Determinants of Capital Structure: Does Growth Opportunity Matter?

Abstract

This study explores the impact of growth opportunities on the capital structure of South African banks, utilising panel data from registered banking institutions covering the period from 2014 to 2023. While a substantial body of literature examines the relationship between growth prospects and corporate leverage, limited attention has been paid to this interaction within the banking sector, particularly in emerging economies. By employing the dynamic panel Generalised Method of Moments (GMM) estimator to address endogeneity concerns, the analysis reveals a statistically significant positive relationship between growth opportunities and both the total debt ratio (TDR) and the long-term debt ratio (LTDR). In contrast, a significant negative association is found between growth opportunities and the short-term debt ratio (STDR). The findings suggest that banks with stronger growth prospects are more inclined to utilise long-term financing, possibly reflecting shareholder preferences for institutions with favourable future outlooks and lower refinancing risks. These results highlight the importance of aligning capital structure decisions with an institution’s growth trajectory, while indicating that this relationship shifts depending on the maturity of the debt considered. This study contributes to the existing literature by contextualising capital structure decisions within the framework of growth opportunities. Structure theory within the context of the banking sector in a developing market offers practical insights for strategic financial planning and regulatory policy.

1. Introduction

The cornerstone of modern finance theory is the seminal paper by Modigliani and Miller in 1958, which posited that a company’s capital structure does not influence its overall value. This view stood in contrast to those of many contemporaneous scholars. Subsequent research (Myers, 1974; Titman & Wessels, 1988; Stulz, 2009; Nguyen & Nguyen, 2020; Abdullah et al., 2022; Ali et al., 2022; Faturohman & Noviandy, 2022; Yilmaz & Alghazali, 2024) has aimed to explain the various factors that affect a company’s capital structure. Among these factors, the most significant are the firm’s size, profitability, growth opportunities, and operating results volatility. Despite extensive exploration of this topic within academic circles, the inconsistent empirical findings indicate that it remains insufficiently examined (Brito et al., 2007).

Growth increases the cost of financial challenges, decreases problems with free cash flow, and exacerbates agency conflicts associated with debt (Frank & Goyal, 2009). Co-investment from stakeholders is significant for expanding companies. Therefore, the trade-off hypothesis states that expansion reduces leverage. On the other hand, growing businesses can discover that their internal resources are insufficient to finance their investment plans with positive net present value, forcing them to look for external funding (Antoniou et al., 2008). The Pecking Order Theory states that a business should issue debt before shares if it needs external financing. Hence, growth opportunity and leverage have a positive relationship based on the pecking order theory.

Finding the appropriate indicator of growth opportunity has generated debate because it is influenced by several factors, many of which are not measurable (Lerner & Flach, 2022). Using the market-to-book ratio as a growth metric, Gropp and Heider (2010) showed a negative connection between growth and book and market leverage. However, Vo (2017) found that growth opportunity, as assessed by Tobin’s Q, has a positive effect on the long-term to short-term debt ratio. Firms that are more valuable in the market tend to take on more debt to finance their investments. Vo (2017) demonstrates that novel share offers in stock markets do not benefit higher-growth firms.

Additionally, Khémiri and Noubbigh (2018) employed three metrics to assess growth opportunities: total, tangible, and intangible asset growth. They showed a negative and significant connection between long-term debt and the rise in total assets. However, Tobin’s Q on long-term debt indicates a positive association between increasing tangible assets (I) and intangible growth assets, which aligns with the expectations of the trade-off theory. Thus, the growth opportunity was measured in this study using the annual growth rate of total assets. The growth parameter indicates that a bank’s chances of expanding increase with the growth rate (Sibindi & Makina, 2018).

Since the banks were at the forefront of the 2008 financial crisis, practitioners and researchers are increasingly concerned about the relative relevance of optimal capital structure (Akbar et al., 2023). High leverage may theoretically cause a company to experience financial difficulties and, in the case of banks, may result in bankruptcies. Furthermore, the growth of different industrial sectors and the economy depends heavily on banks’ function as lenders to balance the supply and demand of capital in an economy (Ching et al., 2016). Many empirical studies have been conducted on the determinants of capital structure (Khan et al., 2024; Hutabarat, 2024; Akbar et al., 2023; Nguyen Kim, 2023; Czerwonka & Jaworski, 2022; Bilen & Kalash, 2020; Nguyen & Nguyen, 2020). However, little research has been conducted on the effect of growth opportunities and capital structure, particularly in the banking industry in South Africa (Khan et al., 2023; Guizani & Ajmi, 2021; Sibindi & Makina, 2018).

While previous studies have largely concentrated on the total debt ratio as a determinant of capital structure (Titman & Wessels, 1988; Rajan & Zingales, 1995), this article extends the analysis by disaggregating debt into long-term debt ratio (LTDR) and short-term debt ratio (STDR). This disaggregation reveals a critical substitution effect between LTDR and STDR, offering a more detailed understanding of how firms structure their liabilities. Empirical findings demonstrate that firms with high growth opportunities tend to rely more heavily on long-term debt while reducing their short-term debt exposure. This is consistent with the predictions of the trade-off theory, which posits that firms balance the costs and benefits of debt financing, and the agency theory, which suggests that long-term debt mitigates agency problems associated with underinvestment (Myers, 1977). Moreover, Barclay and Smith (1995) argue that firms with greater growth prospects prefer long-term debt to avoid refinancing risk and to signal financial stability to investors. The observed substitution effect implies that growth-oriented firms strategically allocate debt maturities to optimise financial flexibility and minimise risk, thereby reinforcing the need for a more differentiated approach to capital structure analysis beyond aggregate debt ratios.

Empirical investigations have predominantly indicated a positive correlation between growth opportunities and capital structure, consistent with the pecking order theory, which asserts that firms with substantial growth prospects favour internal funding prior to resorting to debt or equity issuance (Khan et al., 2023; Abdullah et al., 2022; Guizani & Ajmi, 2021; Sibindi & Makina, 2018). This implies that growth-centred firms frequently resort to external debt when internal resources fall short in financing expansion. Nonetheless, albeit limited, there exists contradictory evidence suggesting a negative relationship between growth opportunities and leverage, as noted by Khémiri and Noubbigh (2018), endorsing the trade-off theory. This theory posits that firms with considerable growth prospects might restrict debt utilisation to mitigate the risks of underinvestment and agency conflicts between shareholders and creditors.

Despite the extensive body of literature on this subject, empirical gaps persist; most of the existing research has concentrated on developed or emerging markets beyond Africa, with sparse studies examining this relationship within African economies, particularly the capital-intensive sector of South Africa, such as banking. Moreover, most studies employ aggregate debt measures, overlooking potential differential effects of long-term versus short-term debt on growth-oriented firms. The distinction between short-term and long-term debt structures in relation to growth opportunities remains underexplored.

Although existing literature provides mixed evidence regarding the impact of growth opportunities on capital structure, the relationship remains inconclusive, particularly within the banking sector. Accordingly, this study investigates the effect of growth opportunities on the capital structure of South African banks over the period 2014 to 2023. The analysis is disaggregated into three dimensions, namely: the total debt ratio, long-term debt ratio, and short-term debt ratio, to provide a comprehensive understanding of how growth opportunities influence different components of capital structure. By focusing on the banking sector, the findings of this study are expected to inform policymakers on financial strategies that could foster economic development and enhance financial decision-making. Ultimately, the paper seeks to contribute empirical evidence to the ongoing debate by addressing key questions related to the capital structure of banks operating in South Africa.

• How do growth opportunities affect the total debt ratio of banks in South Africa?
• How do growth opportunities affect the long-term debt ratio of banks in South Africa?
• How do growth opportunities affect the short-term debt ratio of banks in South Africa?

This study makes significant and original contributions to the extant body of literature on capital structure dynamics. Although theoretical frameworks suggest that growth opportunities should ideally inform adjustments in a firm’s capital structure, scholarly debate persists regarding whether firms ought to increase or decrease leverage in anticipation of such opportunities. Alenaizi et al. (2024) critically examine both positions, asserting that while debt financing can support new growth initiatives, equity issuance, accompanied by a deliberate reduction in leverage, can serve as a strategic alternative. This approach conserves internal liquidity, thereby enabling firms to capitalise on speculative ventures and future growth prospects.

Prior empirical investigations into the determinants of capital structure have predominantly employed three proxies to capture growth opportunities: total asset growth, tangible asset growth, and intangible asset growth (Khémiri & Noubbigh, 2018). In contrast, the present study adopts a more focused metric, annual growth in total assets, as a proxy for growth potential. Sibindi and Makina (2018) contend that a higher growth rate in total assets is indicative of an enhanced capacity for bank expansion.

Furthermore, although a substantial body of research has explored the determinants of capital structure across various contexts (Khan et al., 2024; Hutabarat, 2024; Akbar et al., 2023; Nguyen Kim, 2023; Czerwonka & Jaworski, 2022; Bilen & Kalash, 2020; Nguyen & Nguyen, 2020), there remains a conspicuous gap in the literature concerning the specific influence of growth opportunities on capital structure within the South African banking sector (Khan et al., 2023; Guizani & Ajmi, 2021; Sibindi & Makina, 2018).

Finally, the temporal context of this study, spanning the COVID-19 pandemic and its aftermath, offers a distinctive lens through which to examine the pandemic’s implications on bank leverage. Accordingly, this research not only addresses a critical gap in the literature but also contributes to the evolving discourse on the impact of global crises on corporate financial strategies.

The rest of this paper is structured as follows: the next section reviews the existing theoretical framework on capital structure, the literature on the effect of growth opportunities on capital structure, and hypothesis development. This is followed by the data and methods, which detail our econometric approach. The findings are presented and discussed, with a conclusion and recommendations at the end of the discussion.

2. Literature Review

2.1. Theoretical Framework

Modigliani and Miller’s (1958) and Modigliani and Miller (1963) irrelevance propositions marked the beginning of capital structure questions. These authors contend that regardless of the composition of a company’s funding sources, its worth would remain constant. Until then, other academics have studied whether an ideal capital structure exists and what factors influence it. Numerous studies refuted Modigliani and Miller’s position. The theories of agency, pecking order, and trade-off are the most notable among those put forth. According to the trade-off hypothesis, the best capital structure is a balance between debt and equity that maximises the value of the business. Lerner and Flach (2022) argued that interest expenses are deductible from income tax. However, cash flows from shareholder equity (dividends) are not. Interest on debts owed to third parties is specifically the source of this, and Modigliani and Miller (1963) noted that the more leverage a business has, the less income tax it must pay. As a result, businesses look for the ideal debt-to-income ratio while considering the tax advantages and the expenses of financial distress. In his analysis of the trade-off hypothesis, Myers (1984) demonstrates that its worth rises as a firm’s debt levels rise. However, the expenses of financial troubles increase with debt until they reach a debt position that maximises the firm’s value (Lerner & Flach, 2022).

In response to Donaldson’s and Parr’s (1961) findings, Myers and Majluf (1984) put out the pecking order theory, which holds that management prefers to use internal funds over raising external ones. The Pecking Order suggests that a firm prefers internal finance over loan capital. Essentially, businesses use their funds first, issue external debt, and issue equity funding from outside sources. Myers (1984) asserts that the pecking order theory does not specify an ideal or well-defined amount of debt. Furthermore, the pecking order theory clarifies that businesses seek more outside funding when their internal resources are insufficient to meet their investment requirements (Shyam-Sunder & Myers, 1999). None of the trade-off and pecking models can be disproved, and they both describe the financial behaviour of certain businesses (Fama & French, 2002). However, according to Myers (2003), there is no universal theory of capital structure and no reason to assume that all capital structure models are conditional. According to the pecking order hypothesis, managers of high-growth firms, which usually have substantial financing needs, would be reluctant to pay equity, which will result in a high debt ratio (Saif-Alyousfi et al., 2020).

On the other hand, Jensen (1986) and Jensen and Meckling (1976) put out the agency theory. This theory describes how a company’s financing decisions may be impacted by conflicts of interest between owners and management and between owners and creditors. The premise of the first kind of conflict is that managers may have interests that diverge from those of the company owner, which could cause them to prioritise their own needs over maximising the value of the business. According to Myers (2001), the second type of conflict arises since creditors cannot credit high-risk projects without the accompanying assurances. This is because managers and shareholders may invest in projects with a negative net present value, which the creditors would blame if they failed to meet the expenses. According to the agency theory, firms with high levels of leverage are unable to execute attractive investment possibilities because of the likelihood of financial distress and agency issues (Frank & Goyal, 2009; Padrón et al., 2005; Barclay & Smith, 1999; Myers, 1977). For this reason, previous research has shown a negative relationship between growth opportunities and leverage.

2.2. The Effect of Growth Opportunity on Capital Structure and Hypothesis Development

Several empirical studies use growth as one of the determinants of capital structure in their investigations (Khan et al., 2024; Hutabarat, 2024; Akbar et al., 2023; Nguyen Kim, 2023; Czerwonka & Jaworski, 2022; Bilen & Kalash, 2020; Nguyen & Nguyen, 2020). However, not much research has been conducted on the nexus between growth opportunity and capital structure, particularly in the banking sector in South Africa (Khan et al., 2023; Guizani & Ajmi, 2021; Sibindi & Makina, 2018).

Khan et al. (2024) demonstrated, their study’s findings positively and significantly impacted growth prospects with book and market leverage values. According to their conclusions, growth cannot be utilised as collateral because it is an intangible asset. Similarly, Akbar et al. (2023) found a positive and significant relationship between growth opportunities and leverage. The results of their study are consistent with the prediction of the pecking order theory that there is a positive effect on growth opportunities and capital structure. Moreover, Czerwonka and Jaworski’s (2022) study results show a positive association between growth opportunity and capital structure.

The findings of Nguyen and Nguyen’s (2020) research show that the company’s capital structure is positively impacted statistically by return on assets (ROA), firm size, tangible assets, risks, and growth. Bilen and Kalash (2020) also find a positive and significant nexus between growth opportunities and firm leverage. Their findings are consistent with the prediction of the pecking order theory that there is a positive effect on growth opportunities and leverage. In contrast, Nguyen Kim (2023) discovered that the capital structure of SMEs in Vietnam had a detrimental impact on revenue growth. In contrast, Nguyen Kim (2023) discovered that the capital structure of SMEs in Vietnam had a detrimental impact on revenue growth. Based on such views, they contended that companies with substantial room for expansion typically keep their debt ratio low. Moreover, Hutabarat (2024) found no significant connection between growth opportunity and capital structure. The results indicate that a firm’s capital structure can be advantageous during an economic recession. With an adequate capital structure, a more prominent firm can leverage its size to obtain better financing terms, increasing its potential for investment and growth while effectively managing risks.

Research from the banking industry has shown that growth opportunity is one of the factors that influence capital structure (Khan et al., 2023; Guizani & Ajmi, 2021; Sibindi & Makina, 2018). Using an imbalanced panel data set that included 132 banks operating in 15 different MENA countries between 2012 and 2017, Khan et al. (2023) looked at the critical factors influencing the capital structure decisions made by banks in the MENA region. The study’s conclusions show a significant and negative impact on earnings volatility and profitability with leverage. Furthermore, their study findings showed a significant negative link between growth prospects and leverage. Nonetheless, they discovered that leverage positively and noteworthy impacted GDP growth, inflation, and macroeconomic indicators.

On the other hand, Guizani and Ajmi (2021) found a mixed impact on growth opportunity and capital structure. Their study showed a positive yet insignificant influence on growth opportunity and Islamic Bank (IB) capital structure. However, they found a positive and significant effect on growth opportunities and book leverage for conventional banks (CBs). The results of their study are in line with the pecking order theory. They contended that when internal capital runs out, traditional banks with broader investment options turn to debt financing more frequently. Similarly, Sibindi and Makina (2018) find a positive effect on growth opportunities and bank capital structure. They contend that this demonstrates how the funding practices of South African banks align with the pecking order theory. Considering that many of the studies indicate a positive effect on growth opportunities with leverage, in line with the pecking order theory, the researchers hypothesise as follows:

H₁. 

Growth opportunities have a positive effect on the total debt ratio of banks in South Africa.

H₂. 

Growth opportunities have a positive effect on the long-term debt ratio of banks in South Africa.

H₃. 

Growth opportunities have a positive effect on the short-term debt ratio of banks in South Africa.

3. Materials and Methods

This paper’s population comprises 16 regulated local banks in South Africa. However, the sample used in this article is derived from the demographics of 16 regulated banks and includes 11 South African registered banks from 2014 to 2023. Due to challenges in acquiring financial data for the study’s period, the five tiny banks were excluded from this study. Therefore, the included banks accurately represent South Africa’s licenced bank market from 2014 to 2023. The list of South African banks authorised under the Bank Act 94 of 1990, as of 31 December 2020, was taken from the South African Reserve Bank’s website. In addition, financial and economic data from the South African Reserve Bank (SARB) was extracted monthly and annually. Eleven banks throughout ten years make up the sample size for these registered banks, yielding 110 observations for the banking sample. As previously stated, it is confirmed that there were differences in the procedures of the examined licenced banks, even though only South African licenced banks were selected for this study.

In line with other researchers, the dependent variables in this study are three measures of capital structure: the overall debt ratio, the long-term debt ratio, and the short-term debt ratio (Mabandla & Marozva, 2024; Lazarus et al., 2024). Rajan and Zingales (1995) posit that the ratios of short-term debt, long-term debt, and total debt over total assets are better indicators of financial leverage than the ratio of liabilities to total assets since they offer a clearer picture of the firm’s likelihood of declining shortly and show a more appropriate assessment of its previous funding sources. The growth variable in this study is defined as the annual growth rate of total assets, which aligns with the investigations of Al-Hunnayan (2020) and Guizani and Ajmi (2021). Based on the rationale behind the growth parameter, a bank’s chances of expanding are positively correlated with its growth rate.

Table 1. Summary of variables and proxies.

Variables	Proxies and Definitions	Proxies By	The Expected Sign of the Coefficient
Capital structure proxies (Dependent variable)
Total debt ratio at book value (TDRB)	TDRB is the ratio of the book value of total debt to the book value of total assets.	Mohammad (2022); Lazarus et al. (2024)
The long-term debt ratio (LTDR)	LTDR is the ratio of long-term liabilities over total assets	Mabandla and Marozva (2024); Lazarus et al. (2024)
The short-term debt ratio (STDR)	STDR is the ratio of short-term debts divided by total assets.	Mabandla and Marozva (2024); Lazarus et al. (2024)
Independent variables
Growth opportunity		Al-Hunnayan (2020); Guizani and Ajmi (2021) and Wahyono and Mas’ud (2023)	Negative
Control variable’s
Current Ratio (CURR)	CURR is the current assets divided by current liabilities	Burksaitiene and Draugele (2018); Mabandla and Marozva (2025)	Negative or positive
Economic growth is measured by Gross domestic product (GDP).	GDP: The growth rate of real domestic product.	Khan et al. (2023)	Positive or negative
Inflation rates	Annual consumer price index (CPI)	Khan et al. (2021); Khan et al. (2023); Kebede (2024)	Positive or negative
Interest rates	Effective interest rate	Karpavičius and Yu (2017)	Negative
Size	Size the natural logarithm of total assets	Bandyopadhyay and Barua (2016); Obadire et al. (2023)	Positive or negative

Source: Authors’ own composition.

below displays details of dependent and independent variables and data sources.

Model Specification

The Generalised Method of Moments (GMM) was used in this research. The generic GMM dynamic technique has the following form:

$$Y_{it}={\alpha y}_{it-1}+{\beta X}_{it}+{\mu }_i+{\epsilon }_{it}$$

(1)

where

• $Y_{it}$ indicates the book value of the capital structure metrics for banks i at time t; x is the explanatory variable vector for banks i at time t, signifying the variable unique to the bank. ${\alpha }_0$ represent a constant term; β is the elasticity of the explanatory variables, i.e., the slope of variables; ${\mu }_i$ indicates fixed effects in banks; ${\epsilon }_{it}$ it is a random error term; the subscript i indicates the cross-section, and t indicates the time-series scale.

This study employed the two-step GMM system prediction model of Arellano and Bover (1995) and Blundell and Bond (1998), with the dimension and lag parameters operating as instruments. The one-step GMM system method for forecasting is assumed to supplement the GMM estimate approach of Arellano and Bond (1991). Panel data regression analysis was then used to investigate the effect of growth opportunities on capital structure. To rigorously examine the effect of growth opportunities on capital structure, this study employs the system Generalised Method of Moments (GMM) estimator to address potential endogeneity arising from reverse causality and unobserved heterogeneity. Lagged values of the current ratio and bank size are utilised as internal instruments, premised on the theoretical expectation that firm liquidity and scale influence financing decisions with temporal delay. These lagged instruments are selected to satisfy the relevance and exogeneity conditions fundamental to dynamic panel data estimation. The validity of the over-identifying restrictions is assessed using the Sargan and Hansen tests, both of which yield statistically insignificant results, thereby confirming the appropriateness and orthogonality of the instruments. Furthermore, the insignificance of these statistics suggests that instrument proliferation does not undermine model reliability. The implementation of the system GMM approach, combined with carefully selected lagged instruments, enhances the robustness, consistency, and efficiency of the estimated parameters, reinforcing the empirical soundness of the findings. This paper solely employed South African data since it was our article’s focus. This study investigated the important elements influencing leverage in the South African banking industry by regressing leverage (TDR, STDR, and LTDR) against the components in the following questions (2)–(4).

$$\mathsf{\Delta }{\mathrm{T}\mathrm{D}\mathrm{R}}_{\mathrm{i}\mathrm{t}}=\left(\mathsf{\alpha }-1\right)\mathsf{\Delta }{\mathrm{T}\mathrm{D}\mathrm{R}}_{\mathrm{i}\mathrm{t}-1}+{\mathsf{\beta }}_1\mathsf{\Delta }{\mathrm{G}\mathrm{O}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2\mathsf{\Delta }{\mathrm{CR}}_{\mathrm{it}}+{\mathsf{\beta }}_{\mathrm{j}}{\sum }_{{\displaystyle \frac{j}{t}}=1}^n{\mathsf{\Delta }X}_{ij}+\mathsf{\Delta }{\mathsf{\epsilon }}_{\mathrm{it}}$$

(2)

$$\mathsf{\Delta }{\mathrm{L}\mathrm{D}\mathrm{R}}_{\mathrm{i}\mathrm{t}}=\left(\mathsf{\alpha }-1\right)\mathsf{\Delta }{\mathrm{T}\mathrm{D}\mathrm{R}}_{\mathrm{i}\mathrm{t}-1}+{\mathsf{\beta }}_1\mathsf{\Delta }{\mathrm{GO}}_{\mathrm{it}}+{\mathsf{\beta }}_2\mathsf{\Delta }{\mathrm{CR}}_{\mathrm{it}}+{\mathsf{\beta }}_{\mathrm{j}}{\sum }_{{\displaystyle \frac{j}{t}}=1}^n{\mathsf{\Delta }X}_{ij}+\mathsf{\Delta }{\mathsf{\epsilon }}_{\mathrm{it}}$$

(3)

$$\mathsf{\Delta }{\mathrm{S}\mathrm{D}\mathrm{R}}_{\mathrm{i}\mathrm{t}}=\left(\mathsf{\alpha }-1\right)\mathsf{\Delta }{\mathrm{T}\mathrm{D}\mathrm{R}}_{\mathrm{i}\mathrm{t}-1}+{\mathsf{\beta }}_1\mathsf{\Delta }{\mathrm{GO}}_{\mathrm{it}}+{\mathsf{\beta }}_2\mathsf{\Delta }{\mathrm{CR}}_{\mathrm{it}}+{\mathsf{\beta }}_{\mathrm{j}}{\sum }_{{\displaystyle \frac{j}{t}}=1}^n{\mathsf{\Delta }X}_{ij}+\mathsf{\Delta }{\mathsf{\epsilon }}_{\mathrm{it}}$$

(4)

where

• ${TDR}_{B_{it}}$ represents the total debt ratio at book value for banks i in time t, which is calculated by dividing the total debt book value by the total asset book value.
• ${LTDR}_{B_{it}}$ shows the banks’ long-term debt ratio as a function of long-term/total assets at time t.
• ${STDR}_{B_{it}}$ donates the short-term debt ratio for banks i in time t, calculated by short-term debt over the book value of total assets,
• ${GO}_{it}$ The growth opportunities variable is the annual growth rate of total assets.
• ${CR}_{it}$ is measured as the current assets over current liabilities.
• $X_{ij}$ is a set of macro and microeconomic control variables, including GDPR, inflation rates (IF), and interest rates (INTR), that are analysed in the conclusion.

4. Results

4.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variables	Mean	Median	Maximum	Minimum	Std. Dev.	Skewness	Kurtosis	Jarque–Bera	Obser
TDR	3.27	0.92	145.69	0.63	15.83	7.81	67.62	18,598.87	101
LTDR	0.73	0.21	33.57	0.03	3.65	7.79	67.41	18,481.94	101
STDR	0.52	0.54	0.81	0.02	0.13	(0.30)	4.45	10.40	101
GO	0.87	0.08	78.85	(0.26)	7.84	9.90	98.98	40,414.72	101
CR	1.34	1.37	1.87	0.01	0.22	(2.21)	14.53	641.95	101
RGDP’ Billions	4479.61	4474.21	4599.26	4310.33	78.94	(0.63)	3.04	6.72	101
CPI	4.19	4.10	5.60	3.10	0.84	0.45	1.84	9.07	101
INTR	3.40	3.28	5.86	0.51	1.58	(0.11)	2.24	2.63	101
SIZE	18.39	18.58	21.24	15.26	2.27	(0.01)	1.21	13.50	101

presents summary statistics for the variables used in this study, offering insights into the distribution, central tendencies, and dispersion of the data across the panel of 11 banks over the 2014–2023 period.

During the reviewed period, the average percentage of a bank’s assets financed by deposits and non-deposit loans was shown by the total debt ratio (TDR) capital structure metric, which had a mean of 3.27, with 15.83 as the standard deviation. TDR ranged from a minimum of 0.63 to a maximum of 145.69, with a total range of 145.06. On the other hand, LTDR had an average of 0.73 and a standard deviation of 3.65. The LTDR ranged from 0.03, the lowest, to 33.57, the highest. The mean and standard deviation of the STDR were 0.52 and 013, respectively. STDR ranged from a minimum of 0.02 to a maximum of 0.81. This suggests that some banks may retain less than 2 percent of their liabilities in the form of short-term debt.

Nonetheless, banks are allowed to retain up to 81 percent of their liabilities in the form of short-term debt. The mean value in terms of GOs was 0.87. This suggests that during the investigation period, the average growth rate was 0.87%. The GO standard deviation was 7.86. This suggests that South African banks will be able to grow with a much lower risk. At the same time, the overall growth of assets over the analysis period went from −0.26% to 78.85%. The CR had a mean of 1.34 and a standard deviation of 0.22. The data on the banks’ liquidity parameter has changed less if the standard deviation is smaller than the mean result. However, the minimum CR of 0.01 indicates that over the study period, the South African banks had a CR of at least 1%. Banks can pay up to 1.87% of their short-term debt, according to the maximum CR of 1.87. A bank’s short-term obligations on its current assets decrease as its total CR increases. Moreover, the mean for RGDP was R4,479.61 billion, and the standard deviation was R78.94 billion.

On the other hand, the minimum for RGDP was R4,320.33 billion, and the maximum was R4,599.26 billion. The average inflation rate measured by the annual consumer price index (CPI) was 4.19, with a standard deviation of 0.84. The consumer price index shows the country’s ability to keep prices competitive. A larger scale implies consumer price volatility, which is particularly damaging to the poor and small businesses because they lack a hedging strategy against economic shocks. The minimum CPI was 3.10, and the maximum CPI was 5.60. The IR’s mean was 3.40, and its standard deviation was 1.58. IR ranged from a minimum of 0.51 to a maximum of 5.86, respectively.

4.2. Correlation Analysis

As shown in

Table 3. Correlation analysis.

Variables	TDR	LTDR	STDR	GO	CR	RGDP	CPI	INTR	SIZE
TDR	1.0000
LTDR	0.9998 ***	1.0000
STDR	−0.2054 **	−0.2131 *	1.0000
GO	(0.0162)	(0.0129)	(0.0963)	1.0000
CR	0.1018	0.1133	−0.4116 *	0.1116	1.0000
RGDP	0.0327	0.0334	0.0469	(0.0763)	0.0251	1.0000
CPI	−0.1649 *	(0.1637)	0.0994	0.1470	0.1861 *	0.0036	1.0000
INTR	−0.2038 **	−0.2016 **	0.0002	0.0183	0.2135 **	0.4817 ***	0.4922 ***	1.0000
SIZE	(0.1614)	(0.1479)	0.0840	0.0593	0.1905 *	0.0047	(0.0705)	(0.0352)	1.0000

* p < 0.05, ** p < 0.01, *** p < 0.001.

, the correlation analysis illustrates the relationships between the independent and dependent variables used in the banking industry.

TDR was positive and significantly correlated with LRDR. The TDR and LTDR showed a negative relationship and a significant correlation with STDR. Moreover, there was a negative correlation between TDR, LTDR, and STDR with growth opportunities (GO). The TDR and LTDR had a positive association with the current ratio (CR). However, the TDR and LTDR had a negative connection with CR. The results of the study indicate a negative connection between TDR, LTDR, and STDR with RGDP. In addition, there was a negative and significant relationship between the consumer price index (CPI) and TDR. There was a negative but not significant association between CPI and LTDR. However, there is a positive but not significant connection between CPI and STDR. A negative relationship and significant correlation were found between interest rate (INTR) and TDR and LTDR. However, there is a positive and insignificant connection between INTRs and STDR, and lastly, a negative and insignificant link between size and TDR and LTDR. However, the study showed a positive but not significant association between size and STDR. The correlation coefficients were less than 0.7, which eliminated the potential for multicollinearity (Siddik et al., 2017). In addition, the VIF ranged from 1.30 to 3.10, indicating that multicollinearity was not present. There was no meaningful relationship between any of the equation’s parameters. Due to their minimal impact, several elements were left out.

4.3. Empirical Results and Analysis

The empirical analysis reveals a positive and statistically significant association between interest rates (INTRs) and both the total debt ratio (TDR) and long-term debt ratio (LTDR), while the relationship with the short-term debt ratio (STDR) remains positive but statistically insignificant. These findings suggest that increases in interest rates are more strongly associated with greater reliance on long-term debt, potentially reflecting banks’ preference to lock in borrowing costs over extended periods to mitigate refinancing risks. The results resonate with the findings of Kebede (2024), who documented a similar positive relationship between interest rates and bank leverage, indicating that in some contexts, higher interest rates may not necessarily deter borrowing but may influence the composition of debt

Table 4. Effects of Growth Opportunities on Capital Structure.

	System GMM	System GMM	System GMM
Variables	TDR	LTDR	STDR
LagTDR	−0.260 ***
	(0.0164)
LagLTDR		−0.268 ***
		(0.0158)
LagSTDR			−0.182 *
			(0.0768)
GO	0.577 ***	0.124 ***	−0.00115 *
	(0.0618)	(0.0108)	(0.000380)
CR	−79.42 **	−9.923 *	−0.388
	(22.74)	(3.649)	(0.191)
LRGDP	100.1	35.12	−0.710
	(294.7)	(44.54)	(1.521)
CPI	−21.36 ***	−4.840 ***	0.0331 *
	(4.210)	(0.887)	(0.0132)
INTR	7.502 **	1.616 **	0.00844
	(1.887)	(0.431)	(0.00772)
SIZE	51.91 **	12.02 **	0.151
	(16.37)	(3.313)	(0.0750)
COVID_19	−0.186	−0.0673	−0.00511
	(0.446)	(0.124)	(0.00587)
N	90	90	90
Groups	11	11	11
Instruments	10	10	10
AR(1)	−0.73	−0.61	−1.48
AR(2)	−0.83	−1.09	−0.51
Sargan test	0.10	0.10	4.82
Hansen Test	2.30	2.31	3.94

Robust Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001.

presents the effect of growth opportunities on capital structure.

The role of bank size in determining capital structure is also evident from the positive and significant relationship observed between bank size and both TDR and LTDR. This pattern implies that larger banks may have greater access to external finance, particularly long-term debt, due to their perceived stability, diversified operations, and lower default risk. This outcome aligns with the trade-off theory of capital structure, which posits that firms weigh the benefits of debt, such as tax shields, against the potential costs of financial distress that are often lower for large institutions. Conversely, the findings challenge the pecking order theory, which predicts that firms will prioritise internal financing and regard external debt, especially under high interest rate conditions, as a less desirable option. Interestingly, although the pecking order theory would anticipate a negative correlation between interest rates and debt levels, this study, in line with Assfaw (2020), finds a positive relationship, suggesting that banks may not follow a strict financing hierarchy and may view debt as a strategic tool even in high-rate environments.

Regarding the impact of the COVID-19 pandemic, the results show a negative but statistically insignificant relationship between the pandemic and bank capital structure. While this may reflect heightened economic uncertainty and risk aversion among financial institutions, leading to more conservative leverage decisions, the lack of significance suggests that the impact may be heterogeneous across institutions or time periods. These findings support those of Mohammad (2022), who observed a negative association between COVID-19 and capital structure, likely due to disruptions in credit markets and increased financial fragility. However, they contradict the results of Mabandla and Marozva (2025), who found a positive link, possibly due to policy interventions or liquidity support that encouraged debt uptake. This divergence highlights the complexity of crisis impacts on capital structure and underscores the importance of context-specific analyses when interpreting such relationships.

5. Conclusions

This study investigated the impact of growth opportunities (GOs) on bank capital structure in a panel of banks from 2014 to 2023, employing the Generalised Method of Moments (GMM) to address potential endogeneity issues. The empirical findings reveal a positive and statistically significant relationship between growth opportunities and both the total debt ratio (TDR) and long-term debt ratio (LTDR), suggesting that banks with stronger growth prospects are more inclined to utilise long-term financing. Conversely, GOs exhibited a significant negative relationship with short-term debt ratio (STDR), indicating a tendency among growth-oriented banks to avoid short-term liabilities, likely due to their higher refinancing risk and potential to create liquidity constraints. These findings align with the work of Mabandla and Marozva (2024), who argue that shareholders are more inclined to support larger financial institutions with promising growth trajectories, expecting management to capitalise on such prospects while minimising agency conflicts and reliance on costly external finance. This study contributes to the growing literature on capital structure by focusing exclusively on the banking sector. Future research could extend this analysis by conducting a comparative investigation to examine the heterogeneity in the effects of growth opportunities on capital structure across other African countries or within different segments of the financial sector. Such comparative analyses would provide deeper insights into contextual and institutional factors that may shape the capital structure decisions of firms across diverse economic and regulatory environments. This study focused exclusively on South African banks; however, a key limitation is the exclusion of several small and emerging banks due to the unavailability or insufficiency of data. As a result, the findings may not fully capture the capital structure dynamics across the entire banking sector, particularly those institutions operating on a smaller scale or with less public disclosure. Moreover, the study employed book value measures to assess capital structure, which, while common in empirical research, may not fully reflect the market perception of leverage. Future research could enhance the robustness of such analyses by incorporating both book and market value measures, thereby offering a more comprehensive evaluation of firms’ capital structure decisions and investor expectations. The findings that growth opportunities positively influence long-term debt and negatively affect short-term debt in the banking sector have important implications for banks, regulators, and investors. Banks should align their financing strategies with long-term goals by reducing dependence on short-term debt, while regulators can support this by developing policies that incentivise long-term borrowing and enhance financial stability. Investors can view long-term debt usage as a signal of strong growth prospects and sound financial management.