Capital Structure Resilience During the COVID-19 Pandemic: An Analysis of the Impact of Financial Characteristics on Egyptian Listed Companies

Abstract

Capital structure decisions are among the most critical financial choices for firms, as they directly influence them. There is an ongoing debate among researchers regarding the optimal capital structure, motivating this study to examine the impact of various factors on firms’ capital structure, while also considering the COVID-19 pandemic as an influential external factor. This study investigates the financial behavior of 147 non-financial firms listed on the Egyptian Stock Exchange over the period of 2011–2022, using firm year observations. Firms were classified into 14 sectors, excluding banking and non-banking financial services, due to their unique regulatory environments. Data were collected from multiple reliable sources, including financial statements, corporate reports, and EGX databases. Advanced econometric techniques, including the Panel Generalized Method of Moments (GMM), the Arellano–Bond test, and Johansen cointegration analysis, were employed to address endogeneity and explore long-run relationships. The results show that leverage is persistent over time, is positively associated with firm size, tangible assets, and growth opportunities, and is negatively related to profitability, cash flow, and liquidity. The COVID-19 pandemic had a small but significant positive effect, and sectoral differences were also observed. The findings provide robust insights into corporate financing behavior in emerging markets, highlighting the interplay between firm characteristics, external shocks, and financing decisions.

1. Introduction

Corporate financing decisions become particularly critical during periods of economic instability, where firms are forced to reassess their reliance on internal and external funding sources. The COVID-19 crisis represents a significant liquidity shock that disrupted financial markets worldwide, offering a valuable context to investigate how firms adjust their capital structure under constrained conditions. This is especially relevant in emerging economies such as Egypt, where financial systems are more sensitive to external shocks and institutional limitations (Mohammad, 2022; Brusov & Filatova, 2023) among the most prominent. Rather than restating their conventional interpretations, this study emphasizes the divergence in their implications during periods of financial distress. The Trade-off Theory posits that firms attempt to balance the tax benefits of debt against the costs associated with financial distress (Eldomiaty & Ismail, 2009), whereas the Pecking Order Theory suggests that firms prioritize internally generated funds due to information asymmetry, resorting to external financing only when necessary (Ahmad, 2025). However, in the presence of acute liquidity constraints, such as those observed during the COVID-19 pandemic, these theoretical predictions may not fully align with actual firm behavior (Mohammad, 2022). Additional perspectives, including Agency Cost Theory and Market Timing Theory, further highlight the influence of governance mechanisms and market conditions on financing decisions (Aljamaan, 2018).

Empirical studies generally support the relevance of these theoretical perspectives while also pointing to their context-dependent limitations. Evidence indicates that firm-level characteristics—such as size, profitability, and asset tangibility—play a decisive role in shaping capital structure choices. Firms with larger-scale operations and higher levels of tangible assets tend to access external financing more easily, whereas firms with stronger profitability often depend more on internally generated funds, in line with the Pecking Order Theory (van Binsbergen et al., 2011; Sheikh & Qureshi, 2017). Other determinants, including growth opportunities, non-debt tax shields, and industry-specific factors, also contribute to financing decisions, though their effects vary across institutional and macroeconomic settings. More recent developments, such as the COVID-19 pandemic, global supply chain disruptions, and tighter monetary policies, have significantly influenced firms’ risk assessments and financing patterns, underscoring the need to interpret traditional theories within evolving economic environments (Brusov & Filatova, 2023; Ahmad, 2025).

Within the Egyptian context, the effects of the COVID-19 crisis extended beyond a general global downturn, manifesting in concrete financial constraints such as stricter bank lending practices, heightened uncertainty, and reduced credit availability (Mohammad, 2022). These conditions create an environment in which firms may depart from standard financing hierarchies, raising a key research question regarding the extent to which traditional capital structure theories remain valid when liquidity is limited, and how responsive firms are to both internal characteristics and external economic pressures.

To explore this issue, this study employs panel data techniques to analyze the dynamic responsiveness of leverage decisions, rather than attempting to establish causal identification through quasi-natural experimental designs. This approach allows for a more flexible examination of how firms adjust their capital structure in response to changes in firm-specific variables and macroeconomic conditions over time (van Binsbergen et al., 2011).

The empirical analysis focuses on a sample of 147 non-financial firms listed on the Egyptian Stock Exchange (EGX) over the period from 2011 to 2022, resulting in 1623 firm-year observations. Financial institutions are excluded due to their distinct regulatory environment, which may distort comparability. Both short-term and long-term debt measures are considered, with special attention given to the COVID-19 period as a phase of intensified financial constraints. By combining firm-level determinants with broader economic factors, this study contributes to the existing literature by offering evidence on how firms adapt their financing strategies during liquidity shocks, and by evaluating the applicability of established capital structure theories within the context of an emerging economy (Ahmad, 2025; Brusov & Filatova, 2023).

1.1. Background Capital Structure

1.1.1. Economic Shocks as a Trigger for Financing Adjustments

Economic shocks represent critical turning points that disrupt the financial equilibrium of firms and force a reassessment of their capital structure decisions. These shocks may take different forms, including global financial crises, regional economic downturns, or health-related crises such as the COVID-19 pandemic. Regardless of their origin, such disturbances share common characteristics: heightened uncertainty, contraction in liquidity, and increased volatility in financial markets. These conditions collectively alter the external financing environment in which firms operate.

The literature consistently indicates that firms do not passively absorb these shocks; rather, they actively respond by revising their financing strategies. During periods of economic stability, firms are generally able to access external funding under relatively predictable conditions. However, when a shock occurs, credit markets tighten, borrowing costs increase, and lenders adopt more conservative risk assessment criteria. Consequently, firms experience constraints in accessing debt financing, which compels them to reconsider their reliance on leverage.

Empirical evidence strongly supports this argument. The study by Mohammad (2022), which specifically examined the COVID-19 period, found that banks significantly reduced their leverage ratios while simultaneously strengthening their capital bases. This behavior reflects a defensive financial strategy aimed at enhancing resilience in the face of uncertainty. Similarly, Choi et al. (2024) demonstrated that macroeconomic factors—such as GDP growth, inflation, and credit conditions—play a more dominant role during crisis periods than in stable environments. Their findings suggest that external economic conditions can override firm-specific determinants when uncertainty is elevated.

In addition, earlier research provides supporting evidence from previous crises. Ghosh and Chatterjee (2018) observed that during the global financial crisis, banks—particularly those characterized by high risk and large size—tended to reduce their reliance on debt. This reduction was not merely a reaction to regulatory pressure but also a strategic response to increased financial fragility and market uncertainty.

From a theoretical perspective, economic shocks can be understood as exogenous disturbances that shift firms away from their optimal capital structure. Under normal conditions, firms gradually adjust toward a target leverage ratio. However, shocks disrupt this adjustment process by introducing new constraints and altering expectations about future cash flows and risk. As a result, firms may temporarily deviate from their target structure and adopt more conservative financing policies.

Furthermore, the impact of economic shocks is not uniform across all firms. Differences in firm size, sector, and institutional environment can influence the extent to which firms are affected. For example, firms operating in emerging markets often face more severe financing constraints due to less developed financial systems and limited access to alternative funding sources. Similarly, firms with higher pre-crisis leverage levels may be more vulnerable to shocks, as they already carry significant financial obligations.

Another important dimension is the duration of the shock. Short-term disruptions may lead to temporary adjustments, while prolonged crises can result in structural changes in financing behavior. The COVID-19 pandemic, for instance, combined both immediate disruptions and longer-term uncertainty, thereby intensifying its impact on capital structure decisions subsequent phases of financing constraints, risk reassessment, and leverage adjustment, ultimately leading to observable changes in firm performance and value.

1.1.2. Financing Constraints and Risk Reassessment

Following the occurrence of an economic shock, firms enter a phase characterized by heightened financing constraints and a comprehensive reassessment of financial risk. This stage represents a crucial transmission mechanism through which external disturbances influence internal corporate decisions. The tightening of credit markets, combined with increased uncertainty regarding future cash flows, fundamentally alters firms’ perceptions of risk and their willingness to engage in external financing.

Financing constraints typically manifest in several ways. First, lenders become more selective, imposing stricter borrowing conditions and requiring higher collateral. Second, the cost of debt increases due to higher risk premiums. Third, access to capital markets may become limited, particularly for smaller firms or those operating in riskier sectors. These constraints are particularly pronounced in emerging economies, where financial systems may lack depth and diversification.

Nguyen and Nguyen (2020) provide empirical support for this argument by demonstrating that higher levels of debt are associated with poorer firm performance, especially in state-owned enterprises. This finding highlights the vulnerability of firms that rely heavily on external financing in environments characterized by weak monitoring and limited financial flexibility. As a result, firms facing such constraints are more likely to reduce their dependence on debt and seek alternative financing sources.

At the same time, firms engage in a systematic reassessment of risk. This process involves evaluating the sustainability of existing debt levels, the volatility of earnings, and the potential impact of adverse economic conditions on future performance. Ghosh and Chatterjee (2018) show that high-risk institutions tend to decrease their leverage, reflecting a strategic shift toward more conservative financial structures. This behavior is consistent with the notion that firms prioritize financial stability over growth during periods of uncertainty.

Pandey (2022) adds a theoretical dimension to this discussion by emphasizing the role of information asymmetry and market frictions. According to this perspective, the challenges associated with imperfect information and transaction costs are not unique to specific sectors but are inherent features of financial markets. During periods of crisis, these frictions become more pronounced, further constraining firms’ access to external financing and reinforcing their preference for internal funds.

Moreover, risk reassessment is influenced by both firm-specific and macroeconomic factors. On the one hand, internal characteristics such as profitability, liquidity, and asset structure determine a firm’s ability to withstand financial stress. On the other hand, external factors such as interest rates, inflation, and economic growth shape the overall risk environment. The interaction between these factors creates a complex decision-making framework in which firms must balance risk and return considerations.

Another important aspect of this stage is the role of expectations. Firms’ perceptions of future economic conditions play a critical role in shaping their financing decisions. If managers anticipate prolonged uncertainty, they are more likely to adopt conservative strategies, including reducing leverage and increasing liquidity reserves. Conversely, if they expect a rapid recovery, they may be more willing to maintain or even increase their debt levels.

In addition, institutional and regulatory factors can influence the severity of financing constraints. For example, banks are subject to capital adequacy requirements that may limit their ability to extend credit during downturns. Similarly, government policies, such as monetary easing or fiscal support, can either alleviate or exacerbate financing constraints.

Overall, the stage of financing constraints and risk reassessment represents a critical link between external shocks and internal financial decisions. By shaping firms’ access to capital and their perception of risk, this stage determines the direction and magnitude of subsequent adjustments in capital structure.

1.1.3. Leverage Adjustment Behavior: Theoretical Interpretations

Once firms face financing constraints and reassess their risk exposure, they proceed to adjust their capital structures accordingly. This adjustment process is not random but is guided by well-established theoretical frameworks, primarily the Pecking Order Theory, the Trade-off Theory, and, to some extent, the Agency Theory. These theories provide complementary explanations for how firms balance the costs and benefits of different financing sources under varying economic conditions.

The Pecking Order Theory offers a particularly strong explanation for firm behavior during periods of uncertainty. According to this theory, firms prefer internal financing over external sources due to information asymmetry and the costs associated with issuing new securities. Empirical evidence supports this view. Singh (2016) found a significant negative relationship between profitability and leverage, indicating that more profitable firms rely less on debt. Similarly, Sheikh and Qureshi (2017) reported that both conventional and Islamic banks exhibit a preference for internal financing, especially when profitability is high.

This behavior becomes more pronounced during crisis periods, when external financing is not only more expensive but also more difficult to obtain. Firms, therefore, prioritize the use of retained earnings and reduce their reliance on debt. This shift reflects a strategic effort to minimize financial risk and maintain flexibility.

In contrast, the Trade-off Theory emphasizes the existence of an optimal capital structure that balances the benefits of debt, such as tax shields, against the costs of financial distress. Eldomiaty and Ismail (2009) provide strong empirical support for this theory, showing that target debt ratios and non-debt tax shields are key determinants of financing decisions in Egyptian firms. Their findings suggest that firms actively manage their leverage to achieve a balance between risk and return.

Ukaegbu and Oino (2014) further extend this analysis by examining the speed of adjustment toward target leverage. Their results indicate that banks adjust more rapidly than manufacturing firms, reflecting the influence of regulatory oversight and the need to maintain capital adequacy. This highlights the dynamic nature of capital structure decisions, as firms continuously adjust their leverage in response to changing conditions.

Another important dimension of leverage adjustment is the heterogeneity across sectors and institutional contexts. For example, Guizani (2021) found that Islamic banks adjust more slowly toward their target capital structures compared to conventional banks. This difference is attributed to Sharia compliance requirements and higher financing costs, which limit the flexibility of Islamic banks in managing their leverage.

Similarly, differences between state-owned and private firms have been documented in the literature. State-owned enterprises may have greater access to financing and may therefore exhibit different adjustment patterns compared to private firms. These variations underscore the importance of considering institutional factors when analyzing capital structure behavior.

The Agency Theory also plays a role in explaining leverage decisions. Conflicts between managers and shareholders can influence financing choices, particularly in relation to risk-taking and investment decisions. For instance, higher leverage may be used as a disciplinary mechanism to reduce agency costs, but it may also increase the risk of financial distress.

In summary, leverage adjustment behavior is a complex process influenced by multiple theoretical considerations. Firms do not rely on a single framework but rather integrate insights from different theories to guide their financing decisions. This results in diverse adjustment patterns that reflect both internal characteristics and external constraints.

1.1.4. Observable Outcomes of Capital Structure Adjustments

The final stage in the causal mechanism is reflected in the observable outcomes of capital structure adjustments. These outcomes provide measurable evidence of how financing decisions impact firm performance, value, and financial stability. The literature presents a range of findings, reflecting the complexity of the relationship between leverage and firm outcomes.

A significant portion of empirical studies reports a negative relationship between leverage and firm performance. Chang et al. (2014) found that higher levels of debt are associated with lower returns on assets and equity, suggesting that excessive reliance on debt can reduce profitability. Similarly, Nguyen and Nguyen (2020) confirmed that increased leverage negatively affects firm performance across multiple indicators, particularly in emerging markets.

These findings can be explained by the increased financial burden associated with debt, including interest payments and the risk of default. During periods of economic instability, these costs become more pronounced, further exacerbating the negative impact of leverage on performance.

However, other studies highlight the potential benefits of leverage. Hirdinis (2019) found that capital structure has a positive effect on firm value, as the use of debt can enhance market valuation through tax advantages. This suggests that the relationship between leverage and firm value is not strictly negative but depends on the context in which firms operate.

Akin et al. (2025) provide additional evidence by showing that capital structure and firm size significantly influence enterprise value. Their findings indicate that financing decisions play a central role in shaping market perceptions and investor behavior. At the same time, the impact of profitability appears to be more complex and may vary across firms and market conditions.

The meta-analysis conducted by Dao and Ta (2020) offers a comprehensive perspective by synthesizing the results from multiple studies. Their findings reveal that the relationship between leverage and performance is not uniform, with some studies reporting negative effects, others finding positive effects, and a significant portion showing no relationship. This variation highlights the importance of contextual factors, such as industry, country, and methodological approach.

Another important outcome is the impact of capital structure on financial stability. Firms with high leverage may be more vulnerable to economic shocks, as they have less flexibility to absorb losses. Conversely, firms that maintain moderate levels of debt may benefit from financial leverage without exposing themselves to excessive risk.

In addition, capital structure decisions can influence strategic behavior, such as investment and growth. Firms with lower leverage may have greater flexibility to pursue new opportunities, while highly leveraged firms may be constrained by their financial obligations.

Overall, the observable outcomes of capital structure adjustments provide valuable insights into the effectiveness of different financing strategies. By linking these outcomes to the preceding stages of economic shocks, financing constraints, and leverage adjustment, it is possible to develop a comprehensive understanding of how firms navigate complex financial environments.

The reviewed literature, when reorganized within a causal framework, reveals a coherent and dynamic process through which firms respond to economic shocks. Beginning with external disruptions, moving through financing constraints and risk reassessment, and culminating in leverage adjustments and observable outcomes, this process provides a unified explanation of capital structure behavior. Such an approach not only enhances theoretical clarity but also strengthens the foundation for empirical analysis in the context of crisis-driven financial decision-making.

1.2. Hypothesis Development

Based on the theoretical framework and prior empirical evidence, the following hypotheses are formulated:

H1. 

Firm size has a positive effect on leverage in Egyptian listed firms (Trade-off Theory).

H2. 

Profitability (EPS) has a negative effect on leverage (Pecking Order Theory).

H3. 

Return on assets (ROA) has a negative effect on leverage (Pecking Order Theory).

H4. 

Asset tangibility has a positive effect on leverage (Trade-off Theory).

H5. 

Growth opportunities have a significant effect on leverage (Pecking Order Theory/Trade-off Theory).

H6. 

Cash flow has a negative effect on leverage (Pecking Order Theory).

H7. 

Liquidity has a negative effect on leverage (Pecking Order Theory).

H8. 

The COVID-19 crisis has a significant effect on firms’ leverage decisions (Crisis/Uncertainty Framework).

H9. 

Industry characteristics have a significant effect on leverage (Trade-off Theory/Industry Effects).

2. Materials and Methods

2.1. Data and Sample

This study analyzes 147 non-financial firms listed on the Egyptian Stock Exchange (EGX), yielding 1623 firm-year observations from 2011 to 2022. Firms were categorized into 14 economic sectors based on the EGX classification; however, the banking and non-banking financial services sectors were excluded due to their distinct regulatory and financial frameworks, which could bias the results. Firms with incomplete or missing data were also excluded to maintain consistency and reliability.

The selected period allows for a detailed examination of both short- and long-term capital structure dynamics, with special attention to the COVID-19 pandemic as an exogenous shock that could significantly affect leverage decisions. Data were collected from multiple verified sources, including annual financial statements, corporate reports, general assembly disclosures, and databases such as the EGX website, Mubasher Egypt, and Investing, ensuring a comprehensive and robust dataset.

To achieve this study’s objectives, advanced econometric techniques were employed to account for the dynamic and potentially endogenous relationships among variables. Specifically, the Panel Generalized Method of Moments (GMM) estimator addresses simultaneity and reverse causality, while the Arellano–Bond serial correlation test ensures model validity. Complementary analyses, including descriptive statistics, correlation matrices, and the Johansen cointegration test, were applied to explore data characteristics and long-term relationships. This methodological approach provides precise, reliable, and robust insights into the determinants of capital structure in Egyptian listed firms under varying economic conditions.

2.2. Research Model

Capital structure determinants are potentially endogenous, as explanatory variables such as profitability, firm size, and growth opportunities may correlate with the error term due to simultaneity, reverse causality, or measurement errors. Relying on ordinary least squares (OLS) in such cases could produce biased and inconsistent estimates. To overcome these challenges, this study employs the Panel Generalized Method of Moments (GMM), which uses appropriate instrumental variables that are correlated with the endogenous predictors but uncorrelated with the error term. This approach ensures consistent and efficient parameter estimation while accounting for dynamic panel data characteristics, making it particularly suitable for analyzing the Egyptian non-financial firms over the 2011–2022 period.

The GMM framework allows this study to capture both short- and long-term relationships among capital structure determinants while controlling for firm-specific heterogeneity and potential endogeneity. To validate the model, the Arellano–Bond serial correlation test is applied, confirming that the instruments and lagged variables produce reliable estimations. Additionally, the Johansen cointegration test is employed to examine long-run relationships among variables, complementing the dynamic panel analysis and enhancing the robustness of empirical findings. Descriptive statistics and correlation matrices further provide insights into data characteristics, ensuring a comprehensive understanding of the underlying patterns and interactions.

Table 1. Dependent and independent variables.

Variable Type	Variable	Measurement (Based on Previous Studies)	Relation to Capital Structure Theory	Reason for Using This Measure
Independent	SIZE	Natural log of total assets or total sales (Eldomiaty & Ismail, 2009; Singh, 2016; Sheikh & Qureshi, 2017; Kirshin & Volkov, 2018; Guizani, 2021)	Trade-off Theory & Pecking Order Theory: larger firms can access debt more easily and face lower bankruptcy risk	Measures firm scale, reflects ability to obtain debt and diversify risk
Independent	Earnings per Share (EPS)	Net profit/Number of outstanding shares (Chang et al., 2014; Nguyen & Nguyen, 2020)	Pecking Order Theory: higher profitability leads to preference for internal financing	Proxy for profitability to assess internal financing capacity
Independent	Return on Assets (ROA)	Net income/Total assets (Eldomiaty & Ismail, 2009; Dao & Ta, 2020; Wang & Huang, 2021)	Pecking Order Theory: higher ROA → less reliance on debt	Captures operational efficiency and profitability relative to asset base
Independent	Tangible Assets	Tangible fixed assets/Total assets (Eldomiaty & Ismail, 2009; Kirshin & Volkov, 2018; Choi et al., 2024)	Trade-off Theory: tangible assets serve as collateral → reduce lending risk	Measures collateral availability for debt financing
Independent	Growth Opportunities	Market-to-book ratio (Eldomiaty & Ismail, 2009; Kirshin & Volkov, 2018; Choi et al., 2024)	Pecking Order Theory: high-growth firms need more external financing; Trade-off Theory: debt may be limited due to risk	Captures potential need for financing relative to investment opportunities
Independent	Cash Flow	Net cash from operations/Total assets (Singh, 2016; Dao & Ta, 2020)	Pecking Order Theory: higher internal cash reduces need for debt	Measures internal liquidity and self-financing ability
Independent	Liquidity	Current assets/Current liabilities (Singh, 2016; Dao & Ta, 2020)	Pecking Order Theory: higher liquidity → less debt usage	Reflects short-term financial flexibility and ability to cover obligations
Independent	COVID-19	Dummy variable: 1 if crisis period, 0 otherwise (Mohammad, 2022; Choi et al., 2024; Nguyen & Nguyen, 2020)	Trade-off/Pecking Order: crisis may affect optimal leverage and debt adjustment	Captures external shock affecting leverage decisions
Independent	Industry	Industry classification dummy variables (Eldomiaty & Ismail, 2009; Ukaegbu & Oino, 2014)	Trade-off Theory: risk profiles and asset structures vary by industry → affect leverage	Controls for sector-specific differences in financing patterns
Dependent	Leverage_Total Debt %	Total debt/Total assets × 100 (Eldomiaty & Ismail, 2009; Singh, 2016; Kirshin & Volkov, 2018)	Trade-off Theory: measures overall debt usage for tax shield benefit	Standard measure of financial leverage
Dependent	Total Debt/Equity %	Total debt/Total equity × 100 (Sheikh & Qureshi, 2017; Guizani, 2021)	Trade-off Theory & Pecking Order Theory: indicates balance between debt and equity financing	Evaluates capital structure and relative risk exposure

Source: author’s own creation.

presents the variables used in this study, their measurement, theoretical basis, and relevance to capital structure. These variables were selected based on prior empirical studies and established theories, including the Trade-off Theory and the Pecking Order Theory, ensuring a direct connection between the research model and this study’s hypotheses. By integrating advanced econometric techniques with a carefully constructed variable framework, the methodology provides a precise and firm-specific evaluation of debt policies, directly addressing the research objectives of understanding leverage determinants in Egyptian listed firms.

3. Results

3.1. Descriptive Statistics and Jarque–Bera Test

Table 2. Descriptive statistics of all variables of this study.

	LEV	TD_EQ	SIZE	EPS	ROA	TANG	GRW	LIQ	CASHFLOW
Mean	0.153982	0.284999	20.54580	0.250352	0.035244	0.327354	0.046110	0.578276	0.097102
Median	0.105000	0.178270	20.43000	0.180000	0.040000	0.295000	0.033000	0.611431	0.078972
Maximum	0.620000	1.161743	24.94000	1.020000	0.125000	0.980000	0.395722	0.998558	0.280000
Minimum	0.000000	−0.662833	16.82000	−0.590000	−0.105000	0.000000	−0.273409	0.003341	0.000000
Std. Dev.	0.162042	0.327154	1.647671	0.359917	0.050174	0.235947	0.123747	0.241333	0.074724
Skewness	0.920504	0.757156	0.338104	0.202750	−0.390440	0.579714	0.187665	−0.311053	0.650592
Kurtosis	2.851403	2.753730	2.657174	2.383314	2.586596	2.589443	2.984052	2.294598	2.419788
Jarque–Bera	230.6953	159.1749	38.86994	36.83747	52.79328	102.3053	9.543708	59.82174	137.2601
Probability	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.008465	0.000000	0.000000
Observations	1623	1623	1623	1623	1623	1623	1623	1623	1623

Source: author’s own creation.

shows that the arithmetic means of the variables reflect noticeable differences in the financial and operational characteristics of the firms under study. The average leverage ratio (LEV) is approximately 0.154, with a standard deviation of 0.162, indicating considerable variation in firms’ reliance on debt financing. Similarly, the mean value of the debt-to-equity ratio (TD_EQ) is about 0.285, accompanied by a relatively high standard deviation (0.327), which points to substantial heterogeneity in capital structures across firms. Firm size (SIZE) records an average of 20.55 and a comparatively low standard deviation (1.65), suggesting a reasonable degree of homogeneity in firm size. Earnings per share (EPS) have a mean of 0.25 with a standard deviation of 0.36, reflecting notable fluctuations in profitability levels. The return on assets (ROA) shows a relatively low mean (0.035) and a standard deviation of 0.05, indicating limited dispersion in asset efficiency. Tangibility (TANG) has an average value of around 0.33 with a standard deviation of 0.24, implying moderate variation in asset composition. The growth rate (GRW) records a mean of 0.046 and a relatively high standard deviation (0.124), highlighting wide differences in growth performance among firms. Liquidity (LIQ) has an average of 0.58 with a standard deviation of 0.24, while cash flow (CASHFLOW) reports a mean of 0.097 and a standard deviation of 0.075, both indicating noticeable variability in firms’ short-term financial positions and cash-generating capacity. Regarding distributional properties, the skewness, kurtosis, and Jarque–Bera statistics consistently reject the assumption of normality for all variables at conventional significance levels. Nevertheless, the large sample size mitigates potential concerns related to non-normality, supporting the application of econometric models with appropriate robust standard error adjustments.

The frequency distribution of the COVID-19 variable, which is specified as a binary (dummy) variable taking the values 0 and 1, is based on a total of 1623 observations. The results indicate that the majority of observations fall under the value 0, with 1190 cases, representing 73.3% of the sample, whereas the value 1 is recorded for 433 observations, accounting for 26.7%. The equality between the Percent and Valid Percent columns confirms the absence of missing data for this variable, which enhances the reliability of the dataset. In addition, the cumulative percentage reaches 100% at the category 1, indicating full coverage of the sample. From a statistical perspective, the relatively unbalanced distribution suggests that non-COVID-19 periods (value 0) dominate the sample compared to periods affected by the pandemic (value 1). Such a pattern is commonly observed in longitudinal datasets that span several years before and during the COVID-19 outbreak. Consequently, this dummy variable is well suited for inclusion in regression and econometric analyses to capture the differential impact of the COVID-19 pandemic on the variables under investigation. The frequency distribution of the Industrial variable, which is defined as a binary (dummy) indicator capturing whether a firm belongs to the industrial sector or not, is based on a total of 1623 observations. The results show that most observations are concentrated in category 0, with 1352 cases, representing 83.3% of the sample, while 271 observations (16.7%) are classified under category 1. The identical values of the Percent and Valid Percent columns indicate that there are no missing observations for this variable, confirming the consistency and completeness of the data. Furthermore, the cumulative percentage reaches 100% at the final category, reflecting full sample coverage. From a statistical standpoint, this distribution reveals a relatively uneven representation of industrial firms compared to non-industrial firms within the sample, a pattern commonly observed in multi-sector datasets. Accordingly, this variable can be effectively incorporated into regression and econometric models to control for sector-specific effects and to examine the differential impact of industrial affiliation on the study variables.

3.2. Group Unit Root Test

As illustrated in

Table 3. Group unit root for dependent and independent variables from.

Variables	Augmented Dickey–Fuller (ADF) Test				Phillips–Perron (PP) Test
	t-Stat	Exogenous	Difference	Prob.	Adj. t-Stat	Exogenous	Difference	Prob.
LEV	−11.89634	constant	1st diff	0.001 ***	−10.03346	constant	LEVEL 0	0.001 ***
TD_EQ	−12.32503	constant	2nd diff	0.001 ***	−12.06217	constant	2nd diff	0.001 ***
SIZE	−10.71186	constant	2nd diff	0.001 ***	−8.010038	constant	2nd diff	0.001 ***
EPS	−12.24681	constant	1st diff	0.001 ***	−12.27220	constant	1st diff	0.001 ***
ROA	−14.65064	constant	1st diff	0.001 ***	−14.95006	constant	1st diff	0.001 ***
TANG	−10.62936	constant	1st diff	0.001 ***	−8.676066	constant	1st diff	0.001 ***
GRW	−19.87592	constant	1st diff	0.001 ***	−25.67082	constant	1st diff	0.001 ***
CASHFLOW	−13.68584	constant	1st diff	0.001 ***	−13.79247	constant	1st diff	0.001 ***
LIQ	−11.21128	constant	1st diff	0.001 ***	−8.715224	constant	1st diff	0.001 ***

*** Significant at a level lower than (0.001). ** Significant at a level lower than (0.01). * Significant at a level lower than (0.05). Source: author’s own creation.

, the calculated values for the ADF and PP tests demonstrate statistical significance lower than the threshold of (0.05), leading to the rejection of the null hypothesis, which asserts the presence of a unit root. These findings confirm that the time series data for both exogenous and endogenous variables are stationary over the sample period from 2011 to September 2022. Stationarity is established under the model specification that includes a constant term, with variables identified as being integrated of order one or two, i.e., (0).

3.3. Johansen Cointegrating Test

From

Table 4. Cointegrating test for exogenous and endogenous metric variables Lev_EQ SIZE EPS ROA TANG RW CASHFLOW LIQ from 2011 to 2022.

Hypothesized No. of CE(s)	Unrestricted Cointegration Rank Test (Trace)
	Eigenvalue	Trace Statistic	0.05 Critical Value	Prob.
None *	0.142644	1078.516	159.5297	0.0000
At most 1 *	0.102832	829.5033	125.6154	0.0000
At most 2 *	0.078947	653.9303	95.75366	0.0000
At most 3 *	0.071011	520.8689	69.81889	0.0000
At most 4 *	0.070186	401.6904	47.85613	0.0000
At most 5 *	0.062259	283.9467	29.79707	0.0000
At most 6 *	0.057164	179.9398	15.49471	0.0000
At most 7 *	0.051001	84.69869	3.841465	0.0000
Hypothesized No. of CE(s)	Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
None *	0.142644	249.0127	52.36261	0.0000
At most 1 *	0.102832	175.5730	46.23142	0.0000
At most 2 *	0.078947	133.0615	40.07757	0.0000
At most 3 *	0.071011	119.1785	33.87687	0.0000
At most 4 *	0.070186	117.7437	27.58434	0.0000
At most 5 *	0.062259	104.0070	21.13162	0.0000
At most 6 *	0.057164	95.24107	14.26460	0.0000
At most 7 *	0.051001	84.69869	3.841465	0.0000

Significant at a level lower than (0.01). * Significant at a level lower than (0.05).

and

Table 5. Johansen Cointegrating test for Exogenous and Endogenous metric variables TD_EQ SIZE EPS ROA TANG GRW CASHFLOW LIQ from 2011 to 2022.

Hypothesized No. of CE(s)	Unrestricted Cointegration Rank Test (Trace)
	Eigenvalue	Trace Statistic	0.05 Critical Value	Prob.
None *	0.142634	1084.238	159.5297	0.0000
At most 1 *	0.102074	835.2425	125.6154	0.0000
At most 2 *	0.077441	661.0365	95.75366	0.0000
At most 3 *	0.071784	530.6196	69.81889	0.0000
At most 4 *	0.069960	410.0942	47.85613	0.0000
At most 5 *	0.063721	292.7436	29.79707	0.0000
At most 6 *	0.059854	186.2115	15.49471	0.0000
At most 7 *	0.051968	86.34833	3.841465	0.0000
Hypothesized No. of CE(s)	Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
None *	0.142634	248.9952	52.36261	0.0000
At most 1 *	0.102074	174.2060	46.23142	0.0000
At most 2 *	0.077441	130.4169	40.07757	0.0000
At most 3 *	0.071784	120.5254	33.87687	0.0000
At most 4 *	0.069960	117.3506	27.58434	0.0000
At most 5 *	0.063721	106.5321	21.13162	0.0000
At most 6 *	0.059854	99.86313	14.26460	0.0000
At most 7 *	0.051968	86.34833	3.841465	0.0000

Significant at a level lower than (0.01). * Significant at a level lower than (0.05). Source: author’s own creation.

, the researcher revealed that the Trace and Max-Eigenvalue tests indicate the presence of one cointegrating equation (long-term relationships) at a significant level lower than (0.05). This leads to the rejection of the null hypothesis, thereby confirming the presence of a long-run equilibrium relationship among the endogenous and exogenous metric variables. This finding implies that despite short-term fluctuations, the variables under study tend to move together over time, adjusting toward a shared equilibrium path. Such co-movement supports the theoretical expectation that systematic risk factors have a long-term corrective influence on EGX 100 returns.

3.4. Correlation Matrix

Table 6. Correlation matrix for variables LEV SIZE EPS ROA TANG GRW CASHFLOW LIQ from 2011 to 2022.

Correlation
Probability	LEV	SIZE	EPS	ROA	TANG	GRW	CASHFLOW	LIQ	COVID-19	INDUSTRIAL
LEV	1.000000
	-----
SIZE	0.505856	1.000000
	0.0000	-----
EPS	−0.432929	0.165214	1.000000
	0.0000	0.0000	-----
ROA	−0.512625	0.086493	0.462956	1.000000
	0.0000	0.0005	0.0000	-----
TANG	0.465819	−0.042473	−0.188802	−0.173071	1.000000
	0.0000	0.0872	0.0000	0.0000	-----
GRW	0.451200	0.120319	0.214502	0.262424	−0.137585	1.000000
	0.0000	0.0000	0.0000	0.0000	0.0000	-----
CASHFLOW	−0.384227	−0.031422	−0.042612	0.066181	0.020648	−0.020087	1.000000
	0.0000	0.2058	0.0861	0.0077	0.4058	0.4187	-----
LIQ	−0.406106	−0.072730	0.155381	0.178798	−0.825135	0.130381	−0.033404	1.000000
	0.0000	0.0034	0.0000	0.0000	0.0000	0.0000	0.1786	-----
COVID-19	0.335196	0.068518	−0.023675	−0.028452	−0.010242	−0.103982	0.020885	0.025208	1.000000
	0.0000	0.0058	0.3405	0.2520	0.6801	0.0000	0.4005	0.3102	-----
INDUSTRIAL	0.365074	−0.120399	0.101309	−0.002	−0.184085	0.024650	−0.112938	0.222080	−0.008591	1.000000
	0.0000	0.0000	0.0000	0.9993	0.0000	0.3210	0.0000	0.0000	0.7294	-----

Source: author’s own creation.

and

Table 7. Correlation matrix for TD_EQ SIZE EPS ROA TANG GRW CASHFLOW LIQ.

Correlation
Probability	TD_EQ	SIZE	EPS	ROA	TANG	GRW	CASHFLOW	LIQ	COVID-19	INDUSTRIAL
TD_EQ	1.000000
	-----
SIZE	0.505856	1.000000
	0.0000	-----
EPS	−0.432929	0.165214	1.000000
	0.0000	0.0000	-----
ROA	−0.512625	0.086493	0.462956	1.000000
	0.0000	0.0005	0.0000	-----
TANG	0.465819	−0.042473	−0.188802	−0.173071	1.000000
	0.0000	0.0872	0.0000	0.0000	-----
GRW	0.451200	0.120319	0.214502	0.262424	−0.137585	1.000000
	0.0000	0.0000	0.0000	0.0000	0.0000	-----
CASHFLOW	−0.384227	−0.031422	−0.042612	0.066181	0.020648	−0.020087	1.000000
	0.0000	0.2058	0.0861	0.0077	0.4058	0.4187	-----
LIQ	−0.406106	−0.072730	0.155381	0.178798	−0.825135	0.130381	−0.033404	1.000000
	0.0000	0.0034	0.0000	0.0000	0.0000	0.0000	0.1786	-----
COVID-19	0.335196	0.068518	−0.023675	−0.028452	−0.010242	−0.103982	0.020885	0.025208	1.000000
	0.0000	0.0058	0.3405	0.2520	0.6801	0.0000	0.4005	0.3102	-----
INDUSTRIAL	0.365074	−0.120399	0.101309	−0.002	−0.184085	0.024650	−0.112938	0.222080	−0.008591	1.000000
	0.0000	0.0000	0.0000	0.9993	0.0000	0.3210	0.0000	0.0000	0.7294	-----

Source: author’s own creation.

presents the correlation matrix among the study variables over the period of 2011–2022. The results reveal several statistically significant relationships between financial leverage (LEV), TD_EQ and the variables. In particular, leverage is positively and significantly associated with firm size (SIZE), asset tangibility (TANG), and growth opportunities (GRW), as well as with the COVID-19 dummy and the industrial sector variable, indicating that larger firms and those with more tangible assets and higher growth prospects tend to rely more on debt financing. Conversely, leverage shows a negative and significant relationship with profitability and liquidity indicators, including earnings per share (EPS), return on assets (ROA), cash flow (CASHFLOW), and liquidity (LIQ), suggesting that more profitable and liquid firms prefer internal financing sources over external debt. Moreover, the correlation coefficients among the independent variables are generally low to moderate, implying the absence of serious multicollinearity concerns, with the exception of the strong negative correlation observed between asset tangibility and liquidity. Overall, these results support the suitability of the selected variables for subsequent econometric analysis.

3.5. Panel Generalized Method of Moments

Table 8. Panel Generalized Method of Moments model used to determine the effect of independent variables on LEV.

Variable	Coefficient	Std. Error	t-Statistic	Prob.	VIF
LEV(−1)	0.603919	0.081448	7.414746	0.0000	1.307193
SIZE	0.109056	0.022396	4.869460	0.0000	1.274793
EPS	−0.010347	0.048906	−0.211573	0.8327	1.360051
ROA	−1.399668	0.356576	−3.925298	0.0001	1.390886
TANG	0.729480	0.231286	3.154015	0.0020	3.481980
GRW	0.134187	0.057888	2.318038	0.0218	1.120405
CASHFLOW	−0.304648	0.133998	−2.273526	0.0231	1.039762
LIQ	−0.734292	0.203180	−3.614004	0.0004	3.365168
COVID-19	0.255103	0.118091	2.160224	0.0309	1.022222
INDUSTRIAL	1.301065	0.274251	4.744067	0.0000	1.121486
@LEV(@ISPERIOD(“2013”))	0.000937	0.006451	0.145302	0.8847	-
@LEV(@ISPERIOD(“2014”))	−0.019967	0.006550	−3.048153	0.0027	-
@LEV(@ISPERIOD(“2015”))	0.003225	0.006140	0.525178	0.6003	-
@LEV(@ISPERIOD(“2016”))	−0.021137	0.005890	−3.588353	0.0005	-
@LEV(@ISPERIOD(“2017”))	−0.009280	0.007228	−1.283793	0.2012	-
@LEV(@ISPERIOD(“2018”))	−0.002847	0.006360	−0.447589	0.6551	-
@LEV(@ISPERIOD(“2019”))	−0.006555	0.008058	−0.813452	0.4173	-
@LEV(@ISPERIOD(“2020”))	−0.259448	0.217653	−1.192023	0.2352	-
@LEV(@ISPERIOD(“2021”))	−0.015662	0.011114	−1.409122	0.1609	-
@LEV(@ISPERIOD(“2022”))	0.027678	0.011776	2.350374	0.0189	-

Source: author’s own creation. R² = 0.807518%; RMSE = 0.069; U = 0.15; DW = 1.973253; JB = 5501; SIG = 0.000; BGSC Ftest = 0.535876; SIG = 0.4643. Heteroskedasticity Test: BPG F-test = 1.430864; Sig = 0.1693; Ramsey RESET Test F test = 1.033496; Sig = 0.3095; J-stats = 0.36; sig = 0.822565; Cragg–Donald F-stat = 16.957634 with Stock–Yogo TSLS critical values = 7.03.

and

Table 9. Panel Generalized Method of Moments Model used to determine the effect of independent variables on TD_EQ.

Variable	Coefficient	Std. Error	t-Statistic	Prob.	VIF
TD_EQ(−1)	0.798846	0.073488	10.87039	0.0000	1.078674
SIZE	0.113382	0.041167	2.754212	0.0066	1.223992
EPS	−0.031824	0.113448	−0.280511	0.7795	1.222612
ROA	−2.423552	0.597540	−4.055885	0.0001	2.440610
TANG	0.302002	0.070598	4.277798	0.0000	1.051510
GRW	0.560095	0.154313	3.629593	0.0004	1.007887
CASHFLOW	−0.280258	0.104038	−2.693804	0.0072	2.437085
LIQ	−0.125541	0.054840	−2.289223	0.0222	1.030101
COVID-19	0.020970	0.010007	2.095533	0.0363	1.028985
INDUSTRIAL	0.046881	0.015996	2.930795	0.0034
@LEV(@ISPERIOD(“2013”))	−0.012285	0.012557	−0.978311	0.3295
@LEV(@ISPERIOD(“2014”))	−0.023198	0.014013	−1.655509	0.1000
@LEV(@ISPERIOD(“2015”))	0.001624	0.015814	0.102703	0.9183
@LEV(@ISPERIOD(“2016”))	−0.030555	0.014020	−2.179364	0.0309
@LEV(@ISPERIOD(“2017”))	−0.026462	0.014191	−1.864626	0.0642
@LEV(@ISPERIOD(“2018”))	0.017553	0.015200	1.154793	0.2501
@LEV(@ISPERIOD(“2019”))	−0.009375	0.015067	−0.622190	0.5348
@LEV(@ISPERIOD(“2020”))	−0.055565	0.311912	−0.178143	0.8589
@LEV(@ISPERIOD(“2021”))	0.014582	0.019562	0.745434	0.4572
@LEV(@ISPERIOD(“2022”))	0.111629	0.029142	3.830451	0.0002

Source: author’s own creation. R² = 0.740186%; RMSE = 0.1667; U = 0.19; DW = 1.995095; JB = 4760.66; SIG = 0.000; BGSC F test = 1.301407; SIG = 0.2724. Heteroskedasticity Test: BPG F-test = 1.156691; Sig = 0.3191; Ramsey RESET Test F test = 0.847373; Sig = 0.3574; J-stats = 31.34833; sig = 0.938835; Cragg–Donald F-stat = 19.765763 with Stock–Yogo TSLS critical values = 7.3 (10%).

shows that, according to the panel estimation model using least squares, it can be concluded that:

• The coefficient of determination: R²

The independent variables that were accepted in the model (SIZE-EPS-ROA-TANG-GRW-CASHFLOW-LIQ-COVID-19-INDUSTRY) explain 81–74% of the total variation in the dependent variables ((Lev_TD), (TD_EQ)), and the remaining percentage is due to either the random error in the regression model or other independent variables excluded from the regression model.

• t-test:

The lagged value of the dependent variable (Lev_TD), (TD_EQ) exhibits a strong positive effect, indicating persistence in the model dynamics. Firm size (SIZE) also shows a positive and meaningful association, suggesting that larger firms tend to exhibit higher levels of the studied financial outcome. In contrast, earnings per share (EPS) do not appear to have a significant impact, implying a limited role in explaining the variation in the dependent variable during the sample period. Among the performance indicators, return on assets (ROA) demonstrates a significant negative relationship, which may reflect a tendency for more efficient firms to rely less on external financing. Asset tangibility (TANG) and growth opportunities (GRW) are positively and significantly related to the outcome, consistent with the idea that firms with substantial collateral or expansion prospects are more likely to engage in financing activities. Interestingly, cash flow (CASHFLOW) and liquidity (LIQ) are negatively associated with the dependent variable, indicating that firms with stronger internal funds or higher liquidity may depend less on the studied funding mechanism. Also, distinct patterns in leverage across the periods before, during, and after the COVID-19 pandemic were observed. In the pre-pandemic years (2013–2019), several coefficients, such as those for 2014 and 2016, indicate a slight decline in leverage, suggesting that firms adjusted their debt levels primarily based on internal factors like profitability, liquidity, and firm size, consistent with the Pecking Order Theory, where profitable companies rely more on internal financing. During the pandemic year of 2020, the leverage coefficient declines (−0.259448), although it is not statistically significant, reflecting a cautious approach as firms may have reduced debt or avoided additional borrowing due to heightened economic uncertainty. Following the pandemic (2021–2022), leverage begins to increase again, with the 2022 coefficient (0.027678, p = 0.0189) showing a statistically significant rise, indicating that firms gradually restored their debt levels to take advantage of financing opportunities and tax benefits, in line with the Trade-off Theory. Overall, these patterns demonstrate that leverage persistence over time reflects both internal strategic adjustments and responses to external shocks, highlighting the dynamic interplay between firm behavior and capital structure theories. The industrial sector variable also exhibits a positive effect, highlighting sectoral disparities. Temporal dummy variables yield mixed results, with only certain years showing meaningful effects, reflecting shifting economic conditions across the study period.

• VIF:

Variance Inflation Factors (VIFs) are considered important indicators for detecting the presence of multicollinearity among independent variables, as higher values reflect a greater severity of this issue. Some researchers suggest that a VIF value exceeding 10 indicates a serious multicollinearity problem, while others argue that this threshold is relatively high and recommend acceptable values to be below 4 or 5. Referring to the values presented in Table 6 and Table 7, it is evident that all VIF values are lower than 4, indicating that the proposed statistical model does not suffer from multicollinearity.

• The Jarque–Bera Test:

The test results indicate that the p-value is lower than or equal to 0.05, leading to the rejection of the null hypothesis (H₀), which assumes that the residuals follow a normal distribution. Furthermore, the Pearson skewness coefficient, with a value of 0.28, falls within the acceptable range between −1 and +1, indicating that the data do not exhibit significant skewness (Bluman, 2012).

• Theil’s inequality coefficient U:

Theil’s inequality coefficient (U) is employed to assess the predictive accuracy of the random-effect model. Its values range between zero and one, where a value of zero represents a perfect fit. The obtained values, which range from 0.15 to 0.19, are relatively close to zero, indicating a good level of model fit. Accordingly, the panel data model demonstrates a satisfactory goodness of fit, with an accuracy level ranging from approximately 74% to 81%.

• The Durbin–Watson test statistic:

The Durbin–Watson (DW) test is used to examine whether the residuals from an ordinary least squares (OLS) regression exhibit autocorrelation. Specifically, it tests the null hypothesis that the residuals are uncorrelated against the alternative hypothesis of positive first-order autocorrelation (AR(1)). The DW statistic ranges from 0 to 4, where a value close to 2 suggests no autocorrelation, values near 0 indicate positive autocorrelation, and values approaching 4 indicate negative autocorrelation. In this case, the observed DW values, ranging from 1.97 to 1.99, exceed the upper critical value (dU), implying that the null hypothesis cannot be rejected and the residuals can be considered uncorrelated.

• Breusch–Godfrey Serial Correlation LM Test:

The Breusch–Godfrey (BG) test was used to check for serial correlation in the regression residuals, which include key study variables such as TD_EQ(−1), SIZE, EPS, ROA, TANG, GRW, CASHFLOW, LIQ, COVID-19, and INDUSTRIAL, along with leverage dummies for 2013–2022. All p-values ranged from 0.2724 to 0.4643, above 0.05, indicating no evidence of serial correlation up to lag 2 and confirming the reliability of the estimated coefficients.

• Heteroskedasticity Test

Many statistical methods, including ordinary least squares (OLS), rely on several underlying assumptions. A key assumption is that the variance in the disturbance term remains constant across observations, a condition known as homoskedasticity. When this condition is violated and the variance changes, the errors are described as heteroskedastic. Such variation can occur even when the error terms are assumed to originate from identical probability distributions.

To examine this issue, the Breusch–Pagan–Godfrey test was applied to the residuals of the multiple regression model. The results showed that the probability values associated with both the F-statistic and the Obs*R-squared statistic exceeded the 5% significance level. Consequently, the null hypothesis could not be rejected, supporting the conclusion that the error terms exhibit constant variance.

• Ramsey RESET Test

The Ramsey RESET test was employed to evaluate the correctness of the regression model specification. Since the significance levels of the t-test, F-test, and likelihood ratio test were all above 0.05 (0.3095–0.3574), the null hypothesis was not rejected, indicating that the model is correctly specified with no evidence of omitted variables or functional form misspecification.

• Jstatistic:

The J-statistic is commonly applied in GMM and two-stage least squares frameworks to assess the validity of overidentifying restrictions. Given that the associated probability values range between 0.822565 and 0.93883, which are well above the 5% significance level, the null hypothesis is not rejected. This outcome supports the conclusion that the overidentifying restrictions hold and that the financial inclusion indicators used as explanatory variables can be considered exogenous.

• Weak Instrument Diagnostics:

The strength and validity of the instruments used in the estimation were evaluated using a weak instrument test. The Cragg–Donald statistic was calculated and compared with the Stock–Yogo critical values. The results indicated that the statistic surpassed the 7.03 threshold for the 5–10% significance level. This led to rejection of the null hypothesis that the instruments are weak, confirming that the chosen instruments are strong, reliable, and appropriate for producing valid regression estimates. Assessing instrument strength ensures that parameter estimates remain unbiased and conclusions are trustworthy.

3.6. Arellano–Bond Serial Correlation Test

The tests show in

Table 10. Arellano–Bond serial correlation for Lev and independent variables.

Test Order	m-Statistic	rho	SE(rho)	Prob.
AR(1)	−3.988655	−3.595761	0.901497	0.0000
AR(2)	0.211337	0.095339	0.451122	0.8326

Source: author’s own creation.

and

Table 11. Arellano–Bond serial correlation for Lev and independent variables.

Test Order	m-Statistic	rho	SE(rho)	Prob.
AR(1)	−4.349982	−24.412297	5.612046	0.0000
AR(2)	−1.315283	−5.195284	3.949935	0.1884

Source: author’s own creation.

, that the first-order statistic is statistically significant, whereas the second-order statistic is not, which is what we would expect if the model error terms were serially uncorrelated in levels.

4. Discussion

This study provides a comprehensive examination of the determinants of capital structure in Egyptian non-financial firms, taking into account firm-specific characteristics, sectoral differences, and external shocks, particularly the COVID-19 pandemic. The analysis reveals that leverage exhibits persistence over time, indicating that current financing decisions are strongly influenced by prior debt levels. Firms with larger size, substantial tangible assets, and promising growth opportunities tend to rely more on debt, whereas higher profitability, liquidity, and internal cash reserves reduce dependency on external financing. The COVID-19 pandemic had a measurable, though moderate, effect, reflecting firms’ adjustments to economic uncertainty.

Comparing these findings with prior research and Capital structure theories reveals several agreements and divergences:

• Firm Size

The positive relationship between firm size and leverage is consistent with the Trade-off Theory, which suggests that larger firms face lower bankruptcy risk and financial distress costs, allowing them to utilize higher levels of debt to benefit from the tax shield advantage. In addition, large firms typically enjoy greater access to credit markets and can obtain financing under more favorable conditions. This finding is consistent with the results reported by Eldomiaty and Ismail (2009); Singh (2016); Sheikh and Qureshi (2017); Kirshin and Volkov (2018); and Guizani (2021).

• Tangible Assets

The positive effect of tangible assets on leverage supports both the Trade-off Theory and Agency Theory. Tangible assets can serve as collateral for borrowing, which reduces lenders’ risk and encourages firms to rely more on debt financing. Similar results were reported by Eldomiaty and Ismail (2009); Kirshin and Volkov (2018); and Choi et al. (2024). However, some banking studies, such as that of Sheikh and Qureshi (2017), identified a negative relationship, particularly in Islamic banks, mainly due to Sharia-related restrictions on certain financing instruments.

• Profitability

The negative association between profitability and leverage is consistent with the Pecking Order Theory, which argues that firms prefer internal financing—such as retained earnings—before resorting to external funding sources like debt or equity issuance. Consequently, more profitable firms tend to depend less on borrowing. This result aligns with findings from Singh (2016); Sheikh and Qureshi (2017); and Mohammad (2022). In contrast, Ukaegbu and Oino (2014) documented a positive relationship in Nigerian banks, supporting the Trade-off Theory and reflecting sector-specific differences.

• Liquidity and Internal Cash Flow

The inverse relationship between liquidity and leverage is also consistent with the Pecking Order Theory. Firms with higher liquidity and stronger internal cash flows generally prefer to finance investments internally rather than through external borrowing. This finding is supported by Singh (2016) and Dao and Ta (2020). Nevertheless, in some emerging markets, particularly among certain Asian industrial firms (Chang et al., 2014), liquidity showed a limited influence on leverage.

• Growth Opportunities

The positive impact of growth opportunities on leverage can be interpreted through the Pecking Order Theory, as firms with significant investment prospects often require additional financing and may resort to borrowing when internal funds are insufficient. It may also be partially explained by the Trade-off Theory, where firms attempt to balance the benefits and costs of debt financing. Similar findings were reported by Eldomiaty and Ismail (2009); Kirshin and Volkov (2018); and Choi et al. (2024). However, Guizani (2021) found a negative relationship in Islamic banks, which was attributed to Sharia-compliant financing constraints.

• COVID-19 Impact

The moderate positive effect of the COVID-19 pandemic on leverage indicates that many firms increased borrowing to maintain liquidity and sustain operations during the crisis. This behavior can also be interpreted within the Pecking Order Theory, particularly when internal funds become insufficient. Comparable evidence was provided by Mohammad (2022); Choi et al. (2024); and Nguyen and Nguyen (2020). Conversely, studies such as those of Wang and Huang (2021) and Chang et al. (2014) observed a decline in debt levels during crises in China and Vietnam, reflecting a more cautious financing approach.

• Industry Type

Sectoral differences in leverage levels provide support for the Trade-off Theory, as capital structure decisions often vary across industries due to differences in risk levels, asset composition, and cash- flow stability. Industries with higher proportions of tangible assets and more stable cash flows tend to rely more heavily on debt financing. These find-ings are consistent with those of Eldomiaty and Ismail (2009) and Ukaegbu and Oino (2014).

Overall, the findings confirm that capital structure decisions result from a combina-tion of internal firm characteristics, sectoral conditions, and external shocks. While many results align with previous studies, variations in the effects of profitability, liquidity, growth opportunities, and crisis responses underscore the importance of considering local market and regulatory conditions, as well as firm type, when analyzing financing strate-gies.

From a theoretical perspective, the study this study reinforces the relevance of the Trade-off Theory, the Pecking Order Theory, and the Agency Theory, while emphasizing the need for context-specific adaptations in emerging markets. Practically, the results pro-vide guidance for managers to design financing strategies that account for firm size, asset composition, growth potential, profitability, and liquidity. Policymakers can also leverage these insights to support financial stability and ensure firms maintain flexibility during periods of economic disruption.