The capital market plays an important role in the
global economy as an indicator of economic
stability and as a means of raising funds through
investment. This study aims to examine the
reaction of the Indonesian capital market to the
announcement of the 2024 presidential and vice-
presidential election results released by the General
Elections Commission on March 20, 2024, as well as
the factors influencing this reaction. The research
focuses on companies listed in the LQ45 index on
the Indonesia Stock Exchange (IDX). This study
employs two approaches: event study and
causality. The event study is conducted to
determine whether there is a capital market
reaction by observing abnormal return (AR) and
abnormal trading volume activity (ATVA) within
an 11-day event window, covering 5 days before
the event, the event day itself, and 5 days after the
event. Meanwhile, the causality approach is used to
explore the factors affecting AR and ATVA by
examining the influence of Return on Equity (ROE)
and firm size on the observed AR and ATVA. The
findings indicate that the event did not generate
significant abnormal return (AR) or abnormal
trading volume activity (ATVA) during the 11-day
period. Furthermore, profitability did not have a
significant effect on either AR or ATVA. In contrast,
firm size significantly influenced AR and ATVA,
albeit in different directions. Firm size had a
positive and significant effect on AR, but a negative
and significant effect on ATVA.
Event StudyAbnormal ReturnAbnormal Trading Volume ActivityProfitabilityFirm SizeFile created by JATS EditorJATS Editorissue-created-year2025INTRODUCTION
The Stock Exchange functions as the organizer and provider of
securities trading facilities, as mandated in Article 7 paragraph
(2) of Law No. 8 of 1995. Stocks are one of the most widely
recognized types of securities as investment instruments due to
their accessibility, as reflected in the transparency of stock
prices that are published in real time on online platforms (Gitman
& Zutter, 2015). Stock price fluctuations are influenced by
various circulating information, both fundamental and market
sentiment (Bodie, Kane, & Marcus, 2018). This is in line with
the Efficient Market Hypothesis (EMH), which states that stock
prices always reflect all available information (Fama, 1970).
The EMH is classified into three forms: weak form, semi-strong
form, and strong form. In the weak form, stock prices reflect all
historical information such as past prices and trading volumes,
whereas in the strong form, stock prices reflect all available
information, including private or insider information. This study
focuses on the semi-strong form, where stock prices reflect all
public information, including financial reports, corporate
announcements, economic news, and political events. Fama (1991)
stated that testing the semi-strong form of market efficiency is
conducted through an event study to examine how the market responds
to public information. In this context, the event of interest is the
announcement of the 2024 Indonesian presidential and
vice-presidential election results published by the General
Elections Commission (KPU) on March 20, 2024. Hartono (2022)
emphasized that such public information can influence investors’
expectations regarding economic conditions and national stability,
which are subsequently reflected in stock price fluctuations in the
capital market. If the Indonesia Stock Exchange (IDX) as the
national capital market reacts to the announcement, it is important
to determine whether firms’ profitability and firm size serve as
influencing factors.
The Jakarta Composite Index (JCI) exhibited varying patterns of
movement before and after the announcement of the presidential and
vice- presidential election results in 2009, 2014, 2019, and 2024.
During the 11-day observation window, which included five days
before the announcement, the announcement day, and five days after
the announcement, the JCI increased in 2009 and 2014, whereas in
2019 and 2024, it declined. These differences indicate that the
market’s response to election results is not always consistent
across periods. Basit and Haryono (2021) argued that political
uncertainty can influence investor sentiment and market dynamics,
thereby affecting the JCI.
In line with this phenomenon, various previous studies have
examined capital market reactions to election result announcements.
Luhur (2010) showed that during the 2009 election event on LQ-45
stocks, significant negative abnormal returns occurred on days t−10,
t−5, t−4, t0, and t+7, while significant positive abnormal returns
occurred on days t−10 and t+7. Furthermore, no abnormal trading
volume activity was found during the study period. Feranita and
Hotima (2014) found that during the 2014 election event on LQ-45
stocks, significant negative abnormal returns occurred at t+5.
Sudewa and Sari (2015) revealed that during the 2014 election, no
abnormal returns were observed on LQ-45 stocks. Listyaningsih,
Sariningsih, and Mudrikah (2020) found that during the 2019 election event on Jakarta Islamic Index (JII) stocks,
significant positive abnormal returns occurred on t−10 and t−7. In
addition, significant positive abnormal trading volume activity
occurred on t−10, t−8, t−7, t−6, t−5, t−4, t−3, t−2, t−1, and from
t+1 to t+10. Rohani and Hasti (2020) showed that during the 2019
election, no abnormal returns occurred on LQ-45 stocks. Rahayu
(2020) and Yudiawan and Abundanti (2020) similarly found that no
abnormal returns occurred on IDX80 stocks during the 2019 election
event. Jange (2020) revealed that for banking sector stocks,
significant positive abnormal returns occurred on t−1 and
significant negative abnormal returns on t+2, while no abnormal
trading volume activity was observed. Erwin, Ranidiah, and Mustika
(2021) confirmed that no abnormal returns occurred on LQ-45 stocks
during the 2019 election event. Riyani, Mardiah, and Andriana (2019)
found that no abnormal returns were observed in banking sector
stocks during the 2019 presidential election. Collectively, previous
studies indicate that the Indonesian capital market’s reaction to
presidential election events varies, both in direction and
significance of abnormal returns and abnormal trading volume
activity.
Market reactions are influenced not only by external factors such
as elections but also by internal factors related to firms’
fundamentals (Weston & Brigham, 1993). One of the main
indicators often used to assess a company’s fundamentals is
profitability (Harmono, 2022). Profitability is the measure of a
firm’s ability to generate profit from its business activities
(Sumarto, 2024). In this study, Return on Equity (ROE) is chosen as
the proxy for profitability because it directly reflects the
efficiency of a company in generating profit from shareholders’
invested capital. ROE is considered relevant in measuring market
reactions as it is closely related to investors’ interests in
returns on their equity, and it is one of the most commonly used
profitability indicators in financial analysis (Investopedia, 2024;
Corporate Finance Institute, 2023).
Several studies have examined the effect of profitability on
market reactions. Permana (2017) demonstrated that ROE has a
significantly positive effect on abnormal returns in banking and
insurance sector stocks. Anantha (2017) found that profitability has
a significantly positive effect on cumulative abnormal returns (CAR)
in manufacturing sector stocks. Mujiani, Soraya, and Yuliawati
(2020) showed that profitability has a significantly positive effect
on abnormal returns in mining sector stocks. Meanwhile, Rahayu and
Wardana (2021) found that ROE had no effect on CAR in Jakarta
Islamic Index (JII) stocks. In addition to profitability, firm size
is also an important aspect of fundamentals that may influence
market reactions. This is related to investors’ perception of firms’
stability and resilience (Hery, 2017). Investors tend to be more
confident in investing in large firms as they are perceived to have
more stable and safer long- term prospects (Subroto, 2014). This
confidence is based on the possession of substantial resources,
making large-scale firms more resilient to economic fluctuations and
less prone to bankruptcy. Moreover, if bankruptcy does occur, the
government generally intervenes as a stabilization effort to
mitigate systemic impacts and restore market conditions swiftly and
effectively. To empirically represent firm size, an indicator that
can consistently reflect company scale is needed. In this study, firm size is measured using the natural
logarithm of total assets (ln total assets), as commonly used in
capital market studies to avoid scale bias and normalize data
distribution (Fama & French, 1992).
The effect of firm size on market reactions has also shown mixed
results. Anantha (2017) found that firm size had a significantly
positive effect on CAR in manufacturing sector stocks. Mujiani,
Soraya, and Yuliawati (2020) discovered that firm size had a
significantly negative effect on abnormal returns in mining sector
stocks. Meanwhile, Binfaryanto (2018) reported that firm size had no
effect on CAR in LQ-45 stocks.
Based on the above background, it is necessary to re-examine
market reactions to the announcement of the 2024 Indonesian
presidential and vice- presidential election results released by the
KPU on March 20, 2024. The differences in JCI patterns during
previous elections indicate that the market’s reaction to political
events is not always consistent across periods. Furthermore,
internal factors such as profitability and firm size have been shown
in several studies to influence market responses. Therefore, it is
important to empirically investigate how the election result
announcement, when considering profitability and firm size, affects
abnormal returns and abnormal trading volume activity.
THEORETICAL REVIEWMarket Efficiency
Hartono (2022) stated that in a competitive market, the
equilibrium price of an asset is determined by the available
supply and aggregate demand. This equilibrium price reflects a
consensus among all market participants regarding the asset’s
value based on the available information. When new relevant
information enters the market regarding a particular asset, the
information is used to analyze and interpret the asset’s value.
Consequently, this may lead to a shift to a new equilibrium price,
which will remain in place until new information emerges to change
it again. A market’s reaction to information in reaching a new
equilibrium price is crucial. If the market reacts quickly and
accurately to achieve a new equilibrium price that fully reflects
the available information, such a market condition is referred to
as an efficient market.
Fama (1970) classified market efficiency into three forms of
the Efficient Market Hypothesis (EMH), as follows:
Weak form efficiency. A weak-form efficient market implies
that all past (historical) information has already been
reflected in the current stock price. Therefore, such historical
information (past events, including historical price
developments and trading volumes) can no longer be used to
predict future price changes, as it has already been embedded in
current prices. The implication is that investors cannot predict
future stock market values using historical data.
Semi-strong form efficiency. A semi-strong efficient market
implies that current stock prices reflect historical information
as well as all publicly available information (such as earnings,
dividends, stock split announcements, issuance of new shares,
financial distress, and other
publicly disclosed events that may affect a company’s future
cash flows). In semi-strong efficient markets, abnormal returns
only occur around the time of an announcement as a representation
of the market’s response to that information. A market is
considered semi-strong efficient if the information is absorbed or
responded to rapidly (within one to two trading days around the
announcement). Abnormal returns persisting for longer periods
(more than three trading days) indicate that part of the market
reacts slowly in absorbing or interpreting the information, and
therefore the market is considered inefficient in the semi-strong
form.
Strong form efficiency. A strong-form efficient market
implies that current stock prices reflect historical
information, publicly available information, and also private
(insider) information. In such a market, no investor can earn
abnormal returns.
Fama (1991) refined this classification. Weak-form efficiency
was redefined into a more general classification for testing
return predictability. Under this classification, information
about return patterns (such as the January effect or
day-of-the-week anomalies) cannot be used to obtain abnormal
returns. Meanwhile, semi-strong and strong forms were reformulated
into event studies and private information tests,
respectively.
Event Study
Tandelilin (2017) explained that event study research generally
examines how quickly information entering the market is reflected
in stock prices. The steps for conducting an event study are as
follows:
1. Event Identification
The initial step is to identify events that are deemed to
contain information and are relevant to capital market activities.
Such events must have the potential to influence investors’
expectations, either positively or negatively, toward the returns
of affected companies’ stocks.
2. Determination of Event Date and Event Window
The next step is to determine the event date, i.e., the
official date of the announcement or publication of the event.
Alongside this, an event window is established, covering a certain
number of days before and after the event (e.g., t– 5 to t+5) to
capture possible market reactions. In addition, an estimation
window (e.g., t–120 to t–6) is also determined, which is the
period used to estimate normal returns.
3. Determination of the Normal Return Estimation Model
Brown and Warner (1985) proposed three models for estimating
expected return (E(R_it)):
Mean-adjusted model, which assumes that the expected
return is constant and equal to the average realized return
during the estimation period.
Market model, in which expected returns are calculated in
two stages: first, forming an expectation model using
realized data from the estimation period, and then using it
to estimate returns during the event window. This model is typically derived using
Ordinary Least Squares (OLS) regression.
Market-adjusted model, which assumes that the best
predictor for the expected return of a security is the
return of the market index at that time. In this model, no
estimation period is required because the expected return of
a security is equated with the market index return.
4. Calculation of Abnormal Return (AR)
Abnormal return is calculated as the difference between the
actual return of a security and the expected return based on the
chosen estimation model.
𝐴𝑅 𝑖𝑡 = 𝑅 𝑖𝑡 − 𝐸(𝑅 𝑖𝑡)
Where:
AR_it = abnormal return of security 𝑖 at time t. R_it = actual
return of security 𝑖 at time t.
E(R_it) = expected return on security 𝑖 in period t.
Average Abnormal Return (AAR) Calculation
After obtaining the AR for each stock and each day in the event
window, the average abnormal return across companies is calculated
for each day.
AARt=1N∑i=1NARit
Where:
AAR_t = Average Abnormal Return on day t,
= the average of all abnormal returns for a company on that day.
N = The total number of companies or samples observed in the study.
∑_(i=1)^N = Sigma notation representing the sum from 1 to 2,
= the sum of all abnormal returns for each company.
Calculation of Cumulative Abnormal Return (CAR)
CAR is calculated by summing the AR of a stock over a specific
event window. CAR can reflect the total market reaction to an
event in the short term.
CARi=∑t=t1t2ARit
Where:
CAR_i = Cumulative Abnormal Return for the 𝑖th company,
= the sum of the company's abnormal returns over a given period.
∑_(t=t_1)^(t_2) = Sigma notation representing the sum of abnormal returns from day t_1
= to day t_2, which is the time span during the event window.
t_1 = The first day in the event window.
t_2 = The last day in the event window.
Statistical Significance Test
To assess whether the obtained AR is statistically significant, a t-test is performed by calculating the ratio between the average abnormal return on day t and its estimated standard error.
tstat=AR¯tσˆAR¯t
Where:
t_stat = The t-statistic value used to test significance
= Average abnormal return on day t.
AR ̄_t = Average Abnormal Return of all companies on day t.
σ ̂_(AR ̄_t) = Estimated standard error of the average abnormal return on day t,
= The standard deviation of the AR values relative to the AAR.
Interpretation of Results
The final stage is interpreting the results of the statistical
tests. If a significant abnormal return is found, it can be
concluded that the event under study contains information that
impacts investor behavior. Conversely, insignificance indicates
that the market anticipated the information in advance, or that
the event did not provide sufficient informational value to market
participants.
Signaling Theory
According to Spence (1973), signaling theory explains how
parties with more information (signal senders) can provide signals
to other parties (signal receivers) to reduce information
asymmetry in a transaction. In the context of the capital market,
this theory is used to explain how companies (management) send
signals to investors through published information, such as
financial statements, dividend policies, and investment decisions.
Investors then use these signals to assess the company’s financial condition and prospects before
making investment decisions.
Abnormal Return
Hartono (2022) stated that abnormal return is the difference
between the actual return obtained from a security and the return
expected by investors. The method for calculating expected return
refers to three estimation models, namely the mean-adjusted model,
the market model, and the market-adjusted model (Brown &
Warner, 1985). Thus, abnormal return indicates the excess or
shortfall in return that was not anticipated by investors as a
form of market reaction to new information or certain events.
Abnormal Trading Volume Activity
Abnormal Trading Volume Activity (ATVA) is an indicator that
measures deviations in stock trading volume from normal patterns
over a certain period, namely the difference between actual
trading volume and expected trading volume, and is often used in
event studies (Campbell, Lo, & MacKinlay, 1997). The expected
trading volume is based on specific estimation models, similar to
those used to observe AR. Stock trading volume not only reflects
market liquidity but also the intensity of investor participation
and sentiment, where high volume often signals changes in
expectations or market uncertainty (Lee & Swaminathan,
2000).
Profitability
Profitability describes a company’s ability to generate profits
from its resources while also reflecting its operational
efficiency and financial health (Brigham & Houston, 2019;
Kasmir, 2020). In the capital market context, profitability plays
a vital role because it influences investors’ perceptions of a
company’s performance and prospects, where the publication of
earnings-related information will be reflected in stock prices
(Tandelilin, 2017; Bodie, Kane, & Marcus, 2018).
Quantitatively, profitability is generally measured through
financial ratios such as Return on Equity (ROE), Return on Assets
(ROA), and Net Profit Margin (NPM), which provide a comprehensive
picture of the effectiveness of capital, asset, and sales
utilization in generating profits.
Firm Size
Firm size reflects the scale of a business entity, which can be
measured through total assets, sales, or market capitalization
(Dang, Li, & Yang, 2018). Fama and French (2015) demonstrated
that firm size is systematically related to stock returns, as the
structural characteristics of large firms make the information
they disclose more quickly and strongly responded to by the
market. This occurs because larger firms receive greater media and
analyst attention, have more formal reporting systems that reduce
information asymmetry, and possess more liquid stocks that
accelerate price adjustments to new information (Al-Hadi, Hasan,
& Habib, 2017; Healy & Palepu, 2001; Chordia, Roll, &
Subrahmanyam, 2008).
Political Events
According to Article 6A of the 1945 Constitution of the
Republic of Indonesia, the President and Vice President are
directly elected by the people every five years through general
elections, conducted based on the principles of direct, general, free, confidential, honest, and fair.
Elections are not only a political process but also a significant
economic event, as the uncertainty they generate can affect
investor risk perceptions and market stability (Choo & Chia,
2023). Elections often create temporary uncertainty regarding
fiscal policy direction, regulation, and government stability,
which is then reflected in stock market behavior (Pastor &
Veronesi, 2013). The market’s reaction to election result
announcements reflects investors’ expectations of the new
political configuration and its implications for the business and
investment climate.
Moreover, the market’s reaction to election result
announcements is not uniform (heterogeneous) but is strongly
influenced by perceptions of the new government’s stability, the
policy track record of the winning candidates, and early signals
regarding the direction of economic regulation. In many elections,
investors in developing countries exhibit higher sensitivity
compared to those in developed countries due to their reliance on
foreign investment, fiscal dynamics, and varying levels of
financial literacy (Chia & Lim, 2022). Cross-country studies
in emerging markets show that domestic stock markets tend to react
more strongly to domestic political uncertainty compared to global
markets. This is due to the relatively narrower market structure
and local investors’ dependence on domestic political conditions,
whereas global markets are generally more geographically and
sectorally diversified, thus dampening the impact of a single
country more easily (Brogaard & Detzel, 2015). This approach
highlights that prolonged political uncertainty will not be
absorbed uniformly across all market sectors but depends on how
investors interpret the local political context, institutional
stability, and potential policy agenda of the new government.
Based on the above theoretical review, the research framework is as follows:
Kerangka Pemikiran Penelitian
Alur penelitian mengenai reaksi pasar terhadap pengumuman Pemilu Presiden dan Wakil Presiden 2024.
The hypotheses of this research are:
H1: The announcement of the results of the 2024 presidential
and vice- presidential elections contains information that is
evident in the abnormal returns on LQ45 stocks listed on the
IDX.
H2: The announcement of the results of the 2024 presidential
and vice- presidential elections contains information that is
evident in the abnormal trading volume activity on LQ45 stocks
listed on the IDX.
H3: Profitability influences the abnormal returns resulting
from the announcement of the results of the 2024 presidential and
vice-presidential elections on LQ45 stocks listed on the IDX.
H4: Profitability influences the abnormal trading volume
activity resulting from the announcement of the results of the
2024 presidential and vice-presidential elections on LQ45 stocks
listed on the IDX.
H5: Company size influences the abnormal returns resulting from
the announcement of the results of the 2024 presidential and
vice-presidential elections on LQ45 stocks listed on the IDX.
H6: Company size influences abnormal trading volume activity
that occurs due to the announcement of the results of the 2024
presidential and vice-presidential elections on LQ45 stocks listed
on the IDX.
METHODOLOGY
This study employs two approaches: event study and causality. The
event study is applied to observe the reaction of the Indonesian
capital market to the announcement of the 2024 presidential and
vice-presidential election results. Market reactions are analyzed
through abnormal return (AR) and abnormal trading volume activity
(ATVA) within an 11-day event window, consisting of 5 days before
(H-5), the event day (H), and 5 days after the event (H+5). The
market is considered to react positively when the information
received is perceived as good news, and negatively otherwise.
Abnormal return is calculated using the formula AR_it, with
E(R_it) estimated by the market-adjusted model, where E(R_it) is
assumed to be equal to the actual market return (actual return of
the LQ-45 index) at time t. Abnormal trading volume activity (ATVA)
is calculated using the formula ATVA_it, with E(TVA_it) estimated by
the market-adjusted model, where E(TVA_it) is assumed to be equal to
the actual trading volume activity of the market (trading volume
activity of the LQ-45 index) at time t.
Furthermore, causality is applied to test the effect of
profitability and firm size on AR and ATVA. Profitability is
measured using the Return on Equity (ROE) ratio, defined as the net
income divided by total shareholders’ equity. Firm size is measured
using the natural logarithm of total assets.
This study uses secondary data. Data for calculating abnormal
return (AR) and abnormal trading volume activity (ATVA) are derived
from the daily stock prices of companies listed in the LQ45 index
during the observation period, from February to July 2024. Meanwhile, data for profitability (ROE)
and firm size are obtained from audited annual financial statements
for 2023. All data were accessed through capital market databases
such as the official website of the Indonesia Stock Exchange (IDX)
and other financial data platforms.
The research population comprises all stocks listed in the LQ45
index during the observation period. The LQ45 index was chosen due
to its characteristics of high liquidity, large market
capitalization, and strong fundamentals, making it more
representative of the market’s response to political events. The
sample was selected using non-probability purposive sampling. The
criteria required that companies had not issued other announcements
related to corporate activities mandated by IDX disclosure
regulations during the election result announcement released by the
General Elections Commission (KPU). This criterion was set to avoid
the influence of confounding information that could bias the
interpretation of market reactions. Based on these criteria, 39 LQ45
companies were selected as the study sample.
The data were analyzed using descriptive and inferential
approaches. The descriptive approach was used to present the
variables of the study (AR, ATVA, ROE, and firm size) for general
understanding. The inferential approach was then applied for
statistical testing. First, to test whether there was a significant
market reaction to the observed political event, a t-test was
conducted on AR and ATVA values. Second, multiple linear regression
analysis was used to examine the effect of profitability and firm
size on market reactions. To ensure the regression model was the
Best Linear Unbiased Estimator (BLUE), classical assumption tests
were performed, including normality, multicollinearity,
autocorrelation, and heteroskedasticity tests.
RESULTS
Table 1. Descriptive Statistics of Research Variables
The mean CAR is 0.006. This positive value indicates that the
announcement of the 2024 presidential and vice-presidential
election results received a positive reaction of 0.6%. The minimum
CAR is -0.378, representing a negative reaction observed in PT
Mitra Pack Tbk (PTMP). The maximum CAR is 0.190, experienced by PT
Perusahaan Gas Negara Tbk (PGAS).
The mean CATVA is 0.003, indicating a positive reaction of
0.3%. The minimum CATVA is -0.020, observed in PT Pertamina
Geothermal Energy Tbk (PGEO). The maximum CATVA is 0.367, recorded
by PT Mitra Pack Tbk (PTMP).
The mean profitability is 0.104, indicating profit generation.
This means that every IDR 100 of equity yields an average profit
of IDR 10.4. The minimum profitability is -2.534, representing a
loss, experienced by PT GoTo Gojek Tokopedia Tbk (GOTO). The
maximum profitability is 1.420, observed in PT Unilever Indonesia
Tbk (UNVR).
The mean firm size is 31.761, which in rupiah terms reflects an
average total asset of IDR 198 trillion for companies in the LQ45
index. The minimum firm size is 26.273 (IDR 257 million), recorded
by PT Mitra Pack Tbk (PTMP), while the maximum firm size is 35.315
(IDR 2,174 trillion), recorded by PT Bank Mandiri (Persero) Tbk
(BMRI).
Inferential Statistical Analysis Hypothesis Test 1
Table 2. Abnormal Return Around the 2024 Election Announcement
Event Period
AAR
t-statistic
Significance
H-5
0.0006
0.021
Not Significant
H-4
0.0002
0.008
Not Significant
H-3
0.0003
0.014
Not Significant
H-2
0.0051
0.233
Not Significant
H-1
-0.0070
-0.358
Not Significant
H 0
-0.0022
-0.050
Not Significant
H+1
0.0064
0.323
Not Significant
H+2
-0.0017
-0.065
Not Significant
H+3
-0.0048
-0.192
Not Significant
H+4
0.0031
0.214
Not Significant
H+5
0.0060
0.258
Not Significant
Table 2 shows that both positive and negative abnormal returns
(AR) occurred during the event window. However, based on the
t-test, these AR values were not statistically significant. Thus,
the announcement of the 2024 presidential and vice-presidential
election results did not generate significant abnormal
returns.
Hypothesis Test 2
Table 3. Abnormal Trading Volume Activity Around the 2024 Election Announcement
Event Period
AATVA
t-statistic
Significance
H-5
0.0006
0.191
Not Significant
H-4
0.0001
0.031
Not Significant
H-3
-0.0027
-0.980
Not Significant
H-2
-0.0007
-0.176
Not Significant
H-1
-0.0002
-0.112
Not Significant
H 0
0.0002
0.021
Not Significant
H+1
0.0008
0.170
Not Significant
H+2
0.0008
0.160
Not Significant
H+3
0.0007
0.144
Not Significant
H+4
0.0010
0.100
Not Significant
H+5
0.0022
0.147
Not Significant
Table 3 shows that both positive and negative abnormal trading
volume activities (ATVA) occurred during the event window.
However, based on the t- test, these ATVA values were not
statistically significant. Thus, the announcement of the 2024
presidential and vice-presidential election results did not
generate significant ATVA.
Hypothesis Test 3 and Hypothesis Test 4
Table 4. Classical Assumption Test of Multiple Linear Regression
Test Type
Method
Test Result
Conclusion
Normality
Kolmogorov- Smirnov
Sig. = 0.200
Data are normally distributed
Multicollinearity
Tolerance & VIF
ROE: Tol. = 1.000; VIF = 1.000
No multicollinearity
Size: Tol. = 1.000; VIF = 1.000
Heteroskedasticity
Glejser Test
ROE: Sig. = 0.698
No heteroskedasticity
Size: Sig. = 0.097
Autocorrelation
Durbin-Watson (DW)
DW = 2.103
No autocorrelation
The normality test indicates that the data are normally
distributed, as evidenced by the significance value greater than
0.05. The multicollinearity test shows that all independent
variables have tolerance values greater than 0.10 and VIF values
less than 10, indicating no multicollinearity. The
heteroskedasticity test shows that all independent variables have
significance values greater than 0.05, suggesting no
heteroskedasticity. The autocorrelation test result falls between
the upper limit (du) and 4 – du, confirming no autocorrelation.
Thus, the regression model meets the BLUE (Best Linear Unbiased
Estimator) assumptions and is suitable for further analysis.
Table 5. The Effect of Profitability and Firm Size on Cumulative Abnormal Return (CAR)
Variable
Regression Coefficient
Sig.
Constant
-0.691
0.012
ROE
0.023
0.371
Size
0.022
0.012
F-statistic = 0.182 | R² = 0.027
The F-test produces a significance value of 0.027, indicating
that the model is appropriate. The R² value of 0.182 means that
ROE and size explain 18.2% of the variability in CAR, while the remaining 81.8% is explained
by other factors not included in the model.
The regression equation is:
CAR = -0.691 + 0.023ROE + 0.022Size
The constant value of -0.691 is significant at 0.012, meaning
that if ROE and size are zero, CAR will be -0.691. The regression
coefficient for ROE is 0.023 with a significance of 0.371,
implying that ROE has no significant effect on CAR. The regression
coefficient for size is 0.022 with a significance of 0.012,
indicating that firm size has a positive and significant effect on
CAR. An increase in size by one unit leads to an increase in CAR
by 0.022.
Hypothesis Test 5 and Hypothesis Test 6
Table 6. Classical Assumption Test of Multiple Linear Regression
Test Type
Method
Test Result
Conclusion
Normality
Kolmogorov- Smirnov
Sig. = 0.088
Data are normally distributed
Multicollinearity
Tolerance & VIF
ROE: Tol. = 0.998; VIF = 1.002
No multicollinearity
Size: Tol. = 0.998; VIF = 1.002
Heteroskedasticity
Glejser Test
ROE: Sig. = 0.509
No heteroskedasticity
Size: Sig. = 0.278
Autocorrelation
Durbin-Watson (DW)
DW = 2.070
No autocorrelation
The normality test indicates that the data are normally
distributed, as shown by a significance value greater than 0.05.
The multicollinearity test shows tolerance values greater than
0.10 and VIF values less than 10 for all independent variables,
confirming no multicollinearity. The heteroskedasticity test
results also show significance values greater than 0.05,
indicating no heteroskedasticity. The Durbin-Watson value lies
between du and 4 – du, confirming no autocorrelation. Thus, the
regression model fulfills the BLUE assumptions and is suitable for
further analysis.
Table 7. The Effect of Profitability and Firm Size on Cumulative Abnormal Trading Volume Activity (CATVA)
Variable
Regression Coefficient
Sig.
Constant
1,021.64
0.001
ROE
-5.514
0.672
Size
-2,452.990
0.001
F-statistic = 0.182 | R² = 0.027
The F-test produces a significance value of 0.027, indicating
that the model is appropriate. The R² value of 0.182 shows that
ROE and size explain 18.2% of the variability in CATVA, while the
remaining 81.8% is explained by other factors not included in the
model.
The regression equation is:
CATVA = 1,021.64 – 5.514ROE
– 2,452.990Size
The constant of 1,021.64 is significant at 0.001, meaning that
if ROE and size are zero, CATVA will be 1,021.64. The regression
coefficient for ROE is -5.514 with a significance of 0.672,
indicating that ROE has no significant effect on CATVA. Meanwhile,
the regression coefficient for size is -2,452.990 with a
significance of 0.001, suggesting that firm size has a negative
and significant effect on CATVA. An increase in firm size by one
unit will decrease CATVA by 2,452.990.
DISCUSSION
The results of this study indicate that there was no market
reaction to the announcement of the 2024 presidential and
vice-presidential election results. This finding is consistent with
several previous studies, such as Rohani and Hasti (2020), Rahayu
(2020), and Yudiawan and Abundanti (2020), which also reported no
significant abnormal return during the 2019 election. The
insignificance of this reaction can be explained through the
semi-strong form of the Efficient Market Hypothesis (EMH) proposed
by Fama (1970), which assumes that the market has absorbed all
public information quickly and rationally, such that election
results were already anticipated by investors prior to the official
announcement. In the context of the Indonesian capital market, this
phenomenon may indicate that investors had relatively stable
expectations regarding the election outcome, or even perceived that
the announced results did not bring substantial changes to economic
policy direction or national political stability. Political
uncertainty, which according to Pastor and Veronesi (2013) and Chia
& Lim (2022) could trigger market volatility, appears to have
been reduced earlier due to the dominance of public opinion or quick
count results that circulated before the official announcement.
Furthermore, regression results show that profitability (ROE) has
no significant effect on either AR or ATVA. This contrasts with
several prior studies, such as Permana (2017) and Mujiani et al.
(2020), which found a positive and significant relationship between
ROE and market reaction. This discrepancy may occur because, in the
context of political events such as elections, internal fundamental
variables like ROE tend not to be the main consideration for
investors in making short-term investment decisions. In such
situations, investors are more sensitive to external and macro-level
factors such as government stability, fiscal policy direction, and
systemic risk.
On the other hand, firm size is proven to have a significant
effect. Firm size positively affects AR, which aligns with the
perspective of Hery (2017) and the findings of Anantha (2017), who
argued that larger firms are more trusted by investors because they
have stable reputations, good governance, and lower bankruptcy
risks. The positive reaction to large-cap stocks indicates that when
facing political uncertainty, investors tend to shift their
investments toward large-cap stocks (flight to quality). Conversely,
firm size negatively affects ATVA, meaning that the larger the firm, the smaller the trading
volume surge during the event window. This can be interpreted as
large-cap stocks tending to be traded consistently and steadily,
thus not exhibiting striking volume spikes during political events.
This finding also supports the argument of Chordia et al. (2008)
that large-cap stocks tend to be more liquid and reach market
equilibrium more quickly without sharp increases in trading
volume.
Overall, this study emphasizes that although the Indonesian
capital market did not respond significantly to the announcement of
the 2024 election results, internal factors such as firm size still
play a role in shaping investor perceptions. This indicates that the
market’s reaction to political events is complex, depending on the
combination of macro-level expectations and firm- specific
fundamentals.
CONCLUSION
This study aimed to analyze the reaction of the Indonesian
capital market to the announcement of the 2024 presidential and
vice-presidential election results, focusing on companies included
in the LQ45 index of the Indonesia Stock Exchange. Market reactions
were measured using two indicators: abnormal return (AR) and
abnormal trading volume activity (ATVA). In addition, this study
examined the effect of firm fundamentals, particularly profitability
and firm size, on market reactions (AR and ATVA).
Based on the event study analysis of LQ45 stocks within an 11-day
event window (H–5 to H+5), it was found that AR and ATVA values
throughout the observation period did not show statistical
significance. This indicates that the market did not react to the
2024 election announcement. Furthermore, multiple linear regression
analysis showed that profitability, measured by Return on Equity
(ROE), had no significant effect on either AR or ATVA. Conversely,
firm size had a significant effect on both indicators of market
reaction, albeit in different directions. Firm size had a positive
and significant effect on AR, but a negative and significant effect
on ATVA.
RECOMMENDATIONS
Based on the findings of this study, several recommendations can
be offered for both academic development and practical stakeholders.
From an academic perspective, the absence of a significant market
reaction to the 2024 presidential and vice-presidential election
announcement opens opportunities for further research to explore
this phenomenon in greater depth. One suggested direction is to
extend the event study window, both before and after the
announcement date, to capture possible lagged responses or
anticipatory reactions. This aligns with Khan et al. (2016), who
argue that in emerging markets, investor responses to political
events are often not immediate but spread over several days due to
information constraints and perception asymmetry.
Future research could also consider sectoral differences, for
instance by comparing market reactions between state-owned
enterprises and private companies, or between strategic and non-strategic sectors. Chia
and Lim (2022) show that sensitivity to political risk can be
strongly influenced by firm-specific characteristics and industry
dynamics. To provide a more comprehensive view, additional variables
such as political connection indices, policy uncertainty indicators,
or exposure to new government policies could also be incorporated to
capture other dimensions of the political event’s influence on
investor behavior in the capital market (Brogaard & Detzel,
2015).
From a practical standpoint, the findings of this study carry
important implications for market authorities such as the Financial
Services Authority (OJK) and the Indonesia Stock Exchange (IDX).
Although the market did not show significant reactions to the
election results, it remains crucial for regulators to maintain
transparency and consistency in information disclosure. Clear and
open communication is essential to preserve perceptions of market
efficiency and prevent unnecessary speculation. Fernandes et al.
(2020) emphasized that market liquidity and stock price stability
during political periods are strongly influenced by the credibility
and responsiveness of regulators in managing circulating
information.
For investors, both institutional and individual, this study
suggests that investment decisions should not be made reactively to
political dynamics, including critical events such as the
announcement of presidential election results. Although such events
have long-term implications for national leadership direction, the
insignificant market response suggests that investors perceive no
fundamental changes in macroeconomic policy. Therefore, the more
appropriate investment strategy is to focus on firm fundamentals and
rational risk management. This is consistent with Bouri et al.
(2020), who emphasized that asset allocation strategies based on
fundamentals tend to be more resilient to political uncertainty and
capable of maintaining portfolio stability amid external
dynamics.
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