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Eduvest –
Journal of Universal Studies Volume 3 Number 3, March, 2023 p- ISSN 2775-3735-
e-ISSN 2775-3727 |
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ANALYSIS OF EXPECTED STOCK RETURNS IN
2020 - 2022 USING ARBITRAGE PRICING THEORY (STUDY ON STOCKS
INCORPORATED WITH THE IDX-30 INDEX ON THE INDONESIA STOCK EXCHANGE) |
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ABSTRACT |
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This study aims
to determine which IDX-30 member stock issuers have the highest expected
return values in 2020, 2021, and 2022, and macroeconomics which have a high
influence on the expected returns of IDX-30 member stock issuers in 2020,
2021, and 2022. The population in this study is all stock issuers who are
registered as IDX-30 members in 2020, 2021, and 2022, and the stock data
taken is the issuer's stock data from 2020, 2021, and 2022. Stock price data
and prices The JCI index is taken from the Investing.com website, and data on
stock issuers who are members of the IDX-30 2020 – 2022 are taken from the
doctorsaham.com website. Bond data is obtained from the KSEI website.
Inflation data and USD exchange rate data taken are monthly data from
December 2019 – December 2022. Inflation data is taken from the BI.go.id
website, and USD exchange data is taken from satudata.kemendag.go.id website.
The sample in this study is all members of issuer shares who consistently join
as IDX-30 members in 2020, 2021 and 2022. The number of IDX-30 samples in
2020 is 27 issuers of shares, in 2021 there are 26 issuers of shares, and in
2022 there are 26 stock issuers. Calculating expected stock returns using the
Arbitrage Pricing Theory, and ranking expected stock returns is done in the
Microsoft Excel application, and Linear Regression is used to test the
hypothesis in the JASP application |
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KEYWORDS |
IDX-30, linear regression,
Arbitrage Pricing Theory, JCI, inflation, USD exchange |
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This work is licensed under a Creative
Commons Attribution-ShareAlike 4.0 International |
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INTRODUCTION
In early
2020, the whole world experienced the spread of a new virus called COVID-19
because this virus was only discovered at the end of 2019 in Wuhan City ,
People's Republic of China, and then spread to all over the world. The rapid spread of
the COVID-19 virus around the world has resulted
in most countries around the world
imposing lockdown policies to inhibit
the spread of the COVID-19 virus (Chakraborty & Maity, 2020). The
lockdown policy resulted in several industrial sectors, such as the tourism
industry, aviation industry, retail industry, automotive industry, and aircraft
assembly industry experiencing a decline. Economic activity is due
to lockdown policies (Yunus & Rezki, 2020). However,
not all industries experienced a decline in economic activity, the technology
industry, and the pharmaceutical and medical device industries , greatly
benefiting during the lockdown policy applied. During
2020 – 20121, several countries
in the world implemented policies in the form of
subsidies to private companies, state companies, and the general
public to maintain economic activities It remains
ongoing as long as the lockdown policy is in place (Hastuti et al., 2020). One such
policy is the provision of low-interest loans and high profits of companies in
the technology sector at the time the lockdown was imposed, Technology sector
companies are expanding massively by utilizing capital flows provided by retail
investors and institutional investors such as venture capitalist companies .
Economic policies implemented around the world are not able to help the world
economy escape from economic recession.
Initially, 2022 was expected to
be the year
when the global economy recovered from the impact
of lockdowns carried out from
2020 – 2021 to overcome the spread of
COVID-19 (Kusno, 2020). After most of the
world's population is injected with
vaccines and the rate of
spread of the COVID-19 virus decreases by the end
of 2021, it is expected that
in 2022 economic activity will slowly – land
is recovering as it was before
the COVID-19 pandemic (Tavilani et al., 2021). However,
the reality that has occurred throughout 2022, makes the optimist turn
pessimistic with the forecast that in 2023 most countries in the world,
especially countries that are included in EU (European Union) members, the United
Kingdom, the United States, Japan, Australis, and Canada will enter periods of
economic recession that may exceed or be equivalent to the economic depression
period in the 1920s – 1930s.
The main cause of the difficult
economic recovery occurred in 2022, due to the Russian-Ukrainian
war in February 2022 (Nasir et al., 2022). The
result of this war is that all western countries, including the EU, carry out
economic sanctions, such as freezing russian-owned foreign exchange reserves
stored in western countries, limiting prices. buy oil and gas from Russia, many
companies from western countries exit the Russian market, cancel the operation
of the Nord Stream 2 gas pipeline, and others. the purpose of these economic
sanctions is to restrict Russia from funding its military campaign in Ukraine,
and then make Russia make peace and withdraw its soldiers from Ukraine.
However, Russia retaliated by refusing to sell petroleum, natural gas and other
mineral commodities to western countries, and shifting sales to Asian
countries, such as India and China that bought most of Russia's petroleum and
natural gas at low prices and then sold it at the prevailing prices on the
market to the EU which before the war was the main consumer of the commodity
produced by Russia.
The economic war waged by
western countries and Russia led
to an increase
in prices for raw mineral goods, petroleum and natural gas on the world market (Mahendra, 2022). This
price increase has caused high inflation in countries that have the status of
the most advanced economies in the world, such as the United States which is
experiencing inflation was 7.1% in November, down from 7.7% in October.
Declining inflation in the United States, due to the actions of
the Federal Reserves which raised the
interest rate between 4.25% - 4.50% (Oktorino, 2022). The act
of raising interest rates is also carried out by European and Asian countries.
Indonesia
has also experienced the impact of
the economic turmoil caused by the Russian-Ukrainian
war and the
economic sanctions from western countries
to Russia (Zehfri, 2022). The
impact that has occurred so far is generally positive because Indonesia is one
of the largest exporters of mineral materials in the world, such as coal. Coal
prices that rise in 2022 become a durian collapse for mining companies in
Indonesia such as BUMI Resources, ADARO, Bayan Corporation, and other mining
companies. The increase in prices of other commodities in the international
market, such as bauxite, tin, aluminum and nickel, makes Indonesia's trade
balance positive during 2022. The negative impact experienced by Indonesia is
in the form of an increase in the price of Pertalite subsidized fuel from Rp.
7,500.00 to Rp. 10,000.00 because Indonesia is one of the countries importing
raw oil and fuel from abroad. The increase was also experienced by
semi-finished goods and finished goods that were still imported from abroad due
to the dollar value that rose against the Rupiah to Rp. 15,630.00. The increase
in fuel and some imported products caused Indonesia's inflation to rise to
5.42% in November, down from October and September of 5.71% and 5.95 %.
However, it is still higher than in August where the fuel increase has not been
announced, which is 4.69%. The steps taken by BI to reduce inflation and
protect the rupiah by raising the benchmark interest rate starting from August
to December, which are 3.75%, 4.25%, 4.75%, 5.25%, and 5.5%, respectively.
The Indonesian government's economic policies
and international economic conditions affect the share prices of Indonesian
companies on the Indonesia Stock Exchange. Several issuers in the mining sector
have felt the positive impact of the increase in the price of mining products
in the international market. However, some companies that rely on cheap funds,
such as technology companies, have experienced negative impacts from rising
inflation and interest rate hiking policies. by the majority of central banks
around the world. The macroeconomic impact that occurred during 2020 - 2022,
had a great influence on the expected returns given to investors. This study
uses the APT calculation method to calculate the expected return of the stocks
studied throughout 2020 - 2022.
Arbitrage
Pricing Theory (APT), is the only stock calculation model that includes
inflation, the amount of money in circulation, interest rates, and macroeconomic
components in its calculations. The reasons mentioned in the previous sentence,
caused the APT calculation method to be chosen as a method that calculates the
expected return of shares. Macroeconomics used as a component in the APT
formula will be tested to determine which macroeconomics has a high influence
on the expected return of stocks generated to investors.
This study uses issuers included in the
IDX-30 index which is an index that measures the stock price performance of 30
stocks that are members. The stock issuers listed in IDX-30 are stock issuers
that have the largest liquidity value and stock capitalization value on the
Indonesia Stock Exchange. IDX-30 members are screened from members of the LQ-45
Index, in other words, the stocks included in the IDX-30 are blue chip stocks,
and the composition of stock issuers in the IDX-30 index comes from various
business sectors, so it is very suitable to be the object of research to find
out the current macroeconomic impact on expected stock returns.
Research conducted by Vian Riska Ayuning Tyas,
Komang Dharmawan, and Made Asih (2014), who conducted research on the
application of the Arbitrage Pricing Theory model with the Vector
AutoRegression approach in estimating the expected return on shares of KOMPAS
100 shares in 2010 – 2013 . The results of their study showed that out of 10
KOMPAS100 index stock issuers that were the object of the study, there was one
stock issuer, namely LPKR whose value was influenced by changes in inflation
and currency exchange rates. Keuda research, namely research conducted by Fitri
Halimatus Sadiah, and Esi Fitriani Komara (2022), which conducted research on
the use of Arbitrage Pricing Theory to analyze stock returns on banking
sub-sector companies listed on the Indonesia Stock Exchange for the period 2015
– 2022. The results of the study using simultaneous hypothesis testing showed
that inflation, exchange rates, and Gross Domestic Product (GDP) had a
significant influence on stock returns . Partial testing of the hypothesis
shows that the exchange rate negatively affects stock returns. inflation and
GDP do not have a significant influence on stock returns
RESEARCH
METHOD
Research is a type of quantitative research . This type
of research data is secondary data, that is, data obtained from the second
party, not the first party to issue the data. Data on stock prices and IDX-30
index prices are obtained from the Investing.com website, data on stock issuers
included in the IDX-30 2020 – 2022 members are taken from thedoksaham.com
website, issuer data on inflation and BI-7 Days Repo Rate data is taken from
BI.go.id website, USD exchange rate data is taken from satudata.kemendag.go.id
website, and bond data is obtained from KSEI's website. The data collection
method is carried out by collecting data from the internet (IDX-30 Index, stock
prices of selected IDX-30 issuers, inflation, USD exchange rate, BI-7 Days Repo
Rate, bonds, and previous research articles), and through books on Arbitrage
Pricing Theory models, investment, capital markets, and macroeconomic variables
that were used as objects in this study (inflation, currency exchange rates for
money, and interest rates). Stock data, IDX-30 index, inflation, USD rate, and
BI-7 Days Repo Rate taken are monthly data. The coupon bonds taken come from
the Indonesian Retail National Bond with the FR0044 series, and then divided by
12 months to get an ORI monthly coupon with the FR0044 series. The population
in this study is all stock issuers who are members of IDX-30 from 2020 – 2022.
Evaluation and replacement of members in the IDX-30 index is carried out once
every 6 months, meaning that in a year there are twice (February – July, and
August – January), and because the calculation is done annually, it is not
calculated from the entire year . So, I decided to use IDX-30 issuer member
data in the period February 20 20 – July 2020 0 August 2020 – January 2021,
February 2021 – July 2021, August 2021 – January 2022, February 2022 – July
2022, and August 2022 – January 2023. The sample determination method in this
study uses the purposive sampling method by determining the sample criteria for
this study, as follows:
1. Stock issuers who consistently become members of the IDX-30 within a
one-year period, namely:
a) 2020 (February 2020 – July 2020, and August 2020 – January 2021)
b) 2021 (February 2021 – July 2021, and August 2021 – January 2022)
c) 2022 (February 2022 – July 2022, and August 2022 – January 2023)
2. It has conducted an IPO on the Indonesia Stock Exchange in 2020.
The free variables in this study are Actual Return (Ri),
Excess Return, Risk-free Return (Rf), Market beta, inflation beta, interest
rate beta, USD rate beta, Market (Rm) return, inflation return, interest rate
return, and USD rate return
RESULT AND DISCUSSION
Beta Value of JCI, Beta Inflation, and Beta Exchange Rate of US
Dollar in 2020, 2021, and 2022
The table below shows the Beta values of macroeconomic factors in stock issuers belonging to IDX-30 members in 2020,
2021, and 2021
Table 1 Macroeconomic Beta Value of each stock issuer in 2020
|
Issuer |
β M |
βi |
βK |
|
ACES |
0.021004 |
-0.151139 |
-0.976194 |
|
ADRO |
1.469543 |
0.060340 |
0.869510 |
|
ANTM |
4.080549 |
0.149118 |
2.316756 |
|
ASII |
1.321108 |
-0.287486 |
-0.158245 |
|
BBCA |
1.267490 |
-0.048203 |
0.722845 |
|
BBNI |
1.456949 |
0.259036 |
-1.226988 |
|
BBRI |
1.300532 |
0.057996 |
-0.193708 |
|
BBTN |
1.964195 |
-0.147152 |
-1.134848 |
|
BMRI |
1.517595 |
-0.135411 |
-0.085427 |
|
CPIN |
0.803234 |
-0.432348 |
0.175400 |
|
ERAA |
1.466372 |
-0.150226 |
-0.951450 |
|
GGRM |
0.210908 |
-0.243958 |
-0.995381 |
|
HMSP |
0.412081 |
-0.355874 |
-0.784804 |
|
ICBP |
0.221566 |
-0.045674 |
0.151597 |
|
INCO |
1.847938 |
-0.424613 |
0.774610 |
|
INDF |
0.353120 |
-0.283954 |
0.024927 |
|
INKP |
1.447871 |
-0.683036 |
-0.319409 |
|
INTP |
1.699010 |
0.132849 |
0.840711 |
|
JPFA |
0.998096 |
0.280925 |
-1.223039 |
|
KLBF |
0.352531 |
-0.351388 |
-0.056565 |
|
MNCN |
1.674788 |
0.321064 |
-0.395896 |
|
PGAS |
3.100648 |
-0.043603 |
0.395439 |
|
PTBA |
1.750786 |
0.374596 |
1.673895 |
|
SMGR |
0.831578 |
-0.093058 |
-1.104131 |
|
TLKM |
1.031487 |
0.334074 |
0.031985 |
|
UNTR |
1.903970 |
-0.348482 |
2.283559 |
|
UNVR |
0.129505 |
-0.284833 |
0.185208 |
Source: Researcher's Processed Data Results, 2023
Table 2 Macroeconomic Beta Value of each stock
issuer in 2021
|
Issuer |
β M |
βi |
βk |
|
ADRO |
1.035641 |
0.316853 |
0.131536 |
|
ANTM |
2.172154 |
-0.209529 |
-2.348547 |
|
ASII |
-0.507626 |
-0.133063 |
-4.563142 |
|
BBCA |
0.479014 |
-0.041861 |
-3.551386 |
|
BBNI |
2.951131 |
0.453421 |
0.700098 |
|
BBRI |
1.635977 |
0.057544 |
-0.793454 |
|
BBTN |
4.851604 |
0.355127 |
1.089064 |
|
BMRI |
-0.043825 |
-0.131329 |
-2.993353 |
|
CPIN |
0.788509 |
0.355985 |
5.239313 |
|
EXCL |
0.880550 |
0.279654 |
-0.137933 |
|
GGRM |
-0.647849 |
-1.075326 |
-2.809993 |
|
HMSP |
0.766748 |
0.057111 |
2.160557 |
|
ICBP |
0.056443 |
0.100373 |
1.400219 |
|
INDF |
0.502288 |
0.274953 |
3.470000 |
|
INKP |
-0.318695 |
-0.681014 |
-9.909450 |
|
KLBF |
0.374361 |
0.014054 |
0.269273 |
|
MDKA |
1.657536 |
-0.245356 |
-4.650334 |
|
PGAS |
3.606749 |
0.338885 |
1.602202 |
|
PTBA |
1.867898 |
0.360798 |
2.259957 |
|
SMGR |
0.524396 |
-0.174686 |
-1.543852 |
|
TBIG |
-3.330886 |
-1.388099 |
-10.206542 |
|
TKIM |
0.184096 |
-0.934723 |
-12.095214 |
|
TLKM |
1.721412 |
0.430712 |
2.464396 |
|
TOWR |
2.603215 |
0.096349 |
3.808216 |
|
UNTR |
0.606943 |
0.254713 |
-1.287898 |
|
UNVR |
1.258433 |
0.176043 |
0.550989 |
Source: Researcher's Processed Data Results, 2023
Table 3 Macroeconomic Beta
Value of each stock issuer in 2022
|
Issuer |
β M |
βi |
βK |
|
ADRO |
2.43275 |
0.30827 |
-0.99047 |
|
ANTM |
2.73155 |
-0.15759 |
-3.06205 |
|
ASII |
2.43579 |
0.21891 |
-2.68811 |
|
BBCA |
2.16581 |
-0.09715 |
2.63000 |
|
BBNI |
2.88833 |
-0.00647 |
2.01558 |
|
BBRI |
1.71949 |
-0.07660 |
1.46192 |
|
BMRI |
2.60272 |
0.07782 |
3.57791 |
|
BRPT |
0.84211 |
0.00859 |
1.15455 |
|
OPEN |
1.86091 |
0.11051 |
0.17635 |
|
CPIN |
-1.23972 |
-0.08865 |
-0.11865 |
|
EMTK |
6.00180 |
0.41726 |
-1.07745 |
|
ICBP |
-0.56671 |
-0.18280 |
5.77253 |
|
INCO |
3.06056 |
0.01480 |
-1.35598 |
|
INDF |
-0.79137 |
-0.01263 |
1.13053 |
|
INKP |
2.21887 |
-0.22728 |
5.02172 |
|
KLBF |
0.62458 |
-0.10140 |
3.52800 |
|
MDKA |
2.29726 |
0.11017 |
-5.06603 |
|
PGAS |
1.56531 |
-0.25931 |
1.31532 |
|
PTBA |
1.27214 |
0.14317 |
-3.13909 |
|
SMGR |
1.63342 |
-0.18047 |
6.78229 |
|
TBIG |
-0.45091 |
0.21989 |
-2.09527 |
|
TINS |
2.65814 |
-0.02336 |
-3.52936 |
|
TLKM |
1.80835 |
0.10793 |
-0.85890 |
|
TOWR |
-0.42292 |
0.04430 |
-1.17124 |
|
UNTR |
3.19582 |
0.21110 |
0.96306 |
|
UNVR |
-0.93595 |
0.04587 |
-0.18487 |
Source: Researcher's Processed Data Results, 2023
1. The results of the calculation and ranking of expected
returns from the highest to the lowest based on the Arbitrage Pricing Theory
model in 2020, 2021, and 2022.
The table below displays the results of the
calculation and ranking of expected returns based on Arbitrage Pricing Theory
in 2020, 2021, and 2022, as follows:
Table 4 Arbitrage Pricing Theory Calculation Results and Rankingsa 2020
|
Number |
Issuer |
E.
Return |
|
1 |
INKP |
0.291438 |
|
2 |
HMSP |
0.282906 |
|
3 |
GGRM |
0.265414 |
|
4 |
ACES |
0.240260 |
|
5 |
KLBF |
0.237463 |
|
6 |
UNVR |
0.213968 |
|
7 |
CPIN |
0.208518 |
|
8 |
INDF |
0.198135 |
|
9 |
SMGR |
0.124226 |
|
10 |
ASII |
0.098021 |
|
11 |
ICBP |
0.086109 |
|
12 |
ERAA |
0.067107 |
|
13 |
INCO |
0.039723 |
|
14 |
BBTN |
0.019335 |
|
15 |
BMRI |
-0.006100 |
|
16 |
BBRI |
-0.069417 |
|
17 |
JPFA |
-0.073803 |
|
18 |
BBCA |
-0.075782 |
|
19 |
UNTR |
-0.109000 |
|
20 |
BBNI |
-0.116896 |
|
21 |
ADRO |
-0.163945 |
|
22 |
TLKM |
-0.190863 |
|
23 |
INTP |
-0.225270 |
|
24 |
MNCN |
-0.230953 |
|
25 |
PGAS |
-0.272351 |
|
26 |
PTBA |
-0.409465 |
|
27 |
ANTM |
-0.616976 |
Source: Researcher's Processed Data Results, 2023
Table 5 Arbitrage Pricing Theory Calculation Results and Their 2021
Rankings
|
Number |
Issuer |
E.
Return |
|
1 |
TKIM |
0.981836 |
|
2 |
TBIG |
0.829238 |
|
3 |
INKP |
0.821160 |
|
4 |
MDKA |
0.438168 |
|
5 |
ASII |
0.429140 |
|
6 |
BBCA |
0.356642 |
|
7 |
BMRI |
0.313028 |
|
8 |
GGRM |
0.286754 |
|
9 |
ANTM |
0.268020 |
|
10 |
SMGR |
0.205516 |
|
11 |
UNTR |
0.191834 |
|
12 |
BBRI |
0.154440 |
|
13 |
EXCL |
0.106945 |
|
14 |
ADRO |
0.087599 |
|
15 |
KLBF |
0.072449 |
|
16 |
UNVR |
0.055027 |
|
17 |
BBNI |
0.050353 |
|
18 |
BBTN |
0.023595 |
|
19 |
ICBP |
-0.011341 |
|
20 |
PGAS |
-0.017082 |
|
21 |
HMSP |
-0.067244 |
|
22 |
PTBA |
-0.068959 |
|
23 |
TLKM |
-0.083603 |
|
24 |
INDF |
-0.162631 |
|
25 |
TOWR |
-0.186227 |
|
26 |
CPIN |
-0.292963 |
Source:
Researcher's Processed Data Results, 2023
Table 6 Arbitrage Pricing Theory Calculation Results and Their Rankings
in 2022
|
Number |
Issuer |
E. Return |
|
1 |
TBIG |
0.378188 |
|
2 |
ADRO |
0.313402 |
|
3 |
EMTK |
0.235023 |
|
4 |
ASII |
0.214504 |
|
5 |
UNVR |
0.205322 |
|
6 |
PTBA |
0.195192 |
|
7 |
TOWR |
0.176015 |
|
8 |
UNTR |
0.156955 |
|
9 |
INDF |
0.128747 |
|
10 |
TLKM |
0.121328 |
|
11 |
OPEN |
0.119683 |
|
12 |
MDKA |
0.102684 |
|
13 |
CPIN |
0.06985 |
|
14 |
BRPT |
0.06027 |
|
15 |
BMRI |
0.035328 |
|
16 |
INCO |
-0.05445 |
|
17 |
KLBF |
-0.05584 |
|
18 |
TINS |
-0.07159 |
|
19 |
BBNI |
-0.07403 |
|
20 |
ICBP |
-0.08417 |
|
21 |
BBRI |
-0.08652 |
|
22 |
BBCA |
-0.13692 |
|
23 |
SMGR |
-0.20769 |
|
24 |
ANTM |
-0.22872 |
|
25 |
PGAS |
-0.28481 |
|
26 |
INKP |
-0.29122 |
Source: Researcher's Processed Data Results, 2023
The table above displays the results of
arbitrage pricing theory calculations in 2020, 2021, and 2022. In table 4, it
can be concluded that H1 was rejected, because there were no mining sector
stock issuers who were in the top 5 stock issuers that gave the highest
expected return to investors. The top five stock issuers are INKP (PT. Indah Kiat Pulp & Paper Tbk), HMSP
(PT. Hanjaya Mandala Sampoerna
Tbk), GGRM (PT. Gudang Garam Tbk),
ACES (PT. Ace Hardware Indonesia Tbk), and KLBF ( PT
Kalbe Farma Tbk). The high
expected return provided by INKP is due to INKP's ability to score a net profit
in 2020 of IDR 43.29 trillion, an increase of 7.17% from the year's profit in
2019 amounted to IDR 3.97 trillion. INKP's management ability increased net profit
at a time when world pulp prices were declining throughout 2020, and declining
paper demand was due to the COVID-19 pandemic. HMSP provides a high expected
return in 2020 due to HMSP's ability to continue to distribute dividends from
2019 net profit results even though in 2020 it experienced a decline in net
profit, due to the COVID-19 pandemic, and HMSP maintained its position as the
ruler of cigarette market share in 2020 by 28.8%. GGRM provides high expected
returns in 2020 due to GGRM's ability to increase revenue in 2020 by IDR 144.47
trillion, an increase of 3.57% from revenue in 2019 which amounted to IDR
110.52 trillion. ACES provides a high expected return in 2020 due to ACES'
ability to continue distributing dividends in 2020, the exchange rate of IDR to
USD which tends to be stable in 2020. 2020, namely at the position of IDR
14,000.00, the implementation of tax amnesty on imported goods helped ACES to
sell its products at competitive prices, ACES's status as a market leader in
the household sector with less intense competition, and a less severe decline
in net sales during the COVID-19 period, namely in the third quarter of 2020 of
IDR 5.48 trillion compared to the third quarter of 2019 of IDR 5.97 trillion.
KLBF provides high expected returns due to the demand for medicinal products to
treat COVID-19 disease, which in 2020 there is no commercialized vaccine
because it is still in the trial phase in the lab of a global pharmaceutical
company. KLBF's profit in 2020 was IDR 2.733 trillion, an increase of 9.05%
compared to 2019's profit of IDR 2.506 trillion.
In table 5, it can be concluded that H1 is
accepted, because there is one of the mining sector issuers that is included in
the top five positions, namely the MDKA issuer (PT Merdeka Copper and Gold Tbk). MDKA provides high expected returns due to the
increase in revenue in 2021 of USD 381 million compared to USD 321.9 million in
2020. This increase in revenue led to an increase in net profit obtained by
MDKA from USD 28.9 million to USD 33.4 million. The cause of this increase was
due to the increase in revenue from the Wetar copper
mine from USD 31 million to USD 162 million. The increase in revenue from
copper was also influenced by copper prices soaring throughout 2021 above USD
9,000 per Metric Ton. TKIM (PT Kertas Tjiwi Kimia Tbk) gave a high expected return because the company
recorded an increase in net sales in 2021 of 18.24% to US$ 1.02 billion
compared to US$ 866.45 million in 2020. Revenue from jug exports increased,
from revenue in 2020 from US$ 563.12 million to US$ 634,644 million in 2021 .
TBIG (PT Tower Bersama Infrastructure Tbk) provides a
high expected return because it posted a net profit throughout 2021 of IDR
1,548 trillion, an increase compared to net profit in 2020 amounted to Rp.
1.009 trillion. INKP also provides high expected returns such as TKIM which is
the same as a paper mill issuer due to an increase in net sales of US$ 3.51
billion in 2021, an increase of 17.75% compared to 2020 of US$ 2.98 billion.
The long-term prospects of these two issuers (TKIM and INKP) are also
considered good due to improving global economic conditions and increasing
demand for paper products. Such as shopping bags from paper materials to
replace plastic bags because paper materials are considered environmentally
friendly materials compared to materials from plastic. ASII (PT Astra
International Tbk) provides a high expected return in
2021 because ASII obtained the Group's consolidated net revenue in 2021 of IDR
233.5 trillion higher 33% compared to 2020 with the group's net profit in 2021
reaching IDR 20.2 trillion, 25 % higher than in 2020. The increase in ASII's
performance is inseparable from government programs that provide incentives to
the luxury tax that helps increase sales of ASII automotive products , rising
prices of commodities in global markets, and the easing of pandemic prevention
efforts, which led to better performance of all of the Group's business lines,
particularly the automotive, heavy and mining tools divisions, as well as
financial services.
In table 6, it can be concluded that H1 is
accepted, because one of the mining sector issuers, namely ADRO (PT Adaro
Energy Indonesia Tbk) is included as one of the
issuers that provides high expected returns in 2022. ADRO provides high
expected returns because in 2022 the demand for mining commodities, namely
coal, begins to increase, and is followed by high price increases, causing ADRO
to experience an increase in revenue of 130% as of September 2021 compared to
revenue in the same period in 2020 with details, revenue as of September 2022
of US$ 5.91 billion compared to revenue as of September 2021 was US$2.56
billion. TBIG maintains a high expected return performance in 2022 as in 2021 .
This is due to three things, namely: (1) TBIG which scored a net profit in
semester 1 2022 of IDR 3.3 trillion, an increase of 11.18% compared to semester
1 2021 of IDR 2.97 trillion, (2) TBIG's financial resilience in the face of an
increase in the benchmark interest rate throughout 2022 due to TBIG's ability
to reduce the portion of its debt and the decline in interest on bonds in US
Dollars in 2020 in the range of 2.75% - 4.25%, lower than in 2015 - 2019 at a
level of 5.25%, and bonds in Rupiah in 2020 in the range of 3.60% - 8.00% per
year, lower than in 2015 - 2019 in the range of 8.00% - 9.25%, and (3) remains
strong demand for high data in Indonesia, helping TBIG to obtain rental
contracts of telecommunication towers and fiber optic cables from
telecommunications operators. UNVR (PT Unilever Indonesia Tbk)
provides high expected returns in 2022 due to net sales reaching IDR 10.8
trillion in the first quarter of 2022, an increase of 5.40% compared to net
sales of IDR 10.28 trillion in the first quarter of 2021 with a net profit in
the first quarter of 2022 of IDR 2.02 trillion, an increase of 19.02% compared
to a net profit of IDR 1.69 trillion in the first quarter of 2021 . This
increase was triggered by the recovery of the economy, the return of public
mobility which encouraged an increase in consumer purchasing power, and was
driven by the company's efforts to build fundamentals. strong company
throughout 2021. EMTK (PT Elang Mahkota
Teknologi Tbk) provides a
high expected return in 2022 due to the increase in EMTK's net profit in 2022
by 922.3%, which is IDR 2.70 trillion compared to semester 1 of 2021 of IDR 265
billion. The drastic increase in net profit was caused by investment activities
carried out by EMTK in several companies in Indonesia, such as PT Bukalapak.com
Tbk, PT RANS Satu Bunda, and
PT PSIM Jaya Jogjakarta. ASII provides a high expected return in 2022 the same
as in 2021 because ASII's net profit in the third quarter of 2022 is IDR 22.2
trillion , 49% higher compared to the third quarter of 2021. This increase in
net profit was not only supported by an increase in automotive sales, but also
supported by an increase in commodity prices in the global market which caused
demand for transportation equipment rentals the mine, which is one of ASII's
business lines, is getting higher and higher
Linear Regression results using JASP on
Independent Variables (JCI, Inflation, and USD Exchange Rate) against Dependent
Variables (Expected Return) in 2020, 2021, and 2022.
The table below shows Linear Regression in 2020, 2021, and 2022.
Table 7 Linear Regression In 2020
|
ANOVA
|
|||||||||||||
|
Type |
|
Sum
of Squares |
Df |
Mean
Square |
F |
p |
|||||||
|
H₁ |
Regression |
0.424 |
3 |
0.141 |
5.885 |
0.020 |
|||||||
|
|
Residual |
0.192 |
8 |
0.024 |
|||||||||
|
|
Total |
0.616 |
11 |
||||||||||
|
Note. The intercept model is
omitted, as no meaningful information can be shown. |
|||||||||||||
|
Coefficients |
|||||||||||||||||
|
95% CI |
|||||||||||||||||
|
Type |
Unstandardized |
Standard
Error |
Standardized |
t |
p |
Lower |
Upper |
||||||||||
|
H₀ |
(Intercept) |
-0.013 |
0.068 |
-0.184 |
0.858 |
-0.163 |
0.138 |
||||||||||
|
H₁ |
(Intercept) |
0.003 |
0.051 |
0.062 |
0.952 |
-0.115 |
0.121 |
||||||||||
|
JCI |
0.704 |
0.981 |
0.228 |
0.718 |
0.493 |
-1.558 |
2.966 |
||||||||||
|
Inflation |
0.592 |
0.427 |
0.281 |
1.388 |
0.202 |
-0.391 |
1.576 |
||||||||||
|
USD Exchange Rate |
-2.757 |
1.334 |
-0.653 |
-2.067 |
0.073 |
-5.833 |
0.318 |
||||||||||
The Annova
table shows a calculated F value of 5.885 greater than the table F of 4.0652
with a value of α = 0.05 (5%), df1 = 3 and df2 = 8, and a p value of 0.020 <
the value of the α g used in The study was 0.050. The results of this F
calculation and p value show that all independent variables (JCI, Inflation, and
USD rate) have a significant influence on the dependent variable (expected
return).
The Coefficients table shows the value of t and the coefficients of
each independent variable (JCI, inflation, and USD rate) against the dependent
variable (expected return). Based on the value of t in the table above, the USD
rate has a stronger influence than the JCI and inflation so that H 4 is
accepted, and H5 is accepted due to the influence of the exchange rate USD and
inflation are higher than JCI. The hypotheses H2 and H3 are rejected. The re-gression formula based on unstandardized columns is
described below:
The interpretation of the regression above is as follows:
1. Constant (a)
It has a meaning, that if all free variables are valued at
0, then the value of the dependent variable (expected return) is equal to the value of
the constant, which is 0.003.
2. JCI (X1) to Y (expected return)
The value of the JCI coefficient is 0.704 which shows a positive relationship between JCI and expected returns. This means, that every
increase in one unit of JCI will cause the expected return to rise by 0.704 assuming another free
variable of the regression model is fixed.
3. Inflation (X2) against Y (expected return)
The value of the inflation coefficient is 0.592
which shows a positive relationship between inflation and expected return. This means, that every
increase in a unit of inflation will cause the expected return to rise by 0.592 assuming another free
variable of the regression model is fixed.
4. USD (X3) rate against Y (expected return )
The value
of the USD rate coefficient of
-2.757 indicates a negative relationship between the USD
rate and the expected return. This
means, that every
increase in one unit of the USD
rate will cause the expected return to fall by 2,757 assuming
another free variable of the regression
model is fixed.
Table 8 Linear Regression In 2021
|
Model Summary - E. Return |
|||||||||
|
Type |
R |
R² |
Adjusted R² |
RMSE |
|||||
|
H₀ |
0.000 |
0.000 |
0.000 |
0.203 |
|||||
|
H₁ |
0.485 |
0.236 |
-0.051 |
0.208 |
|||||
|
ANOVA
|
|||||||||||||
|
Type |
|
Sum
of Squares |
Df |
Mean
Square |
F |
p |
|||||||
|
H₁ |
Regression |
0.107 |
3 |
0.036 |
0.822 |
0.518 |
|||||||
|
|
Residual |
0.347 |
8 |
0.043 |
|||||||||
|
|
Total |
0.454 |
11 |
||||||||||
|
Note. The intercept model is
omitted, as no meaningful information can be shown. |
|||||||||||||
|
Coefficients |
|||||||||||||||||
|
95% CI |
|||||||||||||||||
|
Type |
|
Unstandardized |
Standard
Error |
Standardized |
t |
p |
Lower |
Upper |
|||||||||
|
H₀ |
(Intercept) |
0.143 |
0.059 |
2.438 |
0.033 |
0.014 |
0.272 |
||||||||||
|
H₁ |
(Intercept) |
0.208 |
0.080 |
2.590 |
0.032 |
0.023 |
0.394 |
||||||||||
|
|
JCI |
-0.673 |
2.479 |
-0.094 |
-0.271 |
0.793 |
-6.390 |
5.044 |
|||||||||
|
|
Inflation Data |
0.124 |
0.752 |
0.065 |
0.165 |
0.873 |
-1.610 |
1.859 |
|||||||||
|
|
Dollar Exchange Rate |
9.013 |
7.664 |
0.479 |
1.176 |
0.273 |
-8.660 |
26.686 |
|||||||||
The table above shows that the 2021 R is 0.485 (48.5%) which concludes that the correlation between independent
variables (JCI, inflation, and dollar exchange rate) and
dependent variables (expected
return) is more weak compared to the correlation in 2020. An R2 value of 0.236
(23.6%) indicates that the independent variables (JCI, inflation, and dollar rate) can only explain 23.6% of the variance of the dependent variable (expected
return). However,
adjust R2's value
of -0.051 (-5.1%) explains that free
variables cannot explain the variance of dependent variables at all.
The ANOVA table shows
that the calculated F
value of 0.822 is smaller than the table F of 4.0652 with a value of α = 0.05 (5%), df1 = 3
and df2 = 8, and a p value of 0.518 > the α value used in this study of 0.050. The results of this F calculation and p value show
that all independent variables (JCI, Inflation, and USD rate) have an
insignificant influence on the dependent
variables (expected return).
The Coefficients table shows the
value of t and the coefficients of
each independent variable (JCI, inflation, and USD rate) against the dependent
variable (expected return). Based on the t value in the table above, the
USD rate has a stronger influence than the JCI and inflation so H 4 is accepted, but H 2,
H 3, and H5 are rejected because the
influence of JCI is higher than the influence of inflation. The regression
formula based on unstandardized columns is described below:
The interpretation of the regression above is as
follows:
It has a meaning, that if all free variables are
valued at 0, then the value of the dependent variable (expected return) is equal to the value of the constant, which
is 0.208.
2. JCI (X1) to Y (expected return)
The value of the JCI coefficient of
-0.673 indicates a negative relationship between the JCI
and the expected return. This means, that every increase in one
unit of JCI will cause the expected return to fall by 0.673 assuming another free
variable of the regression model is fixed.
3. Inflation (X2) against Y (expected return)
The value of the inflation coefficient is 0.124
which shows a positive relationship between inflation and expected returns. This means, that every increase in a unit of
inflation will cause the expected return to rise by 0.124 assuming another free variable of the
regression model is fixed.
4. USD (X3) rate against Y (expected return)
The value of the USD exchange rate coefficient
of 9.013 indicates a positive relationship between the USD rate and the expected return. This means, that every increase in one unit of the USD rate will
cause the expected return to rise by 9.013 assuming another free variable of the regression
model is fixed.
Table 9 Linear Regression In 2022
|
Model Summary - E. Return |
|||||||||
|
Type |
R |
R² |
Adjusted R² |
RMSE |
|||||
|
H₀ |
0.000 |
0.000 |
0.000 |
0.165 |
|||||
|
H₁ |
0.233 |
0.055 |
-0.300 |
0.188 |
|||||
|
ANOVA |
|||||||||||||
|
Type |
|
Sum of
Squares |
Df |
Mean Square |
F |
p |
|||||||
|
H₁ |
Regression |
0.016 |
3 |
0.005 |
0.154 |
0.924 |
|||||||
|
|
Residual |
0.282 |
8 |
0.035 |
|||||||||
|
|
Total |
0.299 |
11 |
||||||||||
|
Note. The intercept model is omitted, as no
meaningful information can be shown. |
|||||||||||||
|
Coefficients |
|||||||||||||
|
Type |
|
Unstandardized |
Standard
Error |
Standardized |
t |
p |
|||||||
|
H₀ |
(Intercept) |
0.036 |
0.048 |
0.767 |
0.459 |
||||||||
|
H₁ |
(Intercept) |
0.033 |
0.068 |
0.482 |
0.643 |
||||||||
|
|
JCI |
1.202 |
2.902 |
0.176 |
0.414 |
0.690 |
|||||||
|
|
Inflation |
0.105 |
0.404 |
0.092 |
0.261 |
0.801 |
|||||||
|
|
USD Exchange Rate |
4.126 |
7.390 |
0.243 |
0.558 |
0.592 |
|||||||
The table above shows an R
result of 0.233 (23.3%),
which shows the correlation between independent
variables (JCI, Inflation, USD
Exchange Rate) and
dependent variables ( expected return) lower than in 2020 and 2021. An R2
value of 0.055 (5.5%)
indicates that the independent variables (JCI, inflation, and dollar rate) can
only explain 5.5% of the variance
of the dependent variable (expected return), and the rest is explained by
other variables.
However, adjust R2's value of -0.300 (-30%)
explains that free variables cannot explain the variance of dependent variables
at all.
The ANOVA table shows that the calculated F value of 0.154 is smaller than the F of the table of 4.0652
with a value of α = 0.05 (5%), df1 = 3 and df2 = 8, and a p value of 0.924 > the α value used in this study of 0.050. The results of this F calculation and p value show that all
independent variables (JCI, Inflation, and USD rate) have an insignificant
influence on the dependent variables (expected return).
The Coefficients table shows the
value of t and the coefficients of each independent variable
(JCI, inflation, and USD rate) against the dependent variable (expected
return). Based on the t value in the table above, the USD rate has a
stronger influence than the JCI and inflation so H 4 is accepted, but H 2,
H 3, and H5 are rejected because the
influence of JCI is higher than the influence of inflation. The regression
formula based on unstandardized columns is described below:
The interpretation of the regression above is as
follows:
1. Constant (a)
It has a meaning, that if all
free variables are valued at 0, then the value of the dependent variable (expected return) is equal to the value of the constant, which is 0.033.
2. JCI (X1) to Y (expected return)
The value of the JCI coefficient of 1.202 indicates a negative relationship
between JCI and expected return. This means, that every increase in one
unit of JCI will cause the expected return to rise by 1,202 assuming another free
variable of the regression model is fixed.
3. Inflation (X2) against Y (expected return)
The value of the inflation
coefficient is 0.105 which shows a positive relationship between inflation and expected return. This means, that every increase in a unit of
inflation will cause the expected return to rise by 0.1 05 assuming another free variable of the
regression model is fixed.
4. USD (X3) rate against Y (expected return)
The value of the USD rate
coefficient of 4.126 indicates a positive relationship between the USD rate and the expected return. This means, that every increase in one unit of the USD rate will
cause the expected return to rise by 4,126 assuming another free variable of the regression
model is fixed.
DISCUSSION
This research
was conducted to determine IDX-30 member stock issuers in 2020, 2021,
and 2022 that provide expected returns based on
the Abitrage Pricing
Theory calculation model to
investors, and find out independent variables (JCI, inflation,
and USD exchange rate) which most
influence the expected return of IDX-30
stock issuers throughout 2020, 2021, and 2022. The results of the research
above, I can explain as follows:
In 2020, the five stock issuers that provide high expected returns are INKP, HMSP, GGRM, ACES, and KLBF. INKP,
HMSP, and GGRM have good management so
that they are able to maintain profits during the
peak of the COVID-19 pandemic. ACES, in addition
to being
supported by good management,
also gets benefits from
the USD exchange rate which tends to be stable,
and the government's policy of providing tax amnesty for imported goods helps ACES in
the face of the peak period of the COVID-19
pandemic. KLBF, which is a red plate
drug company, earned a high net profit due to the high demand for drugs
during COVID-19.
In 2021, five
stock issuers that provide high expected returns are
TKIM, TBIG, MDKA, INKP, and ASII to get high profits due to recovered product
demand and soaring higher
compared to 2020 Khusus
TBIG, in addition to the increase in tower rental demand due to high internet data demand from consumers, also due to the success of TBIG management in management their debt that is able
to reduce coupon bonds, both denominated in US$ and Rp. is lower
than before 2019. ASII received
an increase in demand because it was supported by the government through a
program to eliminate VAT on luxury goods for automotive products.
In 2022, the five stock issuers
that provide high expected stock returns are
TBIG, ADRO, EMTK, ASII, and UNVR. These five stock issuers have good financial performance reports because they benefit from the current
world conditions. ADRO benefited from the increase in demand for coal exports and rising coal prices due to the geopolitical conditions of the Ukraine-Russian
war. TBIG
results from the company's
ability to control the debt burden with cheaper bond interest than bond interest before 2020, and management's
growth-making decisions organic that does not spend the funds
owned by the company to buy expensive
rival companies. EMTK
benefits from investment policies in other companies that provide high
returns. ASII Ddan UNVR is supported by improving
economic conditions due to the easing of lockdown policies by the government.
The results of the study above concluded that
the USD exchange rate has a significant influence on the expected returns of all IDX-30 issuers throughout 2020, 2021, and
2022. Issuers included in IDX-30 are issuers that carry out raw material import
activities from abroad, such as KLBF or ACES which make their products in the
People's Republic of China and import them back to Indonesia, issuers that
export their products abroad, such as INKP, TKIM or INDF, and
issuers whose transactions his
business uses US$ such as ADRO
which sells its mining products in US $, and rents mining
equipment in the form of
US$ .
Inflation does not have a significant impact
because there are issuers included in IDX-30 that are not directly affected by
inflation, such as ADRO, ITMG, ASII, TOWR, TBIG, TLKM, ANTM, and PGAS that sell
commodity products such as coal, cellular data, and gas whose demand is fixed.
However, high inflation will make it difficult for these issuers to borrow because
high interest rates follow inflation, and if it lasts for a long time, the
demand for their products will decrease because consumers must prioritize their
primary needs. Issuers such as INDF, CPIN, ICBF GGRM, HMSP and UNVR that sell
products such as instant noodles, cut chicken, and daily necessities products
that have become the main consumer products. Such products are resistant to
inflationary pressures. If inflation is high, companies such as HMSP, GGRM, and
UNVR can simply shrink the size of the products sold or reduce the number of
cigarettes in the cigarette
pack to keep them sold at prices before
high inflation.
JCI has a strong influence on the expected returns of all IDX-30 issuers in
2021yy, and 2022. This shows that
the share price of IDX-30 members is
the most traded stock by investors on the Indonesia Stock Exchange. In 2020, the majority of investors implemented a wait-and-see tactic of waiting for the right time to make a transaction. The
implementation of wait-and-see tactics was influenced by the COVID-19 pandemic which caused economic
activity to decline sharply due to the lockdown, and investors were better off waiting until the issuance of financial statements from companies.
CONCLUSION
The conclusions of the study are as follows based
on the calculation results of the Arbitrage Pricing Theory model, new mining
sector issuers will provide high expected returns in 2021 and 2022. The USD exchange rate is an independent
variable that has a high influence on the expected returns of stock issuers in
2020, 2021, and 2022. JCI in 2021 and 2022 has a higher influence than
inflation in the formation of expected returns of IDX-30 stock issuers.
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