Cost of Capital
and Optimal Financing of Corporate Growth of Selected Manufacturing Firms
Listed on the Floor of Nigerian Stock Exchange
Achebelema Damiebi
Sam
Department of Banking and
Finance
Rivers State University,
Port Harcourt
Rivers State, Nigeria
Abstract�
This
dissertation empirically investigated the relationship between cost of capital
and optimal financing of corporate growth of selected manufacturing firms
listed on the floor of Nigerian stock exchange. Annual time series data were
generated from the Annual Reports of the quoted firms and stock exchange fact
book. Fifty manufacturing firms were selected from the population of quoted
manufacturing firms.� Four multiple
regression models were specified and estimated with the aid of Software package
for social services (SPSS). Equity financing measured as equity capital to
total capital, debt financing measured as debt capital to total capital and
return on investment were modeled as the function of cost of debt, cost of
equity and weighted average cost of capital. The generated collinearity
diagnostics result shows that the Eigen values that correspond to the highest
condition index and variable constant are less than 0.5 rule of thumb. The
Durbin Watson test shows absence of autocorrelation. The regression
coefficient shows that cost of debt and cost of equity have negative
relationship on equity financing while weighted average cost of capital have
negative effect, cost of debt and weighted average cost of capital have
positive relationship with debt financing while cost of equity have negative
effect on the dependent variable. Cost of debt and reweighted average cost of
capital have positive effect on return on Investment while cost of equity has
negative effect. Model four found that cost of capital have positive
relationship with financing mix of the quoted firms. From the model summary,
the study conclude that cost of capital have no significant effect on equity
financing and return on investment but significantly affect debt financing. It
therefore recommends that� Management should formulate internal policy
that will enhance the realization of optimal capital structure of the firms,
formulating capital structure of the firm should be well examined with the
investment policy of the firms, the environmental factors should be
acknowledged in formulating cost of capital to avoid risk associated with
inadequate or wrong capital structure, external source of capital such as debt
should be properly appraised and integrated with the investment policy and cost
of equity should be integrated with the objective of maximizing shareholders�
wealth through investment policies.
Keywords: Cost of Capital, Optimal
Financing, of Corporate Growth, Manufacturing Firms, Nigerian Stock Exchange.
1. Introduction
Scholars,
business executives and government decisionmakers all widely acknowledge the
importance of corporate financing on the growth of the firm and the economy at
large. Financing decisions involve the selection of a capital structure that
would minimize the cost of capital of the firm. Apart from the investment and
financing decisions, managers need to decide on the optimal combination of
equity and debt for financing corporate growth. However, the challenge is
determining the optimal combination of equity and debt that reduces the cost of
capital for financing corporate growth.
The relationship
between capital structure and the growth of corporate firms has been a point of
departure amongst scholars in the field of finance. As oppose to the relevance
theory, of Gordon 1958, Miller and Modigliani known as the MM hypothesis opined
that capital structure is irrelevant given some sets of assumption. This debate
has deepened in modern empirical findings as many are in view of Gordon while
few support the MM hypothesis. This is because the assumptions of Miller and
Modigliani are seen not to exist in real world but in an abstract world (Maina
& Kondongo, 2013), (Kaunbuthu, 2011), (Abor, 2005), (Ongore, 2011). For
instance CAPM has been criticize that it is based on many unrealistic
simplifying assumptions (Fama & French 2004, Young & Saade 2011,
Berkman 2013) example all the behave rationally according to their utility
function that they have homogeneous expectations and that funds can be borrowed
or cent at the same interest rate. This is frequently used in settings such as
estimation of equity risk premiums, firm valuation and capital budgeting, and
investment management practices such as portfolio allocation, performance
evaluation, active risk management and attribution analysis (Hou et al. 2012;
C�mara et al. 2009). Therefore, the cost of capital needs to be given attention
in relationship to optimal financing of corporate growth.
Furthermore, an
important condition for corporations to grow is that they have access to
capital for investment in tangible as well as intangible assets. This requires
first of all that capital markets are fit for purpose. That actors and
institutions in capital markets, such as banks, investment funds and stock
markets, have the incentives to properly integrate the internal objective of
optimizing cost of capital. An examination in the Nigerian capital market which
is supposed to be an avenue for sourcing of longterm funds to finance
longterm project is not developed as her foreign counterpart. It has therefore
not been able to judiciously perform its primary obligation of meeting
longterm capital needs of the deficits sectors through efficient accumulation
of capital or mobilization of fund from the surplus units of the economy and
effectively channel mobilized funds for economic use (Ojo, 2012) for instance
the Nigeria capital market is very illiquid, few listed companies with low
volume of equity transaction with low market capitalization which led to the
increasing cost of equity capital.
However, the
financial sector reforms such as the bank consolidation and recapitalization
also aim at making available easy source of debt financing to investors. A
critical examination of the function of the institution prove that this
objective is yet to be determined in relationship with cost of capital and
financing corporate growth.�� Despite the
growing literature, the relationship between cost of capital and optimal
financing of corporate growth remain a knowledge gap as existing studies
focused on cost of capital and corporate profitability of listed firms. Given
the above problems, the controversies surrounding the impact of capital
structure on corporate performance and the knowledge gap, this study seek to
examine the existing relationship between optimal cost of capital and financing
of corporate growth among quoted firms in Nigerian stock exchange.
2. Literature Review
2.1 Cost of Capital
A firm raises funds from various
sources, which are called the components of capital. Different sources of fund
or the components of capital have different costs. For example, the cost of
raising funds through issuing equity shares is different from that of raising
funds through issuing preference shares. The cost of each source is the
specific cost of that source, the average of which gives the overall cost for
acquir�ing capital. The firm invests the funds in various assets. So it should
earn returns that are higher than the cost of raising the funds. In this sense
the minimum return a firm earns must be equal to the cost of rais�ing the fund.
So the cost of capital may be viewed from two viewpoints acquisition of funds
and appli�cation of funds. From the viewpoint of acquisition of funds, it is
the borrowing rate that a firm will try to minimize. On the other hand from the
viewpoint of application of funds, it is the required rate of return that a
firm tries to achieve. The cost of capital is the average rate of return
required by the investors who provide longterm funds. In other words, cost of
capital refers to the minimum rate of return a firm must earn on its investment
so that the market value of company�s equity shareholders does not fall.
2.1.1 Estimating the Cost of Equity Capital
Estimating the
cost of equity involves estimating the expected return on a firm�s common
stock. The cost of equity includes a risk premium to compensate shareholders
for holding a risky equity security rather than a riskfree security:
COE_{i} = E(r_{i})
= r_{f} + rp_{i} ����������������������������������������������������������������������������������������������������������������������������������������� (1)
Where
COE_{i}���������������������� = ��������������������������� firm i�s cost of
equity,
E(r_{i})������������������������� = ������������������������� the expected future
return on firm i�s equity, where returns include capital gains and dividends,
r_{f} ���������������������������� = ���������������������������� the riskfree rate,
and
rp_{i} �������������������������� = ���������������������������� the equity risk
premium for holding firm i�s stock.
There are two general
approaches for estimating the cost of equity at a firm level. We use an implied
approach which measures the cost of equity as the internal rate of return that
equates the present value of forecasted future cash flows to equity holders
with the current stock price. In comparison, the realized returns approach uses
information in realized expost stock returns to generate a cost of equity. In
this section we discuss the different implied cost of equity models and explain
our choice of the implied approach over the realized returns approach.
2.2 Implied Approach
Determining the
cost of equity using the implied approach is analogous to determining the
nominal yield to maturity on a bond; i.e., finding the discount rate that sets
the bond�s price equal to the present value of future cash flows. Similarly,
the implied cost of equity is the discount rate that sets the current stock
price equal to the present value of expected future dividends per share. The
relation between the current stock price (P0), the cost of equity (r), and
future expected dividends per share (d_{1}, d_{2}, d_{3}
�) is represented by the dividend discount model (DDM).
In practice,
determining a bond�s nominal yield to maturity is easier, since future coupon
and principal payments are known. Finding the implied cost of equity is much
more difficult since future dividends must be forecasted into infinity. The
various implied models differ in terms of how this stream of dividends evolves.
2.3 Gordon Dividend Growth Model
The simplest
form of the DDM, the Gordon Dividend Growth Model, assumes a constant perpetual
rate of growth (g) in expected dividends per share. With this assumption,
dividends are an infinite geometric series, and the cost of equity can be
written as a function of the dividend yield plus the constant growth rate:
Two problems are
encountered when estimating the cost of equity using the Gordon Dividend Growth
Model. First, it is difficult to estimate a longterm growth rate as typically
only shorterterm forecasts are available. In practice, many use the fiveyear
dividend growth rate as a proxy for the longterm rate. Second, in the Gordon
Model it is possible to specify that dividends grow at a rate that is greater
than economic growth, which implies that dividends will be larger than the
economy at some future point. We do not use the Gordon Growth Model in our
analysis, but discuss it here to provide a simple intuition as to how growth
and dividend assumptions impact the cost of equity.
2.4 Residual Income Valuation Models
Residual income
valuation (RIV) models address the difficulties in estimating a long term
growth rate by utilizing accounting information. These models equate the
current share price to the sum of two components: (1) the present value of
expected dividends per share over a short or mediumterm horizon (N); and (2) a
discounted terminal value, which is the present value of the expected share
price at the end of the forecast period, assuming that dividends then grow at a
constant rate (gL) in perpetuity:
RIV models
assume clean surplus accounting which requires that earnings are fully
allocated between dividends and retained earnings; i.e., whatever portion of
earnings that is not paid out in dividends is added to book value of equity.
Hence, book value per share (bv_{t}) evolves according to the following
equation:
and as roe_{t������������������������������������������ }
=���������������������������� return on
book equity
= ������������ e_{t }/ bv_{t1}
then bv_{t+1}
Assuming that
return on book equity and the dividend payout ratio after time N+1 remain
constant, the following constraint is imposed on the longterm growth rate of
dividends per share (gL) in Equation (4)
Claus and Thomas
(2001) implement the RIV model using a fouryear forecasting horizon (N=4) and
set the growth rate (g_{L}) equal to the expected inflation rate (pe)
in order to calculate a nominal cost of equity.8 Dividends per share in year
five are backed out from Equation (6) as follows:
So the cost of
equity in the Claus and Thomas model is the value of r that solves the
following equation:
If dividends are
all positive and the cost of equity is greater than the expected inflation
rate, there is only one value of r that will solve this equation.
2.5 Abnormal Earnings Growth Models
Another class of
implied models assumes that the change in abnormal earnings from year to year
grows at a constant rate into perpetuity. This is similar to assuming that the
forecasted change in dividends grows at a constant rate, if the change in
dividends is calculated as:
In the Ohlson
and JuettnerNauroth (2003) version, a closed form solution for the cost of
equity can be backed out from the following relation between price, next year�s
earnings per share estimate and next year�s expected dividends per share:
Where ��
g_{s} ����������� = ������������ shortterm
dividends per share growth rate
=
������������ (e_{2} e_{1})/e_{1}
g_{L} ���������� =������������� a
longterm dividends per share growth rate
Gode and
Mohanram (2003) implement this theoretical model of Ohlson and JuetnnerNauroth
by assuming that the shortterm growth rate (g_{S}) is equal to the
average of the forecasted growth rate between year one and year two and the
average fiveyear growth rate provided by analysts. Furthermore, they assume
that the longterm growth rate (g_{L}) is equal to expected inflation
for all firms.
Easton�s (2003)
model, called the ModifiedPEG ratio model,10 is just a special case of the
Ohlson and JuettnerNauroth model, where the growth rate in the change in
dividends is set equal to zero (g_{L} = 0) so that dividends grow by
the same dollar amount every year into perpetuity. The current stock price is
related to the cost of equity, the next two year�s forecasted earnings, as well
as the next year�s dividend:
The advantage of
the Easton and OhlsonJuettnerNauroth models over RIV models is that they
yield simple formulas for the cost of equity. RIV models have more terms
because they explicitly forecast variables over the shortterm before
calculating a terminal value. In contrast, the abnormal earnings growth models
make assumptions so that the terminal value is calculated immediately, which
allows them to be easily inverted to solve for the cost of equity.
2.6 Hedging and the Cost of Capital
Corporate
finance theory formulates that firm value is the present value of future cash
flows. Therefore, the impacts of hedging on firm value can be from: the effect
on the cash flow stream and/or the impact on the cost of capital by which
future cash flows are discounted. Easley and O�Hara (2004) highlight that the
cost of capital is fundamental in corporate policies because of its impact on
profitability, and hence investment decisions. Recent studies suggest that
hedging has notable influences on the cost of equity. For example, Gay, Lin,
and Smith (2010) report that hedging affects the cost of equity through a
reduction in the covariance of future cash flows. In particular, the cost of
equity is reduced when hedging lowers the effective discount factor of future
investment payoffs. They document that the cost of equity for hedging firms is
24 to 78 bps lower than those firms that do not hedge.
2.7 Reduced Bankruptcy Cost
The
probability of bankruptcy or financial distress is considerably higher when a
firm�s earnings or cash flows are more volatile. Because hedging smoothes
corporate income or cash flows, bankruptcy risk is reduced. In particular,
Smith and Stulz (1985) suggest that hedging reduces a firm�s cash flow
volatility and consequently lowers the expected cost of financial distress. As
a result, we conjecture that hedging should lead to a lower cost of debt. In
addition, highly leveraged firms often cannot afford a large debt capacity
since the cost of debt is high. Graham and Rogers (2002) find that hedging
helps increase debt capacity, leading to an average increase of 1.1% in firm
value.
2.8 Lower Agency Cost of Debt
Myers
(1977) suggests that firms with risky debt may forgo positive NPV projects if
some or all of the value of the project goes toward the bondholders when poor
states occur. Hedging alleviates the underinvestment problem by reducing the
probability of the poor states occurring. Therefore, shareholders have greater
incentives to invest in valueenhancing projects (Bessembinder (1991). Froot,
Scharfstein, and Stein (1993) theorize that hedging curtails the
underinvestment problem when a firm faces growth opportunities and a high cost
of external financing. In this case, hedging leads to managers following the
optimal investment policy by generating sufficient internal funds and having a
low cost of capital. Second, hedging mitigates the riskshifting problem
(Campbell and Kracaw, 1990).
2.9 Lower Level of Information Asymmetry
�Literature
indicates that managers have better information about firm performance than
outsiders. As the release of information is costly and managers may have
incentives to distort or not fully disclose information for private benefits,
investors do not have full information on asset values or their information set
is noisy. As a result, information asymmetry affects equilibrium asset prices
and expected rates of return by influencing the investors� assessments
regarding the distribution of future cash flows. Easley and O�Hara (2004)
demonstrate that investors demand a higher return on stocks with more private
information. Duffie and Lando (2001) argue that information content and quality
based on accounting disclosure are critical for bondholders to retrieve a
conditional distribution of an issuer�s asset value.
2.10 Empirical Review
Khaled and �Samer
(2014) examined the determinants of the rate of return on investment in
stocks and the application of it on industrial enterprises, contributed to the
ASE, which consists of 91 industrial companies during the study period from
1997 2009. The study used Multiple Linear Regression Analysis. The Model
included a number of independent variables which are the cost of capital,
financial leverage, and growth rate of dividends. The results of the study
showed that there is appositive effect and statistically significant for growth
rate of dividends on rate of return on investment (dependent variable). On the
other hand, the study showed no effect with statistical significance for each
of the cost of capital and financial leverage on rate of return on investment
(dependent variable). It turns out that the effect of the growth rate of
dividends on the rate of return on investment is not compatible with the
hypotheses of the study, while that the effect of each of cost of capital and
Financial leverage on rate of return on investment in the stock, consistent with
the hypothesis of the study.
Casmir
and Anthony (2012) found that a capital structure of a firm has a negative
impact on firm�s performance. They proved that highly leverage capital
structure caused negative impact on firm�s performance but it also provides tax
rebate on interest expenses. They used different variables to obtain results
such as return on assets, return on equity, debt to equity ratio, assets
turnover ratio, firm�s size and age, asset tangibility, growth and industrial
sector. They used ordinary least square (OLS) model of estimation. They proved
that ROA, ROE and asset turnover are important measure of firm�s financial
performance. They also concluded that tangibility of assets have great impact
on firm�s performance. They concluded that the firms of their sample size are not
utilizing their tangible assets up to their maximum capacity. So, assets
tangibility is also a vital measure of firm�s performance. They could not prove
the result of industry growth.
Ahmad,
Abdullah and Roslan (2012) proved that capital structure decision has a vital
importance. A wrong decision may cause a negative impact so; great care is
required. There are different theories of capital structure such as Modigliani
Miller theorem, pecking order theory, static trade off theory and agency cost
theory. Pecking order theory focuses on the use of an organization�s internal
funds. They used return on assets, return on equity with short term and long
term debt and total debt, size, asset growth, firm growth and efficiency. They
used series of regression analysis to measure the desired results. They studied
pecking order theory, Modigliani Miller theorem and static trade off theory to
understand the relationship between capital structure and firm�s performance.
The study found that short and long term debts with ROA and ROE and total debt
of capital structure has great impact on firm�s performance.
Chowdhury
and Paul (2010) found that a company injects capital to generate revenue. If
capital of a company is 100% equity than all the earnings after tax goes to shareholders
if capital structure consists debt than a part of profit is also given to
creditors as a rent of their funds� use. According to financial experts use of
debt up to specific point is profitable otherwise it is harmful. They used
different variables as; share price, firm size, profitability, public European
Journal of Business and Management ownership in capital structure, dividend
payout, asset and operating efficiency, growth rate, liquidity and business
risk. They used cross sectional times series regression model to measure the
relationship of all these variables. On the basis of their analysis they
concluded that if capital structure of a firm is designed in a good manner it
multiplies the value of firm. They also proved that if a firm makes amendments
in its capital structures it also causes a positive impact on its value.
Umar,
Tanveer, Aslam and Sajid (2012) proved that capital structure has a vivid
impact on firm�s financial performance. It is a way through which a firm is
financed. They used different variables of financial measure such as return on
asset, return on equity, earning per share, price earnings ratio, earnings
before interest and tax and net profit margin.�
PEriotis,
Frangouli and Ventoura (2011) noted that firms financed with equity are more
profitable as compare to those financed by debt. If debt amount is high than a
part of its profits is given as interest which ultimately reduces its profits.
So, capital structure choice has vital importance. Debt to equity ratio is used
in order to examine its impact on firm�s profitability. Fix effect model and
random effect model are used. It is analyzed that debt negatively impacts a
firm�s profitability because mostly the cost of debt is high than profits of
the firm. They also concluded that firms liked to compete with one and another
rather than cooperating.
Imran (2012)
investigated a relationship between a firm�s performances, equity ownership and
capital structure. Many organizations use debt as a controlling measure. The
external parties keep check and balance on management�s decision making and
generate better results. Debt to equity ratio to measure leverage (capital
structure). Regression model is used to calculate desired outcomes. The results
of this study showed that organizations with high leveraged showed more profit
of those firm which use their extra cash and reduce it from management. Family
ownership has a positive relationship with performance.
Adeyemi and Oboh
(2011) studied a sample size of 66 companies quoted in the Nigerian stock
exchange and found that a significant relationship between capital structure
and the performance of quoted firms in Nigeria using primary data from
questionnaire. Nosa and Ose (2010) used growth opportunity, non debt task
shield, tangibility, profitability and earning volatility also found positive
and significant relationship with the dependent variable which is corporate
performance.
Nicholas et al, (2013) examined Accounting
Information and Cost of Capital: A Theoretical Approach. The primary
goal of the study is to provide a theoretical model that shows explicit
solutions for equilibrium prices and derives the equilibrium required return
for the firm�s stock price. In other words, this theoretical study provides a
direct link between accounting information, related to the firm�s reports, and
the cost of capital within an equilibrium setting. Accounting information is
judged to be of high value because it affects the market�s ability to direct
firms� capital allocation choices. The findings showed that an increase in
expected cash flows, coming from improvements in the quality of accounting
information, leads to a reduction in the firm�s cost of capital.
3. Research Methods
This study used
secondary data which was handpicked from the annual report and statement of
account of selected quoted firms on the Nigerian Stock Exchange for the period.
3.1 Model Specification
Summary
statistics for the variables was calculated. The analysis utilized time series
data with generalized least squares regression. The most basic test involved
regressing the dependent variable, equity financing, debt financing and
corporate growth proxy by profitability of the quoted firms against the four
independent variables which are cost of equity, cost of debt, cost of preference
share and weighted average cost of capital. Thus, in line with the objectives
of the study, the following models are formulated:
Model I
EQF������� = f(CDC, CEQC, WACC)������������������������������������������������������������������������������������������������������������������ (1)
EQF������� =�
α_{0} + β_{1}CDC + β_{2}CEQC +
β_{3}WACC + ei����������������������
�������������������������������������������������������������� (2)
Model II
DF���������� = f(CDC, CEQC, WACC)������������������������������������������������������������������������������������������������������������������ (3)
DF���������� =�
α_{0} + β_{1}CDC + β_{2}CEQC +
β_{3}WACC + eI������������������������������������������������������������������������������������� (4)
Model III
ROI������� = f(CDC, CEQC, WACC)������������������������������������������������������������������������������������������������������������������ (5)
ROI������� =�
α_{0} + β_{1}CDC + β_{2}CEQC +
β_{3}WACC + ei ������������������������������������������������������������������������������������� (6)
Model IV
FM��������� = f(CDC, CEQC, WACC)������������������������������������������������������������������������������������������������������������������ (6)
FM��������� =�
α_{0} + β_{1}CDC + β_{2}CEQC +
β_{3}WACC + ei ������������������������������������������������������������������������������������� (7)
Where:
EQF ���������������������� = ������������ Equity Financing
DF�������������������������� = ������������ Debt Financing
ROI����������������������� =������������� Return on Investment
FM������������������������� =������������� Financing Mix proxy by Debt Equity
Ratio
CDC���������������������� =������������� Cost of Debt Capital
CEQC������������������� =������������� Cost of Equity Capital
WACC�� =������������� Weighted
Average Cost of Capital
α����������������������������� = ������������ Regression Constant
β_{1 � }β_{3}
�������������������� = ������������ Regression Coefficient
ei���������������������������� = ����������� Error Term
4. Techniques of Analysis
The models
stated will be analyzed using the multiple regression models. The Statistical
Package for Social Science (SPSS) will be used to examine the relationship
between the dependent and the independent variables as formulated in the
models. The idea behind regression analysis is the statistical dependence of
one variable, the dependent variable, on one or more variables, the independent
or explanatory variables. The objectives of such analysis are to estimate or
predict the mean or average value of the dependent variable on the basis of the
known or fixed values of the explanatory variables (Gujarati and Porter, 2009).
5. Presentation and Analyses of Results
Table 1:� Tolerance and Variance Inflation Factor (VIF)
MODEL I 
TOLERANCE 
VIF 
CDC 
.229 
4.369 
CEC 
.294 
3.399 
WACC 
.280 
3.574 
Source:
SPSS print out 22.0 (2019)
The variance
inflation factor result shows that all the variables fall below 4.0 and 10.0
which is the minimum and the maximum variance inflation coefficient. From the
above, the study conductively conclude that the variance inflation factor.�
Table 2: Colinearity Diagnostic and Durbin Watson Test
Model 
Eigen value 
Cond index 
Constant 
Variables Proportion 

CDC 
CEQ 
WACC 

1 
6.321 
1.000 
.00 
.00 
.00 
.00 

2 
.418 
3.890 
.51 
.11 
.04 
.00 

3 
.174 
6.030 
.01 
.40 
.95 
.06 

4 
.057 
10.497 
.49 
.49 
.00 
.00 

��� Durbin Watson Test������������������������������������������������������� 1.771
Source: SPSS print out 22.0
(2019)
The Eigen value provides an indication of how many distinct
dimensions they are among the independent variables, when several Eigen value
are close to 0, the variables are highly intercorrelated and the market is said
to be unconditioned; which means small changes in data values will lead changes
in the estimates of the coefficients. From the table above, the Eigen values
are greater than 0, this proves that the variables are not highly correlated,
this means the absence of multicolinearity. A condition index greater than 15
indicates a possible problem and an index greater than 30 suggests a serious
problem. From the table above, the condition index are less than 15 and 30 that
means the absence of serial autocorrelation among the variables. The Durbin
Watson statistics for the models are greater than 1.00 but less than 2.00 which
means the presence of positive autocorrelation among the variables.
Table
3: Effect of Cost of Capital on Equity Financing
VARIABLES 
CDC 
CEC 
WACC 
Unstandarized
β 
5.302 
17.776 
762.808 
Standarized
β 
.208 
.534 
.758 
Standard Error 
13.836 
15.927 
493.540 
Partial
Correlation 
.110 
.307 
.407 
Zero Order 
.016 
.115 
.169 
TStatistics 
.383 
1.116 
1.546 
TSignificant 
.708 
.286 
.146 
R^{2} 
.439 
R^{2} 
.193 
Fratio 
.956 
Fsig 
.445 
Source:� Extracts from SPSS
Window (22.0)
6. Interpretation of Regression Results
The
regression result presented in the above table shows that cost of debt capital
and cost of equity capital have negative relationship on equity financing, this
means that the negative coefficient of 5.302CDC and 17.776CEC would reduce
equity financing by 5.3% and 17.7% for a unit increase in the independent
variables while the positive coefficient of 762.808WACC will add to equity
financing for a unit increase in weight average cost of capital. The models
show that the independent variables can explain 19.3% variation on the
dependent variable. The Tstatistics and the Tsignificant shows that the
models are statistically not significant. The correlation coefficient shows
that the relationship between the dependent and the independent variable is
proxy by 43.9%. The correlation coefficient of the independent variables
confirms the relationship as reveal by the unstandardized and standardize
β coefficient. The Fratio found that the regression model is
statistically not significant.�
Table
5: Effect of Cost of Capital on Debt Financing
VARIABLES 
CDC 
CEC 
WACC 
Unstandarized
β 
1.865 
.843 
17.463 
Standarized
β 
.878 
.304 
.208 
Standard
Error 
.733 
.844 
26.157 
Partial
Correlation 
.592 
.277 
.189 
Zero Order 
.801 
.581 
.703 
TStatistics 
2.543 
.999 
.668 
TSignificant 
.026 
.338 
.517 
R^{2} 
.820 
R^{2} 
.673 
Fratio 
8.227 
Fsig 
.003 
Constant
α_{0} 
708.880 
STStatistics 
2.022 
Sig. 
.668 


Source:� Extracts from SPSS Window (22.0)
7. Interpretation of Regression Results
The
regression result presented in the above table shows that cost of debt and
weighted average cost of capital have positive effect on debt financing, this
means that the negative coefficient of 1865CDC and 17.463WACC proved that an
increase of 10% will lead to 18.6% increase and 17.4% in debt financing while
the negative coefficient of .843CEC will reduce debt financing by 8.4% for a
unit increase in the variables cost of capital. The models show that the
independent variables can explain 67.3% variation on the dependent variable.
The Tstatistics and the Tsignificant shows that CDC is statistically
significant while CEC and WACC are statistically not significant. The
correlation coefficient shows that the relationship between the dependent and
the independent variable is proxy by 82% correlation coefficient of the
independent variables confirms the relationship as reveal by the unstandardized
and standardize β coefficient. The Fratio found that the regression model
is statistically significant.�
Table
6: Effect of Cost of Capital on Return on
Investment
VARIABLE 
CDC 
CEC 
WACC 
Unstandarized
β 
.104 
.299 
120.197 
Standarized
β 
.008 
.018 
.244 
Standard
Error 
7.312 
8.417 
260.838 
Partial
Correlation 
.004 
.010 
.132 
Zero Order
Correlation 
.197 
.179 
.237 
TStatistics 
.014 
.036 
.461 
TSignificant 
.989 
.972 
.653 
R^{2} 
.237 
R^{2} 
.056 
Fratio 
.868 
Fsig 
.868 
Constant
α_{0} 
1958.976 
TStatistics

.560 
Significant� 
.586 


Source:� Extracts from SPSS
Window (22.0)
8. Interpretation of Regression Results
The
regression result presented in the above table shows that cost of debt and
weighted average cost of capital have positive effect on debt financing, this
means that the positive coefficient of .104CDC and 120.197WACC proved that an
increase of 10% will lead to 0.4% increase and 120% in return on investment and
weighted average cost of capital while the negative coefficient of .229CEC will
reduce return on investment by 2% for a unit increase in the variables cost of
capital. The models show that the independent variables can explain 5%
variation on the dependent variable. The Tstatistics and the Tsignificant
shows that all the independent variables are statistically not significant. The
correlation coefficient shows that the relationship between the dependent and
the independent variable is proxy by 23.7% correlation coefficient of the
independent variables confirms the relationship as reveal by the unstandardized
and standardize β coefficient. The Fratio found that the regression model
is statistically not significant.
Table 7: Colinearity Diagnostic and Durbin Watson Test
Model 
Eigen value 
Cond index 
Constant 
Variables Proportion 

CDC 
CEQ 
WACC 

1 
3.998 
1.000 
.00 
.00 
.00 
.00 

2 
.002 
48.747 
.51 
.11 
.04 
.00 

3 
.000 
90.351 
.00 
.40 
.95 
.06 

4 
.000 
117.075 
.49 
.49 
.00 
.94 

Durbin Watson
Test����������������������������������������������������������� 1327
Source:
SPSS print out 22.0 (2019)
The Eigen value provides an indication of how many
distinct dimensions they are among the independent variables, when several
Eigen value are close to 0, the variables are highly intercorrelated and the
market is said to be unconditioned; which means small changes in data values
will lead changes in the estimates of the coefficients. From the table above,
the Eigen values are greater than 0, this proves that the variables are not
highly correlated, this means the absence of multicolinearity. A condition
index greater than 15 indicates a possible problem and an index greater than 30
suggests a serious problem. From the table above, the condition index are less
than 15 and 30 that means the absence of serial autocorrelation among the
variables except weighted average cost of capital with the condition index of
117.075. The Durbin Watson statistics for the models are greater than 1.00 but
less than 2.00 which means the presence of positive autocorrelation among the
variables.
Table
8: Effect of Cost of Capital on Financing Mix
VARIABLES 
CDC 
CEC 
WACC 
Unstandarized
β 
1.201 
3.832 
46.058 
Standarized
β 
.230 
.563 
224 
Standard
Error 
.911 
1.049 
32.497 
Partial
Correlation 
.356 
.726 
.379 
Zero Order 
.880 
.927 
.854 
TStatistics 
1.318 
3.654 
1.417 
TSignificant 
.212 
.003 
.182 
R^{2} 
.196 
Adj. R^{2} 
.895 
Fratio 
43.705 
Fsig 
.000 
Constant
α_{0} 
1420.280 
TStatistics

3.261 
Significant� 
.007 


Source:� Extracts from SPSS Window
(22.0)
9. Interpretation of Regression Results
Analyses
in the regression results presented in the above table indicates that cost of
capital such as cost of debt, cost of equity and weighted average cost of
capital of the selected firms have positive relationship with the financing mix
of the firms. The positive coefficient of 1.201 as the regression parameter for
cost of debt, 3.832 for cost of equity and 46.058 for weighted average cost of
capital reveal that a unit increase on the independent variable will lead to
significant increase such as 12.0%, 38.3% and 460.5% increase on the dependent
variable. The R^{2} and the adjusted R^{2} reveal that 91.6%
and 89.5% variation on the dependent variable can be traced to variation on
cost of capital. The Fstatistics and Fsignificant justifies that the model is
adequate in predicting variation on the dependent variable. The findings of the
model proved that increase in cost of capital increase the financial mix of the
quoted firms. This is contrary to the expectation of the results but justifies
the opinion of Miller and Modigliani as against the Gordon. The positive
relationship between the variables could be traceable to the inability of the
firms to source alternate source of capital for investment but force to borrow
or float equity without considering the cost.��
10. Discussion of Findings
The relationship between corporate capital structure and the performance
of quoted firms has long been a point of controversy among scholars in
corporate finance. Unlike the dividend policy that determines the rate at which
the management determines the proportion of its capital that will be
distributed to shareholders and the proportion to be retain, capital structure
determine the proportion of the company�s capital that is internally generated
known as equity capital and the proportion that is borrowed outside the firm
known as debt capital. Optimal combination of the two components of capital
determines the cost of capital and the financial structure of the corporate
organization. Higher cost of debt capital will results in formulating policies
that will enhance internally generate capital such as higher retention ratio
and lower dividend payout ratio or increasing the equity share capital through
floatation of equities by rights issues or by public offer (Pandey, 2005). This
illustrates the tradeoff theory as formulated by Meyer.
Regression results from model I of this study found that cost of capital
have no significant effect on equity financing of the selected manufacturing
firms in the study as the explained variation reveals that the independent
variables which are cost of debt, cost of equity and weighted average cost of
capital can only explain 19.3% explained variation on the dependent variable.
The β coefficient of the variables proved that cost of debt and cost of
equity capital have negative relationship with equity financing while weighted
average cost of capital have positive relationship. The negative effect of the
variables confirms the apiriori expectation of the results as increase in cost
of capital discourages investment according to economic theory. This means that
increase in cost of capital will discourage investment of the selected
manufacturing firms. The findings confirm the findings of Lotfi (2004) on the
negative effect of cost of capital on fixed assets of selected firms on the
floor of Kenya stock exchange. It also validates the findings of Hussain et
al., (2012) on the effect of cost of capital on corporate profitability of
selected manufacturing firms in India. The positive effect of weighted average
cost of capital can be traced to management factors and policies formulated to
leverage the cost of capital and decreasing dividend payout ratio and
increasing retention ratio.
Model II was formulated to examine the relationship between cost of
capital and debt financing. The regression result found that cost of debt and
weighted average cost of capital have positive but insignificant relationship
with debt financing. It reveals that an increase in cost of debt and weighted
average cost of capital will enhance cost of debt financing. This finding is
contrary to the expectation of the study as the variables are expected to have
a negative effect on the dependent variable. The positive effect can be traced
to opinion of Oseigbu (2005) that found positive relationship between interest
rate and bank lending and was blamed on the ineffectiveness of the financial
sector and the inability of the corporate firms to formulate polices that will
leverage the challenging effect of cost of capital within the business
environment. However, cost of debt has positive and significant effect on debt
financing. This finding confirms the findings of Akani and Lucky (2016) on the
positive effect of capital structure and shareholders� value of quoted
commercial banks in Nigeria.
Model III was formulated to investigate the effect of cost of capital on
the profitability of selected manufacturing companies and the regression
results found that cost of debt and weighted average cost of capital have
positive but insignificant effect while cost of equity have negative but
insignificant effect. The results reveal that the independent variable can only
explain 5% variation on the dependent variable; this shows that cost of capital
is statistically not significant on the profitability of the selected
manufacturing firms. Theoretically, increase in cost of capital discourage
investment and therefore the negative effect of the variable on profitability
confirm the apriori expectation of the results while the positive effect can
be traced to the fact that corporate firms has no valid option on source of
capital and are forced to obtain fund whether the cost is high or not.
11. Conclusion and Recommendation
11.1 Conclusion
This study investigated the relationship between cost of capital and
financing of corporate growth of 50 selected manufacturing firms listed on the
floor of Nigerian stock exchange using time series data from 2000 � 2015.
Financing of corporate growth was proxy as equity capital to total capital,
debt capital to total capital and return on investment while cost of capital
was proxy by the traditional method as formulated by Pandey (2005) as cost of
debt, cost of equity and weighted average cost of capital. From the findings of
the study, we draw the following conclusion:
� That the relationship between cost of capital and equity financing is
statistically not significant. Cost of debt and cost of equity have negative
and insignificant effect while weighted average cost of capital has positive
but insignificant effect on the dependent variable. The model summary shows
that the independent variables could only attest 19.3% variation while the
Fstatistics shows that the model is statistically not significant.
� That cost of capital has significant relationship on debt financing as
the model summary portray that the independent variables have a correlation
coefficient of 82%, an R^{2} of 67.3% and the model is statistically
significant. Cost of equity capital has negative effect while cost of debt and
weighted average cost of capital have negative effect.
�
That cost of capital has no significant effect on
return on investment of the selected manufacturing companies as the multiple R
shows 23.7%, R^{2} of 5.6% and the model tested not significant despite
the positive effect of cost of debt and weighted average cost of capital.
11.2 Recommendation
�
Management should formulate internal policy that
will enhance the realization of optimal capital structure of the firm which
determines the combination of equity capital and debt capital as the capital
structure of the firm and the business environment should be well diagnosed and
tactical measures used to ensure that cost of capital does not affect the
investment decision of the firms and the profitability.
� Formulating capital structure of the firm should be well examined with
the investment policy of the firms to avoid high cost of capital that will not
enhance investment financing of the firms and the environmental factors should
be acknowledged in formulating cost of capital to avoid risk associated with
inadequate or wrong capital structure of the firm to enhance profitability.�����
� External source of capital such as debt should be properly appraised and
integrated with the investment policy of the firms to leverage the high cost of
debt and its effect on investment and profitability and cost of equity should
be integrated with the objective of maximizing shareholders� wealth through
investment policies.
�
The regulatory authorities should formulate policies
of minimizing the cost of capital on the investment decision of the listed
firms and the macroeconomic and the monetary policy should be considered and appraised
in determining the capital structure of the firms.
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