The Influence of Operational Expenses, Dividend Policy and Tax
Expenses on Stock Returns
Laoren
Agustin Goinones
Faculty of Economics, Pamulang
University, South Tangerang, Indonesia
This study intends to
identify the influence of dividend policy
operational expenses along
with tax
burdens on stock
returns. This research
is a
quantitative type of
research carried out
through analyzing the
financial statements of
companies in the
Energy sector listed
on the Indonesia Stock
Exchange (IDX) throughout
the period from 2019
to 2022.
The number
of samples
used in
this study
is 68
samples from 17
energy companies listed
on the Indonesia Stock
Exchange throughout the period from
2019 to
2022 through
the use of the
purposive sampling technique.
The data
used in
this study
is secondary
data in
the form
of financial
statements from each
company that has
become a research
sample. The variables
selected in this
study are
Operating Expense (X1)
as the first independent
variable, Dividend Policy
(X2) as
the second
independent variable, along
with Tax
Expense (X3) as
the third
independent variable as
well as
Return on Shares
(Y) as
the bound
variable. In this
hypothesis test, multiple regression analysis
and panel
data assisted
through the EViews 12 software
program were used. The
results of this
study show
that the
best model
is the
Common Effect Model
(CEM). The results
of this
study show
that Partial
Operating Expenses (t-test)
have an effect on
Stock Returns,
Dividend Policy partially
(t-test) has no effect
on Stock
Returns and Tax Expenses
partially (t-test) have
no effect
on Stock
Returns. Meanwhile, simultaneously
(test F)
Operating Expenses, Dividend Policy
and Tax
Expenses have an
effect on Stock Returns.
The contribution
of the
research to the variables
of Operating Expenses, Dividend
Policy and Tax
Expenses on Stock Return
was 9.6%,
while the
remaining 90.4% was explained
by other
variables outside the research
model.
Keywords: Operational
Expenses; Dividend Policy; Tax expense; Stock returns.
INTRODUCTION
Tax which
is one of the many significant aspects in the world of business and corporate
finance. Tax is
an obligation that must be fulfilled by companies as
a form of contribution to the state. However, Taxes
are also considered a company expense that can affect their net profit
significantly.
From a business perspective, companies will try to reduce the tax burden as much as possible in order to
maximize their net profit
This
happens because the large tax burden can reduce
net profit which ultimately affects returns
share. Companies have a responsibility to make tax payments, in
accordance with applicable tax regulations, and this can be a significant
factor in formulating their financial strategy. A high tax burden can reduce the potential profits that can be
distributed to shareholders as dividends, which in turn can affect returns share
Company which
is listed on the Indonesian Stock Exchange, has a target to provide returns s competitive
shares to their shareholders. Return
Shares are an important measure of financial performance that reflects
how well a company generates profits for its shareholders. In the energy company sector,
it is very popular with the public because market demand for mining
products is still relatively high. Not only that,
mining companies are one of the pillars
of a country's economic development, because of their
contribution as providers of mining resources that are really needed for
a country's
economic growth.
The
development of energy or mining companies from the past
until now,
it has become a favorite in a number of regions in Indonesia and is one
of the pillars of national economic development. Indonesia is a country with abundant natural
resources, automatically many investors are looking to invest their
capital in this sector because they expect high
Return share
is one
of the many key factors that investors consider when making their investment. These
companies must implement smart financial policies, including efficient
management of operational expenses,
decision making regarding appropriate dividend
policies, and wise tax strategies in order
to achieve returns. s shares
expected by shareholders.
Its development investment in the capital
market is increasingly attractive for investors to participate in investing in
a company. Investors allocate their funds through purchasing shares in
companies with the intention of increasing profits in the form of expected returns
Case study on the financial performance of PT Adaro
Energy Indonesia Tbk decreased during the first
semester of 2023. Stone mining issuer this result resulted in the recording
of a net profit attributable to the owners of the parent entity amounting to
US$ 873.83 million and this
realization was reduced by 27.9% from ADRO's net profit in the same period last
year which reached US$ 1.21 billion. As a result, ADRO's profit per share was
reduced to US$ 0.02823 from the previous 0.03900. This
decrease in net profit corresponds to the decrease in ADRO's revenue, where
ADRO recorded revenue of US$ 3.47 billion during the first semester of 2023.
This figure is reduced by 2% when compared to revenue in the same period in
2022 which reached US$ 3.54 billion.
A number
of factors caused ADRO's performance to decline. First, in the midst of an
increase in production volume and sales, the average selling price of stone bara, aka average selling price (ASP),
decreased by up to 18%. Second,
the increase in the number of burdens borne by ADRO. Like the cost of revenue
which rose 34% year-on-year (yoy) to US$ 2.03 billion. This increase was mainly due to the royalty costs of
ADRO's subsidiary, namely Adaro Indonesia (AI), which increased compared to the
same period last year. Royalties
and
dividends to the
Government increased 67% from US$ 511 million to US$ 853 million, while the
income tax burden decreased 65% to US$ 244 million from US$ 696 million (https://investasi.kontan.co.id).
Competitive
business environment,
on energy sector
need to understand how these factors interact
and influence each other. They also need to consider how various policies and
decisions in terms of operational expenses, dividend policy and tax expense management will affect their competitiveness
in the market and
return s shares
they can offer to their shareholders. All these elements create a complex and
dynamic phenomenon.
Energy companies
as entities operating in the mineral and mining sector also face various
challenges and considerations that influence their stock returns. One important aspect that
influences stock returns is the
company's operational expenses. Companies need to monitor their business development and
understand their operational expenses from time to time. Operating expenses are
costs incurred to operate the company's daily activities which include various daily costs such as
employee salaries, transportation and rental costs, which can affect the
company's overall profit.
Revenue
is also an important factor in managing a company's operational expenses.
Dividend
policy also plays a crucial role, namely as a strategic consideration for the
company when distributing
profits to shareholders
or reinvesting these profits in one period.
Tax
Burden is also an important factor,
Recently,
energy companies and other
companies have become increasingly interested in wanting their tax burden to be
as low as possible, as a result, many methods
have been taken to reduce the tax burden to a minimum, and some even use
various methods that should be prohibited by tax regulations (Safitri & Safii, 2022). One of the many
strategies that companies can choose
to minimize their tax burden is usually managing taxable income and deductible expenses. Taxable income
can be managed by evaluating a company's sources of income. All of these factors, namely operating
expenses, dividend policy, and tax burden, are interrelated and influence returns company stock.
Based
on previous research presented in Table 2.1, there are several researchers who
have investigated the influence of certain variables on stock returns in
different industrial contexts. For example, Arifin et
al. (2023) investigate the effect of Tax Burden on Stock Prices in the basic
industry and chemicals sectors, while Ningsih and
Maharani (2022) focus on the Effect of Dividend Policy on Stock Returns in the
Consumer Goods Industry sector. Apart from that, several other studies such as
those conducted by Saputri (2022) and Widianingsih et al. (2021) also shows the influence of
variables such as Financial Performance and Operational Expenses on company
value and share prices. Although the research objects are different, the
current research leads to further understanding of the influence of Operational
Expenses, Dividend Policy, and Tax Expenses on Stock Returns in the context of
Energy companies.
This
research aims to investigate the influence of Operational Expenses, Dividend
Policy, and Tax Expenses on Stock Returns in energy companies. The problem
formulation includes the question of whether these factors individually or
collectively influence stock returns. The research objectives include finding
the influence of each factor on stock returns, as well as the theoretical
benefits for writers, universities and further research. Practically, it is
hoped that this research can help companies improve effectiveness in
operational management, dividend policy and tax understanding, as well as
provide valuable information for investors in evaluation and investment
decisions.
RESEARCH
METHODS
The research method utilized in this study
adopts a quantitative approach, involving systematic and scientific observation
encompassing all aspects closely related to the research object, phenomena, and
correlations. Initially, hypotheses were formulated, followed by hypothesis
testing, data measurement, and generalized conclusions. The study was conducted
at the Indonesia Stock Exchange (IDX) located in Tower 1, 6th floor, Jl. Jend. Sudirman
Kav 52-53, South Jakarta 12190, Indonesia. Secondary
data from financial reports of energy sector companies listed on the IDX from
2019 to 2022 were obtained from www.idx.co.id. The research period spanned from
proposal planning to research report duplication. The operational research
variables encompassed everything determined by the researcher for study purposes,
leading to conclusions (Sugiyono, 2019).
HASIL PENELITIAN DAN PEMBAHASAN
A.
Hasil
Penelitian
The hypothesis in this study was tested using
a
multiple regression model intended
for use get the whole
picture related the
impact of the independent variables,
namely operational expenses, dividend policy and tax burden on the dependent variable,
namely stock returns.
1. Descriptive
statistics
Descriptive
statistics are used to provide an overview of the presentation of data obtained
from the dependent variable and independent variables observed through the average
value, median, maximum value, standard deviation, and minimum value of data.
The dependent variable used in this research is stock returns, while the independent variables are operational expenses, dividend policy and tax burden. In
this discussion A descriptive
statistical analysis test was carried out on Energy sector companies which were recorded on the BEI
in 20 19 -20
22.
Table 1.
Descriptive Statistics Test Results
Source: Data processed by Eviews 12, (2024).
Results
from table 1, on A total of 68 observation data was obtained which came from
multiplying the 4 year research period, namely from 2019-2022, through a
sample size of 17 companies. It was
possible to conclude that:
a.
�Research has a range between -0.765000 to 6.778000.
The lowest share return value
was owned by PT Indo Tambangraya Tbk
in 2019 and the highest value was owned by PT Bayan Resources in 2022 with an
average of 0.373912 and a standard deviation of 1.11967. A standard deviation
that is greater than the average value indicates a large spread of data
variables or the discovery of a relatively large gap between the lowest and
highest stock return percentages.�
b.
"The
results of the analysis through
the use of descriptive
statistics on variable.807191 and a standard deviation of 0.166128.�
c.
"The
results of the analysis through
the use of descriptive
statistics on variable standard deviation 166569.7.�
d.
"The
results of the analysis through
the use of descriptive
statistics on variable 229971 and a standard deviation of 0.090461.�
2. Regression Models and Panel Data
Panel data regression can be carried out through three analysis models namely FEM,
CEM, along with REM. Each model has its
own advantages and disadvantages. The choice of
model depends on the assumptions that the
researcher uses
and the fulfillment of the
requirements for correct statistical data processing, so that the results can be accounted for
statistically. The following is the application of the three
regression models applied in this research:
3. Common Effect Model ( CEM)
CEM method It is assumed that
there will be no differences in intercept values as well as The slope of the regression results is
either based on differences between
individuals or between times. Parameter
estimation method in CEM using
the Ordinary Least Square (OLS) method. The results of
panel data regression with CEM are
presented in the following table:
Table 2. Common Effect Model Panel Data Regression Results
Source: Data processed by Eviews 12, (2024).
4. Fixed Effect Model
Panel data regression estimation method on FEM using the Least Square Dummy Variable
(LSDV) technique
or adding dummy variables. Panel data regression results via The Fixed Effect Model is presented in the following table:
Table 3. Fixed Effect Model Panel Data
Regression Results
Source: Data processed by Eviews 12, (2024).
5. Random Effect Model
BRAKES accommodated past error. The panel data
regression estimation method in REM uses Generalized
Least Square (GLS). Here are output of panel data via BRAKES:
Table 4. Random Effect Model Panel Data Regression Results
Source: Data processed by Eviews 12, (2024).
6. Selection of Panel Data Regression Models
The panel data regression
model is divided into three, namely Common Effect Model (CEM), Fixed Effect Model (FEM), and Random
Effect Model (REM). In
order to determine what model will
be used in this research, as a
result need a number of tests were carried out, namely the Hausman Test,
Chow Test, and Langrange Multiplier Test (LM Test).
a. Test Chow
Test Chow used
to decide which approach is better among the Common Effect Model models through Fixed Effects Model. The following are the output results from the chow test.
Table 5. Chow Test Results
Source:
Data processed by Eviews 12, (2024).
The
results in table 5 can be
obtained that the probability value (Prob) is 0.7345 > 0.05.
As a result,
H0 is accepted, meaning
that the selected model is the model and CEM. Also
continued
by the Langrange
multiplier test.
b. Lagrange
Multiplier (LM) Test
The Lagrange multiplier (LM) test was
carried out to determine the
best model among
CEMs and REM.
The Lagrange multiplier test was
carried out because when testing chow,
the model chosen was CEM.
The selection of this model is
carried out through observing
the probability (prob) value in Breusch-Pagan. The
following are the output results from the LM test.
Table 6. Lagrange Multiplier Test Results
Data source processed by Eviews 12, (2024).
The
results
in table 6 show that
the only
probability value (prob.) in Breusch -Pagan is 0.2675 > 0.05. As a result, H0 is
accepted, meaning the
model is correct, namely
CEM.
c.
Results of Panel Data Regression Model Selection
The results of the panel data regression model test
above are shown below comparing the Chow and Lagrange Multiplier tests, the
conclusion is drawn that the final
model selected and used is the Common Effect Model (CEM).
7. Classic
assumption test
The
classical assumption test is used to test whether the regression model used in
this research is suitable for use or not. The classical assumption tests used
are the Normality Test, Multicollinearity Test, Heteroscedasticity Test,
Multicollinearity Test, and Autocorrelation Test. The results of the classical assumption test in
this research are:
a. Normality test
The
Normality Test is used to test the level of normality of the dependent variable
and the independent variable. A good regression model is a regression model
that has normal or close to normal data distribution. In this study, the normality test was
carried out by comparing the Jarque-Bera (JB) values with
the Chi Square table values.
Ho:
"The residual value is normally distributed"
Ha:
"The residual value is not normally distributed"
The
basis for decision making is carried out through observing probability numbers, through the following provisions:
1)
�Probability
Sig. > 0.05, then H 0 is accepted. So, the residual value is
normally distributed."
2)
�Probability
Sig. < 0.05, then Ho is rejected. So, the residual value is not normally distributed."
Figure 1. Table of Normality Test Results
Source: Data processed by Eviews12, (2024).
The normality test output
results show that the
value is only p value namely 0.147670 > 0.05. As
a result, it can be stated that
the regression model in
this study has
a normal distribution.
b. Heteroscedasticity Test
The
heteroscedasticity test is
a test
that evaluates whether
unequal variances of the residuals are found for all observations in the
linear regression model. This test was carried out to find out whether
deviations were found from
the requirements of classical assumptions in linear regression, where the
regression model needs to fulfill the condition that heterodasticity is not found. If the significance value
(Sig.) is > 0.05, the result is that there are no symptoms of
heteroscedasticity.
Table 7. Heteroscedasticity Test Results
Source: Data processed by Eviews 12, (2024).
Result of table 7 above, the value of Prob. Chi-Square
worth 0.1555 > 0.05. As
a result it
can be concluded that this research did not experience heteroscedasticity
issues.
c. Multicollinearity Test
The
Multicollinearity Test is intended to find out the existence of a relationship between independent variables in
a regression model or being
able to is also considered
useful find
out if the regression
model exists the existence of a relationship between independent variables.
Multicollinearity can be identified from the correlation coefficient value.
If the correlation coefficient value between each
independent variable exceeds 0.80 it
can be concluded
that multicollinearity is occurring whereas if it is
below 0.80 it
can be concluded that
multicollinearity is not
occurring.
Table 8. Table of Multicollinearity Test
Results
Source: Data processed by Eviews 12, (2024).
Results
from table 8 It can be
observed that the
correlation coefficient value for each independent variable is less than 0.80. So it can be concluded that the data tested did not have
multicollinearity or passed the multicollinearity test.
d. Autocorrelation Test
The
autocorrelation test is used to find out whether or not there are deviations
from the classic assumption of autocorrelation, namely the correlation that
occurs between the residuals in one observation and other observations in the
regression model. Frequent testing
methods selected
according to Danang Sunyoto
(2016:97), namely through the Durbin-Watson test
(DW test) through the
provisions:
1)
�Positive autocorrelation occurs if the DW value is below -2
(DW < -2).�
2)
�There is no autocorrelation if the DW value is between -2
and +2 or -2 < DW +2.�
3)
"
Negative autocorrelation occurs,
if DW is above +2 or DW > +2 "
Table 9. Table
of Autocorrelation Test Results
Source: Data processed by Eviews 12, (2024).
Results
table 9 above it can be seen
that the average Durbin
Watson (DW) value is 1.766227
where the DW value is between
-2 and +2
or < -2 DW < +2 as a
result It can be concluded that there is no autocorrelation found or is free from autocorrelation issues.
8. Hypothesis
testing
a. Multiple Linear Regression Analysis
Regression analysis is
used to measure the strength of
the relationship between two or more variables, showing also the direction of the relationship between
the dependent variable and
the independent variable (Ghozali, 2018:21). This analysis is
used to find out the effect of
one dependent variable, namely stock returns,
on three independent variables, namely operational expenses, dividend policy and tax burden.
This analysis is mathematically written using the following equation:
�Y = a + β1X1 + β2X2
+ β3X3 + e�
Table 10. Multiple Regression Test Results�
Source: Data processed by Eviews 12, (2024).
Results in
table 10 this means that a multiple regression equation can be formulated, including:
� Y= 2.447732 + 2.470420 X1 + 2.376378 X2 +
0.405866 X3 + e �
Information:
Y �� :
Stock Returns �������������������������� X1:
Operating expenses
X1 � :
Dividend Policy ������������������������ X3:
Tax Burden
E �� :
Standard error
The results of the regression equation in
table 10 can
be elaborated as follows:
1)
The constant regression coefficient is
worth 2.447732
means that if the operational
expense, dividend policy and tax burden variables are assessed as constant
(value 0), then the average stock return
rate is equal to 2.447732.
2)
The constant regression coefficient is
worth 2.470420
means If
the operational load variable
increases by one unit while the other independent variables remain
constant, the
result is that the level of
operational load will increase worth 2.470420. Vice versa, if operational expenses decrease while
other variables are constant, then operational expenses will increase by 2.470420.
3)
The constant regression coefficient is
worth 2.376378
means if
the dividend policy variable
increases by one unit while the other independent variables remain
constant, the
result is level dividend policy will
later increase worth 2.376378. Conversely, if the dividend policy decreases while
other variables are constant, the dividend policy will
increase in value 2.376378.
4)
The constant regression coefficient is
worth 0.405866
means that if the tax burden
variable increases by one unit while the other independent variables remain
constant, the result is that the
level of the tax burden will decrease by 0.405866. Vice versa, if the tax burden decreases while other
variables remain constant, the result
is that the dividend
policy will increase by -0.405866.
b. Determination (R2)
The coefficient of determination
is intended to see how much influence the independent variable has on the
dependent variable, partially using the
coefficient of determination. The coefficient of determination is the square of
the correlation coefficient as a measure to find out capability of each variable
used. The following is the coefficient of determination table:
Table 11. Coefficient of Determination Test
Results
Source: Data processed by Eviews 12, (2024).
Results t able 11 above, the Adjusted R square
coefficient of determination value obtained is 0.096 or 9.6 %. This shows that the percentage
influence of the independent variables (operational expenses, dividend policy and tax burden)
on the dependent variable (share returns)
is 9.6 %.
So that the variations in the independent variables used in the research model (operational expenses, dividend policy and tax burden) are
able to explain 9.6 %
of the variations in the dependent variable (stock returns) while the remaining 90.4 % is explained or influenced by other variables
outside the research model.
c. Partial Test (t Test)
This
test can be carried out by comparing the t count and t table or by observing the significant
column in each t count. From the significance value of the output of eviews 12, if the Sig. < 0.05 means
that the independent variable has a substantial
impact on the dependent variable, while if the Sig. > 0.05 as a result the
independent variable does not have a substantial
impact on the dependent variable. Following are
the results of the t test used in table 12.
Table 12.
Table of Partial Test Results (t Test)
Source: Data processed by Eviews 12, (2024).
The
results of the t test above compare the t table with the calculated
t and look
at the probability value of each independent
variable, the result is
concluded if:
1) Partial Test ( t Test ) on the Operational Expense variable
The results of this test
are to find the t table value by means of sample size (n) = 68; number of variables
(k) = 4; significant level α = 0.05, the result is df = nk = 68-4 = 64, the
result is t table worth 1,669. Based on table 12. It is known
that the Operational
Expense has a calculated
value of t 3,084 > t table worth
1,669 and a prob value of
0.0 030 <
0.05 so that H1 is accepted, which means that operational expenses have
an effect on stock returns.
2) Partial Test ( t Test ) on the Dividend Policy variable
The results of this test are to find the t table value by means of sample size (n) = 68; number of variables
(k) = 4; significant level α = 0.05, the result is df = nk = 68-4 = 64, the
result is t table worth
1,669. Based
on table 12. It is known that Dividend
Policy has a value of t calculated
0.300 > t table worth 1,669 and a prob value of 0.7644 > 0.05, as a result, H2 is rejected, meaning
that capital structure has no substantial
effect on stock returns.
3) Partial Test (t Test) on the Tax Burden variable
The results of this test are to find the t table value by means of sample size (n) = 68; number of variables
(k) = 4; significant level α = 0.05, the result is df = nk = 68-4 = 64, the
result is obtained
t table worth 1,669. Based on table 12. It is known
that the Tax Burden
has a calculated value
of t
0.277 < t table worth 1,669 with a value of Prob. A
value of 0.7819 > 0.05
as a result H
3 is
rejected which
indicates if
Tax expense
does not have a significant
impact on stock returns.
4) Simultaneous test (F test)
The
F test is a test to test whether the regression model prepared is good/significant or not
good/non-significant. In this test,
the criteria used are observing the probability value (sig.) if
the significant value is <5% or
0.05 As a result, the regression model is suitable for
use. However, if
the significant value is > 5%
or 0.05, the regression model is not used. Following are the results of the F test used in table 13.
Table 13.
Table of Simultaneous Test Results (F Test)
Source: Data processed by Eviews 12, (2024).
The F test results in table 13 reveal
that the calculated f value is 3.393829 with a probability value of 0.023111. Meanwhile,
to find the f table using the number of samples (n)
= 68; number of variables (k) = 4; significant level α
= 0.05. Then df1 = k-1 = 4-1 = 3 and
df2 = nk
= 68-3 = 65, the f table value is
2.746, as a result, f is
calculated 3.393829 > 2.746 f table and
systematically obtained a
significance value of 0.023111 < 0.05, meaning the regression model is suitable for
use.
Analysis
Results and Discussion
The
results of this test use a multiple regression analysis test, this research is
intended to find out the effect of independent variables, namely operational expenses, dividend policy and
tax burden on Stock Return
as the dependent variable. The
following is an explanation of each variable:
1. The
Effect of Operational Expenses on Stock Returns.
The
first hypothesis (H1) in this research is "The Influence of Operational Expenses on Stock Returns in companies operating in the energy sector listed on the Indonesian Stock
Exchange during 201 9 -
20 22.�
From table 13. The significant level for the Operational Expense variable is
0.0 030. The
significance level < 0.05 means that H1 is accepted and it is stated that Operational Expenses have
an impact on Stock Returns. This
matter reveals that the level of a company's operational expenses as
seen from the value of the company's
profit and loss greatly influences the level of
income or profit received. If operational expenses increase and net profit
decreases, it will have an impact on potential stock returns and
company value and vice versa, so that
the expenses received
depend on the company's profitability. This
research is in accordance with research conducted by Siti
Aisyah Ningrum (2021),
which revealed
that operational
expenses have an effect on stock returns.
2. The
Effect of Dividend Policy on Stock Returns.
The
second hypothesis (H2) in this research is "The influence of dividend policy on Share Returns
in companies operating in the energy sector listed
on the Indonesia Stock Exchange during 201 9 -20 22. � From table 13. The
significant level for the Dividend Policy
variable
is 0.7644. A significance level > 0.05 means that H2 is rejected as a result of the Dividend Policy
does not have an impact on
stock returns. Statistically, the dividend
policy is good such
as stable dividend payments, can attract investors and provide a positive
signal on company performance, thus influencing share prices and can be
measured from stock returns. On the other hand, if a company does not
distribute dividends or distributes dividends
in small
amounts, this will affect investors' perceptions of the company's potential
which will ultimately affect the
value and return of the company's shares. This research is in accordance with research
conducted by I Dewa Made Endiana, Ni Luh Yunita Astuti Purnama Dewi, and
I Putu Edy Arizona (2020). However, this
is contrary to research
conducted by Wiwi Widya Ningsih
and Novera Kristanti
Maharani (2022), which revealed that dividend policy has an impact on stock returns and Dian Nurdiana
(2020) also stated that dividend policy has an effect
on stock returns.
3. The
Effect of Tax Burden on Stock Returns.
The
third hypothesis (H3) in this research is "The influence of tax burden on stock returns in companies operating in the energy sector listed on the Indonesia Stock
Exchange during 201 9 -20
22.�
From table 13. At a significant level, the value obtained is 0.7819 > 0.05 which indicates that H3 is rejected as
a result it is said that
the Tax Burden has no effect on Stock returns.
Statistics show that a higher tax burden can reduce the net profit
available to be distributed to investors, which in turn reduces earnings per
share and is less likely to have a negative impact on stock returns.
Conversely, a lower tax burden will increase the net profit available to
investors, which can have a positive impact on stock returns. This research is
in accordance with research
conducted by Sri Ayem and
Pratiwi Nurasjati (2020), M. Aryo Arifin, Wanda Ari Setiawan Oktariansyah, (2023),
which revealed
that the tax burden
does not have an impact on stock returns.
4. The Influence of Operational Expenses, Dividend Policy
and Tax Expenses on Stock Returns.
Fourth hypothesis (H 4) in this research is "The
influence of operational
expenses, dividend policy and tax burden on Stock returns.
In companies operating in the energy sector listed on the Indonesian Stock
Exchange during 201 9 -
20 22.�
Through table
13. The
significant level obtained is the value of Prob. F-statistic worth
0.023111 <
0.05. As a result, H 4 is
accepted, this shows that Operational
Expenses (X1), Dividend Policy (X2), and Tax Expenses (X3) simultaneously have
a significant
impact on Stock Returns (Y).
Judging from the statistics on Operational
Expenses, Dividend Policy and Tax Expenses,
whether they own it directly or
are unable to
do so increase
the net profit of a company.
Increased operational expenses, unfulfilled dividend policies or high tax
burdens reduce a company's net profit and reduce its value and impact potential
stock returns. On the other hand, if operational expenses are reduced, the
dividend policy is profitable, and the tax burden is low, then net profit and
company value will increase, resulting in higher stock returns.
CONCLUSION
This
research investigates the influence of Operational Expenses, Dividend Policy
and Tax Expenses on Share Returns of Energy companies listed on the IDX during
the 2019-2022 period. The results of the analysis show that Operational
Expenses have a significant positive impact on Stock Returns (p = 0.0030, t =
3.084), while Dividend Policy and Tax Expenses have no significant effect (p =
0.7644, t = 0.300; p = 0.7819, t = 0.277). Simultaneously, these three
variables influence Stock Returns (p = 0.0231, F = 3.393). Research limitations
include the contribution of unexamined variables of 90.4 %, a limited sample
(17 companies), and the lack of complete financial reports from several
companies. Researchers recommend expanding the independent variables, extending
the research period, increasing the sample size, and ensuring the completeness
of financial reports for future studies.
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