




































American International Journal of Business and Management Studies  

Vol. 1, No. 1; 2019 

Published by American Center of Science and Education, USA 

 

48 

 

Debt Financing and Corporate Finance Performance:  A Dynamic 

Investigation from Nigeria Quoted Firms 

Uzokwe Grace Onyinyechi 

Department of Banking and finance, 

 Rivers State University, Port Harcourt Nigeria  

Abstract  

There are two components of corporate capital. This paper examined the effect of debt financing on the financial 

performance of quoted firms in Nigeria stock exchange using time series data from 2000-2017. The objective was to 

examine the controversial findings of scholars on the effect of capital structure on corporate performance of firms.  

Return on assets and return on equity was modeled as the function of debt equity ratio, debt ratio, equity ratio, total 

liability ratio and long term debt ratio. Multiple regressions with the aid of statistical package for social sciences 

were used as data analysis techniques. Model one found that a correlation coefficient (r) of .872 this implies that a 

very strong correlation exists between return on assets and explanatory variables. The coefficient of determination 

(r²) is .678 which shows that 67.8% of the variation in Return on Assets is attributable to the variations in the 

financial leverage. Also, the F- value calculated of 8.338 has a correlation corresponding value of .004 which 

implies a good model utility. The test of significance conducted as shown in the tables above states that ROA has a 

calculated value of 242.032 and a corresponding significance value/probability value of .014.   The positive sign of 

t-value (1.653) shows the direction of the variables. This therefore implies that when a financial leverage is well 

used, this leads to a better, reliable and fairer financial result that is objective and represent the true state of affairs in 

the food and beverage companies proportionately. Model two found that a correlation coefficient (r) of .772 this 

implies that a very strong correlation exists between return on assets and explanatory variables. The coefficient of 

determination (r²) is .639 which shows that 63.9% of the variation in return on equity   is attributable to the 

variations in the financial leverage. Also, the F- value calculated of 7.644 has a correlation corresponding value of 

.004 which implies a good model utility. The test of significance conducted as shown in the tables above states that 

ROE has a calculated value of 568.906 and a corresponding significance value/probability value of .003.  The 

positive sign of t-value (3.310) shows the direction of the variables. This therefore implies that when a financial 

leverage is well used, this leads to a better, reliable and fairer financial result that is objective and represent the true 

state of affairs in the food and beverage companies proportionately. We recommend that management of the firms 

should work very hard to optimize the capital structure in order to increase the returns on equity and assets and that 

Management of Nigerian firms should increase their commitments into capital structure in order to improve earnings 

from their business transaction. 

 

Keywords: Debt Financing, Corporate Finance Performance, financial leverage, return on assets, return on equity, 

Nigeria Quoted Firms. 

 

INTRODUCTION 

Every corporate organization exists to maximize shareholders wealth. This operational philosophy depended on 

internal factors of the firms such as financing decision and external factors such as monetary and macroeconomic 

variables. Financial leverage is traditionally viewed as the use debt component capital structure, through the use of 

fixed income securities, such as   loans and bonds. It has a significant influence on the company’s ability to achieve 

its ultimate goal, such as   maximizing the shareholders wealth (Taani, 2012). Generally, increased in leverage 

results increase in return and risk (Tally, 2014). However, the use of leverage is associated with two different 

possible outcomes either positive such as maximizing the profit or negative such as minimizing the profits. 

Financing leverage is determined by profitability, corporate size, liquidity, cash flows, tax and dividend policy 

(Rajin, 2012).  It is measured in terms of debt equity ratio, long term debt to total debt, total debt as percentage to of 

total asset and short term debt to total debt (Rehman, 2013).  Financial leverage is intended to earn more on the fixed 

charges funds than their costs (Tally, 2014). The effect of financial leverage in maximizing  the return of the 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

49 

 

shareholders’ is based on the assumptions that the fixed- charges funds such as the loan and debentures can be 

obtained at a cost lower than the firm’s rate of return on net assets (Damouri, 2013).  

According to the trade off theory (Kraus & Litzenberger, 1973) the optimal capital structure is determined by 

balancing the positives & negative effects of financial leverage. In other words, balancing the benefits of debt 

financing, that includes: tax savings, reducing agency cost, with the cost associated with the debt, that include direct 

and indirect bankruptcy costs.  Furthermore, the use of financial leverage is proven to be beneficial when the 

investment made by the leverage earn returns more than the cost of debt. These disadvantages are relatively small as 

compared to the tax shield that associates the use of financial leverage. On the other hand, financial leverage may 

have a negative impact if the investment that has been made did not achieve sufficient returns having no 

recognizable income to shield, meaning that returns is lower than the cost of debt the company will be at a higher 

risk due to the level of debt they undertook, resulting in reducing the overall value of the company.  

The ambiguity in the theories further deepens the controversies on the relationship between financing leverage and 

firms’ value. The applicability of the theories can better work in the business environment where the degree of 

market imperfection is less compared to the high degree of market imperfection such as the financial  market of the 

developing countries like Nigeria where the market is characterize with information asymmetric and risk that can 

affect the performance of the firms contrary to theories.    financial leverage have the advantages of  tax shield 

benefits  and  the risk level associated with the debt financing makes it less expensive than equity financing., from 

investors point of view investing in debt securities is less risky than investing in publicly trading stocks, as debt 

securities is not subjects to the risks associated with the stock market, through debt financing the company is no 

longer affected by changes in the interest rate that occur in the market, the cost of issuing long-term securities such 

as: bonds & loan contracting is lower than the cost of stock issuing while the disadvantages includes increase in the 

company’s financial risk, the nature of some of the types of debt financing (bonds) requires the company to have a 

large amount of money at maturity, as a result of the high risk that associate the use of the use of debt securities, the 

company becomes subject to more restrictions and obtaining long-term loans may be difficult for small companies 

that are new in the market. While the effect of financial leverage has well been documented in literature, empirical 

findings remain controversial , inconclusive  and difficult to be adopted for policy making, therefore this study 

investigated the effect of financial leverage on the financial performance of quoted Nigeria firms. 

 

LITERATURE REVIEW 

Theoretical Review  

 

 

In the theory of firm's capital structure and financing decisions, the pecking order was first suggested by Donaldson 

in 1961 and it was modified by Myers and Majluf (1984). It states that companies prioritize their sources of 

financing (from internal financing to equity) according to the principle of least effort, or of least resistance, 

preferring to raise equity as a financing means of last resort. Hence, internal funds are used first, and when that is 

depleted, debt is issued, and when it is not sensible to issue any more debt, equity is issued. Pecking Order theory 

tries to capture the costs of asymmetric information. It states that companies prioritize their sources of financing 

according to the law of least effort, or of least resistance, preferring to raise equity as a financing means of last 

resort.  

 

According to Myers (1984), due to adverse selection, firms prefer internal to external finance. When outside funds 

are necessary, firms prefer debt to equity because of lower information costs associated with debt issues. These ideas 

were refined into a key testable prediction by Shyam-Sunder and Myers (1999), that the financing deficit should 

normally be matched dollar-for-dollar by a change in corporate debt. As a result, if firms follow the pecking order, 

then in a regression of net debt issues on the financing deficit, a slope coefficient of one is observed. Fama and 

French (2002) tested some qualitative predictions of the pecking order theory as against the qualitative predictions 

of the tradeoff model. In their findings, they suggested that more profitable firms are less levered and it is consistent 

with the pecking order. And also, those firms with greater investment opportunities are less levered as predicted by 

the tradeoff theory.  

 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

50 

 

The trade-off theory refers to the idea that a company chooses how much debt finance and how much equity finance 

to use by balancing the costs and benefits. Trade-off theory allows the bankruptcy cost to exist. It states that there is 

an advantage to financing with debt (namely, the tax benefit) and that there is a cost of financing with debt (the 

bankruptcy costs and the financial distress costs of debt). The marginal benefit of further increases in debt declines 

as debt increases, while the marginal cost increases, so that a firm that is optimizing its overall value will focus on 

this trade-off when choosing how much debt and equity to use for financing. Empirically, this theory may explain 

differences in D/E ratios between industries, but it doesn't explain differences within the same industry. 

 

The agency theory concerns the relationship between the principal (shareholders) and the agent of the principal. This 

suggests that the firm can be viewed as a nexus of contracts (loosely defined) between resource holders. An agency 

relationship arises whenever one or more individual, called principals, hire one or more other individuals, called 

agents, to perform some service and then delegate decision-making authority to the agents. The agency theory 

concept was initially developed by Berle and Means (1932), who argued that due to a continuous dilution of equity 

ownership of large corporations, ownership and control become more separated. This situation gives professional 

managers an opportunity to pursue their interest instead of that of shareholders Jensen and Runback, (1983). In 

theory, shareholders are the only owners of a company, and the task of its directors is merely to ensure that 

shareholders’ interests are maximized. More specifically, the duty of directors is to run the company in a way which 

maximizes the long term return to the shareholders, and thus maximizes the company’s profit and cash flow Elliot, 

(2002).  

 

The free cash flow (FCF) theory considers the internal source of a firm’s funds. However, FCF has costs associated 

with the way firms’ managers’ deal with FCF. The focus of the FCF theory is how to balance cash flow and the 

costs of FCF. Scott (1981) stated that if a firm has enough cash flow to pay for their expenses, particularly debt, it 

will be able to survive. He argued that firms’ managers will be able to use the firm’s history of cash flows to predict 

the firm’s health and future performance. He linked this with the ability of current cash flows to predict future 

financing status.  

 

Jensen (1986) introduced the FCF theory and its relationship with agency costs. He clarified the effect of cash flow 

and FCF on firms’ performance. He argued that even if cash flow has a positive effect on corporate performance, 

FCF might have a negative effect on corporate performance. In the case of FCF, a firm’s manager might waste it or 

and invest it in negative net present value (NPV) projects. The role of debt in reducing the costs of FCF was first 

explained by Harris and Raviv (1990). Debt can reduce the agency costs of FCF because debt financing ensures that 

management is restricted to making efficient investment decisions. Furthermore, debt prevents managers from 

pursuing individual objectives, as this would increase the firm’s default. As a result, according to this theory, high 

leverage would have a positive relationship with profitability. However, other studies have shown a negative 

relationship between debt and FCF. For example, Hart and Moore (1995) argued that long-term debt controls the 

ability of a firm’s management to finance future investments. They stated that firms with high debt will find it hard 

to raise capital because new security holders will not want to have lower priority than existing creditors. Firms with 

low debt will attract new security holders because security holders will be the first priority to the firm. Hart and 

Moore (1995) believed that there is an optimal debt–equity ratio and mix of senior and junior debts if management 

undertakes unprofitable and profitable investments.  

 

 

The market timing theory is one of the most recent theories discussing capital structure. It suggests that managers, 

depending on their definition of firm value, tend to issue equity when they feel that the market overvalues their 

company (Boudry, Kallberg & Liu 2010). Baker and Wurgler (2002) examined the effect of market timing on 

capital structure and found that low leverage firms are those that raise funds when their market valuations are high, 

while high leverage firms are those that raise funds when their market valuations are low. Thus, firms with low 

leverage are expected to be of high value. However, although issuing equity is often a result of good financial 

performance, it is not necessarily the reason for better financial performance, as this theory suggests. Rather, firms 

may use debt until the market overvalues their firm, and then will issue equity to obtain benefits from the firm’s 

rising share price. This theory contrasts with theories such as the pecking order and trade-off theories.  

Recent Studies of Mansor and Bhatti (2011) and of Mansor, Bhatti and Khan (2012) have showed that the reaction 

of Islamic mutual funds’ performance relative to their conventional peers and to their benchmarks either in bullish or 

bearish markets is similar. During bullish times, they both have positive return, but during bearish markets they have 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

51 

 

negative returns. However, the study of Mansor and Bhatti (2011) has proven that the return performance of the 

Islamic mutual funds is slightly better than the return performance of the conventional mutual funds during the 

bullish market. These findings support the idea of market timing theory from the perspective of financial firms. 

Empirical Review 

Rehman (2013) studies the relationship between financial leverage and financial performance in listed sugar 

companies of Pakistan. The results shows positive relationship of debt equity ratio with return on asset and sales 

growth, and negative relationship of debt equity ratio with earning per share, net profit margin and return on equity. 

This negative relationship between debt equity ratio and earnings per share (EPS) support the fact that as debt 

increases, the interest payment will also rises, so EPS will decrease.  

Rajin (2012) investigates the influence of financial leverage on shareholders return and market capitalization, 

evidence of telecommunication sector companies in India. He found out that the nature of relationship and the state 

of influence of the financial leverage on shareholder’s return and market capitalization individually indicates 

positive relationship between financial leverage and shareholder return but negative relationship between financial 

leverage and market capitalization. 

 Ujah and Brusa (2013) suggest that financial leverage and cash flow impact the degrees to which firms manage 

their earnings. They continue that it depends on economic group or industry a firm belongs to their degree and 

extent of managed earnings varies.  

Obradovich and Gill (2013) indicates that larger board size negatively impacts the value of American firms and 

CEO duality, audit committee, financial leverage, firm size, return on assets and insider holdings positively impact 

the value of American firms.  Pandey (2010) says that the variance and covariance and therefore beta depend on 

three fundamental factors such as; the nature of business, the operating leverage and financial leverage.  

Enuju and Soocheong (2005) examine the effect of financial leverage on profitability and risk of Restaurant firms. 

They find that financial leverage does not influence the restaurant firms’ profitability. It is noteworthy that the sign 

of financial leverage is positive meaning that more leveraged firms had more profits on average even though it was 

not statistically significant.  

Nazir and Saita (2013) studies financial leverage and agency cost, an empirical evidence of Pakistan. The study 

found out that general and admin expense into to sales ratio is negatively related to all four leverage ratio.  

Taani (2012) investigates impact of working capital management policy and financial leverage on financial 

performance. The study shows that firm’s working capital management policy, financial leverage and firm size have 

significant relation to net income and also no significant impact on return on equity (ROE) and return on Assets 

(ROA).  

Akbarian (2013) examines the investigation effect of financial leverage and environment risk on performance of 

firms of listed companies in Tehran stock exchange. The result shows that there is a negative relation between 

financial leverage and dividend per share and between market risk and economic risk with free cash flow per share 

positive significant. It also indicates that financial leverage, market risk and economic risk with return of equity have 

positive significant relationship. 

Alcock, et al (2013) examines the role of financial leverage in the performance of private equity real Estate funds. 

The results indicates that funds overall are unable to deliver significant positive out performance on the basis of 

managerial skill that is unrelated to the exposure to the variation in the underlying market return. It also reveals that 

the impact of transaction costs, fees and other market frictions that are especially prevalent in the direct real estate 

investment industry, given the relatively low level of liquidity of the underlying assets. It further shows that excess 

fund return were approximately proportional to the excess market return, implying that these fund offers their 

investors effective exposure to the performance of the underlying property markets. 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

52 

 

 Akhtar, (2012) conducted a study aiming to discover the impact of leverage on corporate financial performance, 

answering whither the “companies with high rates of profitability are seeking to increase leverage, using a sample 

from the oil & energy companies sector, to measure the effect of leverage on the different performance measures, 

including: rate of return on assets index, return on equity, the number of times to cover benefits and debt, the ratio of 

dividends to equity, net operating profit, growth in sales, and earnings per share. The study concluded that the use 

financial leverage results in improved financial situation, in another words showing that there is a positive 

relationship between leverage & the performance of the companies.  

Subai'i (2012) also studied the relationship between financial leverage & return on assets in the Kuwaiti economic 

sector, having a sample that consisted of fifty-four companies from the Kuwaiti public shareholding companies. The 

study results showed that there is positive relationship between financial leverage & return on investment for all of 

the economy sectors.  

Al-Tally (2014), have also researched the effect of financial leverage on firms financial performance in Saudi 

Arabia’s public listed companies. The study sample was 57 publicly trading firms listed in Saudi stock exchange for 

the years 2002-2010. Independent variable used in the study was financial leverage & zakat whereas financial 

performance was used as dependent variable. To discover the relationship among the variables several techniques 

were used including: maximum & mean factor analysis, standard deviation, ANOVA and SPSS Software.  The 

overall results concluded that positive relationship between financial leverage and performance.  The studies 

mentioned above provided empirical evidence supporting the theory of the financial leverage positive effect on the 

performance of the company; however other studies that have been conducted on different samples showed different 

results.  

Jameel (2013), where he concluded that the financial leverage negatively affects the accounting performance 

measures and the market value of the firms and this impact extends for several subsequent years. The objective of 

the study was to examine the impact of financial leverage on the different performance measures, and to discover 

which one of them would be the more affected by financial leverage. Testing the hypothesis on a sample that was 

extracted from firms listed at Palestinian Security Exchange (consisting of twenty publicly listed corporations during 

the period 2004-2011), using the multi regression model, and return on assets (ROA) return on equity (ROE), return 

on sales (ROS), and sales growth as accounting performance measures, and Tobin’s q to measure & represent the 

market value of the company.  

Hashemi and Zadeh (2012), also concluded from their study that companies that have high leverage will distribute 

less profits to shareholders when compared to companies with low leverage, as result of the reversed correlation 

between financial leverage & dividend policy. The above was concluded when they conducted a study aiming to test 

the effect of financial leverage on dividend policy, using multiple regressions on a sample of 74 public joint stock 

companies of the companies listed on Tehran Stock Market in the period between 2003-2010.  

Tanni, (2012) tested the effect of working capital polices & financial leverage on the performance of 45 Jordanians 

firms listed in the ASE stock exchange. Aiming to find the relationship between debt, size, and profitability using 

the SPSS statistics to determine the nature of the relationships mentioned above, test of correlation, ANOVA, and 

multiple regression analysis were performed.  The finding indicated that firm’s working capital management 

policies, financial leverage, and size have a significant relationship to the net income, ROE, and ROA. Furthermore, 

the study concludes that that working capital polices and size has a positive effect on profitability/ performance, 

while financial leverage has a negative effect on profitability. 

 

RESEARCH METHODLOGY 

The study adopted quasi experimental research design; relevant data were sourced from annual reports and financial 

statement of 10 selected firms in Nigeria stock exchange from 2000 to 2017. 

 

Model Specification 

In this study, the independent and dependent variables will be used in   an equation called multiple regressions. This 

study is a time series study that covers 1990-2016. 

CP = F(FL)           3.1 

Disaggregating equation 1 to achieve the objective of the study, we have the following regression models   



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

53 

 

ROA = F (DER, DR, ER, TLR, LTDR)   `    3.2 

ROE = F(DER, DR, ER, TLR, LTDR)       3.3 

Transforming the equations above, to a testable form, we have the following equation 

ROA =   LTDRTLRERDRDER 543210     3.4 

ROE =   LTDRTLRERDRDER 543210      3.5 

Where  

ROA = Return on assets 

 ROE= Return on capital employed 

DER = Debt Equity Ratio 

DR = Debt Ratio 

ER = Equity Ratio 

TLR = Total Liability Ratio  

LTDR = Long Term Debt Ratio 

 0  = Regression intercept 

 = Error term 

Statistical Approach 

The statistical approaches used in this study include: 

(i) Coefficient of Determination (R
2
): This is used to measure the extent to which the independent 

variables in the model can explain changes on the dependent variable.  

(ii) Correlation Coefficient (R): This measures the strength and the extent to which the dependent and the 

independent variable are related. 

(iii) T-Test: This is used to measure the significance of the independent variables to the dependent variable and 

the hypothesis was tested at 5% level of significance and at 95% confidence interval. The hypothesis for 

this test is stated as follows: 

Null Hypotheses; H0: β = 0, (Statistically not significant) 

Alternate Hypotheses; H1: β  0. (Statistically Significant) 

And the decision rule states that “H0” should be rejected when T-statistics is greater than the critical value. But when 

the T-statistics is lower than the critical value, the “H0” is accepted with its conclusion.  

(iv) F-Test: This is used to find out the overall significance of the regression model at 5% level of significance. 

The hypothesis for this test is stated as: 

Null Hypotheses; H0: β1 –β6 = 0 (all slope coefficients are equal to zero) 

Alternative Hypotheses: H0: β1 –β6  0 (all slope coefficients are not equal to zero) 

The decision rule for this test is that “H0” should be rejected when F-statistics is greater than the critical value of F. 

but when the F-statistics is lower, then the “H0” is accepted while the H1 is rejected. 

(v) Test for Autocorrelation  

The Durbin Watson statistics is used in this research to test for the presence of autocorrelation. When there is 

presence of autocorrelation, the First order autoregressive scheme will be employed to correct it. The hypotheses 

states that: 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

54 

 

H0: P = 0 (There is serial independence in the errors) 

H1: P > 0 (There is first order (AR) positive autocorrelation. 

When the Durbin Watson Statistics (DW-Stat) is lesser than lower Durbin Watson (DL), the null hypothesis (H0) is 

being rejected but if the Durbin Watson statistics is greater than the upper Durbin Watson (Du), the null (H0) is then 

accepted. 

ANALYSIS AND DISCUSSIONS OF FINDINGS 

The chapter deals with the presentation and analysis of the data collected. To test the hypotheses of this study, a 

multiple regression model is used. This is deemed as suitable due to the nature of the variables which are continuous 

rather than dichotomous categorical variables. The table that follows contains the data extracted from the financial 

statement of the quoted food and beverage firms which was used in running the regression and obtaining the results 

of the study. Multiple regressions have been used to estimate the relation between the independent variables of the 

study. The technique of ordinary least square was used to estimate the regression coefficient in the model of the 

study. 

             Presentation of Results 

Test of Colinearity and Autocorrelation of the Variables 

Table i:  Tolerance and Variance inflation factor (VIF) 

 Tolerance VIF 

DER .905 1.104 

DR .726 1.378 

ER .746 1.340 

TLR .834 1.198 

LTDR .823 1.215 

Source: SPSS 20.0 

The tolerance value and the variance inflation factor (VIF) are two advanced measures of assessing multicollinearity 

between the independent variables of the study. In table 4.2, the variance inflation factors are consistently smaller 

than ten indicating complete absence of multicollinearity (Neter et al; 1996 and Johansen, 1999). This shows the 

appropriateness of fitting the model of the study within the four independent variables. In addition, the tolerance 

values are consistently smaller than 1.5 thus further substantiating the fact that there is no multicollinearity between 

independent variables (Tobachmel and Fidell, 1996). 

Table ii:  Durbin Watson and autocorrelation test 

Variables Eigen Value CONDITION INDEX CONSTANT DER DR ER TLR LTDR 

1 5.912 1.000 .00 .00 .00 .00 .00 .00 

2 .046 11.290 .06 .06 .00 .77 .00 .00 

3 .024 15.703 .70 .70 .00 .06 .10 .01 

4 .013 21.342 .09 .09 .07 .01 .58 .03 

5 .003 44.193 .02 .02 .90 .10 .11 .27 

6 .001 70.018 .12 .12 .12 .06 .22 .70 

Durbin Watson Model   2.402 

Source: SPSS 20.0 

 

 

The table above illustrated a co linearity and autocorrelation; the results found that the Eigen values that correspond 

with the highest condition index and variance constants are less than 0.5 rule of the thumb. The Durbin Watson 

statistics of 2.402 shows the absence of multicolinearity, portraying a significant relationship between the dependent 

and the independent variables in the model. 

 

 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

55 

 

Table 4 Correlation Matrices of coefficient of the variables 

 ROA DER DR ER TLR LTDR 

ROA 1.000      

DER .186 1.000     

DR .184 .169 1.000    

ER 0.43 .091 .357 1.000   

TLR -.164 -.063 -.122 .300 1.000  

LTDR .056 -.169 .333 .144 -.121 1.000 

Source: SPSS 20.0 

 

When explanatory variables in the model have correlation of sufficient magnitude to negatively impact the model 

especially beta weight (via standard errors and statistical significance levels associated with beta weight), we say 

there exists collinearity or multi collinearity. According to Gujarati (2004).If multicollinearity is perfect in the sense 

of (exact linear relationship among variables), the regression coefficients of the X variables are indeterminate and 

their standard errors are infinite. If multicollinearity is less than perfect, as in (explanatory variable are inter-

correlated but not perfectly), the regression coefficients, although determinate, possess large standard errors (in 

relation to the coefficients themselves), which means the coefficients cannot be estimated with great precision or 

accuracy. 

 

A prior correlation analysis was carried out of the variables in order to detect multicollinearity problems and 

mitigate against the possible effects it could have on the study. Gujarati (2004), explained the existence of 

collinearity if the pair-wise correlation coefficient is high, and established a threshold of 0.5 and below to explain 

the acceptability of use of the variable in the model. The researcher therefore set a margin of below 0.5 (-0.5) to 

show weak linear correlation (positive or negative) between variables, hence higher degree of acceptability for use 

in the model due to weak possibility of multi-collinearity. Between 0.5 (-0.5) to 0.8 (-0.8) indicates a moderate 

collinearity and 0.8 to 1.0 strong multicollinearity and low acceptability to include the variables within the same 

models. There is no clear method to employ to eliminate multicollinearity, but expansion of observations, 

aggregating similar variables, and eliminating redundant variables from the equation, and so on, may reduce the 

problem of multicollinearity. However, variables need not to be eliminated from the model due to multicollinearity 

problems, because each explanatory variable has a special piece of information about the dependent variable. 

 

Table 5 Effect of Financial Leverage on Return on Assets 

Variables Coefficient  DER DR ER TLR LTDR 

Unstandardized Beta  .556 -47.872 -10.984 -97.310 .256 

Std Error .799 120.830 176.097 423.200 .388 

Standardized Beta .157 -0.50 -.008 -0.037 .256 

T-Statistics  .695 -3.96 -.062 -.230 -.162 

Sig. T .495 .730 .956 .840 -.898 

Constant α0  242.032, T = 1.653, T-Sig =.114,  

R .872, R2 = .678, Adj R2 = .453 

F Ratio = 8.338, F.Sig = .004 

Source: SPSS 20.0 

 

 

Interpretation  
Table 5 above shows a correlation coefficient (r) of .872 this implies that a very strong correlation exists between 

return on assets and explanatory variables. The coefficient of determination (r²) is .678 which shows that 67.8% of 

the variation in Return on Assets is attributable to the variations in the financial leverage. Also, the F- value 

calculated of 8.338 has a correlation corresponding value of .004 which implies a good model utility. The test of 

significance conducted as shown in the tables above states that ROA has a calculated value of 242.032 and a 

corresponding significance value/probability value of .014.   The positive sign of t-value (1.653) shows the direction 

of the variables. This therefore implies that when a financial leverage is well used, this leads to a better, reliable and 

fairer financial result that is objective and represent the true state of affairs in the food and beverage companies 

proportionately.  

 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

56 

 

Table 6 Correlation Matrices of coefficient of the variables 

Table 6 ROE DER DR ER TLR LTDR 

ROE 1.000      

DER .57 1.000     

DR -.115 .169 1.000    

ER .052 .091 .357 1.000   

TLR -004 -.063 -.122 .300 1.000  

LTDR -.343 -.169 .333 .144 -.121 1.000 

Source: SPSS 20.0 

 

As explained in table 3 above when explanatory variables in the model have correlation of sufficient magnitude to 

negatively impact the model especially beta weight via standard errors and statistical significance levels associated 

with beta weight), we say there exists collinearity or multi collinearity. According to Gujarati (2004) if 

multicollinearity is perfect in the sense of exact linear relationship among variables, the regression coefficients of 

the X variables are indeterminate and their standard errors are infinite. If multicollinearity is less than perfect, as in 

explanatory variable are inter-correlated but not perfectly, the regression coefficients, although determinate, possess 

large standard errors in relation to the coefficients themselves, which means the coefficients cannot be estimated 

with great precision or accuracy. A prior correlation analysis was carried out of the variables in order to detect 

multicollinearity problems and militate against the possible effects it could have on the study. Gujarati (2004), 

explained the existence of collinearity if the pair-wise correlation coefficient is high, and established a threshold of 

0.5 and below to explain the acceptability of use of the variable in the model. The researcher therefore set a margin 

of below 0.5 (-0.5) to show weak linear correlation (positive or negative) between variables, hence higher degree of 

acceptability for use in the model due to weak possibility of multi-collinearity. Between 0.5 (-0.5) to 0.8 (-0.8) 

indicates a moderate collinearity and 0.8 to 1.0 strong multicollinearity and low acceptability to include the variables 

within the same models. There is no clear method to employ to eliminate multicollinearity, but expansion of 

observations, aggregating similar variables, and eliminating redundant variables from the equation, and so on, may 

reduce the problem of multicollinearity. However, variables need not to be eliminated from the model due to 

multicollinearity problems, because each explanatory variable has a special piece of information about the 

dependent variable. 

 

Table 7:  Effect of Financial Leverage on Return on Equity 

Variables Coefficient  DER DR ER TLR LTDR 

Unstandardized Beta  -.061 -2.661 1.674 -2.864 -5.374 

Std Error .938 10.465 2.538 6.262 3.411 

Standardized Beta -.014 -.062 .158 -.104 -.360 

T-Test -9.465 -8.254 6.660 -.457 -1.576 

Sig. T .009 .002 .007 .652 0.031 

Constant α0  568.906, T = 3.310, T-Sig =.003,  

R .772, R2 = .639, Adj R2 = .477 

F Ratio = 7.644, F.Sig = .009 
 

 

Table 7 above shows a correlation coefficient (r) of .772 this implies that a very strong correlation exists between 

return on assets and explanatory variables. The coefficient of determination (r²) is .639 which shows that 63.9% of 

the variation in return on equity   is attributable to the variations in the financial leverage. Also, the F- value 

calculated of 7.644 has a correlation corresponding value of .004 which implies a good model utility. The test of 

significance conducted as shown in the tables above states that ROE has a calculated value of 568.906  and a 

corresponding significance value/probability value of .003.  The positive sign of t-value (3.310) shows the direction 

of the variables. This therefore implies that when a financial leverage is well used, this leads to a better, reliable and 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

57 

 

fairer financial result that is objective and represent the true state of affairs in the food and beverage companies 

proportionately.  

The results reveal a positive relationship of 5.5% between the value of debt equity ratio and return on assets and a 

positive relationship of 2.5% between long term debt ratio and return on assets but negative relationship of 47.8%, 

10.9% and 97.3% between debt ratio, equity ratio and return on assets.  The positive impact of the variables 

confirms the a-priori expectation of the results and validates the agency theory, the stakeholders’ theory and the 

tradeoff theory as formulated by Mayer in 1983. The positive impact confirm the findings of Akhtar, et al (2012) 

whose result shows that there is a general perception that a relationship exists between the financial leverage and the 

performance of the companies’ most of the financial performance indicators have positive relationship among 

leverage and the financial performance when compare with debt to equity ratio while the gearing ratio indicates 

negative relationships with the leverage indicators. However, equity ratio, total liability ratio and long term debt 

ratio have positive and significant impact on return on equity of the selected food and beverage firms. This finding is 

expected and confirms corporate theories such as the agency theory and the stake holders’ theory. It validates 

existing empirical findings such as Akhtar, (2012) that the use financial leverage results in improved financial 

situation, in another words showing that there is a positive relationship between leverage & the performance of the 

companies, Subai'i (2012) that there is positive relationship between financial leverage & return on investment for 

all of the economy sectors,  

Al-Tally (2014 that positive relationship between financial leverage and performance and the findings of 

Krivogorsky et al (2009) but contradict the findings of  Enuju and Soocheong (2005) that financial leverage does not 

influence the restaurant firms’ profitability, Nazir and Saita (2013) that general and admin expense into to sales ratio 

is negatively related to all four leverage ratio, Akbarian (2013) that there is a negative relation between financial 

leverage and dividend per share and between market risk and economic risk with free cash flow per share positive 

significant, Rao et al. (2007) also confirm the negative relationship between leverage and performance result, 

Jelinek (2007) that firm experiencing an increase in financial leverage during a five year period gradually compared 

to those which had high leverage degree in the same period has performed less earnings management and the 

findings of Alcock, et al (2013) that funds overall are unable to deliver significant positive out performance on the 

basis of managerial skill that is unrelated to the exposure to the variation in the underlying market return.  

The result implies that the more quoted manufacturing firms mix their equity and debt properly, the more return to 

be generated on equity, assets and investments. The study reveals that the performance indicators of the sampled 

quoted food and beverage firms can be explained by the influence of financial leverage In addition, Nigerian quoted 

firms performed remarkably well within the period of the study as shown by the data computed from the financial 

statement. Leverage has significant effect on their profitability.  

 

CONCLUSIONS AND RECOMMENDATIONS 

This study investigates the effect of financial leverage on the profitability of quoted food and beverage firms in 

Nigeria. The problem of the study therefore emanated from at least two reasons: First, the reform in the Nigerian 

financial market was aimed at simplifying the source of both equity and debt capital for better performance of the 

quoted firms. For instance the deregulation of interest rate and the financial market was aimed at reducing the cost 

of capital which is the prerequisite for corporate profitability, second, to validate existing studies on the relationship 

between financial leverage and profitability of food and beverage firms. 

 

In view of the above, the study hypothesized a  no significant relationship between measures of financial leverage 

and two profitability  indicators of the quoted food and beverage firms namely; return on equity and return on assets. 

The findings of the research are based on the result of the tested hypotheses. The result of the study reveals that 

financial leverage measures have a significant effect on the profitability of the quoted food and beverage firms. 

 

In accordance with the research finding that financial leverage explain the variables of quoted firm’s profitability, 

the study concludes as follows. Firstly, both empirical and statistical evidence on the effect of financial leverage on 

the two profitability indicators namely return on equity and return on assets of the quoted food and beverage firms 

have significant effect on profitability.  From the financial leverage measures, debt equity ratio, debt ratio have 

negative impact on return on assets, while equity ratio, total liability ratio and long term debt ratio have positive 

impact on the dependent variable. Debt equity ratio and long term debt have positive impact on return on capital 

employed while debt ratio, equity ratio and total liability ratio have negative impact on the dependent variable. 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

58 

 

Secondly, the study also concludes that concludes that financial leverage measures of the quoted food and beverage 

firms fluctuates over the period covered in the study. This may be because of management attempt to formulate 

optimal capital structure of the firms.  

 

Recommendations 

 The management of the firms should work very hard to optimize the capital structure in order to increase 

the returns on equity and assets. They can do that through ensuring that their capital structure is optimal. 

 The Management of Nigerian firms should increase their commitments into capital structure in order to 

improve earnings from their business transaction. 

 There is need to caution against the apparent benefits of greater leverage simply as a device for controlling 

managerial opportunistic behavior. First, debt and equity represent different constituencies with their own 

competing, and often mutually exclusive, goals. Second, as the level of debt increases, the capital structure 

can change from one of internal control to one of external control. 

 Investors and stakeholders of the quoted food and beverage firms should also 

 consider the leverage level of any firm before committing their hard earned money as the strength of a firm 

financing mix determine the quantum of their returns. 

REFERENCES 

 Abdallah B., (2012). Trends in working capital management and its impacts on firm's profitability and value. 

Journal of studies and business research Issue. 2(2), 12-30.  

Akbarian, S., (2013). The investigation effect of financial leverage and Environment Risk on Performance firms of 

listed companies in tehran Stock Exchange. 8(3), 249 – 255.  

Akhtar, S; Javed, B; Maryam, A., & Sadia, H., (2012). Relationship between financial leverage and financial 

performance: Evidence from fuel and energy sector of Pakistan European Journal of Business and 

Management 4(11), 7 – 17.  

Akinmulegun, S.O., (2012). The effect of financial leverage on corporate performance of some selected companies 

in Nigeria Canadian Social Science.  8(1), 85 – 91.  

Al-Najjar, B., (2011). The inter-relationship between capital structure and dividend policy: empirical evidence from 

Jordanian data. International Review of Applied Economics, 25 (2), 209-224.  

Alocock, J; Baum, A; Colley, N & Steiner, E., (2013). The role of financial leverage in the performance of private 

equity real estate funds. Journal of portfolio management.9 (9), 99 – 110.  

Amalendu B., (2012). Leverage Impact on Firms Investment Decision: A Case Study of Indian Pharmaceutical 

Companies. International Journal of Contemporary Business Studies, 3(1), 35-45.  

Damouri, D; Khanagha, J.B., & Kaffash, M., (2013). The relationship between changes in the financial leverage and 

the values of the Tehran listed firms. 5(3), 56-89. 

Gill, A., Mathur, N., (2011). Factors that Influence Financial Leverage of Canadian Firms. Journal of Applied 

Finance & Banking, 1(2), 19-37.  

Hashemi, S., A., & Zadeh Fatemeh, Z., K.,. (2012). The impact of financial leverage operating cash flow and size of 

company on the dividend policy -case study of Iran- interdisciplinary. Journal of contemporary research in 

business. 3(10), 45-80.  

Jameel H., A., (2013). The Effect of Financial Leverage on the performance of firms listed at Palestinian Security 

Exchange - An Empirical Study Alazhar University. Journal, 15(1), 281-318.  

Jensen, M. (1986). Agency costs of free cash flow, corporate finance and takeovers. American Economic Review. 

7(6), 2, 323-329.  

Jensen, M.C., & Meckling, W.H.,  (1976). Theory of the firm: managerial behavior, agency costs and ownership 

structure. Journal of Financial Economics,1(3), 305-360.  

Karaduman, H.A ; Akbas, H.E; Caliskan, A.O and Durers (2011). The relationship between working capital 

management and profitability: Evidence from an emerging market. International Research Journal of 

Finance and Economics 6(2), 61 – 67.  

Khan, A., Kaleem, A.,  Mian Sajid N., M., S., (2012). Impact of Financial Leverage on Agency cost of Free Cash 

Flow: Evidence from the Manufacturing sector of Pakistan. Journal of Basic and Applied Scientific 

Research, 2(7), 6694-6700.  

Modigliani, F. and Miller, M.H., (1963). Corporation income taxes and the cost of capital; A correction. American 

Economic Review, 5(3), 433-443.  



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

59 

 

Modiglini, F., & Miller, M.,  H.,  (1958). The Cost of Capital, Corporate Finance and the Theory of Investment. 

American Economic Review 4(8), 261-297.  

Myers, S., (1977). Determinants of corporate borrowing. Journal of Financial Economies, 9(4) 147-76.  

Myers, S., (1984). The Capital Structure Puzzle. The Journal of Finance, 3(9), 575-592.  

Myers, S., C., & Majluf, N., S., (1984). Corporate Financing and Investment Decisions When Firms Have 

Information Investors Do Not Have. Journal of Financial Economics, 13,187-222.  

Nasrollah, T, Mohammad, A.O, & Seyed, H.S.E., (2013). Effect of financial leverage and investment diversification 

on income – increasing Earnings management Middle-East Journal of Scientific Research 16(6), 836 – 844.  

Nazir, M.S  & Saita, H.K., (2013). Financial leverage and Agency Cost: An Empirical evidence of Pakistan 

International Journal of Innovative and Applied Finance 1(9), 1 – 16.  

Qureshi, M.A., Imdadullah, M., Ahsan, T. (2012). What determines leverage in Pakistan? A panel data analysis. 

African Journal of Business Management, 6(3), 978-985.  

Qureshi1, M. A., Imdadullah, M., Ahsan, T., (2012). What determines leverage in Pakistan? A panel data analysis. 

African Journal of Business Management, 6(3), 978-985.  

Rajin, S., (2012). Impact of Financial Leverage on Shareholders returns and market Capitalization: Empirical 

evidence of telecommunication sector Companies India. International Journal of Research in IT, 

Management and Engineering 2(12).  

Rehman, S.S., (2013). Relationship between financial leverage and financial performance: Empirical Evidence of 

listed sugar companies of Pakistan. Global Journal of management and Business Research finance 13(8), 

33 – 40.  

Sheikh, N. A., & Wang,  Z., (2011). Determinants of capital structure: An empirical study of firms in manufacturing 

industry of Pakistan. Managerial Finance, 37(2), 117-133.  

Tally, H., (2014). An Investigation of affect of financial leverage on firm financial performance in Saudi Arabia’s 

Listed Companies , Victoria Graduate School of Business, Melbourne, Australia. 30  

Ujah, U &  Brusa, O. (2012). The effect of financial leverage and cash flow volatility on Earnings management 

Texas A & M International University.  

Zhao, B., Wijewardana, W. P., ( 2012 ). Financial leverage, firm growth and financial strength in the listed 

companies in Sri Lanka', Procedia - Social and Behavioral Sciences 40(4), 709-715. 

 

 

Copyrights  

Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an 

open-access article distributed under the terms and conditions of the Creative Commons Attribution license 

(http://creativecommons.org/licenses/by/4.0/). 

 

 

 

 

 

 


