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REACTION OF THE TOP 10 CAPITALIZATION SHARES IN THE
2020 PILPRES EVENT
Rahma Nur Praptiwi, Tri Widjatmaka
Politeknik Negeri Jakarta
Received:April
10th, 2021
Revised: April
19th, 2021
Approved:April
17th, 2021
Abstract
This research is a study that aims to see whether there is empirical
evidence of the reaction of the Indonesian capital market to
international political events, namely the 2020 US Presidential
Election using abnormal return indicators and trading volume
activity. The research methodology used is event research. This
study uses primary data taken from the first party and researchers
get data directly from a questionnaire given to all employees of IT
companies in the Special Region of Yogyakarta. The population in
this study are stocks that are included in the top 10 capitalization
companies in Indonesia. The sampling technique in this study used
convinience sampling. The data collection technique in this study
used a questionnaire equipped with an answer level as the choice
of respondents to answer questions. The data analysis technique
used multiple linear regression and the application used SPSS
version 22. The results of the paired sample t-test statistical
calculation both abnormal return and trading volume activity
showed that there was no difference in the average abnormal
return and trading volume activity before and after the event.
Keywords: Capital Market, Event Study, Abnormal Return,
Trading Volume Activities
This work is licensed under a Creative Commons
Attribution-ShareAlike 4.0 International
INTRODUCTION
One of the most attractive investments for investors is stocks. Because stocks
have a high-profit opportunity, but they also have a high potential risk. Stocks allow
investors to get multiple benefits, namely in the form of capital gains (the difference
between the selling price and the buying price) and dividends. However, shares of going
public issuers which are capital goods are very sensitive to changes, both at home and
abroad.
In an economic and non-economic environment, there are various events on the
stock exchange in which information and data are contained, so that information and data
cannot be separated from the stock exchange. According to Suryawijaya and Setiawan as
referred to in (Wardhani, 2012), the increasingly important role of the stock market in
economic activity, makes the stock market more sensitive to events in its surroundings,
whether related or not directly related to economic events.
The capacity of the company or company, changes to company plans, publication
of financial reports or dividends are influenced by economic conditions, and always get a
response from market players in the capital market. Moreover, various government
regulations and economic deregulation also affect changes in price and transaction
volume on the stock exchange (Gunanta, 2012). The impact of non-economic conditions,
although not directly related to changes in the stock market, cannot be free from stock
trading activities (Diniar & Kiryanto, 2016). Non-economic conditions, for example,
Rahma Nur Praptiwi, Tri Widjatmaka
Reaksi 10 saham kapitalisasi teratas pada peristiwa Pilpres AS tahun 2020 195
various problems related to ecological issues, human rights, and political conditions, are
often the main factors that trigger changes in stock prices on global stock exchanges.
Political issues are part of non-economic conditions and will affect the state of
the capital market. Because changes in the political situation will have an improved or
even worsening impact on economic stability in which investors want to trade in the stock
market. Political stability and stable economic conditions will create a sense of security
for investors to invest their funds in the stock market. In this way, investors usually have
high expectations of political events, and high expectations will be reflected in changes in
stock prices or movements in stock exchange volume in stock trading.
The United States is a superpower that is the mecca of many countries. The
election of the President of the United States (Pilpres) of the United States of America in
2020 determines the climate around the world. The US Presidential Election which was
held on November 3, 2020, was the 59th US Presidential Election. The presidential
candidates for the Presidential Election are Joe Biden-Kamala Harris and Donald Trump-
Mike Pence.
This study has the objective of whether there is a significant difference before
and after the US presidential election on abnormal returns and whether the trading
volume activity between before and after the US presidential election is significantly
different.
According to (Sudarsono, 2014) return is the result of the acquisition of
investment activities. There are two differences in returns, namely the return that is
realized and the return that is expected or expected (investors want this return). Stock
return refers to the value of profits that investors feel when investing. According to
(Hermuningsih, 2018) return is the result of investing in shares, profits or losses can also
be negative or positive. If it is positive, it means that you can gain capital or get realized
results, while negative means that you have suffered a loss or capital loss.
According to Jogiyanto (2009) referred to in (Hutami, 2015) abnormal return is
the difference between the actual return and the return expected by investors. The
difference in return will be positive if the return obtained is greater than the expected
return or the return that is calculated.
According to Jogiyanto (2009) in (FIrga, 2012), there are three estimation models
of expected return, namely:
a. Mean-adjusted model.
The model assumes that the expected return is constant, with the model the
average return realized before the estimated period (estimation period). Using the average
through adjustment model, the expected return computes it by dividing the firm's actual
realized return over the estimated period by the duration rather than the estimated period.
Where the estimated period is the period the event or event occurred (event period) or the
event window (event window).
b. Market model.
The market treatment model takes two stages, namely: (1) an expectation model
that is formed by using the realization during the estimation period (2) the use of the
expectation model to estimate the expected return in the event window. The expectation
model calculates the amount of the expected return that is not affected by market shifts,
market index gains, and expected returns that are not affected by market shifts. How to
calculate the expected return using the market model is calculated using the beta. Beta on
the IDX (Indonesia Stock Exchange). It has been proven to be biased due to thin trading
because the IDX is a market where trading transactions rarely occur.
c. Market adjusted model.
The market-adjusted model assumes that the best predictor for estimating the
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Reaksi 10 saham kapitalisasi teratas pada peristiwa Pilpres AS tahun 2020 195
index return in a particular event period, and the expected return for all securities in that
particular event period is the same.
To get a return or rate of return, investors must pay attention to the potential risks
that will be faced if they want to get a certain return. Risk is the difference that may occur
between the expected return and the actual return received. The more likely the
difference, the greater the investment risk. Risk consists of various reasons, including
interest rate risk, market risk, inflation risk, liquidity risk, business risk, and exchange
rate risk, financial risk. Types of risk are divided into two categories, namely general
risks (risks related to market changes as a whole) and specific risks experienced by
companies, namely risks not related to market changes (Nurhaeni, 2009).
Tandelilin (2001) in (Desiyanti, 2011) defines abnormal return as an increase in
the rate of return or rate of return from investors' acceptance in one trading session,
generally, in stock trading, the return on profits occurs only once, due to achieving the
highest profit value. only get once to then decrease slowly and then stabilize again.
This research is relevant to research conducted by (Praptiwi, 2017) entitled
Reactions of the Top 10 Capitalization Shares Against the 2014 Ri Presidential Election.
This research is an event study that aims to determine whether there is empirical evidence
of the reaction of the Indonesian capital market to one of the events. politics in the
country, namely the 2014 Presidential Election, using indicators of abnormal return and
trading volume.
The difference with previous studies only lies in the variables that affect it,
namely this study uses the issue of the 2020 US Presidential Election, which is a more
global scale. This research is important to do so that investors and other interested parties
know the effect of the 2020 US Presidential Election on the reaction of the capital market
in Indonesia.
RESEARCH METHOD
The research methodology applied is event research. According to (Suganda,
2020), the capital market event study is an empirical study used to analyze the effects of
stock exchange events in countries. Another meaning, the study is applied to determine
the response of the capital market to various events. In capital market research,
particularly for market efficiency testing, event study is a semi-strong form of market
efficiency test methodology.
This study uses primary data taken from the first party and the researchers get the
data directly from the questionnaire given to all employees of IT companies in the Special
Region of Yogyakarta. The object of this research is organizational culture and
procedural justice for organizational citizenship behavior (OCB). The independent
variable is organizational culture (X1) and procedural justice (X2). While the dependent /
dependent variable in this study was organizational citizenship behavior (Y). The
population in this study were all employees of IT companies in the Special Region of
Yogyakarta. The sample selection is by using the purposive sampling method, which
means that those who fit the criteria as desired will be used as research samples.
The sampling technique in this study used convinience sampling. The data
collection technique in this study used a questionnaire that was equipped with an answer
level as the choice of respondents to answer questions. The data analysis technique used
multiple linear regression and SPSS version 22 as the application used.
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RESULTS AND DISCUSSION
A. Results of Linear Regression Testing
Table 2
Summary of Regression Test Results for Companies with Top Capitalization Returns
with IHSG Returns
No
Company Name
Regression Equation
R
Coefficient of
Determination
1.
BBRI
Y=1.619x + €
0.807
0.651249
2.
BMRI
Y=0.002+1.580x + €
0.809
0.654481
3.
BBCA
Y=0.001+1.172x + €
0.779
0.606841
4.
BBNI
Y=0.001+1.613x + €
0.823
0.677329
5.
ASII
Y=1.310x + €
0.665
0.442225
6.
BBTN
Y=1.792x + €
0.735
0.540225
7.
BNGA
Y=0.001+1.476x + €
0.685
0.469225
8.
TLKM
Y=0.002+1.402x + €
0.702
0.492804
9.
PNBN
Y=1.052x + €
0.6
0.36
10.
BDMN
Y=0.001+1.443x + €
0.75
0.5625
Based on table 2, of the top 10 capitalized companies, six of them have positive α
values, namely BMRI, BBCA, BBNI, BNGA, TLKM, and BDMN, meaning that if the
independent variable (IHSG) is considered constant, the company return value is positive.
Four companies, namely BBRI, ASII, BBTN, and PNBN have an α value of zero, which
means or if the independent variable (IHSG) is considered constant, the company return
value is zero.
The beta value or beta coefficient is used to estimate the occurrence of systematic
risk. According to Scheneller (1975) referred to in (Paramitasari, 2011), systematic risk is
a risk that cannot be avoided through diversification or commonly called market risk or
(market risk) which is the cause due to factors affecting the entire market or economy.
Beta of 1 means that every 1% increase / decrease in profits of the market makes an
increase / decrease in stock profits by 1%. Thus, the greater the beta, the more sensitive
the security profits are to the turnover of market profits and the more risky the stock is.
Based on the regression results, the order of companies with the highest to lowest beta are
BBTN (1,792), BBRI (1,619), BBNI (1,613), BMRI (1,580), BNGA (1,476), BDMN
(1,443), TLKM (1,402), ASII (1,310 ), BBCA (1,172), and PNBN (1,052).
According to Gozali (2009) referred to in (Paramitasari & Mulyono, 2016) the
coefficient of determination (R2) measures how far the model's ability to explain
variations in the dependent variable. The coefficient of determination ranges between
zero (0) and one (1). A small R2 value means that the ability of the independent variable
(free) to interpret the variation in the dependent variable is so limited. Value close to one
(1) means that the independent variables show almost all the information or data needed
to predict the variation in the dependent variable. The coefficient of determination ranges
from 0.36 to 0.6773.
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Table 3
Average abnormal return (AAR) and standard deviation
N
Minimum
Maximum
Average
Standard
Deviation
Variation
AAR
before
5
-0.0353
0.054214
0.015413
0.026763
0.00
AAR
after
5
-0.00703
0.017437
0.004576
0.008911
0.00
According to table 3, the average abnormal return (AAR) after the event has
decreased. Before the event, the average abnormal return was 0.015413, the standard
deviation was 0.026, while after the event the standard deviation was 0.004576, which
was 0.0089. The standard deviation has decreased quite a lot, which is equal to -0.0178. it
can be shown that the abnormal return condition after the event is more homogeneous
than before the event.
Table 4
Average abnormal return before and after the event
t
Average abnormal return
5
0.001184
4
0.006186
3
0.007475
2
0.006329
1
0.001944
-1
0.009857
-2
-0.00642
-3
0.000875
-4
-0.00212
-5
0.00305
During the event period, the average abnormal return based on the calculation
results (five days after and five days before the event) shows that most of them are
positive for the average abnormal return. The highest average abnormal return value
occurs on the first day before the incident, which is 0.001944, while the lowest average
abnormal return value on the second day after the event is -0.006425. There is a
difference in the average abnormal return (AAR) value in the observation period before
and after the incident, indicating that market players responded to the events of the US
Presidential Election.
Trading volume activity (TVA) is a tool used to show the response of the stock
market to information or data by measuring the mobility parameters of the movement of
buying and selling shares in the market. The calculated TVA describes the ratio between
the total shares traded and the total shares running during a certain period of time.
Table 5
Average trading volume activity
N
Minimum
Maximum
Mean
Std.
Deviation
TVA_SBLM
5
0.253132
0.276543
0.265667
0.011113
TVA_STLH
5
0.177196
0.438251
0.28511
0.10787
According to table 5, the average TVA after the incident has increased. The
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average TVA before the event was 0.265667, which is a standard deviation of 0.011113,
while the average TVA after the event was 0.28511 with a standard deviation of 0.10787.
The standard deviation has increased quite a lot, which is equal to 0.0967. This shows
that the condition after the event was more varied than before the event, namely the stock
sale.
Table 6
Average trading volume activity during the observation period
T
Rata-rata Trading Volume Activity
5
0.177196
4
0.252966
3
0.35043
2
0.438251
1
0.206709
-1
0.276543
-2
0.254706
-3
0.253132
-4
0.275045
-5
0.268911
The difference in the average TVA value at the time observed before and after
the event shows that the situation and conditions before and after the event have enough
information to smooth market response. (Mahaputra & Purbawangsa, 2015) in their
research said that an event or a condition that is created can be said to be information if it
is able to change or be taken into consideration by market players.
B. Hypothesis Testing
1. Hypothesis Testing I
The first hypothesis in this study is that there is an influence between 10 stocks
with top capitalization and the Composite Stock Price Index (IHSG) during the US
Presidential Election.
2. Hypothesis Testing II
The second hypothesis in this study is that there is a difference in the average
abnormal return obtained by investors before and after the US Presidential Election.
3. Hypothesis Testing III
The third hypothesis in this study is that there is a difference in the average
trading volume obtained by investors before and after the US Presidential Election.
Hypothesis Testing I
The first hypothesis is that there is a difference in the average abnormal return
obtained by investors before and after the US Presidential Election. According to
(Hidayat, 2019), abnormal returns occur due to certain events, for example national
holidays, the beginning of the month, an uncertain political atmosphere, extraordinary
events, stock splits, initial public offerings, and others.
Hypothesis testing will be carried out, so before that it must be known in advance
the data from the variables, namely the abnormal return before and after the event is
normally distributed or not. This normality test will use the Z-test formula from
Kolmogorov Smirnov, with the following rules:
1. If sig≤ 0.05, then the data are not normally distributed
2. If sig> 0.05, then the data is normally distributed.
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Table 7
Normality Test of Variable Data Abnormal return
AR Before
AR After
Score Z
0.495
0.670
Sig
0.967
0.761
Source: processed data (2021)
Table 7 shows the Z value or Z-value of each abnormal return. Based on table 7,
it can be concluded:
1. The Z value for the abnormal return variable before the event is 0.495 with a
significance of 0.967. The sig value> 0.05, then the conclusion is that the
abnormal return data before the event is normally distributed
2. The Z value for the abnormal return variable after the event is 0.670 with a
significance of 0.761. because the sig value> 0.05, the conclusion is that the
abnormal return data after the event is normally distributed.
Based on the Z-test with Kolmogorov Smirnov, it is concluded that the two
variables have a normal data distribution, so that the analysis can continue into a
parametric statistical analysis.
Tabel 8
Paired sample t-test Variabel Abnormal return
Mean
St. Deviation
t
df
Sig.(2-tailed)
0.009985
0.02
1.639
9
0.136
Source: processed data (2021)
Based on table 8, the Paired sample t-test shows a t value of 1.639 with a
significance of 0.136. Because sig> 0.05, it can be concluded that there is no difference in
the average abnormal return obtained by investors between before and after the US
Presidential Election at the 95% confidence level. The rules that apply in the paired
sample t-test are:
1. If the t value> t table, then H0 is rejected or is in the area of acceptance for H1
(there is a significant difference in the average abnormal return between before
and after the US Presidential Election.)
2. If the t value <t table, then H0 is accepted or is in the H1 rejection area (there is
no significant difference in the average abnormal return between before and after
the US Presidential Election)
The findings of this study are in accordance with the research (Hidayat, 2019)
namely that there is no difference in the average abnormal return before and after the
event. Market response related to general election events in terms of stock abnormal
returns tends to be momentary and not prolonged.
Hypothesis Testing II
The second hypothesis is that there is a difference in the average TVA or trading
volume obtained by investors before and after the US Presidential Election. Hypothesis
testing will be carried out, so before that, the data from the trading volume activity
variables before and after the event must be normally distributed or not. This normality
test will use the Z-test formula from Kolmogorov Smirnov, with the following rules:
1. If sig≤ 0.05, then the data are not normally distributed
2. If sig> 0.05, then the data is normally distributed.
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Table 9
Normality Test of Stock Trading Volume Variable Data
TVA Before
TVA After
Score Z
0.486
0.532
Sig
0.972
0.940
Source: processed data (2021)
Table 9 above shows the Z value of each share trading volume, namely:
1. The Z value for the variable stock trading volume before the US Presidential
Election is 0.486 with a significance of 0.972. The value of sig> 0.05, then the
conclusion is that the stock trading volume data before the event is normally
distributed.
2. The Z value for the variable stock trading volume after the US Presidential
Election is 0.532 with a significance of 0.940. The value of sig> 0.05, then the
conclusion is the stock trading volume data after the event is normally
distributed.
Table 10
Paired Sample t Test Variable Stock Trading Volume
Mean
St. Deviasi
t
df
Sig.(2-tailed)
-0.01944
0.11393
-0.540
9
0.603
Source: processed data (2021)
The paired sample t test above shows the t value of -0.540 with a significance of
0.603. The significance value is 0.603> 0.05, so the conclusion is that there is no
difference in the average stock trading volume activity before and after the US
Presidential Election at the 95% confidence level.
C. Discussion
Figure 1 presents the fluctuations in the abnormal return that occurred when an
event occurred, namely at intervals of five days before and five days after the event. In
general, before the event, the average abnormal return increases until the third day and
decreases until the first day before, while the average abnormal return after the event
generally fluctuates up and down.
Figure 1 Average Abnormal return Around the US Presidential Election
-0.008
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
0.008
0.01
0.012
1 2 3 4 5
AAR Sebelum AAR Setelah
Rahma Nur Praptiwi, Tri Widjatmaka
Reaksi 10 saham kapitalisasi teratas pada peristiwa Pilpres AS tahun 2020 201
Investing funds in the capital market carries a risk that is closely related to stock
price fluctuations, and price fluctuations are influenced by various information or data.
Information with good news means the stock price will go up, and information with bad
news will decrease the stock price (Nurhaeni, 2009).
According to the paired sample t-test, it shows the t value of 1.639 with a
significance of 0.136. Because sig> 0.05, it can be concluded that there is no difference in
the average abnormal return obtained by investors between before and after the US
Presidential Election at the 95% confidence level. This is due to the actions of players on
the stock exchanges who tend to prepare speculations that are considered favorable for
them on the days around the events. Not showing the relevant difference between the
average abnormal returns before and after the US presidential election proves that
although the events contain information, investors' reactions did not cause turmoil for the
capital market.
Figure 2 Average Abnormal Return by Company
Based on Figure 2, the general conclusion is before the event and, the company
gets a positive abnormal return, and after that, the abnormal event is negative. Before the
incident, PNBN had a negative tendency, while after the ASII incident it tended to be
negative.
Figure 3 Average Trading volume activity (TVA) Around the US Presidential Election
Figure 3 illustrates the fluctuations in TVA that occurred during the event period,
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
BBRI BMRI BBCA BBNI ASII BBTN BNGA TLKM PNBN BDMN
Seblum Setelah
0
0.1
0.2
0.3
0.4
0.5
1 2 3 4 5
setelah sebelum
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namely the five days before and five days after the event. There was a fairly stable
condition in the days before the event, and there was a downtrend after the fourth day
before the event. Selling action tends to color trading activities as a strategy for market
players to secure their portfolios. From the paired sample t test, the t test above shows the
t value of -0.540 with a significance of 0.603. The significance value is 0.603> 0.05, so
the conclusion is that there is no difference in the average stock trading volume before
and after the US Presidential Election at the 95% confidence level.
Figure 4 Average Trading Volume Activity by Company
Based on the picture above, the conclusion is that before or after the event there
is no change in average trading volume activity. Telkom has the highest average trading
volume activity. Figure 4 shows that there is no change in average trading volume activity
on a large scale.
CONCLUSION
Based on the research results, it can be concluded that 1). From the paired sample
t test, it can be seen that there is no difference in the average abnormal return obtained by
investors between before and after the US Presidential Election at the 95% confidence
level. The existence of an abnormal return indicates that the market has not been effective
in a semi-strong form if the information that is fully reflected in the price is public
information and market information. 2). From the paired sample t test, it can be seen that
there is no significant difference in the average trading volume before and after the US
Presidential Election. This is due to the actions of stock exchange players who tend to
prepare speculations that are considered favorable for them on the days around the events.
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