increased liquidity lowers bank profitability as measured by ROA and ROE.

The impact of liquidity on performance has been the subject of several research studies (Claeys and Vander Vennet 2008; Trujillo-Ponce 2013). The outcomes have been varied. According to several studies, decreasing liquidity risk favours a bank’s financial performance (Bordeleau and Graham 2010; Bourke 1989; Lartey et al. 2013). Some have found the reverse true (Alim et al. 2021; Konadu 2009).

Ruziqa (2013) provides valuable insights into this relationship in the context of Indonesian banks, finding that both credit and liquidity risks significantly influence bank financial performance. This study emphasises the importance of considering multiple risk factors when examining bank performance.

Thus, we propose the following hypothesis:

Hypothesis (H3).

Banks’ liquidity risk in the Jordanian banking sector significantly impacts their financial performance.

2.5. The Mediating Role of Liquidity Risk between Capital Structure and Financial Performance

Prior studies have discussed the nexus between capital structure and financial performance (Chadha and Sharma 2015; Nelson and Peter 2019; Saeed et al. 2013; Zeitun and Tian 2014). Some scholars have emphasised the direct impact of capital structure on financial performance (Das and Swain 2018; Pinto et al. 2020; Vătavu 2015). Alternatively, some scholars have argued that capital structure does not always impact firm performance directly, but indirectly improves performance through mediating and moderating factors (Ahmed et al. 2023; Aslam et al. 2014; Ronoowah and Seetanah 2024; Shahwan 2018).

In this study, we proposed that liquidty risk is an essential conduit that plays a mediating role between capital strucutre and financial performance. While direct relationships between capital structure, liquidity risk, and financial performance have been established in previous research, the mediating role of liquidity risk in this relationship is a relatively unexplored area. Acosta-Smith et al. (2020) propose that a bank’s capital influences liquidity risk via two pathways: capital’s role in absorbing losses and as a catalyst prompting banks to reconfigure their capital structure for more effective liquidity risk management. Capital structure is a crucial factor which has a significant influence over liquidity risk (Sumani and Roziq (2020). Bank risk is considered a key channel through which intellectual capital influences financial performance (Cahyaningrum and Atahau 2020). In addition, liquidty risk management mediates the path between the liquidity factors and performance of UAE Islamic banks (Alqemzi et al. 2022). Boamah et al. (2023) found that liquidity risk mediates the relationship between capital regulation and bank performance, suggesting that the impact of capital structure on performance is not direct but operates through its effect on liquidity risk. Moreover, liquidity risk and credit jointly influence bank financial performance (Ruziqa (2013). Thus, this study proposes that liquidity risk might act a mediating mechanism through which the capital structure impacts the financial performance of Jordanian banks. Based on this empirical foundation, we propose the following hypothesis:

Hypothesis (H4).

Liquidity risk mediates the relationship between capital structure and financial performance in the Jordanian banking sector.

The study’s proposed research framework, incorporating these relationships and control variables, is depicted in Figure 1.

3. Methodology

3.1. Population and Sample

This study employs a quantitative approach to investigate how capital structure affects financial performance and how liquidity risk mediates this connection, utilising secondary data from financial reports of Jordanian banks. The population consists of 25 Jordanian banks (Central Bank of Jordan n.d.), from which we selected a sample of 13 commercial banks over eight years (2015–2022). There are two reasons to focus on the financial industry over traditional non-financial firms. First, financial industries are highly concerned with mitigating risk and capital structure. Hence, they are always concerned with risk mitigating strategies to enhance their operations. However, on the other hand, non-financial firms are keenly focusing on competitive advantage, reputation, and growth (Anwar 2018; Ullah et al. 2023).

The focus on Jordanian banks is justified by the sector’s crucial role in the country’s economy and its resilience amid regional instabilities. This geographical limitation allows for a more controlled study environment, minimising the impact of varying regulatory frameworks and economic conditions across different nations. Furthermore, Jordan’s banking sector has undergone significant reforms in recent years, making it an interesting case study for understanding capital structure dynamics and financial performance.

Our sample of 13 banks was carefully selected based on the availability of consistent and complete financial data for the entire study period. Due to their unique regulatory structures and operating models, we excluded foreign and Islamic banks, focusing instead on commercial banks to ensure comparability. This sample represents over 50% of the total population and is considered representative in financial studies (Krejcie and Morgan 1970).

The study period of six years (2015–2020) was chosen to balance the need for a comprehensive dataset with the requirement for recent and relevant information. This timeframe captures a significant period of economic development and regulatory changes in Jordan’s banking sector, allowing for meaningful analysis of trends and relationships. The resulting 78 bank–year observations (N = 78) provide a robust dataset for our analysis.

While a larger sample size or more extended period might be preferable in some contexts, our sample of 78 observations is well justified. The sample covers more than half of the Jordanian banking sector, ensuring high representativeness. By focusing on a carefully selected sample and period, we ensure the consistency and completeness of our data, which is crucial for reliable analysis. Using panel data allows us to exploit cross-sectional and time-series variations, partially mitigating the limitations of sample size (Baltagi 2015).

From a statistical standpoint, our sample size of 78 is sufficient to detect medium to large effect sizes with adequate statistical power in our analyses, including regression analysis and structural equation modelling (Cohen 2016). Moreover, our sample size is comparable to, and in some cases more significant than, those used in published studies focusing on banking sectors in individual countries (Berger and Bouwman 2013).

3.2. Data Collection

Data collection involved gathering financial information from the selected banks’ annual reports, publicly available through the Amman Stock Exchange and the banks’ official websites. We also cross-referenced these data with information from the Central Bank of Jordan to ensure accuracy and consistency, thereby enhancing the reliability of our dataset.

3.3. Measuring Variables

3.3.1. Dependent Variable

In this study, to measure the dependent variable (bank financial performance), two proxies were used: return on assets (ROA) and net income-to-expenditure ratio (NIER). The capacity of a bank’s management to generate profits from its assets is shown by its ROA. NIER shows how well the bank can cover its costs through financial and operating income. Parvin et al. (2020), Chiaramonte and Casu (2017), and Sahyouni and Wang (2019) all used these measurements in their studies.

3.3.2. Independent Variable

In this study, to gauge the explanatory variables (capital structure), three proxies were used, namely, equity/asset ratio (EAR), debt-to-loan (DTL), and deposit-to-asset ratio (DAR). The EAR represents the percentage of a company’s overall equity allocated towards its operations. The DTL ratio quantifies a firm’s borrowing about its loans. Increasing debt usage results in a higher debt-to-loan ratio (DTL ratio). Parvin et al. (2020) state that the DAR is a standard ratio that gauges a firm’s deposits relative to its assets.

3.3.3. Mediating Variable

In this study, to measure the mediating variable (liquidity risk), two proxies were employed, namely, the loan-to-deposit ratio (LTD) and cash-to-deposit ratio (CTD). The LTD ratio is commonly employed to assess liquidity and credit risk. It is calculated by dividing a bank’s total loans or financing by its total deposits. Adam (2014) states that a bank’s loans are funded through deposits, regardless of the situation, as indicated by this ratio. The CTD measures the bank’s lending capacity based on deposits and indicates its primary banking operations (Burksaitiene and Draugele 2018; Goel and Kumar 2016).

Table 1. Study variables.

Description and Variables	Symbol	Measurement of Variables	Source
Financial Performance	FP	ROA = Net Income/Total Asset NIER = (Total Revenue-Total Expenses)/Total Expenses	Parvin et al. (2020)
Capital Structure	CS	EAR = Equity/Asset DTL = Debt/Loan DAR = Deposit/Asset	Parvin et al. (2020)
Liquidity risk	LR	LTD = Total Loans/Total Deposits CTD = Total Cash/Total Deposit	Hacini et al. (2021); Burksaitiene and Draugele (2018)

displays the variables and their respective measurements utilised in the study.

Using an established conceptual framework, this study focuses on the mediating role of liquidity risk in the relationship between capital structure and financial performance. The theoretical framework allows an exploration of complex relationships without the need for control variables. While control variables can be helpful, they may complicate this initial investigation into these relationships. We aim to understand the dynamics between capital structure, liquidity risk, and financial performance. Adding control variables at this stage may obscure these core dynamics. However, future research could benefit from including control variables for a more nuanced understanding, contributing to a more comprehensive view of these phenomena.

3.4. Econometric Equation

The econometric model, sometimes referred to as an algebraic model, is mentioned below.

$${FP}_i = {\propto }_i + {\beta }_1{CS}_i + {\epsilon }_i$$

(1)

$${LR}_i = {\propto }_i + {\beta }_1{CS}_i + {\epsilon }_i$$

(2)

$${FP}_i = {\propto }_i + {\beta }_1{LR}_i + {\epsilon }_i$$

(3)

$${FP}_i = {\propto }_i + {\beta }_1{CS}_i + {\beta }_2{LR}_i + {\epsilon }_i$$

(4)

In the above model,

• α is the intercept term;
• β1, β2 are coefficients of variables;
• FP represents financial performance;
• CS represents capital structure;
• LR represents liquidity risk.

4. Data Analysis

The study aims to investigate the influence of capital structure on the financial performance of Jordanian banks and explore the mediating role of liquidity risk. To achieve these objectives, we employ a multifaceted analytical approach that combines descriptive analysis, correlation analysis, panel data estimation methods, and structural equation modelling (SEM).

Our descriptive analysis provides a comprehensive overview of the data, including measures of central tendency and dispersion, helping to identify patterns and trends in the variables over the study period. This is complemented by correlation analysis, which assesses the strength and direction of associations between variables, offering preliminary insights into potential relationships.

Given the nature of our data (cross-sectional and time-series), we utilise advanced panel data techniques such as fixed effects or random effects models, depending on the results of specification tests (e.g., Hausman test). These methods allow us to control for unobserved heterogeneity across banks and over time, which is particularly valuable given our sample size. The panel data approach enhances the robustness of our findings by accounting for both individual bank characteristics and temporal changes.

To investigate the mediating role of liquidity risk in the relationship between capital structure and financial performance, we employ structural equation modelling (SEM). This sophisticated technique is beneficial for our sample size as it allows for the simultaneous estimation of multiple relationships and the incorporation of latent variables, providing a more holistic understanding of the complex interplay between our variables of interest.

We implement several strategies to address potential limitations associated with our sample size. We conduct rigorous data validation, including thorough checks for outliers, missing data, and potential reporting errors to ensure the quality of our dataset. Where appropriate, we use bootstrapping techniques to generate robust standard errors and confidence intervals, which can help mitigate concerns about the normality assumption in smaller samples.

The descriptive analysis findings of the study variables are shown in

Table 2. Descriptive analysis.

Variables	Mean	SD	Q1	Median	Q3	Min	Max	Z-Value	N
ROA	0.0094	0.0045	0.0059	0.0093	0.0128	−0.0016	0.0185	1.1450	78
NIER	0.4532	0.1757	0.3099	0.4391	0.5797	0.1273	0.8054	1.9814	78
EAR	0.1286	0.0295	0.1032	0.1227	0.1624	0.0750	0.1725	2.4071	78
DTL	−0.1483	0.2119	−0.2921	−0.1441	−0.0058	−0.7046	0.2067	0.8141	78
DAR	0.7033	0.0867	0.6474	0.6961	0.7575	0.5069	0.8660	0.6747	78
LTD	0.7343	0.1056	0.6854	0.7583	0.8033	0.4801	0.8912	0.0948	78
CTD	0.1739	0.0571	0.1298	0.1633	0.2143	0.0668	0.2967	1.5855	78

Note: ROA = return on assets, NIER = net income-to-expenditure ratio, EAR = equity/asset ratio, DTL = debt-to-loan, DAR = deposit-to-asset ratio, LTD = loan-to-deposit ratio, CTD = cash-to-deposit ratio. SD = standard deviation, Min = minimum, Max = maximum, N = sample size.

. Several vital measures were employed to explore financial performance, capital structure, and liquidity risk among the firms under study, each providing unique insights into the companies’ operations and financial health. Analysing financial performance via the proxies of ROA and NIER, we observed average values of 0.0094 and 0.4532, respectively. These values suggest that, on average, the firms are generating a return of 0.94% on their assets and that their net income comprises approximately 45.32% of total expenditures. The respective standard deviations of 0.0045 and 0.1757 underscore the variability within our dataset, hinting at the distinct financial performance strategies employed by the firms. Turning to capital structure, assessed through EAR, DTL, and DAR, we find mean values of 0.1286, −0.1483, and 0.7033, respectively. These values indicate that, on average, equity makes up about 12.86% of total assets, while debts are typically less than loans, and deposits are less than assets. The associated standard deviations for EAR (0.0295), DTL (0.2119), and DAR (0.0867) reflect significant variability within the data, implying substantial divergence in capital structure strategies across the firms. Lastly, the liquidity risk, evaluated through the LTD and the CTD, presented mean values of 0.7343 and 0.1739, respectively. This suggests that the firms’ loans constitute about 73.43% of their total deposits, while cash on hand forms approximately 17.39% of deposits. With standard deviations of 0.1056 for LTD and 0.0571 for CTD, the data illustrates considerable variance among the firms, reflecting the diversity of liquidity risk profiles they have adopted. DTL scores have negative values because the variable DTL was transformed by multiplying it by −1.

Table 3. Correlation analysis.

	ROA	NIER	EAR	DTL	DAR	LTD	CTD	VIF
ROA	1							-
NIER	0.7034 ***	1						2.13
EAR	0.3555 ***	0.3720 ***	1					1.78
DTL	−0.2687 **	−0.2303 **	−0.1978 *	1				3.42
DAR	−0.0382	−0.1054	−0.3630 ***	−0.6720 ***	1			2.95
LTD	0.1178	0.2253 **	0.3942 ***	0.5389 ***	−0.6436 ***	1		2.67
CTD	0.1906 *	0.3249 *	0.2593 **	−0.6800 ***	0.267 **	−0.2409 **	1	1

Note: ***. Correlation is significant at the 0.01 level (2-tailed), **. Correlation is significant at the 0.05 level (2-tailed), *. Correlation is significant at the 0.10 level (2-tailed). ROA = return on assets, NIER = net income-to-expenditure ratio, EAR = equity/asset ratio DTL = debt-to-loan, DAR = deposit-to-asset ratio, LTD = loan-to-deposit ratio, CTD = cash-to-deposit ratio. VIF = variance inflation factor.

shows the correlation among the study variables. Essentially, the results show no value higher than 0.9; otherwise, there will be a perfect correlation. In addition, the results of the conducted collinearity test show that all VIF values are less than 5, which means that the model does not suffer from a collinearity issue (Gujarati and Porter 2009). Capital structure measures are inversely related with the liquidity risk variables. Overall, these correlation findings emphasise the interconnectedness of various financial measures with firms’ profitability, as gauged by ROA, and point towards the potential drivers of financial performance. However, a further SEM investigation could provide more depth and clarity to these relationships.

Path Analysis

Using structural equation modelling (SEM), we test if liquidity risk mediates the relationship between financial performance and capital structure. To provide a more granular analysis, we test each capital structure measure individually in separate models. This approach allows us to examine the specific impact of each capital structure component on firm performance and its interaction with liquidity risk.

We use both return on assets (ROA) and net income-to-expenditure ratio (NIER) as indicators of firm performance to capture both profitability and efficiency aspects. ROA provides insight into overall profitability, while NIER focuses specifically on the efficiency of managing interest expenses relative to income. This dual approach allows for a more comprehensive assessment of bank performance. However, to ensure robustness, we will also conduct separate analyses using each performance measure individually.

Our analysis occurs in two stages: initially, excluding the mediator variable to display the capital structure’s direct effect on firm performance. The second stage involves incorporating the mediator variable, liquidity risk, into the existing model. In both stages, we report standardised results for all path coefficients and effect sizes. Standardisation allows for easier comparison of effects across different variables and facilitates interpretation of the relative importance of each path in the model.

Our structural equation model is evaluated using multiple fit indices to ensure robust assessment of model fit. In addition to the Chi-square test, we report the comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardised root mean square residual (SRMR) in Appendix A 

Table A1. The goodness of fit statistics.

Model Fit Criteria	Actual	Threshold	Remakes
CMIN	11.021	-
DF	4	-
CMIN/DF	2.75	Less than 3	Acceptable
CFI	0.851	Greater than 0.80	Acceptable
TLI	0.862	Greater than 0.80	Acceptable
RMSEA	0.039	Less than 0.06	Acceptable
SRMR	0.023	Less than 0.0	Acceptable

. This comprehensive approach to model evaluation aligns with current recommendations in the SEM literature.

Furthermore, we conduct sensitivity analyses to assess the stability of our results. This includes testing alternative model specifications and using bootstrapping techniques to generate confidence intervals for indirect effects in our mediation analysis.

The first mediation condition is met if the coefficient value decreases compared to step one and average variance extracted (AVE) increases when liquidity risk is included. The persistence of significant evidence post-inclusion of the mediator hints at partial mediation, while its disappearance indicates full mediation. Our model comprises latent variables depicting the proposed and endogenous variables, demonstrating the dimensions’ sentences.

The results presented in

Table 4. Summary of the results.

	Estimation Results	Estimate	S.E.	C.R.	p-Value	Adjusted R-Square
Direct effect	Capital structure -----> Firm performance	0.019	0.009	2.013	0.044	0.487
Indirect effect	Capital structure -----> Firm performance	−0.014	0.27	−0.535	0.592	0.489
	Capital structure -----> Liquidity Risk	−1.122	0.21	−5.334	0.000
	Liquidity Risk ------> Financial Performance	−0.027	0.022	−1.255	0.209

provide insights into the complex relationships between capital structure, firm financial performance, and liquidity risk in Jordanian banks. These findings warrant a careful interpretation considering both direct and indirect effects.

Direct Effect: The direct association (see Figure 2) between capital structure and firm financial performance is positive (0.019) and statistically significant at the 5% level (p = 0.044). This suggests that, on average, banks with higher levels of debt in their capital structure tend to exhibit better financial performance. The adjusted R-square for this direct effect model is 0.487, indicating that capital structure explains 48.7% of the variance in firm financial performance. This moderate explanatory power suggests that while capital structure is an important predictor of firm performance, other factors not included in this model also play substantial roles.

This positive relationship aligns with findings from several recent studies in the banking sector. For instance, Pham et al. (2022) found a positive impact of capital structure on the financial performance of Vietnamese commercial banks, while Javed et al. (2014) reported similar results in their study of Pakistani firms.

Indirect Effect and Mediating Role of Liquidity Risk: The analysis of the indirect effect (See Figure 3) reveals a more complex picture. The indirect effect of capital structure on firm performance through liquidity risk is negative (−0.014) but not statistically significant (p = 0.592). This result requires a nuanced interpretation of the mediating role of liquidity risk.

According to contemporary mediation analysis principles (Hayes 2018), a significant indirect effect is not always necessary to establish mediation. Instead, we should consider the following components of our model:

• The significant direct effect of capital structure on firm performance (0.019, p = 0.044);
• The significant effect of capital structure on liquidity risk (−1.122, p < 0.001);
• The non-significant effect of liquidity risk on financial performance (−0.027, p = 0.209).

These results suggest a pattern of inconsistent mediation (MacKinnon et al. 2007). While capital structure significantly influences both firm performance and liquidity risk, the pathway from liquidity risk to firm performance is not statistically significant. This could indicate the following: (a) liquidity risk may be influencing firm performance through mechanisms not captured in our current model; (b) the relationship between liquidity risk and firm performance may be non-linear or contingent on other factors; and (c) the effect of liquidity risk on firm performance might be too small to detect with our current sample size, suggesting the need for further research with larger datasets.

The adjusted R-square for the model including the indirect effect is 0.489, only marginally higher than that of the direct effect model. This small increase suggests that while liquidity risk does play a role in the relationship between capital structure and firm performance, its contribution to explaining overall variance is limited.

Importantly, our results show that capital structure negatively affects liquidity risk (−1.122) and is significant at the 1% level. This suggests that banks with higher levels of debt in their capital structure tend to have lower liquidity risk. This finding is consistent with the “risk absorption” hypothesis proposed by (Bhattacharya and Thakor 1993), which suggests that banks with higher capital ratios are better able to absorb liquidity shocks.

However, the non-significant relationship between liquidity risk and financial performance (−0.027, p = 0.209) is somewhat surprising. This result diverges from some previous studies (Ramzan and Zafar 2014) that found significant relationships between liquidity risk and bank performance. This discrepancy might be due to the specific context of Jordanian banks or could indicate that the relationship is more complex than our linear model assumes.

These findings align with recent literature suggesting complex, context-dependent relationships between capital structure, liquidity risk, and firm performance in the banking sector (Pham et al. 2022). Our results highlight the need for more sophisticated models that can capture the nuanced interplay between these factors.

In conclusion, while our initial hypothesis of full mediation is not supported, the results reveal a complex relationship between capital structure, liquidity risk, and firm performance in Jordanian banks. The significant direct effect of capital structure on firm performance, coupled with its significant impact on liquidity risk, suggests that capital structure decisions play a crucial role in bank management. However, the non-significant relationship between liquidity risk and firm performance indicates that this aspect of bank operations may require further investigation.

For a comprehensive overview of the study objectives and hypothesis results, please refer to

Table 5. Remarks table.

S.no	Research Objectives	Hypothesis	Remarks
1	To scrutinise the influence of banks’ capital structures on Jordanian banking performance	H1. Banks’ capital structures in the Jordanian banking sector significantly impact their financial performance.	H1: Accepted
2	To check the role of capital structure of banks on liquidity Risk	H2. Capital structure of banks in the Jordanian banking sector significantly impacts liquidity risk.	H2: Accepted
3	To examine the role of liquidity risk in Jordanian banking financial performance	H3. Banks’ liquidity risk in the Jordanian banking sector significantly impacts their financial performance.	H3: Not Accepted
4	To scrutinise the mediating role of liquidity risk between the relationship of capital structure and financial performance in the Jordanian banking sector	H4. Liquidity risk mediates the relationship between capital structure and financial performance in the Jordanian banking sector.	H4: Partially Accepted

, which summarizes the key findings of this research.

5. Discussion

The study’s findings highlight the intricate relationships between capital structure, liquidity risk, and financial performance within the banking sector. These results provide valuable insights and align with various recent studies, contributing to the ongoing discourse in financial research.

Firstly, the positive and significant direct effect of capital structure on financial performance aligns with recent findings by Pham et al. (2022), who reported similar positive impacts in Vietnamese banks. This suggests that capital structure enhances a bank’s financial performance. However, this study’s findings diverge from those of Mumtaz et al. (2013), and Onaolapo and Kajola (2010), who reported either a negative or non-significant relationship between capital structure and financial performance. These discrepancies underscore the complexity of this relationship and suggest that contextual factors, such as regulatory environments and market conditions, may play a crucial role.

The study assesses the relationship between capital structure and liquidity risk, revealing a significant negative association. This finding aligns with the work of Lipson and Mortal (2009) and Šarlija and Harc (2012), who found that higher levels of debt in the capital structure are associated with lower liquidity risk. This negative relationship can be explained through the “risk absorption” hypothesis proposed by Bhattacharya and Thakor (1993), suggesting that banks with higher capital ratios are better equipped to absorb liquidity shocks. Additionally, Acosta Smith et al. (2019) argue that banks with larger capital bases can more effectively manage liquidity risk by maintaining sufficient liquid assets, even though these assets may yield lower returns compared to illiquid ones.

Regarding liquidity risk’s impact on financial performance, this study finds an inverse relationship, consistent with the findings of Arif and Nauman Anees (2012) and Chen et al. (2018). These studies suggest that elevated liquidity holdings can dampen financial performance due to the higher costs associated with maintaining liquidity. Bordeleau and Graham (2010) further posit that the relationship between liquidity and financial performance is not linear but rather complex and context-dependent. This complexity is echoed by Ehiedu (2014), who emphasises the trade-off between profitability and liquidity, suggesting that excessive liquidity can erode profitability, while insufficient liquidity can lead to financial instability.

Regarding the mediating role of liquidity risk between capital structure and financial performance of Jordanian banks, the study’s findings reveals that liquidity risk mediates the relationship between capital structure and financial performance. This finding aligns with the theoretical perspectives of Diamond and Rajan (2000), who suggest that banks must balance their capital structures to effectively manage liquidity risk. The significant mediating role of liquidity risk is also supported by the work of Adeyanju (2011), who highlight the critical influence of liquidity on bank profitability and stability. These findings are partially supported by the outcomes of Ruziqa (2013), who concluded that liquidity risk and credit jointly influence bank financial performance.

Contributions

This research contributes and enriches the existing literature on risk and capital structure in two ways. First, these findings contribute to the existing literature by providing empirical evidence from Jordanian banks and highlighting the complex interplay between capital structure, liquidity risk, and financial performance. The study underscores the importance of capital structure and effective liquidity risk management in enhancing bank performance and stability. In particular, we tested the mediating role of liquidity risk between capital structure and financial performance of banking industries. Although there are previous studies of similar relationships, the concentrated model is not tested in the Jordan banking industry. Hence, we enrich the literature on how liquidity risk mediates the path between capital structure and financial performance of banks. Second, our research contributes to the theory of capital structure through empirical evidence. For instance, the theory determines the values and percentage of the overall cost of capital in a company’s outcomes and financial performance. Our research extended the scope of the theory by employing liquidity risk as a mediating factor and confirmed that the relationship is significantly affected.

6. Conclusions

This study, utilising robust structural equation modelling (SEM) applied to a comprehensive dataset encompassing Jordanian banks from 2015 to 2022, unveils a nuanced understanding of the interconnectedness between capital structure, liquidity risk, and a bank’s financial performance. Our findings demonstrate a statistically significant positive influence of capital structure, measured by the equity/asset ratio, debt-to-loan ratio, and deposit-to-asset ratio, on financial performance metrics like return on assets (ROA) and net income-to-expenditure ratio (NIER). This empirical evidence reinforces the established theoretical construct that capital structure is a cornerstone of achieving superior financial outcomes for banks operating in the Jordanian market.

However, the study reveals a crucial mediator in this relationship. A particularly noteworthy contribution lies in the complete mediation of liquidity risk within the association between capital structure and financial performance. This unveils a previously unexplored mechanism through which capital structure choices indirectly influence financial health. Our analysis suggests that capital structure decisions significantly impact a bank’s liquidity risk profile. A well-capitalised bank, with a higher equity/asset ratio and a lower debt-to-loan ratio, can maintain a more robust buffer against unexpected withdrawals or market fluctuations. This translates into lower liquidity risk. Conversely, banks with a higher reliance on debt financing are more susceptible to liquidity disruptions, increasing their risk. Subsequently, this altered liquidity risk profile has a demonstrably inverse effect on financial performance. Lower liquidity risk, facilitated by the capital structure, allows banks to operate more efficiently, minimise funding costs reflected in the net income-to-expenditure ratio, and potentially generate higher returns, as evidenced by the positive association with ROA.

This cascading effect underscores the crucial role of liquidity management as the bridge between capital structure decisions and tangible financial results. It highlights the intricate dance Jordanian banks must undertake, balancing the need for adequate liquidity to fulfil obligations with the simultaneous pursuit of profit generation through asset deployment. Effective liquidity risk management strategies become paramount in translating capital structure into sustained financial well-being.

In conclusion, this study offers a significant contribution to the existing body of knowledge on banking in emerging markets. By unveiling the intricate interplay between capital structure, liquidity risk, and financial performance, it provides a comprehensive framework for understanding how capital structure decisions ripple through a bank’s operations, impacting its financial health. These findings are particularly valuable for the Jordanian banking sector, informing strategic decisions and shaping regulatory policies. However, the broader theoretical implications extend to similar emerging market contexts, offering valuable insights for navigating the complexities of capital structure management and liquidity risk mitigation in the pursuit of sustainable financial performance.

6.1. Practical Implications

The results of this study hold significant implications for both banking practice and regulatory policy within Jordan. Understanding the intricate interplay between these factors allows for a more informed approach to financial management in Jordanian banks.

For Banking Practice: The issues are capital structure optimisation with a liquidity focus. This study underscores the critical need for Jordanian banks to optimise their capital structure while maintaining a keen focus on mitigating liquidity risk. A capital structure that combines appropriate levels of debt and equity not only directly enhances financial performance, but also facilitates a more robust liquidity profile. This allows banks to operate with greater efficiency and potentially generate higher returns. Implementing robust liquidity risk management strategies becomes paramount in achieving these goals.

For Regulatory Policy: Capital Adequacy and Liquidity Risk Integration: Regulatory bodies in Jordan should consider integrating the intricate interplay between capital structure, liquidity risk, and financial performance when formulating policies. This might involve developing capital adequacy requirements that account for a bank’s liquidity risk profile. Banks with a more equity-weighted capital structure, as evidenced by factors like a higher equity/asset ratio and lower debt-to-loan ratio, could potentially be granted some flexibility in capital adequacy requirements, provided they maintain a strong liquidity risk management framework. This approach would incentivise capital structure while ensuring financial stability through effective liquidity management.

6.2. Limitations

While this study offers valuable insights, it is essential to acknowledge its limitations to guide future research endeavours.

Generalisability to Broader Emerging Markets: While the study provides valuable insights for Jordanian banks, the generalisability of these findings to other emerging markets may be limited. Future research can explore whether the observed relationships between capital structure, liquidity risk, and financial performance hold true in other emerging economies with different banking system characteristics and regulatory environments.

Longitudinal Analysis for Causal Strength: The study employs cross-sectional data, which limits the ability to establish definitive causal relationships. Future research employing longitudinal data analysis over an extended period can provide stronger evidence for causal links between capital structure decisions, changes in liquidity risk, and their subsequent impact on financial performance in Jordanian banks.

Metrics for Liquidity Risk Nuances: While the study explores liquidity risk, future research can delve deeper by incorporating additional metrics that capture specific aspects of liquidity risk faced by Jordanian banks. This could involve including measures like the loan-to-deposit ratio or liquidity coverage ratio, providing a more nuanced understanding of how capital structure choices influence different facets of liquidity risk and ultimately impact financial performance.

6.3. Future Research Directions

Building upon this study’s foundation, future research should prioritise two key areas to refine our understanding and offer more actionable insights for Jordanian banks and regulators:

1. Refining Liquidity Risk Measurement:

Incorporate additional liquidity risk metrics specific to the Jordanian banking context. This could include the loan-to-deposit ratio (LDR) and liquidity coverage ratio (LCR). Analysing how capital structure choices influence these metrics would provide Jordanian banks with a more granular understanding of their liquidity risk profile.

2. Macroeconomic Factors and Regulatory Changes:

Impact Assessment: Investigate how macroeconomic factors (e.g., recessions) and regulatory changes (e.g., capital adequacy requirements) influence the relationship between capital structure, liquidity risk, and financial performance. This would offer Jordanian regulators a more holistic view of the dynamics at play, allowing them to formulate policies that incentivise capital structure for Jordanian banks and ensure financial stability through effective liquidity management practices.

By focusing future research on these two areas, we can create a more comprehensive understanding of how capital structure decisions translate into financial performance through the lens of liquidity risk in the Jordanian banking context. This knowledge will be valuable for both banks and regulators in promoting a robust and financially healthy banking sector in Jordan.