This study aims to determine and analyze the influence between
Profitability, Liquidity, Company Size, and Sales Growth on Capital Structure
in public companies in the property and real estate sub-sector on the Indonesia
Stock Exchange for the period 2011-2015. In this study, four variables were
taken that allegedly affect Capital Structure, namely Profitability, Liquidity,
Company Size, and Sales Growth with multiple linear regression analysis
equations using the Stepwise method, classical assumption testing consisting of
normality tests, multicollinearity tests, heteroscedasticity tests and
autocorrelation tests using the Statistical Product for Social Science
(SPSS) program version 22.0 for windows and Microsoft Excel 2016. The
sample of companies was taken as many as 33 companies from 49 populations of
property and real estate sub-sector companies on the Indonesia Stock Exchange. The
results of this study show that partially, Liquidity, and Company Size have a
significant influence on Capital Structure. Meanwhile, in terms of model
feasibility there is a significant influence between Liquidity, Company Size,
and Sales Growth on Capital Structure. Should be able to pay more attention to
the movement or change of Capital Structure based on the variables of
Profitability, Liquidity, Company Size, and Sales Growth. Because these
variables can affect the rise or fall of the Capital Structure in public
companies, propert and real estate sub-sectors.
Keywords:
Profitability,
Liquidity, Company Size, Sales Growth, Capital Structure.
INTRODUCTION
In the current era of
globalization, competition in the business world makes companies have to strive
to be able to achieve their company's main goals. The main goal of any company
in general is to maximize the value of the company. Therefore, one of the
things that must be done by the company is the decision to determine the
Capital Structure which must be considered properly through Capital Structure
management.
One of the important
decisions faced by companies in relation to the continuity of company
operations is funding or Capital Structure decisions i.e., balance or
comparison between foreign capital and own capital. Foreign capital is defined
in this case as debt, both long-term and short-term debt.
In this study took the
sub-sector of property and real estate. The property sector as one of the business instruments is usually
chosen by investors. Property and Real
Estate is one of the investment
alternatives that investors are interested in where investment in this sector
is a long-term investment and property is a multipurpose asset that can be
used by companies as collateral, therefore property
and real estate companies have a high
Capital Structure.
The existence of several
factors that affect the company's Capital Structure is important as a basis for
consideration in determining the composition of the company's Capital
Structure. In this study, researchers limited several factors studied that
allegedly affect Capital Structure, including Return On Equity, Current Ratio, Company Size, and Sales Growth.
This factor is used to show how much the company's ability to meet total debt
based on the company's total assets or capital structure. This study takes four
variables that are thought to affect Capital Structure, namely: Profitability,
Liquidity, Company Size, and Sales Growth.
RESEARCH METHOD
The population in this
study is public companies in the property and real estate sub-sector on the
Indonesia Stock Exchange (IDX) for the period 2011-2015 as many as 49
companies. The sample in this study that meets certain criteria is as many as
28 public companies in the property and real estate sub-sector on the Indonesia
Stock Exchange (IDX) for the period 2014-2016.
Research conducted by Indrajaya (2011) entitled
"The Effect of Asset Structure, Company Size, Growth Rate, Profitability,
and Business Risk on Capital Structure". The results show that asset
structure variables have a positive and significant influence on capital
structure. The variable size of the company has a positive and significant influence
on the capital structure. The profitability variable has the strongest
explanatory influence or power compared to other variables, with a negative and
significant influence on capital structure.
Research conducted by Mardiansyah (2012)
entitled "The Effect of Profitability and Operating Leverage on Capital
Structure". The results show that profitability variables have a negative
and significant effect on capital structure. Variable operating leverage has a
negative but not significant effect on capital structure.
Research conducted by Putri (2012) entitled
"The Effect of Profitability, Asset Structure, and Company Size on Capital
Structure". The results showed that the profitability variable had a
positive but not significant influence on the capital structure. Asset
structure variables have a positive and significant influence on capital
structure. The variable size of the company has a positive and significant
influence on the capital structure of the company.
Research conducted by
Putra and Kesuma (2013) entitled "The Effect of Profitability, Liquidity,
Size, Growth on Capital Structure". The results show that profitability
and liquidity variables partially have a negative and significant influence on
capital structure. Conversely, the variable size of the company was not shown
to have any influence on the capital structure. Growth variables have a
positive and significant influence on capital structure.
Descriptive Statistical Test Results
Descriptive statistics provide a picture or description of data into
information that is clearer and easier to understand. In this study, the data
described is seen from the lowest value (minimum), largest value (maximum),
average (mean), and standard deviation of the data. Based on table 1 it can be
known that the number of samples (N) is 84 company data, the number is the
total of the sample of public companies in the
property and real estate sub-sector
during 3 years of observation, namely 2014 to 2016, the variables studied are Return
On Equity, Current Ratio, Growth
Sales, Company Size and Capital
Structure. Below are the results of the descriptive Statistical Test.
Table 1. Descriptive
Statistical Test Results
|
Descriptive Statistics |
|||||
|
|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
X1_ROE |
84 |
.00 |
.41 |
.1177 |
.09318 |
|
X2_CR |
84 |
.21 |
19.07 |
2.6643 |
3.29556 |
|
X3_GS |
84 |
.00 |
8.43 |
.2982 |
1.03493 |
|
X4_SIZE |
84 |
25.16 |
31.45 |
29.0812 |
1.56571 |
|
Y_DER |
84 |
.04 |
2.02 |
.8521 |
.48900 |
|
Valid N (listwise) |
84 |
|
|
|
|
Source : Secondary data processed using SPSS
Table 1. shows the descriptive
statistics of each variable presented below:
1. The results of the
Return On Equity calculation show that
from 84 samples, Return On Equity has the smallest (minimum) value of
(0.15), namely at PT. Cowell Development Tbk. in 2015, the largest value
(maximum) 0.41 at PT. Fortune Mate Indonesia Tbk. in 2016, the average value
(mean) was 0.12 and standard deviation was 0.93.
2. The results of the
Current Ratio calculation show that out of 84 samples, the Current Ratio has
the smallest (minimum) value of 0.21 at PT. Bukit Darmo Property Tbk. in 2016,
the largest value (maximum) of 19.07 at PT. Metro Realty Tbk. in 2016, the
average value (mean) was 2.66 and standard deviation was 3.30.
3. The Growth Sales
variable shows that 84 samples, Growth Sales have the smallest (minimum) value
of (0.65) at PT. Danayasa Arthama Tbk. in 2016, the largest value (maximum) of
8.43 at PT. Bukit Darmo Property Tbk. in 2014, the average value (mean) was
0.30 and the standard deviation value was 1.03.
4. The Company Size
variable shows that 84 samples, Company Size has the smallest (minimum) value
of 25.16 at PT. Metro Realty Tbk. in 2016, the largest value (maximum) of 31.45
at PT. Lippo Karawaci Tbk. in 2016, the average value (mean) was 29.08 and the
standard deviation value was 1.57.
5. The result of the
calculation of Capital Structure that 84 samples, Capital Structure has the
smallest value (minimum) of 0.04 at PT. Indonesia Prima Property Tbk. in 2016,
the largest value (maximum) of 2.02 at PT. Cowell Development Tbk. in 2015, the
mean value was 0.85 and the standard deviation was 0.49.
Classical Assumption Test Results
Because this study used multiple regression analysis methods, it is
important to test classical assumptions to
take bias analysis or BLUE (Best Linear
Unbiased Estimator). Classical assumption tests are used to determine the
presence or absence of residual normality, multicollinearity, autocorrelation
and heteroscedasticity. All classical assumption test requirements must be met
for test results to be reliable.
The residual normality test in the regression model is used to test
whether the residual values resulting from the regression are normally
distributed or not. A good regression model has a normal distribution of residual
values. This study used One Sample Kolmogorov - Smirnof to test residual
normality.
The residual normality test by One Sample Kolmogorov - Smirnov is then
used to find whether the data distribution, whether it follows the normal,
poisson, distribution is uniform or exponential. The residual is normally
distributed if the significance value > 0.05.
The results of the normality test data obtained are as follows:
Table 2. Normality Test
Results of One Sample K-S
|
One-Sample Kolmogorov-Smirnov Test |
||
|
|
||
|
|
Unstandardized Residual |
|
|
N |
84 |
|
|
Normal Parametersa,b |
Mean |
.0000000 |
|
Std. Deviation |
.43025723 |
|
|
Most Extreme Differences |
Absolute |
.097 |
|
Positive |
.097 |
|
|
Negative |
-.057 |
|
|
Test Statistics |
.097 |
|
|
Asymp. Sig. (2-tailed) |
.050c |
|
|
a. Test distribution is Normal. |
||
|
b. Calculated from data. |
||
|
c. Lilliefors Significance Correction. |
||
Source : Secondary data processed using SPSS
From the output table 2 shows the significance value (Asymp.Sig
2-tailed) is 0.05 (<0.05) then it is concluded that the residual value is
normally distributed.
The multicolonicity test aims to test whether the regression model
found a correlation between independent variables. A good regression model
should not have correlations among independent variables. This study used the tolerance
value method and Variance Inflation
Factor (VIF) to test multicollinearity.
The decision is made by looking at the value of variance inflation factor (VIF) and Tolerance, if Tolerance >0.1
and VIF <10, then it can be concluded that there is no multicollinearity
(Ghozali, 2005).
Table 3. Multicolonicity
Test Results
Coefficientsa
Source :
Secondary data processed using SPSS
The output of table 3 concludes that there is no multicollinearity
among independent variables because all independent variables are at a
tolerance threshold of >0.01 and VIF <10.
Heteroscedasticity is a residual variance that is not the same in all
observations in the regression model. And there should be no heteroscedasticity
in good regression. This study used the Park test to test for heteroscedacity.
The Park test proposes to progress the absolute value of the residual
to the independent variable. If the significance value between the independent
and residual variables is > 0.05 then there is no heteroscedasticity.
Table 4. Park Method
Heteroscedasticity Test Results
|
Coefficientsa |
|||||||||||
|
Type |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
|
||||||
|
B |
Std. Error |
Beta |
|
|
|
||||||
|
1 |
(Constant) |
8.243 |
23.894 |
|
.345 |
.732 |
|
||||
|
Lnx1 |
.069 |
.444 |
.023 |
.155 |
.878 |
|
|||||
|
Lnx2 |
-.336 |
.522 |
-.095 |
-.644 |
.523 |
|
|||||
|
Lnx3 |
-.239 |
.226 |
-.157 |
-1.059 |
.295 |
|
|||||
|
Lnx4 |
-3.474 |
7.041 |
-.076 |
-.493 |
.624 |
|
|||||
|
a.
Dependent Variable: Lnei2 |
|||||||||||
Source : Secondary data processed using SPSS
As shown in table 4 the significance value for the variables Return On Equity, Current Ratio, Sales
Growth and Company Size is >0.05 so it can be concluded that there is no
heteroscedasticity problem.
The autocorrelation test aims to test whether in the linear regression
model there is a correlation between confounding errors in period t with
confounding errors in period t-1 (previous). A good regression model should not
have autocorrelation. Researchers used the Durbin-Watson (DW test) in
conducting autocorrelation tests.
Decision making on the Durbin-Watson test is as follows:
DU < DW < 4-DU
then Ho is accepted, meaning there is no autocorrelation
DW < DL or DW >
4-DL then Ho is rejected, meaning there is autocorrelation
DL < DW < DU or
4-DU < DW < 4-DL, no conclusions can be drawn.
Table 5. Autocorrelation Test
Results of Durbin-Watson Method
|
Model Summaryb |
|||||
|
Type |
R |
R Square |
Adjusted R Square |
Std. Error of the
Estimate |
Durbin-Watson |
|
1 |
.475a |
.226 |
.187 |
.44102 |
1.940 |
|
a. Predictors: (Constant), X4_SIZE, X3_GS, X1_ROE, X2_CR |
|||||
|
b. Dependent Variable: Y_DER |
|||||
Source : Secondary data processed using SPSS
Based on table 5 of the Durbin-Watson statistics with a significance
level of 0.05, with a sample of n = 84 and an independent variable k = 4, the
researcher obtained DL = 1.55 and DU = 1.75 values. Therefore, the values 4-DU
= 2.26 and 4-DU = 2.25. As seen in output table 4.5, the DW value is 1.94.
Since the values of DU < DW < 4-DU (1.75 < 1.94 < 2.26) it can be
concluded that there is no autocorrelation problem.
Model Feasibility Test Results
Test Coefficient of Determination (R2)
In multiple regression to test the coefficient
of determination, researchers use a summary model with the aim of knowing how
big the combination of independent variables consisting of Return On Equity, Current Ratio, Growth Sales and Company Size. The
following are the results of the determination test coefficient test (table
4.7):
Table 6. Coefficient of Determination Test Results
|
Model Summary |
||||
|
Type |
R |
R Square |
AdjustedR Square |
Std. Error of the Estimate |
|
1 |
.475a |
.226 |
.187 |
.44102 |
|
a. Predictors: (Constant), X4_SIZE, X3_GS, X1_ROE, X2_CR |
||||
Source : Secondary data processed using SPSS
Test results above R² value =
0.226. From these values, it can be seen that Capital Structure is influenced
by aspects of Return On Equity, Current
Ratio, Growth Sales and Company Size as much as 0.226 or 22.6% and the
remaining 77.4% is influenced by other variables that are not studied.
F Test (Anova)
Simultaneous influence testing or the purpose of the F test is to show
whether all independent variables included in the model have a reciprocal
influence on the dependent variable or not (Ghozali, 2005). The confidence
degree used is 0.05. If F is calculated > F table, it is stated that all
independent variables simultaneously have a significant effect on the dependent
variable
Table 7. Statistical
Simultaneous Test Results F
|
ANOVA |
||||||
|
Type |
Sum of Squares |
Df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
4.482 |
4 |
1.120 |
5.761 |
.000b |
|
Residuals |
15.365 |
79 |
.194 |
|
|
|
|
Total |
19.847 |
83 |
|
|
|
|
|
a. Dependent Variable: Y_DER |
||||||
|
b. Predictors: (Constant), X4_SIZE, X3_GS, X1_ROE, X2_CR |
||||||
Source : Secondary data processed using SPSS
The table above shows a calculated F value of 5.76 and a significance
level of 0.00. The F value is calculated (5.76) > the F table (2.49), and
the sig value. less than the probability value of 0.000 (<0.05). Thus the
regression equation model based on research data is significant, meaning that
the linear regression model meets the criteria of linearity. Or it can be said
that together (simultaneously) the independent variables Return On Equity, Current Ratio, Sales Growth and Company Size
affect the dependent variable Capital Structure.
Multiple Linear Regression Analysis
Multiple regression analysis is used to determine the shape (of the
variable relationship). According to Gunawan Sudarmanto (2013), multiple linear
regression analysis is used by researchers if researchers intend to predict how
things will be (the rise and fall of the dependent variable when two or more
independent variables as predictor factors are manipulated (increased in
value).
Table 8. Multiple Linear Regression Test Results
|
Coefficientsa |
|||||||
|
Type |
Unstandardized Coefficients |
Standardized
Coefficients |
t |
Sig. |
|
||
|
B |
Std. Error |
Beta |
|
|
|
||
|
1 |
(Constant) |
-1.679 |
1.011 |
|
-1.660 |
.101 |
|
|
X1_ROE |
-.232 |
.545 |
-.044 |
-.425 |
.672 |
|
|
|
X2_CR |
-.039 |
.016 |
-.265 |
-2.445 |
.017 |
|
|
|
X3_GS |
-.052 |
.048 |
-.110 |
-1.083 |
.282 |
|
|
|
X4_SIZE |
.092 |
.034 |
.295 |
2.679 |
.009 |
|
|
|
a. Dependent Variable: Y_DER |
|||||||
Based on the output results in table 8 above, a constant value of
-1.679, β1 value of -0.232, β2 of -0.039, β3 of -0.052 and β4 of 0.092 were
obtained. Thus can be formed multiple linear regression equations as follows:
Y = -1.679 – 0.232 X1 – 0.039 X2 – 0.052 X3 + 0.092 X4 + ἐ
Based on the above equation, it can be
interpreted as follows:
1. The constant -1.679
states that if the independent variable is constant, then the value of Y
(Capital Structure) is -1.679.
2. The regression
coefficient X1 (Profitability Variable) of negative value of 0.232 states that
every increase in profitability by 10% causes a decrease in the value of debt
or Capital Structure by 10% X 0.232 which is 2.232% assuming the variables of
liquidity, company size, and growth rate remain constant
3. The regression
coefficient X2 (Liquidity) of negative value of 0.039 states that every
increase in risk by 10% will lead to a decrease in the value of debt or Capital
Structure by 10% X 0.039. That is 0.39% assuming the variables of
profitability, company size, and growth rate remain constant
4. The regression
coefficient X3 (Growth Sale)s negative
value of 0.052 states that every increase in risk by 10% will lead to a
decrease in the value of debt or Capital Structure by 10% X 0.052 which is
0.52%. assuming the variables of profitability, liquidity, and growth rate
remain constant
5. The regression
coefficient X4 (Company Size) of positive value of 0.092 states that every
increase in risk by one unit will cause an increase in the value of debt or
Capital Structure by 10% X 0.092. That is 0.92% assuming the variables of
profitability, liquidity, and company size, remain constant
Test Results t
The partial test or t-test is used to show how far the influence of the
independent variable individually or partially explains the dependent variable
tested at a significance level of 0.05. The results of testing data with t test
as in table 9.
Table 9. Statistical
Individual Parameter Test Results t
|
Coefficientsa |
||||||
|
Type |
Unstandardized
Coefficients |
Standardized
Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
|
|
||
|
1 |
(Constant) |
-1.679 |
1.011 |
|
-1.660 |
.101 |
|
X1_ROE |
-.232 |
.545 |
-.044 |
-.425 |
.672 |
|
|
X2_CR |
-.039 |
.016 |
-.265 |
-2.445 |
.017 |
|
|
X3_GS |
-.052 |
.048 |
-.110 |
-1.083 |
.282 |
|
|
X4_SIZE |
.092 |
.034 |
.295 |
2.679 |
.009 |
|
|
a. Dependent Variable: Y_DER |
||||||
Source : Secondary data processed using SPSS
Researchers use a two-way
t-test, which means that the influence of the independent variable on the
dependent variable can have a positive or negative effect. Based on the table t
bidirectional test at a significance of 0.05 with degrees of freedom df = n-k
or 84-4 = 80, the results obtained for t table 1.66.
If the significance value is less than the degree of confidence then we
accept the alternative hypothesis, which states that an independent variable
partially affects a dependent variable.
Return On Equity to
Capital Structure
In table 4.9 the sig value X1 Return on Equity is 0.672. The sig value
is greater than the probability value of 0.05, or the value of 0.672 > 0.05,
then H1 is rejected and Ho is accepted. The variable X1 has a calculated t of
-0.425 with table t = 1.66. So t calculate < t table it can be concluded
that the variable X1 has no contribution to Y. The value of t is negative that
the variable X1 has the opposite relationship with Y. So it can be concluded
that X1 Return on Equity has no
significant effect on Y.
Liquidity (Current
Ratio) toCapital Structure
In table 9. the sig X2 Current Ratio value is 0.017. The sig value is less than the
probability value of 0.05, or the value of 0.017 < 0.05, then H1 is accepted
and Ho is rejected. The variable X2 has a calculated t of -2.445 with table t
=- 1.66. So t calculate < t table it can be concluded that the variable X2
has no contribution to Y. The value of t is negative that the variable X2 has a
relationship in the opposite direction with Y. So it can be concluded that X2 Current Ratio has a significant effect
on Y.
Growth Sales to Capital Structure
In table 9 the sig value of X3 Growth Sales is 0.282. The sig value is
greater than the probability value of 0.05, or the value of 0.282 > 0.05, then
H1 is rejected and Ho is accepted. The variable X3 has a calculated t of -1.083
with table t = 1.66. So t calculate < t table can be concluded that variable
X3 has no contribution to Y. The value of t is negative that variable X3 has a
relationship in the opposite direction with Y. So it can be concluded that X3 Growth Sales has an insignificant
effect on Y.
Company Size to Capital
Structure
In table 9. the sig value X4 Company Size is
0.009. The sig value is less than the probability value of 0.05, or the value
of 0.009 < 0.05, then H1 is accepted and Ho is rejected. The variable X4 has
a calculated t of 2.679 with table t = 1.66. So t calculate > t table can be
concluded that the variable X4 has a contribution to Y. The value of t is
positive that the variable X4 has a unidirectional relationship with Y. So it
can be concluded that X4 Company Size has a significant effect on Y.
Discussion of Research Results
The
effect of profitability (X1) on Capital Structure. (Y)
The
results of this study show that profitability proxied with Return On Equity (ROE) indicates the company's ability to generate
net profit. The more optimally the company uses its capital, it is said that
the better the company's performance. The greater the ROE value, the more
profitable the company is considered and the return expected by investors is
also large. In general, companies that have a high level of profit use
relatively kecil.ini debt according to theory according to Bringham and Houston
(2011). That companies that have a high level of profitability will be able to
produce and share more companies so that they can be used as cover obligations
or funding that will have an impact on the lack of debt use by the company and
vice versa with a low level of profitability, the company will use a lot of
debt to fund the company's operations In this study after statistical
calculations with z
SPSS version 2.0 indicates that companies with
a high level of profitability do not affect capital structure or debt although
the effect is not significant but the negative direction between profitability
and debt policy is in accordance with theory according to Bringham and Houston
(2011), this is because companies with a high level of profitability mean that
the company is able to manage assets or assets well and tends to borrow more
funds little. The results of this study are not in line with the research of
Riski Dian Infantri (2015) which states that Return On Equity has a significant effect on Capital Structure.and
the results of this study are in accordance with the original research of
Sulisyowati (2009) which states that profitability has a negative effect not
significantly on capital structure
Effect of Liquidity (X2) on Capital Structure
Liquidity variables have a negative influence on capital structure and this
negative influence on capital structure contradicts the initial theory, but a
similar study conducted by Seksak (2011) also found that the use of long-term
debt in the capital structure in companies in Thailand is getting less and less
along with the increase in company liquidity. This happens because companies
that already benefit from more liquid equity will be more motivated to use more
of their own capital than to use long-term debt. The result of this fact is
that the company will reduce the use of its long-term debt as the company's
liquidity level increases. Other American studies by Lipson and Mortal (2010)
and Martell (2006) also found that more liquid companies use less long-term
debt, resulting in a negative relationship between liquidity and capital
structure. This result may also occur because more liquid companies will pay
their debts which results in a decreased level of use of seedling debt. The
results of this study are in line with Rianingsih's (2015) research which
states that the Current Ratio has a
significant effect on Capital Structure. However, in this study, liquidity has
a significant negative effect, this is in accordance with the results of Paydar
and Bardai's (2012) research which states that liquidity has a significant
negative effect on capital structure.
Effect of Sales Growth (X3) on Capital
Structure
The negative influence of growth variables on capital structure
variables, is not in accordance with the initial theory, but there are studies
that support this phenomenon, Larry (1995) found that there is a negative
relationship between company growth and the use of debt in companies. This can
happen if the growth of these companies is not recognized by the capital
market, or for companies whose growth is not significant enough to cover their
debts. This result may also occur because the company studied is a company with
a high growth rate, but it is no longer a company that is at a young age. So
that the company's increased growth here shows good company performance. This
gives the company an advantage to attract stock investors so that the company
decides to reduce the use of long-term debt. The results of this study are not
in line with Laily's (2013) research which states that Growth Sales has a significant effect on Capital Structure.
The Effect of Company Size on Capital Structure
The larger the size of a company makes it easier
for it to obtain the flow of funds from outside the company. This is because
the large assets owned by the company provide certain confidence for investors
to invest their funds. Similarly, creditors to distribute debt funds to the
company. So that the size of a company affects the amount of debt that can be
obtained by the company and also affects the amount of debt needs from the
company. The results of this study are in line
CONCLUSION
For
both companies and investors in the property and real estate sub-sector, it is
crucial to focus on monitoring and understanding changes in Capital Structure.
Public companies within this sector should particularly emphasize the variables
of Return On Equity, Current Ratio, Size, and Growth Sales, as these factors
play a significant role in influencing the fluctuations of Capital Structure.
For investors seeking opportunities in shares of these companies, a careful
analysis of Return On Equity, Current Ratio, Company Size, Sales Growth, and
Capital Structure is recommended to make informed and profitable investment
decisions.
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