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Edunity

Volume 2 Number 7, July 2023

p- ISSN 2963-3648- e-ISSN 2964-8653

Doi:

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ANALYSIS OF FAMA AND FRENCH 3-FACTOR MODEL

VARIABLES IN THE FORMATION OF EXPECTED STOCK

RETURNS (ISSUERS OF LQ-45 INDEX MEMBER STOCKS FOR

THE PERIOD 2020 – 2022)

Harold Kevin Alfredo

University of Lampung, Lampung, Indonesia

Email: haro[email protected]

ABSTRACT

Abstract: Fama and French Three Factor Model is one of the models for calculating expected

return on stock portfolios that can be used by investors. This model was developed by Eugene

F. Fama and Kenneth R. French by adding two factors, namely company size (SMB), and

company book value (HML) to the CAPM calculation model. The purpose of this study is to

determine stock issuers that can provide high expected returns to investors, determine the

overall influence and each variable in Fama and French Three Factor Model (market return,

SMB, and HML) on the expected return of each portfolio used in this study consisting of 6

portfolios, namely Big High, Big Medium, Big Low, Small High, Small Medium, and Small

Low, and to all 6 portfolios in 2020, 2021, and 2022 respectively. This study used 28 selected

stock issuers listed on the LQ-45 Index consecutively from 2020 - 2022 using the purposive

sampling method from the period 2020 - 2022. The Multiple Linear Regression method is used

to determine the level of influence of the whole and each independent variable on the

dependent variable. The results show that mining sector issuers are the issuers that provide

the highest expected return to investors during the period 2020 - 2022. Based on the results of

Linear Regression, there is a significant difference in results, between doing linear regression

for each portfolio (Big High, Big Medium, Big Low, Small High, Small Medium, and Small

Low) and doing linear regression on portfolios divided by observation year (2020, 2021, and

2022).

Keywords: JCI; LQ-45; Fama and French Three Factor Model; Market return; SMB and HML

Introduction

The COVID-19 pandemic has caused the economy in Indonesia to suffer a recession as a

result of the Indonesian government’s policy of limiting social activity outside the home.

This policy resulted in small and medium-sized enterprises (SMEs) in Indonesia not

being able to operate during the social restriction due to where their business must be

closed, companies in the service sector implement the WFH system as well as maximize

the use of telecommunications services in serving complaints of their customers, and

manufacturing companies must limit the number of employees working within the

factory to prevent the spread of the COVID-19 virus resulting in reduced production

[Analysis of Fama and French 3-Factor Model Variables in

the Formation of Expected Stock Returns (Issuers Of Lq-45

Index Member Stocks for The Period 2020 – 2022)]

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capacity. Employees also experience salary reductions because they work from home

(WFH), and if they refuse they are welcome to resign. The Indonesian government

decided to loosen the social restriction policy after six months of implementation of the

policy based on complaints from UMKM entrepreneurs who suffered large losses, and

even some who went bankrupt due to social restrictions, and helped boost the country’s

economic activity during the pandemic. The latest government policy, helping UMKM

entrepreneurs, especially five-foot merchants to sell back. However, UMKM

entrepreneurs face another problem of falling demand as many consumers tighten their

belts because of their wages cut by the company during the period of social restriction.

The government has implemented a new policy of providing direct cash assistance

(BLT), and soft loans from banks to UMKM entrepreneurs so that they can survive

during the period of social restriction.

Large companies have also suffered a sharp blow to the government’s social restriction

policies. Companies in the consumerism sector were hardest hit by government policies,

causing them to experience a drastic decline in profits compared to 2019, such as PT

Mitra Adi Perkasa, Tbk (MAPI), PT Ace Hardware Indonesia (ACES), PT. Pizza Hut

Indonesia (PZZA), PT Unilever Indonesia (UNVR), and PT Matahari Department Store

Tbk (LPPF). The above-mentioned companies are companies that sell consumer

products that can be said to be secondary or tertiary products. However, not all

companies operating in the consumer goods sector experienced a decrease in profits,

companies such as PT Kalbe Farma Tbk (KLBF), PT Chemia Farma T bk (KAEF), PT

Indofood Success Makmur T bc (INDF), and PT Indafood CBP Success Macmur Tbc

(ICBP) high profit performance during COVID-19. The reason is because the previously

mentioned companies sold high-demand health products during COVID-19, and food

products such as milk are already considered as basic food by Indonesian society.

Companies in the mining sector such as PT Adaro International TBK (ADRO), PT Bumi

Resources Tbk (BUMI), PT Bukit Asam T bk (PTBA), PT Medco Energy International Tbc

(MEDC), and PT Indoraya Mining Megah Tbg (ITMG) also experienced a decline in

profits during the implementation of social restrictions due to COVID-19 which caused

demand for mining commodities to decline drastically. One evidence of a decrease in

demand for mineral commodities during the COVID-19 pandemic, i.e. the demand for

coal used as fuel for PLTU, has been reduced due to decreased electricity consumption

as a result of many factories leaving their employees or operating with limited capacity,

and offices being closed during the implementation of social restriction policies. The

decline in demand also occurs for other energy commodities such as petroleum, one of

which is the BBM, which due to social restriction policies makes the consumption of bbm

decrease drastically, and natural gas, which is also like coal, becomes a source of fuel for

power plants and manufacturing industries. (Bei, Xinyue,et all, 2014)

The impact of COVID-19 and the policy of social restriction were also felt by the

Indonesian Stock Exchange. The volume of transactions in 2020 on the Indonesian Stock

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Index Member Stocks for The Period 2020 – 2022)]

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Exchange was 27.495.947.445, down from 2019 by 36.534.971.048. Another negative

impact, is the decline in IHSG values during the COVID-19 pandemic from area 6300 to

area 3900 over 3 months, indicating the emergence of panic among investors due to the

announcement of Covid-19 as an epidemic, and accompanied by the enforcement of

social restriction policies. The only positive impact of the COVID-19 pandemic on the

Indonesian Stock Exchange is the increase in the number of local retail investors from

2,484,354 in 2019 to 7,489,337 in 2021.

The index used in this study is the LQ-45 Index, as this Index is based on the market

capitalization value and liquidity of an issuer of shares as a condition for entering as a

member of the issuer LQ-45. Most stock issuers that are members of the LQ-45, such as

BBCA, ASII, BMRI, BBNI, and BBRI, are known as blue-chip stocks that have a good

foundation and are the primary target for institutional and retail investors as stock

components in their portfolios. These blue chip stocks are also known to have a major

influence on IHSG’s movements, and when these stocks go down, it can affect the value

of the IHSG to go down as well and vice versa. (Siddiq, 2020)

The Fama and French Three Factor Model is used as a method of calculation in this

study, as this method calculates the expected return of stocks in addition to the market

return, also based on the size of capitalization, and the stock book value that describes

the intrinsic value of the stocks (Hendra, 2017) . This calculation model was developed

by Fama and French based on their criticism of the CAPM model that relies only on

market return, and they argue that there are other factors that affect the price of a stock.

Later, Fama and French introduced the size of the market and the value of the stock book

as factors that influenced the return on stocks. The main difference between the Fama

and French Three Factor Model and the Arbitration Pricing Theory is that the Fama and

French three-assumes Model assume that the book value and market capitalization

already represent the macroeconomic influence on the stock, while the Arbitrage Price

Theory assumes that macro-economics directly affects the issuer of the stock. (Fawziah

& Naning, 2016)

The research aims to find out the expected return on shares of the selected LQ-45 stock

issuer for the period 2020-2022. The calculation model used in this study is the Fama and

French Three Factor Model. The reason for choosing this method is because the Fama

and French model uses variables that represent the fundamental value of the stock itself,

namely the size of capitalization and stock book value. LQ-45 was chosen as a member

of the LQ45 Index, which is considered to be a blue chip stock and has a major influence

on IHSG’s value.

Previous Research

[Analysis of Fama and French 3-Factor Model Variables in

the Formation of Expected Stock Returns (Issuers Of Lq-45

Index Member Stocks for The Period 2020 – 2022)]

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Research conducted by Citra Amanda, and Zaafri Ananto Husodo entitled Empirical

test of Fama and French Three Factor Model and Illiquidity Premium in Indonesia

(Amanda & Husodo, 2015). The purpose of their research was to determine the effect of

market beta, size, book value, and liquidity on excess stock returns in Indonesia. This

study used Amihud illiquidity (2002) as a representative of illiquidity, used Ordinary

Least Squares (OLS) regression monthly data taken over 10 years, from 2003 - 2013, and

used dummy variables to make a difference in crisis and non-crisis times. The study

divided the portfolio into 12 portfolios, sorted by size-illiquidity and book-to-market

(BM/)-illiquidity (Sari & Alteza, 2019). The results show that market beta consistently

has a positive and significant impact on each portfolio when sorted according to these

two criteria (Setiawan, 2017). The size factor (SMB) has an influence to explain the

illiquidity factor and vice versa. The research also found that stocks with small

capitalization beat stocks with large capitalization. The HML coefficient increases when

book-to-market also increases, while the SMB coefficient increases when liquidity

decreases. This indicates that small-cap stocks are more difficult to trade on the stock

exchange (illiquid) (Sutrisno & Ekaputra, 2016).

Research conducted by Teddy Chandra entitled Testing Fama and French Three Factor

Model in Banking Companies in Indonesia Stock Exchange. This study aims to examine

the effect of the Fama and French Three Factor Model and CAPM on returns generated

by banking sector stocks in Indonesia. This study used 29 samples of stock issuers listed

in the banking sector from January 2010 to December 2013. This study used multiple

linear regression. The results showed that CAPM can be used to predict the return of

stock issuers in the banking sector. On the other hand, Fama and French Three Factor

Model cannot be used in its entirety in Indonesia. Only excess market returns and

company size can affect changes in stock returns, while book-to-market equity shows no

significant effect. (Chandra, 2015)

Research Method

This type of research is quantitative research and uses secondary data. The data

collection method is as follows: (1) monthly IHSG price data, and shares taken from

Investing.com., (2) ORI data (Indonesia Retail Bonds) used is FR0081 published on

July 30, 2019 obtained from danamon.co.id., (3) summary financial reports or fact

sheets of issuers of selected shares from IDX.com, (4) LQ-45 member data taken from

doktersaham.com., (5) data on research articles taken from Google Scholar.com, (6)

materials on investments, stocks, bonds, income, risk, IHSG, LQ-45 Index, and Fama

and French 3 Factor Model taken from lectures. (Aghdam, 2022)

The population in this study is all stock issuers selected to be members of the LQ-45

Index for the period January 2020 - December 2022. The LQ-45 Index is updated 2

times a year, and there are always changes in stock issuers that enter and exit become

members of the LQ-45 Index. Thus, it was decided to carry out a purposive sampling

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the Formation of Expected Stock Returns (Issuers Of Lq-45

Index Member Stocks for The Period 2020 – 2022)]

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method in determining the sample of stock issuers used in this study. The criteria

for selected samples are as follows:

1. Stock issuers that are consistently members of the LQ-45 Index for the period 2020

- 2022.

2. Issuers of shares that have conducted an IPO in January 2019.

This study uses 6 portfolios formed based on small minus big, and high minus

low.

The study used a linear regression analysis technique that was performed twice, the

first performed a lineary regression to each of the portfolios of the period 2020 - 2022,

and the second carried out a Linear Regression to all of the respective portfolio - each

observation year. The formula of Fama and French 3 Factor Model is as follows:

𝑌 = 𝛼 + 𝛽1𝑋1 + 𝛽2𝑋2 + 𝛽3𝑋3 + ɛ

Where as:

Y = Expected return Fama-French Three Factor

Model

α = constant value

, β

= Coefficient regression/stock volatility

X1 = Excess of Market Return

X2 = SMB (Small minus Big)

X3 = HML (High minus Low)

ɛ = residual error

The research uses six portfolios that are formed based on small minus big, and high

minus low.

Result And Discussion

Table 1 Ranking of Expected Return of LQ-45 Index Stock Issuers from Highest to

Lowest for the 2020 - 2022 Period Based on Fama and French 3 Factor Model

Issuer Code

Expected return

ITMG

1.441364916

ANTM

1.245245576

ADRO

0.998140744

INCO

0.797220443

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the Formation of Expected Stock Returns (Issuers Of Lq-45

Index Member Stocks for The Period 2020 – 2022)]

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TBIG

0.688370025

PTBA

0.321121543

TOWR

0.307399086

BBNI

0.296536140

INKP

0.275356156

BMRI

0.246941943

ERAA

0.245122067

UNTR

0.188404099

KLBF

0.135582882

BBCA

0.122578380

BBRI

0.092696623

PGAS

0.063917273

BBTN

(0.051112911)

JPFA

(0.083549738)

TLKM

(0.156284686)

ASII

(0.192787027)

ICBP

(0.212942791)

CPIN

(0.221491282)

INDF

(0.281335178)

EXCL

(0.341890318)

SMGR

(0.551851890)

INTP

(0.675962338)

MNCN

(0.746554314)

HMSP

(0.957886934)

Source: Data processed by researchers

Table 2 Ranking of Expected Return of LQ-45 Index Stock Issuers from

Highest to Lowest in Each Observation Year Based on Fama and French 3

Factor Model

FF3FM 2020

FF3FM 2021

FF3FM 2022

Issuer

Code

Expected

return

Issuer

Code

Expected

return

Issuer

Code

Expected

return

ANTM

1.14793036

TBIG

0.665464020

ITMG

0.68867083

INKP

0.45785460

ADRO

0.519768755

ADRO

0.53404291

ERAA

0.39427916

ITMG

0.398721936

INCO

0.47362107

INCO

0.38993452

ERAA

0.314309448

BMRI

0.30872510

ITMG

0.35397215

JPFA

0.183356166

PTBA

0.30218334

TBIG

0.31909665

TOWR

0.174890726

BBNI

0.28306957

UNTR

0.22190119

ANTM

0.168422324

PGAS

0.23362147

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TOWR

0.20230280

TLKM

0.158666306

KLBF

0.20513472

BBTN

0.18314443

EXCL

0.132694054

UNTR

0.14720358

SMGR

0.09203211

BBNI

0.092327491

BBRI

0.13863876

JPFA

0.05472633

BMRI

0.059266908

ICBP

0.11219847

PTBA

0.05384784

BBTN

0.050037547

BBCA

0.11161536

CPIN

0.00666967

KLBF

0.044877645

INKP

0.07367794

PGAS

(0.00472563)

BBCA

0.024605155

INDF

0.01056572

BBCA

(0.01364214)

BBRI

(0.028183129)

ASII

(0.02474209)

BBRI

(0.01775901)

PTBA

(0.034909638)

TOWR

(0.06979444)

ADRO

(0.05567092)

INCO

(0.066335138)

ANTM

(0.07110710)

ASII

(0.07180514)

ASII

(0.096239802)

CPIN

(0.09572653)

BBNI

(0.07886092)

INDF

(0.131920953)

SMGR

(0.11960124)

EXCL

(0.08941126)

CPIN

(0.132434423)

TLKM

(0.12068346)

KLBF

(0.11442948)

ICBP

(0.148672215)

HMSP

(0.16603779)

BMRI

(0.12105006)

INTP

(0.153613372)

MNCN

(0.22317981)

INDF

(0.15997994)

PGAS

(0.164978573)

INTP

(0.25415395)

ICBP

(0.17646905)

UNTR

(0.180700677)

BBTN

(0.28429489)

TLKM

(0.19426753)

MNCN

(0.250262253)

TBIG

(0.29619065)

INTP

(0.26819502)

INKP

(0.256176386)

JPFA

(0.32163223)

MNCN

(0.27311225)

HMSP

(0.483631089)

EXCL

(0.38517311)

HMSP

(0.30821806)

SMGR

(0.524282754)

ERAA

(0.46346654)

Source: Data processed by researchers

Table 3 Portfolio Formation Results

Portfolio 2020

Number

B/H

B/M

B/L

S/H

S/M

S/L

BMRI

BBRI

BBCA

ADRO

INCO

INTP

BBNI

ASII

TLKM

PGAS

PTBA

TOWR

INDF

ICBP

HMSP

BBTN

EXCL

ANTM

INKP

UNTR

CPIN

MNCN

JPFA

TBIG

SMGR

KLBF

ERAA

ITMG

Portfolio 2021

Number

B/H

B/M

B/L

S/H

S/M

S/L

BMRI

BBRI

BBCA

INDF

INCO

ANTM

ASII

ICBP

TLKM

SMGR

EXCL

INTP

BBNI

UNTR

HMSP

INKP

PTBA

ADRO

CPIN

PGAS

ITMG

KLBF

BBTN

JPFA

TBIG

MNCN

ERAA

TOWR

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the Formation of Expected Stock Returns (Issuers Of Lq-45

Index Member Stocks for The Period 2020 – 2022)]

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Portfolio 2020

Number

B/H

B/M

B/L

S/H

S/M

S/L

Portfolio 2022

Number

B/H

B/M

B/L

S/H

S/M

S/L

BMRI

ADRO

BBCA

INKP

ANTM

TOWR

ASII

INCO

BBRI

SMGR

ITMG

TBIG

UNTR

BBNI

TLKM

BBTN

PGAS

INDF

ICBP

MNCN

PTBA

KLBF

ERAA

INTP

HMSP

EXCL

CPIN

JPFA

Data sources processed by researchers

Table 4 Linear Regression Results of Portfolio

Based on Each - Maing Portfolio

Informat

ion

Big High

Big

Medium

Big Low

Small

High

Small

Medium

Small

Low

0.559

0.715

0.690

0.481

0.118

0.899

R²

0.313

0.512

0.476

0.232

0.014

0.808

Adjusted

R²

0.018

0.329

0.371

0.039

(0.197)

0.665

1.062

2.797

4.539

1.205

0.066

5.628

t JCI

1.307

0.240

1.743

(1.211)

0.305

(1.699)

t SMB

(0.043)

2.089

1.401

1.093

(0.368)

1.897

t HML

(0.336)

(1.513)

(1.482)

(0.938)

(0.165)

(2.451)

By Year

Informat

ion

Portfolio

2020

Portfolio

2021

Portfolio

2022

0.932

0.801

0.943

R²

0.868

0.642

0.890

Adjusted

R²

0.670

0.104

0.724

4.381

1.193

5.371

t JCI

1.534

(0.487)

3.598

t SMB

(0.869)

(0.821)

2.620

t HML

1.667

1.681

(2.848)

Discussion

Table 1 shows that hypothesis H 1 which assumes that issuers of shares in the mining

sector have a higher expected return than the expected return of stock issuers from other

industrial sectors is accepted. The top five issuers with the highest expected return are

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Index Member Stocks for The Period 2020 – 2022)]

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dominated by four issuers in the mining sector, namely ITMG, ANTM, ADRO, and

INCO. Only one non-mining sector issuer entered the top five, namely TBIG (Fakriah et

al., 2020).

The high expected return of the four mining sector stocks above is directly proportional

to the increase in mining commodity prices throughout 2020 - 2022. Although in 2020

the performance of these four mining companies experienced a decline in financial

performance due to the COVID-19 pandemic, these four companies reported high-profit

profits in 2021 - 2022 due to demand for coal mining commodities after several countries

began to relax social distancing policies during 2021, and increased demand for nickel

mines which is one of the main components in electric car batteries (Woen & Patricia,

2022).

TBIG provides a high expected return influenced by the increasing tower rental income

they have from telecommunication operators in Indonesia such as PT. Indosat Tbk, PT.

Telkom Tbk, PT. XL Axiata, Tbk, and PT. Smartfren Tbk. The company's ability to carry

out corporate expense efficiency, and increased revenue from the acquisition of 3,000

towers owned by PT. Inti Bangun Sejahtera Tbk in 2021.

Table 2, presents data on the total expected return of issuers from the highest to the

lowest in 2020, 2021, and 2022. The hypothesis of H2 was partially accepted, and rejected,

because, in 2020, the issuer that provided a high expected return was the issuer of the

mining sector, namely ANTM. Throughout 2020, ANTM's share price increased due to

rumors of cooperation between ANTM and Tesla to build a car battery factory in

Indonesia with ANTM as the main supplier of nickel ore to Tesla's battery factory in

Indonesia. Other causes are the increasing nickel ore price and nickel sales volume in the

domestic market as well as ANTM's management ability to reduce production cash costs

throughout 2020 (Yunita, 2023).

TBIG, which provides the highest expected return in 2021, makes the H2 hypothesis

partially accepted. The factors that influence the high expected return provided by TBIG

are inseparable from the increase in TBIG's net profit in 2021 by 53.4% compared to net

profit throughout 2020 which was influenced by TBIG's success in acquiring 3,000 towers

owned by PT. Inti Bangun Sejahtera contributed to an increase in revenue of 16% year-

on-year and positive investor sentiment toward the increase in revenue growth of

technology sector issuers during COVID-19 throughout 2021 (Zainuri et al., 2021).

Hypothesis H3 which states that mining sector issuers provide the highest expected

return compared to other sector issuers is accepted. ITMG is a mining sector issuer and

LQ-45 Index issuer that provides the highest expected return throughout 2022 (Putri,

2018). The contributing factor is that the coal produced by ITMG is coal that has a fairly

high calorific value, and ITMG's coal price exposure to Newcastle coal prices is quite

high. Another factor is that ITMG is the dominant stock issuer in coal exports,

[Analysis of Fama and French 3-Factor Model Variables in

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throughout 2022, except for Q1 2022 due to the high rainy season, ITMG's coal

production growth is quite solid, and investors' high expectations that ITMG will

distribute dividends at a ratio of 70% of 2022 net profit like the previous year's dividend

distribution ratio.

Table 4 shows the results of linear regression which can be explained as follows:

1. Based on R

, R2, and

Adjusted R2, Small Low portfolios are the only portfolios that have

values close to 1 (0.899, 0.808, and 0.606), which means that the independent variable

has a high correlation (89.9%0, and is able to explain 80.8%/60.6% variance of the

dependent variable. Meanwhile, the Small Medium portfolio has the lowest value

among the six portfolios with values of 0.118, 0.014, and -0.197. The meaning of this

value is, the independent variable has a low correlation of 11.8%, and is only able to

explain or not able to explain at all when viewed from the negative Adjusted R2

value to the dependent variable. If the linear regression of the portfolio is based on

the year, it can be seen that the R

, R 2, and Adjusted R2 values of the portfolio in 2020, and 2022 have values close to 1

(R, and R 2

), and above 0.5 (

Adjusted R2). Different results were obtained in the 2021

portfolio which had the lowest value compared to the other two portfolios.

However, when compared to the linear regression results of each 6 portfolios, the

values of R, and R 2 are only inferior to the values of R, and R2 Small Low. However,

for the Adjusted R2 value, the 2021 portfolio is only higher than the Big High, Small

High, and Small Medium portfolios.

2. Based on the F value, the High-Low portfolio is the only portfolio where hypothesis

H4 is accepted. In this portfolio, a set of independent variables has a strong influence

on the formation of dependent variables. In other portfolios, hypothesis H4 is

rejected. Based on the results of linear regression it was found that a set of

independent variables has a weak influence on the formation of expected return.

However, when the six portfolios are combined and separated only by year (2020,

2021, and 2022), the results show that a set of independent variables has a weak

influence on the formation of expected returns.

3. Based on the t value, the majority of portfolios (Big High, Big Low, and Small High)

the formation of expected return is influenced by the JCI variable, which means H5

is accepted. SMB has only the greatest influence on the formation of expected return

2 portfolios (Big Medium, and Small Medium), meaning H6 is received. The

independent variable HML has only the greatest influence on the formation of the

portfolio's expected return of 1% (Big Low), meaning H7 is received. However,

when the six portfolios are combined and separated only by year (2020, 2021, and

2022), it is found that HML has a strong influence on the formation of expected

returns for all portfolios in 2020, and 2021, which means H7 is received. However,

in 2022, JCI is an independent variable that has a strong influence on the formation

of expected returns for the entire portfolio, which means H5 is received.

Conclusion

Vol. 2, No. 7, 2023

[Analysis of Fama and French 3-Factor Model Variables in

the Formation of Expected Stock Returns (Issuers Of Lq-45

Index Member Stocks for The Period 2020 – 2022)]

804

Harold Kevin Alfredo

The conclusions from the results of the above research are as follows: There are 28 stock

issuers that are consistently included in the LQ-45 Index members for the period 2020 –

2022, Issuers in the mining sector are the issuers that provide the highest expected return

to investors during the period 2020 – 2022, Based on the results of Linear Regression,

there is a significant difference in results, between doing linear regression for each

portfolio (Big High, Big Medium, Big Low, Small High, Small Medium, and Small Low)

and doing linear regression on portfolios divided by observation year (2020, 2021, and

2022).

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