Agus Ismaya Hasanudin, Risma Nindya Primawresti, Tri Lestari
Faculty of Economics and Business, Sultan Ageng Tirtayasa University, Jakarta, Indonesia
Email:
[email protected],
[email protected],
[email protected]
|
ARTICLE INFO |
ABSTRACT |
|
Date received : 25 February 2022 Revision date : 05 March 2022 Date
received : 17 March 2022 |
This study aims to examine the effect of profitability and liquidity
on company value with capital structure as a moderating variable. Company
value is essential to know because it reflects growth and performance. The object of this research is pharmaceutical
sub-sector companies listed on the Indonesia Stock Exchange (IDX) during
2015-2019. because the 2015-2035 National Industrial Development Master Plan
states the pharmaceutical and pharmaceutical ingredients industry is one of
the mainstay manufacturing sectors that get development priority because it plays
a significant role as the main driver of the national economy. This quantitative research is included in
associative research to obtain information about the influence or
relationship between two or more variables. The results of study shows that
one company that has just been listed on the IDX from 2018 so that it becomes
a deduction in the sample used. |
|
Keywords: Capital structure; company value; liquidity; profitability |
INTRODUCTION
This
study examines the effect of Profitability and liquidity on company value with
capital structure as a moderating variable. According to Fuad et al. (2006),
company value is the value of a company related to its share price. Moreover,
company value is of interest to its stakeholders, who can utilize information
about the value in a variety of ways to achieve their objectives, such as
investing or managing the company (Tarczyński,
Tarczyńska-Łuniewska, & Majewski, 2020).
This opinion is based on the idea that a price increase is identical to an
increase in the prosperity of shareholders and an increase in the value of the
company.
According
to the Ministry of Industry, the pharmaceutical industry has a vital role in
investing in the Indonesian economy, so investors need to know the development
of the value and performance of companies in the pharmaceutical industry. The profitability ratio can calculate the company's
ability to generate profits. The profitability ratio used aims to assess the
company's ability to seek profit in a certain period. This ratio can also show
its ability to control its operational costs. In addition, profitability is
viewed as a measure of progress, improvement, and a factor indicating the
company's long-term sustainability (Seissian,
Gharios, & Awad, 2018).
Companies that have bad liquidity in the long term can
affect the company's solvency level. Because, the use of debt for corporate
funding has a large risk, it requires careful consideration. �Whether other factors can weaken or strengthen
the influence of Profitability and liquidity in influencing company value. For
this reason, researchers are interested in adding moderating variables to this
study. The moderating variable itself is an independent variable that can
strengthen or weaken the influence of other independent variables on the
dependent variable (Ghozali,
2018).
According to Barauallo (2011),
the capital structure is everything related to company financing internally and
externally within a certain period. The capital structure is essential to note
because it can result in different financial conditions.
The
purpose of this research include knowing the effect of Net Profit Margin on company
value, knowing the effect of the Current Ratio on the company's value, knowing
the effect of capital structure as a moderating variable net profit margin on company
value, and knowing the effect of Capital Structure as a moderating variable
Current Ratio on company value.
The
following are some of the previous studies that have been found which have
similarities in terms of theory, objects and variable.
Table 1
Previous Research
|
No |
Researcher |
Research Title |
Variables used |
Research
result |
|
1 |
|
The Effect of Profitability on Company
Value with Capital Structure as Moderating Variable |
Dependent �The
value of the company Independent �
Profitability Moderation � Capital Structure |
Profitability
has a positive effect on company value. The capital structure acts as a quasi
moderator (pseudo moderator). |
|
2 |
|
Effect of Profitability, Company Size
and Growth Rate on Company Value with Capital Structure as Moderating
Variable |
Dependent �The
value of the company Independent �
Profitability �
Company size �
Growth rate Moderation � Capital Structure |
Profitability,
company size and growth rate affect company value. Capital structure can only
moderate Profitability and growth rate on company value. |
|
3 |
|
Effect of Liquidity and Profitability
on Company Value with Capital Structure as Moderating Variable |
Dependent �
The value of the company Independent �
Profitability �
Liquidity Moderation � Capital Structure |
Profitability
and liquidity result in a negative and significant company value. The capital
structure acts as a pure moderator to moderate Profitability and liquidity to
company value. |
|
4 |
|
Effect of Current ratio, Net Profit
Margin, Debt to Equity Ratio dan Earning per Share terhadap Nilai Perusahaan |
Dependent � Cooperation
value Independen �
Current ratio �
Net profit margin �
Debt to equity ratio �
Earning per share |
Current
ratio, net profit margin dan earning per share berpengaruh positif
dan signifikan terhadap nilai perusahaan. Untuk debt to equity ratio berpengaruh positif namun tidak signifikan. |
|
5 |
|
Effects of ROA, DER, CR, DPR and Company
Size on Company Value. |
Dependent �The
value of the company Independent �
ROA �
DAR �
CR �
Company size �
DPR |
ROA and company
size positively affect company value (PBV), while DER, CR, and DPR negatively
affect. |
|
6 |
|
Impact of Capital Structure on Company
Value: Evidence from Indian Hospitality Industry |
Dependent �
The value of the company �
Independent company quality �
leverage �
size �
profitability �
tangibility �
growth �
liquidity �
growth in gros domestic product �
inflation |
Research
findings reveal a significant relationship between company value and company
quality, leverage, liquidity, size and economic growth. |
|
7 |
|
Analysis of the Effect of Financial
Performance and Corporate Social Responsibility on Company Value |
Dependent �The
value of the company Independent �
ROA �
ROE �
OPM �
NPM �
CSR |
Only NPM has a negative and
significant effect on company value. While ROA, ROE, OPM and CSR have a
positive and significant effect. |
|
8 |
|
Effect of Profitability, Liquidity and
Growth Opportunities on Company Value with Capital Structure as Intervening
Variable |
Dependent �The
value of the company Independent �
Profitability �
Liquidity �
Growth opportunities Intervening �
Capital structure |
Only the
liquidity and growth opportunities variables that affect the capital
structure. Only the profitability variable produces a positive direction for
its effect on company value. The capital structure can only intervene in the
profitability variable influencing company value. |
|
9 |
|
Effect of Profitability and Leverage
on Company Value and Capital Structure Intervening Variables |
Dependent �The
value of the company Independent �
Profitability �
Leverage Intervening � Capital Structure |
Profitability
and leverage have a positive and significant effect on capital structure.
Profitability, leverage, and capital structure significantly affect company
value. Capital structure can mediate Profitability and leverage in
influencing company value. |
|
10 |
|
Analysis of the Effect of Financial
Performance, Growth Opportunity and Company Size on Capital Structure and Company
Value |
Dependent �The
value of the company �
Capital structure Independent �
Liquidity �
Profitability �
Growth Opportunity �
Company size |
Only
Profitability and capital structure have a negative and significant impact on
company value. Meanwhile, the capital structure is only affected by
Profitability and liquidity significantly negatively. |
Source: a review of
research results
Framework
a. Effect of Net Profit Margin (NPM) on Company Value
The ultimate goal to be achieved by the company is to get
the maximum profit or profit. NPM is a profitability ratio that describes the
management's ability to compensate margin for owners who have provided their
capital for risk. The company's ability to generate profits from sales is the
information needed by investors.
By looking at the value of NPM, information held by
outsiders will increase, and according to good information, signal theory can
be a signal that influences decisions. Thus, knowing the NPM can influence
investors' interest in buying company shares. This is in line with previous
research conducted by (Hardiyanto, 2020), showing that the net profit margin has a positive and significant effect
on the projected company value with the PBV ratio.
b. Effect
of Current Ratio (CR) on Company Value
One of the liquidity ratios is the CR. The ratio
describes the ratio of current assets to current liabilities. Current assets
are considered the most liquid or the easiest to liquidate if intended to pay
debts immediately. By knowing the company's ability to pay debts with current
assets, the health of the company's financial condition can be seen. This ratio
can also potentially increase profits and supervision of management in using
its current assets.
c. Capital Structure Moderates Net Profit Margin (NPM) on Company Value
Capital structure is the proportion of internal capital
with external capital. When a company uses a fair amount of debt composition as
a source of capital, it indicates that the company has better performance to
increase investor confidence (Kusna & Setijani, 2018).
Moreover, the increase in company value is in line with the increase in
corporate leverage (Ross, 1977).
Some of the results of several previous studies found empirical evidence that
capital structure can strengthen the effect of Profitability on company value (Munthe,
Inge, 2018; Anggraini, 2017; Santoso, 2018; and Cahyono et al., 2019).
d. Capital
Structure Moderates the Effect of Current Ratio (CR) on Company Value.
Liquidity
is the level of the company's ability to meet its current obligations.
Companies with high liquidity will gain the trust of external investors because
they are considered capable of fulfilling their short-term obligations. The
company will also be considered to generate the returns that investors want.
Sulistiowati (2020) states that
there is a role for DER as a ratio to calculate the capital structure that can
bridge the influence of CR on company value.
Figure
1.
The research model of the relationship
between Profitability and Liquidity on Company Value with Capital Structure as
a moderating variable
Description:
Y : Dependent variable company value
X1: Independent variable net profit value
X2: Independent variable current ratio
Z : Modal structure moderating variable
H : Path and path coefficient
Hipothesis
Based
on the explanation of the framework and research model above, the researcher
proposes several hypotheses as quick answers to the formulation of the research
problem as follows:
H1: Net Profit 333rt5 Margin (NPM) positively affects company
value.
H2: Current Ratio (CR) positively affects company
value.
H3: Capital structure can strengthen the influence of
Net Profit Margin (NPM) on Company Value.
H4: Capital structure can strengthen the effect of the
Current Ratio (CR) on Company Value.
METHOD
The
scope of research
In
this study, the researcher wanted to know the relationship between
Profitability and liquidity as independent variables in influencing company
value as the dependent variable, also using capital structure as a moderating
variable. This study includes companies in the pharmaceutical sub-sector that
have been listed on the IDX for the 2015-2019 period.
Population
and Sample
The
population in this study is the annual financial statements of pharmaceutical
sub-sector companies from 2015 to 2019 that have gone public and are listed on
the Indonesia Stock Exchange (IDX). The five years is used to see the
consistency of the influence of the independent variable on the dependent
variable.
This
study uses a purposive sampling method, selecting a sample group or subject
with specific criteria. The sample in this study also used the following
criteria:
a) Pharmaceutical sub-sector companies listed on the Indonesia Stock Exchange
from 2015 � 2019.
b) Companies that issue audited financial statements as of December 31 in
2015-2019.
c) The company has the 2015-2019 financial data needed during the research.
Data
Types and Sources
The
data was taken from secondary data. Secondary data comes from second-hand
sources that were already available before the research was conducted. Sources
of data are taken based on each company's website, the sample of the study, and
the official website of the Indonesia Stock Exchange to obtain financial
reports and the necessary financial data.
Data
collection technique
The
method used in this research is documentation, namely by collecting, recording,
reviewing and processing secondary data in financial reports and financial data
of pharmaceutical sub-sector companies listed on the Indonesia Stock Exchange
through the official IDX website.
Research variable
Dependent Variable (Company Value)
The
dependent variable used is company value in this study. Company value in this
study is proxied by Price to Book Value (PBV). Namely, the ratio can describe
the performance of stock prices with book value. PBV can be calculated by the
following formula:
![]()
The formula can
calculate the book value (book value) of shares:
![]()
Independent Variable
Independent
variables are considered to be able to influence the dependent variable. The
independent variables in this study are:
a.
Profitability
According
to the company's ability to generate profits and
measure the level of management effectiveness can be measured by the
profitability ratio. One of them are NPM and ROA.
NPM
is a calculation or ratio showing the percentage of net profit from sales (Kasmir, 2016). The formula for calculating NPM is:
![]()
(Source:
Kasmir,
2016)
b.
Liquidity
Liquidity is the level of a company's ability to meet its
current obligations (Kasmir, 2016). One of the liquidity ratios is the Current Ratio (CR). CR is a ratio that
describes the ratio of current assets to current liabilities.
This ratio can reflect the level of current assets in
covering current liabilities to increase the company's value. The formula for
calculating CR is as follows (Kasmir, 2016):
![]()
Kasmir
(2016) explains that if the resulting CR
value is more than 200%, it can be said that the company is liquid. If less
than 200%, the company can be illiquid (not liquid).
Moderating Variable (Capital Structure)
Moderating
variables can strengthen or weaken the relationship between the independent and
dependent variables (Sugiyono, 2013). In this study, the moderating
variable used is capital structure.
The
capital structure in this study is proxied by the Debt to Equity Ratio (DER). Since
2016, the safe scale of the DER ratio has been set to 4:1. Kasmir
(2016) states
the formula for calculating DER is as follows:
![]()
�� Variable Operational
Table 2
Variable Operational
|
No
|
Variable
|
Definition
|
Indicator
|
Scale
|
|
1 |
Company Value (PBV) (Y) |
Price to Book Value
(PBV) is a ratio that can describe the comparison of stock prices in the
market with their book value (Sutrisno, 2014:12) |
|
Ratio |
|
2 |
Profitabilitas (NPM) (X₁) |
Net Profit Margin is a ratio that reflects the ability of the company
to generate profits in certain sales (Kasmir, 2016) |
� |
Ratio |
|
3 |
Likuiditas (CR) (X₃) |
The current ratio is
a ratio that can show the company's ability to meet maturing obligations (Kasmir, 2016) |
� |
Ratio |
Data
Analysis Methodology
This
study tested the regression with moderating variables that tested the
interaction. In (Ghozali, 2018), testing with moderating variables can use Moderated Regression Analysis
(MRA), a particular application of multiple linear regression that contains an
element of interaction, namely multiplication between independent variables. In
this study, data management uses SPSS 25. Operation in this program uses
descriptive menus and simple dialogue boxes but still with a relatively high
level of statistical analysis (Ghozali, 2016).
Descriptive
Statistical Analysis
The
descriptive statistical analysis aims to emphasize the characteristics of each
variable without comparing it with other variables.
1. Classical Assumption Test
Before performing the linear regression test, it is
necessary to test the classical assumptions first. This test is carried out so
that the data used is free from bias and is by the classical assumption
criteria.
2. Normality Test
The normality test determines whether the residual variable
has a standard data distribution (Ghozali, 2018).
Decision making in the Kolmogorov-Smirnov test is based on:
a) If the result of the Kolmogorov-Smirnov test is below 0.05, then the
distribution pattern can be declared abnormal, then the regression model does
not meet the assumption of normality.
b) If the result of the Kolmogorov-Smirnov test is above 0.05, then the
distribution pattern is regular and meets the assumption of normality.
3. Multicollinearity Test
Multicollinearity test aims to determine whether there is
a correlation between the independent variables in the regression model (Ghozali, 2018).
It can be measured from the tolerance and VIF (variance
inflation factor) values to determine multicollinearity.
4. Heteroscedasticity Test
The heteroscedasticity test aims to test whether there is
an inequality of residual variance from one observation to another in a
regression model (Ghozali, 2018).
If there is an equation of variance, it is called homoscedasticity. Moreover, if vice versa, it is called
heteroscedasticity. A good research model has results that do not
contain heteroscedasticity (Ghozali, 2018).
5. Autocorrelation
Test
The autocorrelation test is used to
determine whether there is a correlation between consecutive observations over
time that are related to each other (Ghozali, 2018).
Autocorrelation
test can be known by using the Durbin Watson test (DW Test).
Table 3
Autocorrelation retrieval
|
Zero Hypothesis |
Decision |
iF |
|
There is no positive autocorrelation |
Reject |
0 < d
< dl |
|
There is no positive autocorrelation |
No
decision |
dl ≤
d ≤ du |
|
There is no negative autocorrelation |
Reject |
4 � dl
< d < 4 |
|
There is no negative autocorrelation |
No
decision |
4 � du
≤ d ≤ 4 � dl |
|
There is
no autocorrelation, positive or negative |
No reject |
du < d
< 4 - du |
����������������������� ��� (Ghozali, 2018).
Hypothesis testing
1. Partial Test (t Test)
A
partial test determines how significant the independent variable is to explain
the dependent variable.
2. �Simultaneous Significance Test (F Test)
The
F test is used to determine the effect of the independent variables (Net Profit
Margin and Current Ratio) together on the dependent variable (Price to Book
Value).
3. Coefficient of Determination Test R�
Coefficient
of determination test is used to test how far the variation of the dependent
variable is explained (Ghozali, 2018). The
coefficient value of this test is between 0 (zero) and 1 (one). If the
resulting coefficient value is small, it indicates that the independent
variable's ability to explain the variation of the dependent variable is
limited.
4. Moderated Regression Analysis
The
regression analysis method using Moderated Regression Analysis (MRA) can test
the effect of moderator variables. To test the hypothesis, the formula that can
be used is:
Model I
Regression Equation:
Y �= α + β1X1 + β2X2 + e
Model II
Regression Equation:
Y
= α + β1X1 + β2X2 + β3Z + β4X1*Z + β5X2*Z + e
Description:
Y = Dependent
variable (PBV)
α = Konstanta
β1 � β5 = Koefisien regresi
X1��� = Net
Profir Margin (NPM)
X2��� = Current
Ratio (CR)
β4X1*Z
= NPM interaction with DER
β5X2*Z
= Interaction of CR with DER
Z���� = Capital Structure (Debt to Equity Ratio)
e ��� = Standard error (error term)
��������������
Types of moderating
variables (Ghozali, 2018):
1.
Homologizer Moderator
This
variable means that in moderating the independent variable (X), the moderating
variable does not interact and is significantly related to either the
independent variable (X) or the dependent variable (Y).
2.
Quasi Moderators
Quasi
moderating variable is related to the variables Y and X. It is related to
variable X. This variable can modify the relationship between the independent
variable (X) and the dependent variable (Y).
3.
Pure Moderators
Pure
variable interacts with the independent variable (X) but is not related to the
dependent variable (Y) or the independent variable (X).
RESULTS AND
DISCUSSION
The object of this research is pharmaceutical sub-sector
companies listed on the Indonesia Stock Exchange (IDX) during 2015-2019.
because the 2015-2035 National Industrial Development Master Plan states the
pharmaceutical and pharmaceutical ingredients industry is one of the mainstay
manufacturing sectors that get development priority because it plays a
significant role as the main driver of the national economy.
The Industrial Information Booklet published by Pusdatin
in 2017 concluded that the pharmaceutical sector is one of the main sectors
that investors aim to encourage growth through investment.
Based on this information, the pharmaceutical industry
has an essential role in investing in the Indonesian economy, so investors need
to know the development of the value and performance of companies in the
pharmaceutical industry. The final sample of this study was nine companies in 5
years of observation, so the total number of samples was 45 observations. Table
4 presents the selection process.
Table 4
Sample Selection Procedure
|
Sample
Selection Criteria |
Amount |
Pharmaceutical sub-sector companies listed on the Indonesia Stock
Exchange from 2015 � 2019.
|
10 |
Companies publish audited financial reports and annual reports from
December 31, 2015-2019.
|
0 |
|
The company has the 2015-2019 financial data needed
during the research. |
(1) |
|
Amount |
9 |
The results
of purposive sampling show that the final sample used in the study is nine
companies per year.
Table 5
Sample of Pharmaceutical Sub-Sector
Companies
|
No |
Company
Code |
Company
name |
|
1 |
DVLA |
PT.
Darya-Varia Laboratoria Tbk |
|
2 |
INAF |
PT.
Indofarma Tbk |
|
3 |
KLBF |
PT.
Kalbe Farma Tbk |
|
4 |
KAEF |
PT.
Kimia Farma Tbk |
|
5 |
MERK |
PT.
Merck Tbk |
|
6 |
PYFA |
PT.
Pyridam Farma Tbk |
|
7 |
SCPI |
PT.
Merck Sharp Dohme Pharma Tbk |
|
8 |
SIDO |
PT.
Industri Jamu dan Farmasi Sido Muncul Tbk |
|
9 |
TSPC |
PT.
Tempo Scan Pacific Tbk |
Source:
analyzed secondary data (2021)
In
this study, the variables used include the Company Value (PBV), Net Profit
Margin (NPM), Current Ratio (CR), and Debt.
Table 6
�Description
of Research Variables
|
Descriptive Statistics |
|||||
|
|
N |
Minimum |
Maximum |
Mean |
Std. Deviation |
|
NPM |
45 |
-2,84 |
190,20 |
12,3051 |
27,90708 |
|
CR |
45 |
1,00 |
9,28 |
3,1624 |
1,88314 |
|
DER |
45 |
7,61 |
494,65 |
81,7878 |
90,33037 |
|
PBV |
45 |
,17 |
38,90 |
5,0662 |
8,03655 |
|
Valid N (listwise) |
45 |
|
|
|
|
Source: Output SPSS 25
Classical Assumption
Test
Classical assumption
testing concompanys that the resulting regression equation has estimation
accuracy, is consistent and does not produce bias.
Normality Test
The normality test is used to determine whether the
residuals in the regression model have a normal
distribution. In this study, the One-Sample Kolmogorov Smirnov was used. The
results of the normality test in this study can be seen in table 7.
Table 7
Mode Normality Test Results I
|
One-Sample Kolmogorov-Smirnov Test |
||
|
|
Unstandardized Residual |
|
|
N |
45 |
|
|
Normal Parameters,b |
Mean |
,0000000 |
|
Std. Deviation |
7,85986623 |
|
|
Most Extreme Differences |
Absolute |
,220 |
|
Positive |
,220 |
|
|
Negative |
-,156 |
|
|
Test Statistic |
,220 |
|
|
Asymp. Sig. (2-tailed) |
,000c |
|
|
a. Test distribution is Normal. |
||
|
b. Calculated from data. |
||
|
c. Lilliefors Significance Correction. |
||
��������
Based
on table 7 the normality test results show that the distribution of the data
used in this study is not normal because of the value of Sig. (2-tailed) is
0.000 < 0.05. It can also be seen from observing the histogram image and the
resulting normal P-Plot:
The histogram image of the normality test above shows the
data spread that is far skewed to the left and is not normal. Meanwhile, the
P-Plot shows points far from the diagonal line in the regular line drawing.
Thus, by observing the results of the two images above, it can be concluded
that the regression model used violates the normality test rules.
The regression results in this research model
show five outlier data above the whisker called extreme data (Junaidi, 2015). So
that the final observations are 40 observations. The results of the Kolmogorov
Smirnov One-Sample test after the outliers are:
Table 8
Normality Test Results after Outlier
Model I
|
One-Sample Kolmogorov-Smirnov Test |
||
|
|
Unstandardized Residual |
|
|
N |
40 |
|
|
Normal Parameters,b |
Mean |
,0000000 |
|
Std. Deviation |
1,65778065 |
|
|
Most Extreme Differences |
Absolute |
,115 |
|
Positive |
,115 |
|
|
Negative |
-,086 |
|
|
Test Statistic |
,115 |
|
|
Asymp. Sig. (2-tailed) |
,196c |
|
|
a. Test distribution is
Normal. |
||
|
b. Calculated from data. |
||
|
c. Lilliefors
Significance Correction. |
||
Tabel 9
Normality
Test Results after Outlier Model II
|
One-Sample Kolmogorov-Smirnov Test |
||
|
|
Unstandardized
Residual |
|
|
N |
40 |
|
|
Normal Parameters,b |
Mean |
,0000000 |
|
Std. Deviation |
,68058538 |
|
|
Most Extreme Differences |
Absolute |
,088 |
|
Positive |
,088 |
|
|
Negative |
-,068 |
|
|
Test Statistic |
,088 |
|
|
Asymp. Sig. (2-tailed) |
,200c,d |
|
|
a. Test distribution is Normal. |
||
|
b. Calculated from data. |
||
|
c. Lilliefors Significance Correction. |
||
|
d. This is a lower bound of the true
significance. |
||
Source:
Output SPSS 25
Based on Tables 8 and 9,
the normality test results using the one-sample Kolmogorov-Smirnov test show
the Asym.Sig value. (2-tailed) of 0.196. And 0.200. This shows that the data
are distributed generally because the significance level is
above 0.05. In addition to the significance value, the normality test can also
be seen by observing the standard P-Plot image presented in the figure 4.

Figure
4. Normal P-Plot
Sumber: Output SPSS 25
Based on Figure 4, it can be observed that the plotting points spread
around the diagonal line. Thus, the Normal P-Plot Figure shows a typical
distribution pattern.
Multicollinearity Test
The multicollinearity test was used to determine whether there was a
correlation between the independent variables. The following is the
multicollinearity test used in this research variable:
Table 10
Multicollinearity Test Results
|
Coefficients |
||||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
Collinearity Statistics |
|||
|
B |
Std. Error |
Beta |
Tolerance |
VIF |
||||
|
1 |
(Constant) |
,908 |
,856 |
|
1,061 |
,296 |
|
|
|
NPM |
,309 |
,063 |
,796 |
4,920 |
,000 |
,478 |
2,090 |
|
|
CR |
-,148 |
,211 |
-,112 |
-,703 |
,487 |
,498 |
2,009 |
|
|
DER |
-,001 |
,005 |
-,035 |
-,261 |
,796 |
,708 |
1,412 |
|
|
a. Dependent Variable: PBV |
||||||||
Source: Output SPSS 25
It can be
seen in Table 10 that the results of the multicollinearity test of the
variables in this study can be concluded that there is no multicollinearity
between the variables in the regression model in this study.
Heteroscedasticity
Test
The heteroscedasticity test aims to determine whether
there is an inequality of variance from the residual of one observation to another
observation in the regression model. The
results of the heteroscedasticity test from this study are:
Table 11
�Heteroscedasticity Test Results
|
Coefficients |
||||||
|
Model |
Unstandardized
Coefficients |
Standardized
Coefficients |
t |
Sig. |
||
|
B |
Std.
Error |
Beta |
||||
|
1 |
(Constant) |
,564 |
,594 |
|
,948 |
,349 |
|
LnX1 |
,079 |
,087 |
,183 |
,905 |
,372 |
|
|
LnX2 |
-,431 |
,206 |
-,483 |
-2,091 |
,044 |
|
|
LnZ |
,100 |
,111 |
,192 |
,900 |
,374 |
|
|
a.
Dependent Variable: ABS_RES3 |
||||||
Source: Output SPSS 25
The results
of the heteroscedasticity test in Table 11 show no heteroscedasticity or
relationship between the independent variables and the absolute value of the
residuals in the regression model.
Autocorrelation
Test
The autocorrelation test aims to test whether there is a
correlation between the confounding error in period t and the confounding error
in period t-1 (previous) in the linear regression model (Ghozali, 2018). The
following are the results of the autocorrelation test in this study:
Table 12
Model I . Autocorrelation Test
Results
|
Model Summary |
|||||
|
Model |
R |
R Square |
Adjusted
R Square |
Std. The
error of the Estimate |
Durbin-Watson |
|
1 |
,741a |
,549 |
,511 |
1,72547 |
,679 |
|
a. Predictors: (Constant), DER, CR, NPM |
|||||
|
b. Dependent Variable: PBV |
|||||
Source: Output SPSS 25
Based
on Table 12, the results of the autocorrelation test using Durbin Watson are
0.679 with a dl value of 1.3832 and a du value of 1.6662, where the decision making
value (d) is in the 0 < d < dl position, which means that it is included
in the
null hypothesis has no positive autocorrelation with the rejected decision. So,
the conclusion that can be drawn is that H0 is rejected because there are
symptoms of autocorrelation. However, to overcome this, this study carried out
a transformation to overcome the symptoms of autocorrelation. The selection of data transformations in this study is based on
whether or not the autocorrelation coefficient (p) or Rho is known.
Table 13
Model I. Autocorrelation Test
Results
|
Model Summary |
|||||
|
9 |
R |
R Square |
Adjusted R Square |
Std. The error of the Estimate |
Durbin-Watson |
|
1 |
,768a |
,590 |
,555 |
1,30098 |
2,068 |
|
a.
Predictors: (Constant), Lag_Z, Lag_X1, Lag_X2 |
|||||
|
b.
Dependent Variable: Lag_Y |
|||||
Source: Output SPSS 25
Based on Tables 12 and 13, the results of the
autocorrelation test using the Cochrane Orcutt method, it can be concluded that
there is no autocorrelation in the regression model.�
Hypothesis Test
1. Simultaneous Test (F Test)
A
simultaneous test (F test) was conducted to determine whether all the
independent variables used in the study had a simultaneous or joint effect on
the dependent variable. The
results of the
simultaneous test on the regression model I can be seen in table 14.
Table
14
Simultaneous
Test Results (Model I)
|
ANOVA |
|||||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
||||
|
1 |
Regression |
130,249 |
2 |
65,124 |
22,439 |
,000b |
|||
|
Residual |
107,384 |
37 |
2,902 |
|
|
||||
|
Total |
237,633 |
39 |
|
|
|
||||
|
a. Dependent Variable:
PBV |
|||||||||
|
b. Predictors:
(Constant), CR, NPM |
|||||||||
Source: Output
SPSS 25
�� Based on Table 14,
it can be seen that the result of the calculated F value is 22.439 > F table
3.24. The resulting significance value is 0.000 < 0.05. So it can be
concluded that the independent variables NPM and CR simultaneously affect the
dependent variable Price to book value (PBV). These results indicate that
regression model I is in good condition. The simultaneous test (F test) on the
regression model II is as follows:
Table 15
Simultaneous Test Results (Model
II)
|
ANOVA |
||||||
|
Model |
Sum of
Squares |
df |
Mean
Square |
F |
Sig. |
|
|
1 |
Regression |
138,259 |
5 |
27,652 |
9,461 |
,000b |
|
Residual |
99,374 |
34 |
2,923 |
|
|
|
|
Total |
237,633 |
39 |
|
|
|
|
|
a. Dependent Variable: PBV |
||||||
|
b. Predictors: (Constant), MRA2, CR, MRA1, NPM,
DER |
||||||
Source:
Output SPSS 25
�������� Based on Table 15, the
calculated F value is 9.461 > from F table 2.63. The resulting significance
value is 0.000 < 0.05. This indicates that the simultaneous test on the
regression model II is acceptable, which means that all independent variables
NPM, CR, MRA1 (moderation) and MRA2 (moderation) have a simultaneous or joint
effect on the dependent variable Price to book value (PBV).
2.
Coefficient
of Determination (R�)
The coefficient of
determination (R�) was carried out to determine how far the regression model
could explain the dependent variable in the study. The results of the
coefficient of determination in this study are as follows:
Table 16
Coefficient of Determination Test Results
(Model I)
|
Model Summary |
||||
|
Model |
R |
R Square |
Adjusted
R Square |
Std. The
error of the Estimate |
|
1 |
,740a |
,548 |
,524 |
1,70361 |
|
a. Predictors: (Constant), CR, NPM |
||||
Source: Output SPSS 25
In table 16, it can be seen that the value of Adjusted R� in
regression model 1 of this study is 0.524 or 52.4%. The independent variable
used in this study can explain the effect of 52.4% on the dependent variable.
In comparison, the remaining 47.6% is explained by other variables not examined
in this regression model. Meanwhile, the coefficient of determination test
performed on the regression model II can be seen in table 17.
Table 17
Coefficient of Determination Test
Results (Model II)
|
Model Summary |
||||
|
Model |
R |
R Square |
Adjusted
R Square |
Std. The
error of the Estimate |
|
1 |
,763a |
,582 |
,520 |
1,70961 |
|
a. Predictors: (Constant), MRA2, CR, MRA1,
NPM, DER |
||||
Source: Output SPSS 25
Based on Table 17, the
adjusted R square value is 0.522 or equal to 52%, which means that the
independent variable in regression model II can explain the dependent variable
as much as 52%. The remaining 48% is not explained in this research model.
3.
Multiple Regression Analysis
Based on the classical
assumption test results that have been done previously, it can be concluded
that the data in this study shows a normal distribution and does not occur heteroscedasticity,
multicollinearity, and autocorrelation.
In addition, the regression model used can also be said to
be a good regression model because it has passed the simultaneous test and the
coefficient of determination.
Multiple regression
analysis was used to determine the magnitude of the regression coefficient
values in the research model and the resulting significance value
to become the basis for testing the research hypothesis. Research model I is
the result of the equation of net profit margin (NPM) and current ratio (CR) as
the independent variable with Price to book value (PBV) as the dependent
variable. Table 4. 16 shows the results of the regression analysis carried out
on model I:
Table 18
Results of Regression Analysis (Model I)
|
Coefficients |
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
,741 |
,560 |
|
1,323 |
,194 |
|
NPM |
,313 |
,059 |
,808 |
5,270 |
,000 |
|
|
CR |
-,137 |
,204 |
-,103 |
-,672 |
,506 |
|
|
a. Dependent Variable:
PBV |
||||||
Source: Output
SPSS 25
Model I:
PBV = 0,741 + 0,313
NPM - 0,137 CR
H1:
net profit margin (NPM) has a positive effect on company value (PBV)
Based on
table 18, the regression analysis results in the model I above, it can be seen
that NPM, which is an indicator of Profitability, has a significant positive
effect on company value (PBV).
The
regression equation results show that the constant coefficient value is 0.741,
which can be interpreted if the Profitability (NPM) is constant, then the company
value (PBV) becomes 0.741. Furthermore, the profitability regression
coefficient (NPM) of 0.313 indicates that when Profitability (NPM) increases by
one unit, the company value (PBV) will increase by 0.313 units. The higher the
Profitability generated, the higher the company's value generated by the
company. This means that Profitability has a positive effect on company value.
So it can be concluded that Ho is rejected and Ha is accepted.
H2:
Liquidity (CR) has a positive effect on company value (PBV)
The second
hypothesis assumes a positive effect of liquidity calculated by the Current
Ratio (CR) on the company value calculated by Price to book value (PBV). Based
on the results of the regression analysis model I in table 4.16, it can be seen
that the resulting regression coefficient value is -0.137 with a significance
value of 0.506 greater than 0.05.���� It
can be concluded that liquidity (CR) has an opposite or negative effect on company
value (PBV). The resulting significance value is 0.506, more significant than
the 5% or 0.05 degree of confidence. This indicates that the independent
variable liquidity (CR) has no significant effect on the dependent variable of company
value (PBV). Thus, in this study, Ha rejected Ho acceptance.
Furthermore, multiple regression analysis was performed on regression
model II. This model is an equation form of the independent variables
profitability (NPM) and liquidity (CR), the dependent variable company value
(PBV) and the interaction of the independent variable with the moderating
variable capital structure (DER). The results of the moderated regression
analysis (MRA) test in regression model II can be seen as follows:
Table 19
Moderated Regression Analysis (MRA) Test
Results Equation II
|
Coefficients |
||||||
|
Model |
Unstandardized
Coefficients |
Standardized
Coefficients |
t |
Sig. |
||
|
B |
Std.
Error |
Beta |
||||
|
1 |
(Constant) |
,692 |
,873 |
|
,793 |
,434 |
|
NPM |
,292 |
,093 |
,753 |
3,154 |
,003 |
|
|
CR |
,028 |
,268 |
,021 |
,103 |
,919 |
|
|
DER |
,009 |
,010 |
,230 |
,932 |
,358 |
|
|
MRA1 |
,000 |
,002 |
-,040 |
-,162 |
,873 |
|
|
MRA2 |
-,005 |
,004 |
-,276 |
-1,313 |
,198 |
|
|
a. Dependent Variable: PBV |
||||||
Source: Output SPSS 25
Y = 0,692 + 0,292 NPM + 0,028 CR + 0,009 DER + 0,000 MRA1 - 0,005 MRA2
Based on the results of the moderated regression analysis (MRA) test, the
significance value of the capital structure variable (DER) was 0.358 > 0.05.
A variable is moderators homologized when it is insignificant for creators or
predictors and insignificant when interacting with them (Ghozali, 2018).
Discussion
1. Effect
of Profitability (NPM) on Company Value (PBV)
The first hypothesis proposed assumes a positive
influence of Profitability on company value. So, it can be concluded that the
first hypothesis in this research is accepted. The results of the SPSS test on
the first hypothesis explain that the higher the Profitability of the company,
the higher the value of the resulting company.
By looking at the value of NPM, information held by
outsiders will increase, and according to good information, signal theory can
be a signal that influences decisions. So, when Profitability increases, it
will also increase the level of confidence and interest to invest so that
investor perceptions will be reflected well. That way, the demand for shares
will increase, and the company's value will also increase.
Profitability or the company's ability to earn a
profit is valuable information for investors to determine the company's
financial condition and prospects in the future. So, if the profit generated
increases, investor confidence in the company will also increase. The results
of this study are in line with research conducted by (Santoso, 2018), (Harahap, Septiani, & Endri,
2020)
and (Hardiyanto, 2020),
which states that Profitability is calculated by net profit margin (NPM) has a positive
effect on company value.
2. Effect
of Liquidity (CR) on Company Value (PBV)
The results showed that the resulting liquidity
regression coefficient (CR) was -0.137, which means that when liquidity (CR)
increases by one unit, the company value will decrease by -0.137.
The results of the SPSS test on the second hypothesis
can be concluded that liquidity has a negative direction on company value.
In the pharmaceutical sub-sector listed on the IDX
based on descriptive statistical analysis results, the mean or average
liquidity is 3.16. A good liquidity standard is 2:1 or 200% (Kasmir, 2016).
It is believed that companies in this sub-sector are assessed with good
liquidity. There is no doubt from external parties regarding the liquidity
capacity of the pharmaceutical sub-sector.
The results of this study are in line with the results
of research conducted by Husna et al. (2019) and Lumentut
et al. (2019) which concluded that liquidity calculated by the
Current Ratio (CR) does not affect company value.
3. Effect
of Capital Structure in Moderating Effect of Profitability (NPM) on Company
Value (PBV)
The moderated regression analysis (MRA) test results
prove that the capital structure cannot significantly strengthen the influence
of Profitability on company value. It can be concluded that in the
pharmaceutical sub-sector, the capital structure cannot be used as a moderating
variable for Profitability in its influence on company value. This is because
when the company has a high level of Profitability, it will reduce its
dependence on capital from outside parties. After all, the company will use
internal funding or retained earnings for its operational activities.
4. Effect
of Capital Structure in Moderating Effect of Liquidity (CR) on Company Value
(PBV)
The results of the moderated regression analysis (MRA)
test prove that the capital structure cannot moderate the effect of liquidity
on company value. The results of this study are not in line with the results of
Sulistiowati's research (2020), which concludes that the capital structure acts
as a pure moderator in the influence of liquidity on company value (Sulistiowat,
2020).
CONCLUSION
Profitability
calculated by net profit margin significantly affects the company value
calculated by Price to book value. Liquidity calculated by
the Current Ratio has no significant effect on the company value calculated by
Price to book value. Capital structure calculated by debt
to equity ratio cannot moderate the effect of Profitability calculated by net
profit margin on the company value calculated by Price to book value .
Capital structure calculated by debt to equity ratio
cannot moderate the effect of Profitability calculated by current Ratio on the company value calculated by Price to book value.
Research
limitations
One
company has just been listed on the IDX from 2018, so it becomes a deduction
from the sample used.This study uses two independent variables and one
moderating variable with two regression models that have adjusted R square
values of 52.4% and 52%, respectively. The independent and
moderating variables used in this study were only able to explain the influence
of 52.4% and 52% on the dependent variable. In contrast, the rest was explained
by other variables not examined in this study.
Suggestions
For
the company, the company should pay more attention to the factors that affect
the company's value, such as Profitability and maintaining the stability of the
profits obtained, to convince investors about the company's prospects and
performance.
For
investors, before deciding which companies are believed to be worthy of
investment, investors need to pay attention to several things, such as
Profitability, which can describe the company's financial condition.
For
further researchers, it is better to add the research sector considering that
companies' Profitability, liquidity, and capital structure have different
conditions so that it is possible to produce different research results. In addition,
it is recommended to add several research variables that can more strongly
influence the company's value.
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�