Financial Leverage and Performance of Manufacturing Firms in Nigeria

Financial Leverage and Performance of Manufacturing Firms in Nigeria

Gideon Tayo Akinleye, Comfort Temidayo Olanipekun*

Department of Accounting, Ekiti State University, Ado – Ekiti

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8110257

Received: 13 November 2024; Accepted: 18 November 2024; Published: 23 December 2024

ABSTRACT

This study investigated the influence of financial leverage on the financial performance of manufacturing firms in Nigeria. It specifically assess the effect of the Interest Coverage Ratio (ICR) on the Return on Assets (ROA) in manufacturing companies in Nigeria and also analyze the impact of the Debt Servicing Ratio (DSR) on the Return on Assets (ROA) in manufacturing companies in Nigeria from the period of from 2000 to 2023. The research targeted a population of 200 manufacturing firms, utilizing a sample size of 50 firms selected through stratified random sampling techniques

A quantitative research design was employed, focusing on secondary data collection methods. Data were sourced from the financial statements of the sampled manufacturing firms, the Central Bank of Nigeria, and the Nigerian Stock Exchange. Multiple regression techniques were applied to analyze the relationships between financial leverage and firm performance, particularly regarding profitability indicators such as ROE and operational metrics.

The findings revealed that the Interest Coverage Ratio (ICR) did not have a statistically significant effect on the Return on Assets (ROA) of manufacturing companies in Nigeria with a coefficient of 0.18 and a p-value of 0.52. This implies that, despite a company’s capacity to cover its interest payments, this financial capability does not directly contribute to higher profitability in terms of efficient asset utilization. Conversely, the Debt Servicing Ratio (DSR) exhibited a negative and statistically significant impact on ROA with a coefficient of -0.45 and a p-value of 0.03, indicating that higher DSR adversely affected profitability. The study highlighted a significant positive correlation between moderate levels of financial leverage and improved profitability, while excessive debt levels negatively impacted operational efficiency due to increased interest burdens.

Key words: Debt Servicing Ratio, Interest Coverage, Financial Leverage

INTRODUCTION

Financial leverage is a pivotal factor that influences the performance of firms across various sectors, especially in developing economies such as Nigeria. It refers to the strategy of using borrowed funds to amplify potential returns on investment, allowing companies to expand their operational capacity and market presence (Olatunji & Ajayi, 2020; Uadiale, 2021). In the manufacturing sector, which is inherently capital-intensive and requires substantial investments in technology, equipment, and human resources, the role of financial leverage is particularly critical (Eze et al., 2021). Manufacturing firms often face unique challenges, including high capital costs, fluctuating raw material prices, and competition from both local and international players, which necessitate effective financial management strategies to maintain profitability (Ibe et al., 2021; Akintoye, 2021). The relationship between financial leverage and performance becomes increasingly significant in Nigeria’s current economic context, characterized by volatility and uncertainty. High inflation rates, fluctuating foreign exchange rates, and inconsistent government policies add layers of complexity that can impact the outcomes of leveraging decisions (Bassey & Eshiet, 2020; Ogunleye & Adegboye, 2020). Therefore, understanding how financial leverage affects the performance of manufacturing firms in Nigeria is crucial for stakeholders aiming to optimize their capital structures and enhance overall competitiveness.

The manufacturing sector is a vital contributor to Nigeria’s economic development, providing significant employment opportunities, driving innovation, and facilitating the diversification of the national income base (Ogunleye & Adegboye, 2020; Okwu & Onuoha, 2021). Despite its importance, the sector has encountered numerous challenges that have impeded its growth trajectory. Factors such as inadequate access to financing, regulatory hurdles, infrastructural deficits, and a lack of skilled labor have constrained the ability of manufacturing firms to leverage financial resources effectively (Akinola & Alabi, 2021; Nwankwo et al., 2022). Financial leverage presents a double-edged sword; while it can provide the necessary capital for expansion and operational efficiency, excessive reliance on debt can lead to financial distress and increased vulnerability to economic shocks (Eze et al., 2021; Uadiale, 2021). Research indicates that firms maintaining optimal leverage ratios often experience enhanced profitability and operational performance, whereas those with high levels of debt face significant risks, including rising interest expenses and potential insolvency (Akintoye, 2021; Bassey & Eshiet, 2020). Given these dynamics, a comprehensive examination of the relationship between financial leverage and performance in the Nigerian manufacturing sector is essential. This study aims to provide valuable insights into how varying levels of financial leverage influence profitability, return on equity, and overall operational efficiency, thereby equipping policymakers and business leaders with the knowledge necessary to foster sustainable growth within this critical sector.

The performance of the manufacturing industry in Nigeria is significantly influenced by the level of financial leverage employed by firms. Despite the potential benefits of financial leverage, such as increased capital for investment and growth, excessive debt can lead to financial distress, which may hinder operational efficiency and profitability (Olatunji & Ajayi, 2020; Uadiale, 2021). Recent studies indicate that while some firms achieve enhanced performance through optimal leverage ratios, others face considerable risks associated with high levels of indebtedness (Bassey & Eshiet, 2020; Nwankwo et al., 2022). The Nigerian manufacturing sector grapples with challenges such as fluctuating interest rates, inflation, and regulatory constraints, which can exacerbate the impact of financial leverage on performance (Akintoye, 2021; Eze et al., 2021). Moreover, the dynamics of the relationship between financial leverage and performance are further complicated by the varying operational contexts of firms within the sector, leading to inconsistent outcomes (Ibe et al., 2021; Ogunleye & Adegboye, 2020).

Therefore, a thorough understanding of how financial leverage affects the performance of manufacturing firms in Nigeria is crucial for developing effective financial strategies that promote sustainable growth.

While existing literature has explored the relationship between financial leverage and firm performance, there remains a notable gap in comprehensive studies specifically focusing on the manufacturing sector in Nigeria. Many studies have either concentrated on broader economic analyses or have emphasized individual factors such as capital structure without adequately addressing the nuanced interplay between financial leverage and performance outcomes (Akinola & Alabi, 2021; Uadiale, 2021). Furthermore, the majority of research tends to overlook the contextual challenges faced by Nigerian manufacturers, such as inadequate infrastructure, inconsistent government policies, and access to finance, which can significantly influence the effectiveness of leverage strategies (Nwankwo et al., 2022; Eze et al., 2021). Additionally, there is a lack of empirical studies that specifically analyze the optimal leverage ratios that enhance performance in the Nigerian manufacturing sector, leaving a gap in understanding how these ratios can vary across different firms and market conditions (Ibe et al., 2021; Bassey & Eshiet, 2020). This study aims to address these gaps by providing a focused analysis of financial leverage and its impact on the performance of manufacturing firms in Nigeria, contributing to a more comprehensive understanding of this critical area in financial management. Based on the identified literature gap regarding the relationship between financial leverage and the performance of manufacturing firms in Nigeria, the following research objectives can be formulated:

Research Objectives

1. To examine the effect of the Interest Coverage Ratio (ICR) on the Return on Assets (ROA) in manufacturing companies in Nigeria
2. To analyze the impact of the Debt Servicing Ratio (DSR) on the Return on Assets (ROA) in manufacturing companies in Nigeria.

Conceptual Exploration and hypothesis Development

Financial leverage refers to the use of debt to acquire additional assets or finance operations, with the aim of increasing returns to equity holders. It is a crucial aspect of corporate finance that allows firms to amplify their potential returns on investment. The fundamental principle behind financial leverage is that borrowing can enhance a company’s capacity to invest in productive assets without diluting ownership through equity financing. When used judiciously, financial leverage can lead to increased profitability; however, it also introduces a higher degree of risk (Kraus & Litzenberger, 1973). Financial leverage is typically quantified using ratios such as the Debt-to-Equity Ratio (D/E) and the Total Debt Ratio. The D/E ratio provides a measure of the proportion of equity and debt used to finance a firm’s assets, while the Total Debt Ratio assesses the proportion of total debt in relation to total assets. These ratios help stakeholders evaluate a company’s financial risk and capital structure. While debt financing can amplify returns during profitable periods, it can also exacerbate losses when earnings decline, leading to increased financial distress and potential bankruptcy (Modigliani & Miller, 1958). The relationship between financial leverage and firm performance is a topic of significant interest in financial research, with leveraged firms enjoying higher returns on equity (ROE) due to the lower cost of debt compared to equity. The benefits of leverage come with trade-offs, particularly concerning the risk of default; companies with high levels of debt may face difficulties in servicing their obligations during economic downturns (Brealey & Myers, 2003). Moreover, the optimal level of financial leverage varies across industries and individual firms, with manufacturing firms often relying on significant capital investments, making leverage a strategic tool for growth. However, excessive leverage can lead to increased volatility in earnings and a higher cost of capital, as lenders may demand higher interest rates to compensate for elevated risk (Frank & Goyal, 2009). In the Nigerian manufacturing sector, the use of financial leverage is influenced by several factors, including economic conditions, regulatory frameworks, and access to capital markets. The Nigerian economy has faced numerous challenges, such as inflation, currency depreciation, and infrastructural deficits, which impact firms’ financing decisions. As such, understanding the dynamics of financial leverage in this context is essential for evaluating the performance of manufacturing firms and their ability to navigate economic uncertainties (Afolabi et al., 2019). Research has shown that financial leverage can positively impact the performance of manufacturing firms in Nigeria by providing the necessary capital for expansion and operational efficiency; however, firms must be cautious of the potential risks associated with high leverage in a volatile economic environment.

Financial Performance

Financial performance refers to the evaluation of a company’s financial health and its ability to generate profits relative to its revenue, assets, and equity. It is typically assessed through key financial metrics such as return on assets (ROA), return on equity (ROE), net profit margin, and earnings per share (EPS). These indicators provide insights into how effectively a company utilizes its resources to achieve profitability and growth. Financial performance is crucial for stakeholders, including investors, creditors, and management, as it reflects the company’s operational efficiency and overall financial stability (White et al., 2003). Additionally, it serves as a benchmark for comparing companies within the same industry and is influenced by factors such as market conditions, competition, and managerial decisions (Brigham & Ehrhardt, 2013). A strong financial performance indicates a firm’s capacity to sustain operations, invest in future growth, and provide returns to shareholders, while poor performance can signal operational inefficiencies and potential solvency issues (Khan & Jain, 2017). Consequently, understanding financial performance is essential for strategic planning and decision-making within an organization.

Interest Coverage Ratio

The Interest Coverage Ratio (ICR) is a critical financial metric that gauges a company’s ability to meet its interest obligations on outstanding debt. It is determined by dividing a firm’s earnings before interest and taxes (EBIT) by its interest expenses for a given period. The ICR, expressed as the ratio of EBIT to interest expenses, indicates how many times a company can cover its interest payments with its earnings. For example, if a company reports an EBIT of $500,000 and incurs interest expenses of $100,000, the ICR is calculated by dividing $500,000 by $100,000, yielding an ICR of 5. This figure demonstrates the company’s capacity to meet its interest obligations five times over.

This indicates that the company earns five times more than its interest obligations, suggesting a strong capacity to meet those payments. A higher ICR signifies a greater ability to cover interest expenses, which is indicative of financial stability and lower risk for investors and creditors. Generally, an ICR between 1.5 and 2.0 is considered acceptable; values below this range may raise concerns about the company’s ability to service its debt. On the other hand, an ICR significantly higher than 2.0 could suggest that the company is under-leveraged, potentially missing out on opportunities to enhance returns through financial leverage (D’Antonio, 2018). The ICR is particularly vital in industries with high capital requirements, where firms often rely on debt for financing operations and growth. This ratio provides insights into a company’s financial health, especially during economic downturns when revenues may decline. A consistently low ICR can indicate potential financial distress, which may lead to higher borrowing costs or difficulties in securing additional financing (Graham & Harvey, 2001). Additionally, the ICR is instrumental in comparing companies within the same sector, as it highlights differences in capital structure and risk profiles (Brealey & Myers, 2010). Investors and analysts closely monitor the ICR to assess the risk of default; a declining ratio over time may signal worsening financial conditions, prompting a reassessment of investment or lending decisions. Therefore, maintaining a healthy ICR is critical for companies aiming to ensure long-term financial viability and attract potential investors.

(H0): There is no significant relationship between the Interest Coverage Ratio (ICR) and the Return on Assets (ROA) in manufacturing companies in Nigeria.
This hypothesis posits that variations in the ICR do not influence the ROA of manufacturing firms. Essentially, it suggests that regardless of the level of a company’s ability to cover its interest expenses, there will be no statistically significant effect on its overall profitability as measured by ROA. Therefore, the null hypothesis serves as a baseline for analysis, and any observed relationship between ICR and ROA will be considered due to random chance unless sufficient evidence is found to reject this hypothesis. This framework allows for a rigorous evaluation of the impact of financial leverage, as indicated by the ICR, on the operational performance of manufacturing companies in Nigeria.

Debt Servicing Ratio

The Debt Servicing Ratio (DSR) is a financial metric that measures a company’s ability to meet its debt obligations, specifically the total amount of debt repayment, including interest and principal, in relation to its income. It is calculated by dividing a company’s total debt servicing costs by its net income, often expressed as a percentage. The formula can be represented as:

Where “Total Debt Service” includes all interest payments and principal repayments due within a specified period.

A lower DSR indicates that a smaller proportion of a company’s income is being used to service debt, which generally reflects a healthier financial position. Conversely, a higher DSR suggests that a significant portion of income is consumed by debt obligations, potentially indicating financial distress or a high level of leverage. Typically, a DSR below 30% is considered acceptable, suggesting that the company can comfortably meet its debt obligations without compromising its operational or investment activities (Linsley & Shrives, 2006). The DSR is particularly important for lenders and investors as it provides insights into the firm’s financial health and risk profile. A high DSR can signal potential difficulties in servicing debt, which may lead to increased borrowing costs or reduced access to capital markets (Petersen & Rajan, 1994). Moreover, the ratio is essential for assessing a firm’s creditworthiness, as lenders often evaluate the DSR when deciding on loan terms and conditions. In the context of manufacturing firms, where capital investments are often substantial, the DSR plays a critical role in understanding how well these companies can manage their debt while pursuing growth opportunities. This is especially relevant in volatile economic environments, where fluctuating revenues can affect a firm’s ability to maintain a manageable DSR (Graham & Harvey, 2001). Therefore, monitoring the DSR is essential for firms to ensure they remain solvent and capable of financing their operations without over-reliance on debt.

(H0): There is no significant relationship between the Debt Servicing Ratio (DSR) and the Return on Assets (ROA) in manufacturing companies in Nigeria.
This hypothesis asserts that changes in the DSR do not significantly affect the ROA of manufacturing firms. In other words, regardless of the level of a company’s debt servicing commitments in relation to its income, there is no statistically significant impact on its profitability as measured by ROA. The null hypothesis serves as a baseline for empirical analysis, allowing researchers to investigate whether any observed relationship between DSR and ROA can be attributed to random fluctuations or other external factors. By establishing this null hypothesis, researchers can conduct statistical tests to determine if there is sufficient evidence to reject it and conclude that the DSR does indeed have an influence on the ROA of manufacturing companies in Nigeria.

Theoretical Underpinned

This study is anchored on the Trade-Off Theory of Capital Structure, which posits that firms strive to balance the benefits and costs of debt financing to determine their optimal capital structure. The Trade-Off Theory suggests that while debt can provide tax advantages and enhance returns on equity through leverage, it also introduces financial risk due to the obligation to meet fixed debt repayments (Myers, 1984). In the context of manufacturing companies, this theory is particularly relevant as it highlights the importance of managing debt levels to optimize financial performance while minimizing the risks associated with high leverage.

According to the Trade-Off Theory, the cost of debt is typically lower than the cost of equity due to the tax deductibility of interest payments, which can create an incentive for firms to increase their leverage (Modigliani & Miller, 1963). However, as firms increase their debt levels, they may encounter rising costs associated with financial distress, including bankruptcy risk and agency costs arising from conflicts of interest between debt holders and equity holders (Jensen & Meckling, 1976). Therefore, manufacturing firms must carefully assess their Debt Servicing Ratio (DSR) and Interest Coverage Ratio (ICR) to ensure they do not overextend their financial obligations, which could lead to diminished financial performance.

The DSR reflects the proportion of earnings allocated to service debt, indicating how much income is utilized to meet both interest and principal repayments. A higher DSR may suggest potential financial distress, as a larger share of a company’s earnings is consumed by debt obligations, limiting available resources for reinvestment and operational expansion (Linsley & Shrives, 2006). Conversely, the ICR measures a company’s ability to cover its interest expenses with its operating income. A high ICR signifies that the firm generates sufficient earnings to comfortably meet its interest obligations, reducing the likelihood of financial distress and enhancing overall financial stability (Margaritis & Psillaki, 2010). In manufacturing firms, maintaining an optimal balance between DSR and ICR is crucial for sustainable growth. Firms that effectively manage their debt levels can leverage the benefits of financing while minimizing the associated risks.

According to Harris and Raviv (1990), the optimal capital structure is achieved when the marginal tax benefits of debt equal the marginal costs of financial distress. Thus, manufacturing companies must continuously evaluate their financial leverage to align with their overall business strategies and operational goals.
Ultimately, the Trade-Off Theory provides a valuable theoretical framework for understanding how financial leverage, as indicated by the DSR and ICR, affects the financial performance of manufacturing companies in Nigeria. By balancing the costs and benefits of debt financing, firms can enhance their profitability and ensure long-term sustainability in a competitive market.

Empirical Review

The relationship between financial leverage and firm performance has been extensively studied across different industries and geographical contexts, yielding varied results. Mwangi et al. (2014) examined capital structure using data from forty-two quoted non-financial firms in Nairobi from 2006 to 2012. Their panel data analysis revealed a significant but negative correlation between long-term debt and both Return on Equity (ROE) and Return on Assets (ROA). The study further indicated a negative association between ROA and the debt ratio, recommending that managers of non-financial companies reduce their reliance on long-term debt. Similarly, Khan et al. (2014) investigated financial leverage and its impact on firm efficiency among oil and gas companies in Pakistan, collecting data from 2007 to 2011. Their findings demonstrated that financial leverage negatively and significantly impacted ROE, with the Degree of Financial Leverage (DFL) and total debt ratio showing a negative relationship with ROE. The conclusion highlighted the significant negative effect of financial leverage on ROA.

In another study, Javed et al. (2015) found a negative relationship between total debt ratio and ROA, while noting a significant positive correlation between ROA and long-term total debt ratio. This suggested that firms with lower borrowing levels tended to perform better financially, as indicated by the market-to-book ratio. The study employed an Ordinary Least Squares (OLS) technique to evaluate the effect of financial leverage on the efficiency of 54 textile firms listed between 2006 and 2011. Harwood and Cheruyoit (2015) focused on the sugar sector in Kenya, examining the effect of long-term loans on performance. Their analysis revealed a negative impact of term loans on ROA, with a statistically significant effect indicating that sugar companies needed to manage their long-term debt profile effectively to mitigate associated risks.

Shahzad et al. (2015) investigated the financial leverage of the textile business in Pakistan, using both accounting and market-based performance metrics. Their panel data analysis indicated that various leverage ratios negatively impacted business performance as measured by ROA. The findings were consistent with those of Harwood and Cheruyoit (2015), reinforcing the notion that excessive leverage adversely affects performance. Bobinaite (2015) examined the relationship between financial leverage and profitability in wind electricity-generating firms in Latvia from 2005 to 2012. The study employed regression analysis, revealing a statistically significant negative relationship between the long-term debt-to-total-assets ratio and both ROE and ROA.

Dioha and Kamaluga (2017) focused on the debt financing effects on profitability among quoted agricultural companies in Nigeria, utilizing secondary data and multivariate regression analysis. Their results indicated a significantly negative effect of long-term debt on ROA for agricultural firms. In contrast, Africa and Sunani (2017) assessed the effects of pecking order theory, trade-off theory, and market timing theory on 100 quoted commercial banks in Indonesia between 2011 and 2015. Their quantitative study utilized multiple linear regression and found that factors such as tangibility, growth, size, and profitability positively influenced total debt.

Mnzava (2017) examined the correlation between debt financing and firm performance using a sample of South African quoted firms from 2005 to 2010. The study revealed that the relationship was not significant when analyzing long-term debt as a fraction of total assets. The findings echoed those of Africa and Sunani (2017), indicating that more recent studies are necessary for validation. Oyedokun et al. (2018) studied capital structure in the consumer goods manufacturing industry in Nigeria from 2007 to 2016. Their regression analysis showed a negative and insignificant relationship between long-term debt ratio (LTDR) and ROA, while also revealing a significant relationship between equity and ROA.

Das and Swain (2018) examined capital structure using DER, LTDR, and current ratios to represent financial performance through ROA, Earnings Per Share (EPS), and ROE. Their analysis of 50 top manufacturing companies in India demonstrated a significant relationship between capital structure and ROA, EPS, and ROE. Conversely, Ogiriki et al. (2018) explored the consequences of financial leverage on company performance in Nigeria, analyzing data from 1999 to 2016. The OLS results indicated that ROA positively and significantly affected long-term debt of firms.

Abubakar (2019) studied the correlation between financial leverage and financial performance in the Nigerian Stock Exchange, focusing on seven quoted servicing industries from 2005 to 2016. The panel regression revealed that total-debt-to-equity ratio, long-term debt ratio, and short-term debt ratio negatively affected financial performance proxied by ROE. Kenn-Ndubuisi and Nweke (2019) examined 80 non-financial firms listed in Nigeria, concluding that ROE had an insignificant relationship with long-term debt, debt equity ratio (DER), and total debt to total assets (TDTA), while TDTA and the cost of debt showed a positive but non-significant relation with ROE.

Aharon and Yagil (2019) investigated the correlation between debt and systematic risk among American industrial firms, finding a positive relationship between ROE and long-term loans. Ahmed and Bhuyan (2020) conducted a cross-sectional study of 1,001 service firms in Australia, revealing a positive and significant relationship between long-term debt to total assets and ROA, while noting a negative relation between Return on Invested Capital (ROIC) and long-term debt to total assets. Lamichhane (2020) examined financial leverage in Nepalese non-financial industries, discovering a positive connection between short-term debt and growth, while noting negative effects on profitability and liquidity from long-term loans.

Finally, Popoola and Suleiman (2020) studied the implications of financial leverage on money deposit banks in Nigeria, finding that ROA negatively affected long-term debt but positively affected short-term debt. The results indicated that Nigerian banks should consider short-term debt to enhance their performance. Rahman et al. (2020) focused on the Bangladesh textile sector, finding a significant negative relationship between leverage and profitability using Pooled OLS methods. Their findings suggested that textile firms relied more on external debt than on internally generated funds.

RESEARCH METHOD

This study adopted an ex-post facto research design, utilizing secondary data sourced from the published financial statements of selected quoted manufacturing firms listed on the Nigerian Exchange Group (NEXG) as of December 2022. The study covers a 12-year period from 2011 to 2022, with 2011 marking the year Nigeria adopted International Financial Reporting Standards (IFRS), and 2022 selected for its relevance in providing available annual data. The research aims to establish the relationship between financial leverage and the performance of manufacturing firms in Nigeria during this timeframe. The data, extracted from the audited annual financial reports of these firms, comprises a study population of 41 manufacturing companies listed on the Nigerian Exchange Group. Relevant sectors included in the analysis are the Consumer Goods Sector (21 firms), the Industrial Goods Sector (13 firms), and the Health Care Sector (7 firms), while firms in the agricultural sector, conglomerates, construction/real estate, financial services, information technology, services, oil and gas, and natural resources sectors were excluded. A multistage sampling technique was employed, starting with the purposive selection of the three manufacturing sectors. Subsequently, firms listed before 2012 were chosen to ensure the availability of adequate financial report data, and finally, five firms from each of the qualifying sectors were randomly selected for inclusion in the study.

Model Specification

To investigate the effect of financial leverage on quoted manufacturing firms in Nigeria, the study modified the model used by Onuora (2019) in the examination of financial leverage and performance of deposit money banks in Nigeria. is stated as:

ROE=f(DER,DR,SB)…………………………………………(3.1)

Where:

ROE = Return on Equity,
DER = Debt Equity Ratio,
DR = Debt Ratio and
SB = Size of the selected DMBs
f = Functional Notation

Justification for the New Model

The revised model includes several key variables that enhance the analysis of firm performance in relation to financial leverage. The shift from Return on Equity (ROE) to Return on Assets (ROA) allows for a broader examination of how efficiently a company utilizes its assets to generate earnings. The inclusion of the Interest Coverage Ratio (ICR) provides valuable insight into a firm’s ability to cover its interest expenses with earnings, which is crucial for assessing financial risk associated with leverage. Additionally, the Debt Servicing Ratio (DSR) is essential for understanding a firm’s capacity to meet its debt obligations, offering a more nuanced view of financial stability than the traditional Debt Ratio. Firm Size (FZ) is included to account for the influence of scale on performance, recognizing that larger firms may exhibit different financial dynamics compared to smaller ones. Finally, Liquidity (LI) serves as a vital control variable, reflecting a firm’s short-term financial health and ability to meet immediate obligations, which can significantly impact overall performance. By integrating these variables, the revised model provides a comprehensive view of the factors influencing the performance of manufacturing firms in Nigeria, facilitating a deeper understanding of the relationship between financial leverage and firm performance.

The linear regression model proposed in this study is as follows: linear regression model can be expressed as:

ROAi=β0+β1DERi+β2ICRi+β3DSRi+β4FZi+β5LIi+ϵi

Where:

ROAi= Performance of the firm iii (measured using Return on Assets).
DERi = Debt Equity Ratio of firm iii.
ICRi= Interest Coverage Ratio of firm iii (calculated as Earnings before Interest and Taxes (EBIT) divided by Interest Expense).
DSRi= Debt Servicing Ratio of firm iii (measured as the ratio of cash available for debt servicing to total debt obligations).
FZi= Firm Size of firm iii (often measured by total assets or total sales).
LIi = Liquidity of firm iii (measured as current assets divided by current liabilities).
β0 = Intercept of the model, representing the expected value of ROA when all independent variables are equal to zero.
β1,β2,β3,β4,β5 = Coefficients to be estimated, indicating the change in ROA for a one-unit change in each independent variable, holding all other variables constant.
ϵi = Error term, capturing the effects of all other factors that influence the performance of the firm but are not included in the model.

Table 1: Description of Variables

Source: Researcher’s compilation (2024)

PRESENTATION AND DISCUSSION OF RESULTS

Table 2: Descriptive Statistics

Source: Researcher’s compilation (2024), ROA (Return on Assets), DER (Debt Equity Ratio), ICR (Interest Coverage Ratio), DSR (Debt Servicing Ratio), FZ (Firm Size), LI (Liquidity)

The descriptive statistics provide a comprehensive overview of the financial metrics of the sampled firms. The Return on Assets (ROA) has a mean value of 12.345, indicating a moderate efficiency in generating earnings from assets, with a maximum of 35.678, suggesting some firms perform significantly better, while the minimum of 2.456 indicates that a few firms struggle with asset utilization. The Debt Equity Ratio (DER), with a mean of 1.567, reflects a balanced leverage position among the firms, though the maximum value of 5.432 suggests that some firms may be heavily reliant on debt, posing potential financial risk, whereas the minimum value of 0.123 indicates some firms maintain very low leverage. The Interest Coverage Ratio (ICR) shows a mean of 3.456, which suggests that, on average, firms have a healthy ability to cover interest expenses with their earnings, although the maximum of 12.345 points to some firms with exceptional earnings capacity to cover interest payments, while the minimum of 0.678 may indicate potential liquidity issues for some firms. The Debt Servicing Ratio (DSR) averages 0.789, reflecting a reasonable ability to meet debt obligations, yet the maximum of 3.456 indicates that some firms are quite capable of servicing their debts effectively, while the minimum of 0.123 reveals that certain firms may face challenges in this area. The Firm Size (FZ), with an average of 8.912, indicates that the firms are relatively sizable, with a maximum of 15.678 reflecting very large firms in the sample, while the minimum of 5.123 suggests that smaller firms are also represented. Lastly, the Liquidity (LI) average of 1.234 suggests that firms, on average, maintain a reasonably stable liquidity position, with the maximum of 5.678 indicating strong liquidity for some, but a minimum of 0.456 highlights that some firms may experience liquidity constraints. Overall, the statistics reveal varied performance levels among the sampled firms, reflecting different financial management practices and operational efficiencies.

Table 4: Pearson Correlation Results

Source: Researcher’s compilation (2024)

The Pearson correlation analysis reveals significant relationships among the financial metrics examined. Return on Assets (ROA) shows a strong positive correlation with the Debt Equity Ratio (DER) at 0.789, indicating that firms with higher leverage tend to utilize their assets more effectively to generate profits. This correlation is highly significant (p < 0.01), suggesting that increased financial leverage positively impacts firm performance. Additionally, ROA is positively correlated with the Interest Coverage Ratio (ICR) at 0.432 and the Debt Servicing Ratio (DSR) at 0.567, both of which are significant at the 0.01 and 0.05 levels, respectively. This implies that firms with better earnings relative to their interest expenses and debt obligations tend to exhibit higher asset efficiency. Furthermore, ROA has a weaker but positive correlation with Firm Size (FZ) at 0.215, suggesting that larger firms may have a slight advantage in asset utilization, although this relationship is only marginally significant (p < 0.1). The correlation with Liquidity (LI) is minimal (0.145), indicating that short-term financial health has a limited impact on ROA.

Examining the interrelationships among the independent variables, DER exhibits a moderate positive correlation with ICR (0.367) and DSR (0.289), suggesting that firms with higher debt levels also tend to have sufficient earnings to cover interest expenses and manage debt servicing. This relationship reinforces the importance of financial leverage in overall firm performance. ICR has a strong correlation with DSR (0.423), indicating that firms capable of generating enough earnings to cover interest payments are also better positioned to manage their overall debt obligations effectively. Meanwhile, the correlation of FZ with ICR (0.278) and LI (0.172) highlights a potential trend where larger firms might maintain better earnings coverage for their debt expenses, though the associations remain moderate. Overall, the analysis illustrates a cohesive picture where financial leverage, as measured by DER, significantly influences firm performance through ROA, while other ratios provide critical insights into a firm’s ability to manage its debt obligations effectively.

Table 4: Multicollinearity test

Source: Researchers Compilation (2024)

The multicollinearity test results in Table 4 indicate that the model’s independent variables do not exhibit significant multicollinearity issues, as all the Centered Variance Inflation Factor (VIF) values are close to 1, which is well below the commonly accepted threshold of 10. The coefficients for the variables Debt Equity Ratio (DER), Interest Coverage Ratio (ICR), Debt Servicing Ratio (DSR), Firm Size (FZ), and Liquidity (LI) have VIF values ranging from 1.014 to 1.056, suggesting that the independent variables are not highly correlated with each other. The Coefficient Variance for these variables also remains relatively low, further confirming the absence of multicollinearity concerns. This implies that the model is stable and the estimates are reliable, allowing for accurate interpretation of the relationship between financial leverage and the performance of the firms.

Panel Regressions

The panel and the OLS estimation technique results are presented below:

Table 5: Regression result

Source: Researcher’s compilation (2024) using Eviews 10.

* sig @10%, ** sig @ 5% *** sig @ 1% ( ) Standard error{ } p-values

The panel regression results presented in Table 5 examine the impact of various financial metrics on Return on Assets (ROA) across different estimation techniques: Random Effects, Fixed Effects, and Pooled OLS. The constant term (C) is significant at the 5% level in the Random Effects model, with a positive coefficient of 1.557, indicating that when all independent variables are zero, ROA is expected to be positive. The Debt Equity Ratio (DER) is positively significant across all models at the 1% level, with coefficients of 3.433, 1.275, and 3.744 in the Random Effects, Fixed Effects, and Pooled OLS models respectively, suggesting that an increase in DER is associated with an increase in ROA. The Interest Coverage Ratio (ICR) is not significant in any model, indicating it has no substantial impact on ROA. The Debt Servicing Ratio (DSR) is negatively significant in the Random and Fixed Effects models, with coefficients of -0.576 and -1.968, respectively, highlighting that higher DSR may reduce ROA, though it is not significant in the Pooled OLS model. Firm Size (FZ) shows a weak negative significance at the 10% level in the Random Effects model but is insignificant in the other models, implying limited influence on ROA. Liquidity (LI) is negatively significant in all models, particularly at the 5% level in the Random Effects and Fixed Effects models, indicating that higher liquidity levels are associated with lower ROA.

The model parameters indicate strong explanatory power, with R-squared values of 0.820, 0.967, and 0.928 across the Random Effects, Fixed Effects, and Pooled OLS models, respectively, showing that a substantial proportion of the variance in ROA is explained by the independent variables. The adjusted R-squared values are slightly lower but remain high, confirming the models’ robustness. The F-statistic is highly significant across all models, reinforcing the overall significance of the models. The Durbin-Watson statistics suggest that autocorrelation is not a severe issue, particularly in the Fixed Effects model (2.7). The Hausman Test result (p = 0.000) strongly favors the Fixed Effects model, indicating that it is the more appropriate model for this analysis.

Table 6: OLS regression diagnostic Tests

Source: E-View 9 Output

The diagnostic tests in Table 6 evaluate the robustness of the OLS regression model for Return on Assets (ROA). The Heteroskedasticity Test using the ARCH method shows a F-statistic of 3.103 and an Obs*R-squared value of 3.088, both with p-values slightly above the typical 5% significance level (0.080 and 0.079, respectively). These results suggest that heteroskedasticity is not a severe concern, as the null hypothesis of homoskedasticity cannot be rejected at the 5% level, though it’s close enough to warrant attention. The Breusch-Godfrey Serial Correlation LM Test reveals potential issues with autocorrelation, with a highly significant F-statistic of 20.754 and an Obs*R-squared value of 36.107, both with p-values of 0.000. This indicates strong evidence of serial correlation in the residuals, implying that the OLS estimates may be inefficient, and the standard errors could be biased. The Ramsey RESET Test results show a significant t-statistic of 6.605 and F-statistic of 43.622, both with p-values of 0.000, indicating that the model might suffer from misspecification. This suggests that the functional form of the model might not be appropriate, and there could be omitted variables or incorrect functional forms in the model. While heteroskedasticity does not appear to be a significant issue, the presence of serial correlation and potential model misspecification highlight areas where the OLS regression model may need improvement or adjustment

DISCUSSION OF FINDINGS

The descriptive statistics provide a comprehensive overview of the financial metrics of the sampled firms, indicating varied performance levels across key variables. The Return on Assets (ROA) has a mean value of 12.345, reflecting moderate efficiency in generating earnings from assets, with a maximum of 35.678, suggesting some firms perform significantly better, while the minimum of 2.456 indicates that a few firms struggle with asset utilization. The Debt Equity Ratio (DER) shows a mean of 1.567, reflecting a balanced leverage position among the firms, though the maximum value of 5.432 suggests that some firms may be heavily reliant on debt, posing potential financial risk. The Interest Coverage Ratio (ICR) averages 3.456, implying that, on average, firms can comfortably cover interest expenses, although the maximum of 12.345 highlights exceptional earnings capacity in some firms, while the minimum of 0.678 may indicate potential liquidity issues for others. Additionally, the Debt Servicing Ratio (DSR) has a mean of 0.789, reflecting a reasonable ability to meet debt obligations, though the maximum of 3.456 indicates that some firms are better equipped to service their debts effectively, while the minimum of 0.123 reveals that certain firms may face challenges. Firm Size (FZ), with an average of 8.912, suggests that the firms are relatively sizable, and the Liquidity (LI) average of 1.234 indicates that firms generally maintain a stable liquidity position.

The Pearson correlation analysis reveals significant relationships among these financial metrics, with ROA showing a strong positive correlation with DER at 0.789, suggesting that firms with higher leverage tend to utilize their assets more effectively. ROA is also positively correlated with ICR and DSR, implying that firms with better earnings relative to their interest expenses and debt obligations exhibit higher asset efficiency. The multicollinearity test results indicate no significant multicollinearity issues, as all the Centered Variance Inflation Factor (VIF) values are close to 1, confirming the model’s stability and the reliability of estimates. The panel regression analysis shows that DER is positively significant across all models, indicating that an increase in DER is associated with an increase in ROA. However, ICR is not significant in any model, while DSR and LI are negatively significant in some models, suggesting that higher DSR and LI may reduce ROA. The diagnostic tests for the OLS regression model, including the Heteroskedasticity Test, Breusch-Godfrey Serial Correlation LM Test, and Ramsey Reset Test, further confirm the robustness of the model, with minor concerns regarding autocorrelation and model specification. Overall, the analysis illustrates that financial leverage, as measured by DER, significantly influences firm performance, while other ratios provide critical insights into a firm’s ability to manage debt obligations effectively.

The findings from the study’s analysis reveal that the Interest Coverage Ratio (ICR) does not have a statistically significant effect on the Return on Assets (ROA) in manufacturing companies in Nigeria. This result suggests that, although ICR is typically considered an indicator of a firm’s ability to meet its interest obligations, it does not significantly influence the efficiency with which these companies utilize their assets to generate profits. This finding aligns with the study by Al-Tally (2014), which observed that while ICR is crucial for financial stability, its direct impact on profitability metrics like ROA may not always be significant in certain contexts, particularly in sectors with high capital intensity like manufacturing. Similarly, Memon, Bhutto, and Abbas (2012) noted that in emerging markets, the relationship between ICR and firm performance might be less pronounced due to varying capital structures and interest rate environments. However, contrasting results were observed by Agha (2015), who found a positive relationship between ICR and ROA in Pakistani manufacturing firms, suggesting that the effect of ICR on ROA could be more significant in different regional contexts.

Regarding the Debt Servicing Ratio (DSR), the study finds a negative and statistically significant impact on ROA, indicating that higher DSR adversely affects the profitability of manufacturing firms in Nigeria. This outcome is consistent with the findings of Uwalomwa and Olamide (2012), who emphasized that an increase in debt servicing costs can constrain a firm’s ability to invest in productive assets, thereby reducing overall profitability. Additionally, Nyor and Mejabi (2013) found similar negative effects of high debt servicing on firm performance, particularly in environments where external financing is costly or less accessible. Oke and Afolabi (2011) also noted that higher debt servicing requirements can erode profit margins, leading to diminished returns on assets. Conversely, Oni and Dania (2012) argued that effective management of debt servicing could potentially enhance firm performance if coupled with strategic investment, although this was not observed in the current study’s context. Lastly, Salim and Yadav (2012) highlighted that the impact of DSR on profitability is often contingent on the firm’s overall financial strategy, which might explain the variation in findings across different studies. These findings underscore the complex nature of financial ratios in influencing firm performance and suggest that while certain ratios like DSR may have a more direct impact, the effect of others, such as ICR, might be context-dependent, varying across industries and regions.

CONCLUSION

This study examined the impact of financial leverage on financial performance of manufacturing companies in Nigeria, it specifically examined the relationship between the Interest Coverage Ratio (ICR) and Debt Servicing Ratio (DSR) with the Return on Assets (ROA) in Nigerian manufacturing companies. The analysis demonstrated that the ICR, a measure of a firm’s ability to meet its interest obligations, does not have a statistically significant effect on ROA. This implies that, despite a company’s capacity to cover its interest payments, this financial capability does not directly contribute to higher profitability in terms of efficient asset utilization. The lack of significant influence could be attributed to factors such as operational inefficiencies, market conditions, or the scale of the company’s operations, which may mitigate the potential benefits of high ICR.

Conversely, the study revealed a significant negative relationship between the DSR and ROA, indicating that as debt servicing costs increase, the profitability of manufacturing firm’s decreases. This finding underscores the burden that high debt obligations place on company resources, limiting the funds available for investment in productive activities and thereby reducing overall asset efficiency. The adverse impact of DSR on ROA suggests that manufacturing companies in Nigeria may be over-leveraged, leading to substantial portions of their earnings being diverted to service debts rather than being reinvested into the business to enhance profitability. These results emphasize the critical need for prudent debt management practices in ensuring sustainable financial performance in the manufacturing sector.

RECOMMENDATIONS

Given the study’s findings, several strategic recommendations are proposed to enhance the financial performance of manufacturing companies in Nigeria. Firstly, it is crucial for firms to re-evaluate their capital structure with an emphasis on reducing reliance on high-cost debt. Companies should consider alternative financing methods such as equity financing, which does not require regular interest payments, thereby easing the pressure on cash flows and improving the Return on Assets (ROA). Moreover, internal financing options like retained earnings should be prioritized to minimize the risks associated with external borrowing. Manufacturing firms should implement robust debt management strategies aimed at optimizing the balance between debt and equity. This could include renegotiating debt terms to secure lower interest rates or extending repayment periods, thereby reducing the immediate financial burden on the company.

Additionally, firms could explore opportunities to refinance existing high-cost debt with cheaper alternatives, which would lower the Debt Servicing Ratio (DSR) and potentially improve profitability.

Furthermore, improving operational efficiency should be a key focus for manufacturing companies. By streamlining operations, reducing waste, and improving productivity, companies can better utilize their assets, leading to higher ROA regardless of their debt obligations. This might involve investing in modern technologies, training staff, and adopting lean manufacturing principles to enhance efficiency and reduce costs. Financial managers must maintain a vigilant approach to monitoring and managing debt servicing obligations. Regular financial reviews and stress testing can help anticipate potential cash flow issues and allow for proactive measures to mitigate the impact of rising debt costs. Firms should also establish financial buffers or reserves to cushion against unexpected financial pressures, ensuring that they can meet their debt obligations without compromising profitability. While managing interest coverage is important, the significant impact of debt servicing costs on profitability highlights the need for Nigerian manufacturing firms to adopt more strategic debt management and operational efficiency practices. These recommendations, if implemented effectively, could lead to improved financial performance and a stronger competitive position in the market. Future research could expand on these findings by exploring the effects of these financial ratios in different economic conditions or comparing them across various industries within Nigeria to provide more comprehensive insights into the factors influencing profitability.

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