JRSSEM 2023, Vol. 02 No. 9, 2023 – 2035
E-ISSN: 2807 - 6311, P-ISSN: 2807 - 6494
DOI : 10.59141/jrssem.v2i09.432 https://jrssem.publikasiindonesia.id/index.php/jrssem/index
CALCULATING EXPECTED STOCK RETURN USING
ARBITRAGE PRICING THEORY MODEL AND ANALYZING
INDEPENDENT VARIABLES THAT AFFECT STOCK
EXPECTED RETURN (ANALYSIS CONDUCTED ON
KOMPAS100 STOCK ISSUERS FOR THE PERIOD 2020 –
2022)
Harold Kevin Alfredo
University of Lampung, Indonesia
*
Email: haroldkevinalfredo@gmail.com
*Correspondence: kevinalfredo@gmail.com
Submitted
: April 02
th
2023
Revised
: April 14
th
2023
Accepted
: April 25
th
2023
Abstract: This study aims to determine which variable independent (IHSG, USD exchange,
money supply (M2), and inflation) that has the most influence of expected returns using the
Arbitrage Pricing Theory Model for selected stock issuers from the KOMPAS100 Index for the
period 2020 – 2022. The population in this study are all stock issuers registered as members
of the KOMPAS100 Index for the period 2020 – 2022 using a purposive sampling technique to
obtain 57 selected stock issuers from the KOMPAS100 Index population for the period 2020 –
2022. Data on stock issuers who are members of the KOMPAS100 Index for the period 2020 –
2022 is taken from the doktorsaham.com website. The monthly stock price data for selected
issuers and the monthly JCI stock data are taken from the Investing.com website. Monthly
inflation data for the period 2020 - 2022 is taken from the BI.go.id website. Monthly data on
the amount of money in circulation (M2) and monthly data on the USD exchange rate for the
period 2020 – 2022 are taken from the saturated. kendang.go.id website. Arbitrage Pricing
Theory is used to calculate the expected return of a sample of selected stock issuers using the
Microsoft Excel application. JASP application is used to test the hypothesis using Linear
Regression. The Linear Regression results conclude that inflation has a strong influence on the
expected return of selected stock issuers who are members of the KOMPAS100 Index for the
period 2020 - 2022, followed by the amount of money in circulation (M2), JCI, and the USD
exchange rate which has the lowest influence.
Keywords: Jakarta Composite Index; KOMPAS100; inflation; money supply (M2); USD exchange;
Arbitrage Pricing Theory; and Linear Regression.
Harold Kevin Alfredo | 2024
INTRODUCTION
The early years of the 2020s were a
bleak year for economies around the world.
In the early 2020s, countries in the world
experienced two events that shook their
economies, namely the COVID-19
pandemic which caused most countries in
the world to lockdown, and caused an
economic recession even though it was still
in a mild stage because economic growth
was still supported by large profits
obtained by companies in the technology
industry sector, and logistics following the
WFH (Work From Home) policy ), and many
purchases of goods through online stores
such as Tokopedia, and Shopee.
Governments around the world have also
issued capital economic assistance
programs to businesses, which are
experiencing economic hardship during
COVID-19 lockdowns, and low interest
rates during the pandemic, causing
booming technology companies to get a
lot of funds from investors because their
yields are higher than yields on bonds and
deposits. Low-interest rates also make
consumers prefer to buy luxury consumer
goods using credit cards or in Indonesia
using the services of online loan
companies.
The outbreak of the COVID-19
pandemic was declared over at the end of
2021, and many economic observers and
government officials around the world
expect the world economy to begin to
recover in 2022. However, these hopes were
dashed following the Russia-Ukraine war
on February 24, 2022, which resulted in the
imposition of economic sanctions by the
United States and its allies on Russia, in the
hope that Russia would experience severe
economic difficulties, which prompted the
fall of Putin's government, and then
replaced with a new government that
would be pro-western and conclude a
peace treaty with Ukraine. At first,
everything went according to the plan of
the United States and its allies, but in the
end, throughout 2022 there was an
increase in inflation in western countries
due to rising prices of energy, commodities,
and foodstuffs which increased interest
rates to overcome inflation. This increase in
inflation and interest rates is expected to
cause a recession in Western countries,
reduce demand for consumer products that
are mostly imported from developing
countries in Asia-Pacific, and consequently
will cause economic recessions in
developing countries due to reduced
demand for their export products. The
opposite happened with Russia after
experiencing the highest inflation for 4
months, getting durian collapsed after
Russia exported the commodity to
developing countries in the Asia Pacific
despite selling it at a discount.
Indonesia has also experienced
negative and positive impacts caused by
the COVID-19 pandemic, and the Russian-
Ukraine war. The negative impact caused by
COVID-19 is the decline in people's
economic activities, especially those who
do MSME businesses and have to
temporarily close their sales places for 5
months due to the lockdown policy
implemented by the government. When
the government allowed the reopening of
street MSME markets and shops in malls
throughout Indonesia by implementing
strict restrictive policies, the number of
2025 | Calculating Expected Stock Return Using Arbitrage Pricing Theory Model and Analyzing
Independent Variables That Affect Stock Expected Return (Analysis Conducted on Kompas
100 Stock Issuers For The Period 2020 – 2022)
sales was not as much as before the COVID-
19 pandemic which resulted in many MSME
businesses closing. The government
conducted a monthly direct cash transfer
program to MSME businesses during the
lockdown period, and this program helped
reduce the number of MSME businesses
that closed. Another negative impact is due
to declining demand for products, causing
companies to reduce business expenses by
laying off employees with deducted
salaries, asking employees to resign
voluntarily, and dismissing employees
directly with severance pay. The positive
impact of COVID-19 is the increasing
number of Indonesians registering as
investors on the Indonesia Stock Exchange,
and the lockdown policy has helped
increase the profits of online marketplace
companies such as Tokopedia, and logistics
companies that get delivery orders for
goods sold on online marketplaces.
The negative impact of the Russia-
Ukraine war on the Indonesian economy is
the increasing cost of imported products
from abroad due to the decline in the value
of the Rupiah against the US $ due to the
Fed's policy of raising the benchmark
interest rate making storing funds in the
Fed's Treasury more profitable than storing
funds in Indonesia. The victims of the
US$ exchange rate increase against Rp
were the increase in subsidized fuel prices
in Indonesia at the end of last year which
helped increase inflation in Indonesia, and
to help the poor due to the increase in
subsidized fuel, the government reissued a
BLT policy of Rp 600,000.00 / month.
Another negative impact is that JCI often
falls because foreign investors prefer to sell
their shares on the IDX, and move these
funds to foreign Stock Exchanges that
provide more profitable returns. The
increase in the exchange rate of
US$ against Rp, rising inflation, and rising
commodities in the world market caused
the share prices of several issuers to fall,
such as Barito Pacific (BRPT), Bank Jago
(ARTO), Gojek Tokopedia (GOTO), and
Tower Bersama Infrastructure (TBIG). The
positive impact of the Russia-Ukraine war is
that rising prices of mining commodities
such as coal, natural gas, and nickel have
collapsed for mining companies in
Indonesia that reported an increase in net
profits and provided high returns for
investors. Other profitable companies are
companies that provide mining equipment
such as United Tractors which has increased
profits from leasing mining equipment to
its clients.
This research was conducted to
determine the returns provided by stock
issuers during COVID-19 and the Russia-
Ukraine war which is still ongoing today.
The calculation model used is Arbitrage
Pricing Theory. The reason for using the
Arbitrage Pricing Theory model is that this
calculation model includes external factors,
namely macroeconomic conditions in its
calculations. The issuers used in this study
are issuers listed as members of the
KOMPAS100 Index from 2020 – 2022. The
reason for choosing KOMPAS100 is
because KOMPAS100 measures the level of
the market capitalization of an issuer on the
Indonesia Stock Exchange, and the number
of index members totaling 100 issuers
makes this index a wider spectrum of
industrial sectors assessed than the LQ-45
Harold Kevin Alfredo | 2026
Index.
The research was conducted by (Gusni
&; Suskim Riantani, 2017) who researched
the use of Arbitrage Pricing Theory to
analyze Islamic stock returns from the
period 2009 - 2014 with a regression model
approach in the eviews9 application. The
results of the model test found that there is
a linear relationship between
macroeconomic variables (inflation,
exchange rates, and interest rates) and
stock return variables. The hypothesis test
results show that only variable interest rates
hurt stock returns. Meanwhile, inflation and
exchange rates do not affect the return of
Islamic stocks incorporated in JII.
The research was conducted by Elly
Zunara and Sri Hartoyo (2016) who
conducted research on the influence of
macroeconomic factors on stock return and
risk premium using the Arbitrage Pricing
Theory model on 90 selected stocks on the
Indonesia Stock Exchange from January
2009 to December 2013. The observations
from January 2009 – December 2013 found
that beta inflation and price had a negative
and significant influence on the expected
return of the majority of selected shares. As
the beta exchange rate of US Dollars has a
positive and insignificant influence on the
expected return of the majority of selected
stocks
MATERIALS AND METHODS
This research is a type of quantitative
research, which is research that uses
calculations to obtain research results. The
type of data in this study is secondary data,
which is data obtained from a second party
that obtains, compiles, and publishes raw
data from the first party. Stock price data
and JCI data are obtained from the
Investing.com website, data on stock
issuers included in KOMPAS100 members
from 2020 - 2022 are obtained from the
doktersaham.com website, inflation data is
obtained from the BI.go.id site, USD
exchange rate data, and the amount of
money in circulation is obtained from the
saturated. kendang.go.id site and bond
data is obtained from the KSEI website.
The data collection method is carried
out by collecting data from the internet
(JCI, stock prices of selected KOMPAS100
issuers, inflation, USD exchange rates,
money supply, bonds, and previous
research articles), and through books on
the Arbitrage Pricing Theory model,
investment, capital markets, and
macroeconomic variables that are used as
objects in this study (inflation, currency
exchange rates, and money supply).
Monthly data include data on selected
stocks, JCI, inflation, USD exchange rate,
and the amount of money in circulation.
The bond coupons taken come from the
Indonesian Retail National Bond with the
FR0042 series and are then divided by 12
months to get a monthly coupon for ORI
with the FR0042 series. The population in
this study is all stock issuers that are
members of KOMPAS100 from 2020 – 2022.
Evaluation and replacement of members in
the KOMPAS100 index are carried out every
6 months, meaning that in a year there are
twice (February – July, and August –
January), and because the calculation is
carried out annually, it is not calculated
from the whole year. So, I decided to use
KOMPAS100 issuer member data in the
period February 2020 – July 2020, August
2027 | Calculating Expected Stock Return Using Arbitrage Pricing Theory Model and Analyzing
Independent Variables That Affect Stock Expected Return (Analysis Conducted on Kompas
100 Stock Issuers For The Period 2020 – 2022)
2020 – January 2021, February 2021 – July
2021, August 2021 – January 2022,
February 2022 – July 2022, and August
2022 – January 2023. The sampling method
in this study uses the purposive sampling
method by determining the sample criteria
of this study.
RESULTS AND DISCUSSION
A. JCI Beta Calculation Results, Inflation
Beta, Money Supply Beta (M2), and US
Dollar Exchange Rate Beta
The table below displays the
results of the calculation of beta
inflation, the beta of JCI, the beta of
money supply (M2), and the beta of the
US Dollar exchange rate as follows:
Table 1. Beta Inflation, beta JCI, Beta Money Supply (M2), and Beta US
Dollar Exchange Rate
Issuer Name
bM
bk
bM2
bi
AALI
1.45152
-1.47195
0.9728
-0.0594
ACES
-0.02529
-1.38969
0.3304
-0.0518
CHURCHYARD
1.09717
-0.77458
3.7776
0.3242
ACRA
1.83789
-0.05695
0.5987
0.2010
ANTM
3.30263
1.47299
-0.3502
-0.2104
ASIA
0.92087
-1.41164
1.9570
0.0785
ASSA
2.36720
0.77000
-2.0686
-0.4250
BBCA
1.18837
0.54979
-0.1309
0.0026
BBNI
1.85193
-0.72015
-0.1778
0.1697
BBRI
1.27871
-0.44608
0.8795
0.0425
BBTN
2.58493
-0.25473
-0.9747
-0.0675
BMRI
1.36502
-0.30527
0.1697
0.0738
BRPT
0.80965
-1.78358
-3.1127
-0.0142
BSDE
1.29808
-0.83260
0.3459
0.1220
BTPS
1.19892
-1.75457
1.6154
-0.2693
CPIN
0.37771
-0.49746
1.0444
-0.0981
CTRA
1.91030
-1.03606
0.1323
-0.0257
ELSA
2.11318
-0.20845
-0.7166
-0.1056
ERAA
0.73771
-1.45117
-0.8203
-0.3133
EXCL
0.58608
-1.95732
1.2619
0.1870
GGRM
0.30959
-0.77112
0.3885
-0.2159
HMSP
0.57989
-0.79383
1.0309
-0.0856
ICBP
0.10480
0.36778
-0.4457
-0.0161
INCO
2.01004
0.70085
-0.3709
-0.0516
INDF
0.20377
-0.14722
0.2796
-0.0150
INDY
3.71970
3.10947
-2.2046
0.0917
Harold Kevin Alfredo | 2028
INKP
1.45594
0.16891
-2.3752
-0.3228
INTP
0.82910
-0.88024
2.4085
0.1191
ITMG
1.94077
0.38909
0.5115
0.2290
JPFA
0.52438
-1.34307
-0.4307
-0.0031
JSMR
0.44424
-2.87898
1.0057
0.1337
KLBF
0.42505
0.01063
0.0716
-0.0622
LPKR
1.47570
-2.00904
0.8438
0.0018
LPPF
1.27933
-2.45728
3.4722
0.2493
LSIP
2.38318
1.76812
-0.3428
-0.0322
MAPI
0.93503
-2.18996
-0.3397
0.0894
MDKA
1.29361
-0.52318
0.5185
-0.0887
MEDC
2.27715
0.09115
-1.0958
-0.0431
MIKA
0.13853
-0.49447
0.6952
0.0470
MNCN
1.62206
-0.36673
0.0879
0.1244
PGAS
2.93464
0.38399
-0.3445
-0.0358
PTBA
1.62526
1.43253
0.1421
0.1929
PTPP
2.57262
-0.94379
-0.4782
0.0148
PWON
1.37805
-1.05495
-0.6639
0.0558
SCMA
1.32029
-1.05695
0.5124
-0.0507
BEEN
0.51786
0.60449
-0.5757
-0.0057
SMGR
0.60350
-1.51248
1.4350
-0.0575
SMRA
1.44574
-1.57599
0.6678
-0.0834
TBIG
0.34789
-0.13394
-2.8610
-0.2451
TINS
3.48775
1.92408
-1.8329
-0.2147
TKIM
2.86755
1.65090
-3.8501
-0.2948
TLKM
1.24585
0.42109
-0.6582
0.1443
TOWR
0.35171
-0.57466
-1.7941
-0.1541
TPIA
0.83183
-0.70884
-3.3834
-0.1914
UNTR
1.70098
1.73120
-0.0542
0.0587
UNVR
0.33953
0.58549
-0.8620
0.0072
WSKT
2.49876
-1.10547
-0.9994
0.0688
Source: Data processed by researchers
B. Total Expected Return of KOMPAS100 Shares for the 2020 - 2022 Period Selected
Based on Calculation Results Using Arbitrage Pricing Theory
Table 2. Total Expected Return of KOMPAS100 Shares for the 2020 – 2022 Period
Issuer Name
Total APT
AALI
0.27405
ACES
0.51570
2029 | Calculating Expected Stock Return Using Arbitrage Pricing Theory Model and Analyzing
Independent Variables That Affect Stock Expected Return (Analysis Conducted on Kompas
100 Stock Issuers For The Period 2020 – 2022)
CHURCHYARD
0.57155
ACRA
0.14093
ANTM
-0.69419
ASIA
0.48518
ASSA
-0.60710
BBCA
-0.00150
BBNI
0.20424
BBRI
0.20402
BBTN
-0.19506
BMRI
0.16663
BRPT
0.35732
BSDE
0.30504
BTPS
0.23821
CPIN
0.28251
CTRA
0.11803
ELSA
-0.13632
ERAA
0.17126
EXCL
0.69267
GGRM
0.23901
HMSP
0.30330
ICBP
0.20343
INCO
-0.22069
INDF
0.29239
INDY
-0.88277
INKP
-0.27898
INTP
0.45505
ITMG
0.06539
JPFA
0.42053
JSMR
0.82663
KLBF
0.18697
LPKR
0.39793
LPPF
0.75636
LSIP
-0.45109
MAPI
0.55376
MDKA
0.11188
MEDC
-0.18284
MIKA
0.41710
MNCN
0.16327
PGAS
-0.32432
Harold Kevin Alfredo | 2030
PTBA
-0.08619
PTPP
-0.00672
PWON
0.25217
SCMA
0.22202
BEEN
0.09271
SMGR
0.44943
SMRA
0.26686
TBIG
0.01311
TINS
-0.84806
TKIM
-0.80373
TLKM
0.09355
TOWR
0.17969
TPIA
0.04325
UNTR
-0.24910
UNVR
0.12914
WSKT
0.05649
Source: Researchers' preparations
C. Linear Regression Results using
JASP to Determine the
Independent Variables (JCI, USD
Exchange Rate, Inflation, and
Money Supply (M2)) that Most
Affect the Dependent Variable
(Expected Return of Shares) for the
2020 – 2022 Period
The table below displays
the Linear Regression results of
all selected stocks from the
KOMPAS100 Index for the
period 2020 – 2022.
Table 3. Linear Regression Period 2020 - 2022
Model Summary - Total APT
Model
R
R²
Adjusted R²
RMSE
H₀
0.000
0.000
0.000
0.391
H₁
0.496
0.246
0.148
0.361
ANOVA
Model
Sum of Squares
df
Mean Square
F
p
H₁
Regression
1.318
4
0.329
2.524
0.061
Residual
4.046
31
0.131
Total
5.364
35
2031 | Calculating Expected Stock Return Using Arbitrage Pricing Theory Model and Analyzing
Independent Variables That Affect Stock Expected Return (Analysis Conducted on Kompas
100 Stock Issuers For The Period 2020 – 2022)
ANOVA
Model
Sum of Squares
df
Mean Square
F
p
Note. The intercept model is omitted, as no meaningful information can be shown.
Coefficients
95% CI
Model
Unstandardized
Standard Error
Standardized
t
p
Lower
Upper
H₀
(Intercept)
0.166
0.065
2.538
0.016
0.033
0.298
H₁
(Intercept)
0.153
0.066
2.326
0.027
0.019
0.288
JCI
-1.999
1.904
-0.245
-1.050
0.302
-5.882
1.884
USD course
1.835
3.179
0.153
0.577
0.568
-4.648
8.319
M2
-5.462
4.176
-0.248
-1.308
0.201
-13.979
3.055
Inflation
0.770
0.476
0.260
1.617
0.116
-0.201
1.741
The Summary Model Table gives the
results of R, R 2, and Adjusted R
2
values
respectively 0.496, 0.246, and 0.148. The
meaning of each of the values mentioned
earlier is, the value of R = 0.496 (49.6%)
shows that the independent variables (JCI,
USD exchange rate, money supply (M2),
and inflation), have a correlation of 49.6%
with the dependent variable (expected
return). The value of R
2
= 0.246 (24.6%)
indicates that the independent variables
(JCI, USD exchange rate, money supply
(M2), and inflation) can only explain the
24.6% variance of the dependent variable
(expected return). The value of Adjusted R
2 = 0.148 (14.8%) shows that the
independent variables (JCI, USD exchange
rate, money supply (M2), and inflation) can
only explain 14.8% of the variance of the
dependent variable (expected return) after
adjustment to R
2
.
ANOVA table. It was found that the
calculated F value of 2.524 had a slight
difference in value from the table F value of
2.679 with α = 0.05, df1 = 4, and df2 = 31.
The value of ρ-value = 0.061 is smaller than
the α value used in this study, which is
0.050. The calculated F value and ρ-value
are so thin the difference in value with the
F table and α, shows that the independent
variables (JCI, USD exchange rate, money
supply (M20, and inflation) have little
influence on the dependent variable
(expected return).
The Coefficient table shows the t value
and coefficient value of each independent
variable (JCI, USD exchange rate, money
supply (M2), and inflation) against the
dependent variable (expected return).
Based on the value of t above, it can be
concluded that inflation strongly influences
the formation of an expected return of
1,617, followed by the money supply (M2)
of -1,308, JCI of -1,050, and finally the USD
exchange rate of 0.577. The results of the t
value of each independent variable support
the third hypothesis (H
3
) which states that
there are one or two macroeconomic
Harold Kevin Alfredo | 2032
variables that have a stronger influence
than JCI, but there are one or two
macroeconomic variables that have a lower
influence than JCI. The regression formula
is based on unstandardized standard values
as follows:
The interpretation of the above
formula is:
1. Constant (a)
It means that if all values of the
independent variable are 0, then the value
of the dependent variable (expected return)
is equal to the value of the constant, which
is 0.153.
2. JCI (X
1
) to Y (expected return)
A negative JCI coefficient value
indicates that there is a negative
relationship between JCI and expected
return. This means that every increase in
one unit of JCI will cause the expected
return to decrease by 1,999 assuming other
independent variables from the regression
model are assumed to be fixed.
3. Exchange rate USD (X
2
) to Y
(expected return)
A positive USD exchange rate
coefficient value indicates that there is a
positive relationship between the USD
exchange rate and expected return. This
means that every increase of one unit of the
USD exchange rate will cause the expected
return to increase by 1,835 assuming the
other independent variables of the
regression model are assumed to be fixed.
4. Money supply (M2) (X
3
) to Y
(expected return)
The negative value of the money
supply coefficient (M2) indicates that there
is a negative relationship between the
money supply (M2) and the expected
return. This means that every increase of
one unit of money supply (M2) will cause
the expected return to decrease by 5.462
assuming the other independent variables
of the regression model are assumed to be
fixed.
5. Inflation (X
4
) to Y (expected return)
A positive inflation coefficient value
indicates that there is a positive
relationship between inflation and
expected return. This means that every
increase in one unit of inflation will cause
the expected return to increase by 0.770
assuming the other independent variables
of the regression model are assumed to be
fixed.
Discussion
This study was conducted to determine
the independent variables (JCI, USD
exchange rate, money supply (M2), and
inflation) that most influence the formation
of expected returns of selected stock
issuers from the KOMPAS100 Index for the
2020-2022 period. The results of the
research above can explain as follows:
The results show that inflation has a
major influence on the expected return of
selected stock issuers of the KOMPAS100
Index for the 2020-2022 period. Most of the
selected stock issuers from the KOMPAS100
Index in this study focus on domestic
business, and the products sold by some of
these issuers are inflation-sensitive
products, such as SMRA, PWON, CTRA,
BSDE, LPKR, WSKT, JSMR, and PTPP which
focus on property and infrastructure
2033 | Calculating Expected Stock Return Using Arbitrage Pricing Theory Model and Analyzing
Independent Variables That Affect Stock Expected Return (Analysis Conducted on Kompas
100 Stock Issuers For The Period 2020 – 2022)
development in Indonesia such as houses,
apartments, and toll roads that are paid
using rupiah currency. If inflation increases,
the issuers mentioned above will have to
increase the price of the real estate they sell
due to the increase in the price of building
raw materials, and companies such as
WSKT, and JSMR will have to increase toll
road entrance fees to their users due to the
increase in toll road maintenance costs due
to inflation.
Issuers such as SMGR, and INTP, which
are cement-producing companies in
Indonesia, have high sensitivity to rising
inflation. Inflation will directly result in a
decrease in the value of the IDR currency
against the USD currency which is often
done in energy purchase contracts for the
cement plants they operate. The main
market share of SMGR, and INTP is the
domestic market and they accept it in the
form of Rp, and if there is an increase in
inflation which is then followed by a
decrease in the value of the IDR currency
against the USD currency causes these two
issuers to have to increase their cement
prices to prevent losses if they insist on
using the same price as before the increase
in inflation.
Other issuers such as GGRM, and
HMSP whose main business is to sell
cigarette products that face cigarette excise
duty increases every year, are also sensitive
to rising inflation which will lead to higher
prices of cigarette raw materials and
increase cigarette prices if they do not
stimulate will divert consumers to cheaper
cigarette products. Issuers such as UNVR,
INDF, MAPI, ICBP, LPPF, and JPFA sell
processed food products, clothing, and
other consumerist products, when there is
an increase in inflation, these companies
have two choices, increase the price or
reduce the content of the products they sell
to maintain the price of their products at
the same price. For ICBP issuers that sell
instant noodle products that have been
considered as one of the staple foods, the
choice is to increase product prices due to
the increase in prices of wheat staples
imported from abroad. MAPI issuers and
LPPFs that have clothing-selling business
lines will experience a decrease in the
number of sales during times of rising
inflation because consumers prefer to buy
necessities rather than buy clothes. MAPI
issuers that sell foreign branded products
to the upper middle class, do not
experience a decline in sales when inflation
only rises 0.5-1% per year, but they will
experience a decline in sales when inflation
rises above 1% per year because
consumers of its target market prefer to
keep their money in deposits or other
securities to protect their wealth.
Issuers such as ADRO, ANTM, INCO,
MEDC, MDKA, PTBA, PGAS, ITMG, and TINS
have the resilience to inflation because they
sell mining products such as coal, oil and
gas, tin, nickel, bauxite which they usually
enter into sale and purchase contracts
using USD both with business partners
abroad and domestically. The USD currency
is higher than the Rp, causing their income
derived from the export of mining materials
which is valued in USD to protect them
from the increase in production costs, and
labor costs which are valued in Rp.
The relationship between inflation and
the money supply (M2), USD exchange rate,
Harold Kevin Alfredo | 2034
and JCI can be explained as follows: High
inflation will cause the money supply (M2)
to increase, due to rising prices, people
need more money when buying a product
than in the previous year. Inflation will
decrease the currency's value, resulting in
the value of other currencies such as the
USD higher than the previous year. Inflation
if it cannot be controlled in one quarter or
one year will result in a loss of confidence
of foreign investors in the country's
economic condition and they will attract
investment on the Indonesia Stock
Exchange which will result in local retail
investors panicking so the value of JCI will
drop dramatically.
CONCLUSIONS
The conclusions that can be obtained
from this study are:
a. 57 stock issuers are consistently
included as members of the
KOMPAS100 Index for the 2020-2022
period.
b. Inflation has a major influence on the
expected return
of KOMPAS100 Index
stock issuers for the 2020-2022 period.
The USD exchange rate weakly
influences the
expected return
of
KOMPAS100 Index stock issuers for the
2020-2022 period.
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© 2023 by the authors. Submitted
for possible open-access
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under the terms and conditions of the Creative
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