THE INFLUENCE OF FINANCIAL RATIOS AND
MACROECONOMIC FACTORS TOWARD STOCK
PRICES OF BANK CATEGORY KBMI 3 & 4
By
Venantius Ivan Widijono
014202000028
A Thesis submitted to the
School of Business, President University
in partial fulfillment of the requirements for
the Degree in Management Science
September 2023
PANEL OF EXAMINERS
APPROVAL SHEET
The Panel of Examiners declare that the Thesis entitled THE INFLUENCE
OF FINANCIAL RATIOS AND MACROECONOMIC FACTORS
TOWARD STOCK PRICES OF BANK CATEGORY KBMI 3 & 4 that
was submitted by Venantius Ivan Widijono majoring in Management from the
School of Business was assessed and approved to have passed the Oral
Examinations on …….
Panel of Examiners
……….
Chair - Panel of Examiners
……….
Examiner 2
……….
Examiner 3
ii
STATEMENT OF ORIGINALITY
In my capacity as an active student of President University and as the author
of the thesis/final project/business plan stated below:
Name
Student ID number
Study Program
Faculty
: Venantius Ivan Widijono
: 014202000028
: Management
: Business
I hereby declare that my thesis/final project/business plan (underline one of
these) entitled THE INFLUENCE OF FINANCIAL RATIOS AND
MACROECONOMIC FACTORS TOWARD STOCK PRICES OF
BANK CATEGORY KBMI 3 & 4 is to the best of my knowledge and belief,
an original piece of work based on sound academic principles. If there is any
plagiarism detected in this thesis/final project/business plan, I am willing to
be personally responsible for the consequences of these acts of plagiarism,
and will accept the sanctions against these acts in accordance with the rules
and policies of President University.
I also declare that this work, either in whole or in part, has not been submitted
to another university to obtain a degree.
Cikarang, 13 September 2023
(Venantius Ivan Widijono)
iii
CONSENT FOR INTELLECTUAL PROPERTY RIGHT
Title of Thesis
THE INFLUENCE OF FINANCIAL RATIOS
AND MACROECONOMIC FACTORS
TOWARD STOCK PRICES OF BANK
CATEGORY KBMI 3 & 4
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iv
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aware of this agreement and has also agreed to the above warranties.
Name:
Venantius Ivan Widijono
Date:
13 September 2023
Signature:
v
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ACADEMIC INTEREST
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undersigned:
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Student ID number
Study program
: Venantius Ivan Widijono
: 014202000028
: Management
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approve to give President University a non-exclusive royalty-free right upon
my final report with the title:
THE INFLUENCE OF FINANCIAL RATIOS AND
MACROECONOMIC FACTORS TOWARD STOCK PRICES OF
BANK CATEGORY KBMI 3 & 4
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This statement I made in truth.
Cikarang, 13 September 2023
(Venantius Ivan Widijono)
vi
ADVISOR APPROVAL FOR JOURNAL OR
INSTITUTION’S REPOSITORY
As an academic community member of the President's University, I, the
undersigned:
Name
NIDN Number
Study program
Faculty
: Assoc. Prof. Dr. Drs. Chandra Setiawan, M.M., Ph.D
: 201205336
: Management
: Business
declare that following thesis :
Title of thesis
: The Influence of Financial Ratio and Macroeconomic
Factors Toward Stock Prices of Bank Category KBMI
3&4
Thesis author
: Venantius Ivan Widijono
Student ID number : 014202000028
will be published in journal or institution’s repository
Cikarang, 13 September 2023
(Assoc. Prof. Dr. Drs. Chandra Setiawan, M.M., Ph.D)
vii
PLAGIARISM RESULT
viii
RESULT OF GPT ZERO
ix
ABSTRACT
This study aims to investigate the influence of financial ratio and macroeconomics factors
toward stock price of bank category KBMI 3 & 4. This research used quantitative
approach. There are seven independent variables used within this study, which is BOPO,
LDR, NPL, CAR, Inflation Rate, Exchange Rate USD/IDR and BI Rate. Through
purposive sampling, this research got 312 observations from 13 companies using quarterly
data from 2017 – 2022. All variables is examined by descriptive analysis, classical
assumption test, multiple linear regression and hypotheses testing. The outcome of this
study reveals that partially, BOPO, NPL, Inflation Rate and Exchange Rate have a negative
significant influence toward stock price of bank category KBMI 3& 4, while CAR and BI
rate have a positive significant influence toward the stock price. In addition, LDR partially
have positive but not significant effect toward stock price of bank KBMI 3 & 4.
Simultaneously, all independent variables exerts significant influence toward stock price
of bank category KBMI 3 & 4 during 2017 – 2022 period, with the value of coefficient
determination 27.69%.
Keywords: Stock Prices, BOPO, LDR, NPL, CAR, Inflation Rate, Exchange Rate, BI Rate
x
ACKNOWLEDGEMENT
First and foremost, the researcher would like to thank God because with His
blessing and amazing grace, the researcher could finish this thesis. During the
completion of this thesis, there are many struggles and limitations experienced
by researcher, but gratefully all the difficulties experienced were always
supported by several parties. Therefore, the researcher would express his
gratitude for everyone who had helped him in completing this thesis:
1. Researcher family, father, mother and little brother who are always
sending prayers, support, motivation, and cheers the researcher in every
situation.
2. Mr. Assoc. Prof. Dr. Drs. Chandra Setiawan, M.M., Ph.D., as a thesis
advisor who has given time, advice, guidance, and support during the
creation of this thesis from the beginning until this thesis complete
3. All lectures and employees of President University for their guidance,
knowledge, and support while the researcher studying in President
University.
4. The researcher soul mate, close friends and seniors who always
accompany, assist, support, cheers and motivate researcher all the time.
5. All friends from PUMA Management and PUCatSo for all support,
beautiful memories and happiness while studying at the President
University
xi
6. Lastly, to those who indirectly contributed to this thesis, the researcher
would like to thank you for every support in the completion of this
study.
Researcher hope this thesis can be useful for everyone in the future regarding
the related topic who might need it
Cikarang, 13 September 2023
(Venantius Ivan Widijono)
xii
TABLE OF CONTENTS
PANEL OF EXAMINERS APPROVAL SHEET ......................................................... ii
STATEMENT OF ORIGINALITY ............................................................................... iii
CONSENT FOR INTELLECTUAL PROPERTY RIGHT ......................................... iv
SCIENTIFIC PUBLICATION APPROVAL FOR ACADEMIC INTEREST.......... vi
ADVISOR APPROVAL FOR JOURNAL OR INSTITUTION’S REPOSITORY . vii
PLAGIARISM RESULT .............................................................................................. viii
RESULT OF GPT ZERO ............................................................................................... ix
ABSTRACT ....................................................................................................................... x
ACKNOWLEDGEMENT ............................................................................................... xi
TABLE OF CONTENTS .............................................................................................. xiii
LIST OF TABLES ........................................................................................................ xvii
LIST OF FIGURES ..................................................................................................... xviii
LIST OF ACROYNMS ................................................................................................. xix
CHAPTER I INTRODUCTION ..................................................................................... 1
1.1 Background ............................................................................................................... 1
1.2 Problem Statement .................................................................................................... 6
1.3 Research Questions ................................................................................................... 7
xiii
1.4 Research Outline ....................................................................................................... 7
CHAPTER II LITERATURE REVIEW ........................................................................ 9
2.1 Review of Literature ................................................................................................. 9
2.1.1 Bank ................................................................................................................... 9
2.1.2 KBMI ................................................................................................................. 9
2.1.3 Capital Market ................................................................................................. 10
2.1.4 Stock ................................................................................................................ 11
2.1.5 Financial Ratio Analysis .................................................................................. 14
2.1.6 Operational Efficiency Ratio (BOPO) ............................................................. 15
2.1.7 Loan to Deposit Ratio (LDR) .......................................................................... 16
2.1.8 Non-Performing Loan (NPL) ........................................................................... 17
2.1.9 Capital Adequacy Ratio (CAR) ....................................................................... 17
2.1.10 Macroeconomic.............................................................................................. 18
2.1.11 Inflation Rate ................................................................................................. 19
2.1.12 Exchange rate ................................................................................................. 20
2.1.13 Bank Indonesia Rate (BI Rate) ...................................................................... 20
2.2 Previous Research ................................................................................................... 21
2.3 Research Gap .......................................................................................................... 29
2.4 Theoretical Framework ........................................................................................... 30
xiv
2.5 Hypothesis............................................................................................................... 31
CHAPTER III METHODS ............................................................................................ 32
3.1 Research Design...................................................................................................... 32
3.2 Research Framework .............................................................................................. 32
3.3 Sampling Design ..................................................................................................... 34
3.3.1 Size of Population ............................................................................................ 34
3.3.2 Size of Sample ................................................................................................. 34
3.4 Data Collection Design ........................................................................................... 37
3.5 Operational Definition ............................................................................................ 37
3.6 Data Analysis Design .............................................................................................. 39
3.6.1 Descriptive Analysis ........................................................................................ 39
3.6.2 Panel Data Analysis ......................................................................................... 41
3.6.3 Classical Assumption Test ............................................................................... 43
3.6.4 Multiple Regression Analysis .......................................................................... 45
3.7 Hypothesis Testing.................................................................................................. 46
3.7.1 Significant Level .............................................................................................. 47
3.7.2 T-test ................................................................................................................ 47
3.7.3 F-test ................................................................................................................ 49
3.7.4 Coefficient of Determination (R2) ................................................................... 50
xv
CHAPTER IV RESULTS, ANALYSIS AND DISCUSSION ..................................... 51
4.1 Descriptive Analysis ............................................................................................... 51
4.2 Data Analysis .......................................................................................................... 53
4.2.1 Panel Data Regression ..................................................................................... 53
4.2.2 Classical Assumption Test ............................................................................... 55
4.2.3 Multiple Linear Regression Analysis............................................................... 58
4.3 Hypothesis Testing.................................................................................................. 61
4.3.1 T-test ................................................................................................................ 61
4.3.2 F -test ............................................................................................................... 62
4.3.3 Coefficient of Determination (R2) ................................................................... 63
4.4 Interpretation of the Result ..................................................................................... 63
CHAPTER V CONCLUSIONS AND RECOMMENDATIONS ............................... 68
5.1 Conclusions ............................................................................................................. 68
5.2 Recommendations ................................................................................................... 70
REFFERENCES ............................................................................................................. 71
APPENDICES ................................................................................................................. 81
xvi
LIST OF TABLES
Table 2.1
Previous Research ....................................................................
21
Table 3.1
Sample Proportion ....................................................................
35
Table 3.2
Operational Definition..............................................................
37
Table 4.1
Descriptive Statistics Result .....................................................
51
Table 4. 2
Chow Test ................................................................................
53
Table 4.3
Hausman Test ...........................................................................
54
Table 4.4
Lagrange Multiplier Test ..........................................................
55
Table 4.5
Correlation Matrix ....................................................................
56
Table 4.6
Heteroscedasticity Test ............................................................
57
Table 4.7
Autocorrelation Test .................................................................
58
Table 4.8
Multiple Linear Regression Result ...........................................
59
Table 4. 9
F-Test .......................................................................................
62
Table 4.10
Coefficient of Determination ...................................................
63
xvii
LIST OF FIGURES
Figure 1.1
Growth of IDX Composite .......................................................
1
Figure 1.2
Growth of Total Assets of Banks in Indonesia ........................
2
Figure 2.1
Theoretical Framework ............................................................
30
Figure 3.1
Research Framework ................................................................
33
Figure 4.1
Normality Test..........................................................................
56
xviii
LIST OF ACROYNMS
BOPO
= Beban Operational Pendapatan Operasional (operational efficiency ratio)
LDR
= Loan to Deposit Ratio
NPL
= Non-Performing Loan
CAR
= Capital Adequacy Ratio
IR
= Inflation Rate
EXR
= Exchange Rate
BIR
= Bank Indonesia Rate
OJK
= Otoritas Jasa Keuangan
KBMI
= Kelompok Bank berdasarkan Modal Inti
IDX
= Indonesia Stock Exchange
xix
CHAPTER I
INTRODUCTION
1.1 Background
In the present day, almost every societal group has a greater awareness of the capital
market. The capital market provides finance for businesses and the government, as well as
investment opportunities for fund owners. The capital market has evolved into an important
component of the economy, serving as a conduit for funds to be transferred from investors
wanting a return to capital users who require cash to finance various projects or business
operations. Long-term investment products having maturities of more than one year are
available in the capital market, such as equities, bonds, mutual funds, and different
derivative instruments derived from securities (OJK, 2019). In Indonesia, the main
exchange that provides a marketplace for trading various financial instruments such as
stocks, bonds, and mutual funds is called the Indonesia Stock Exchange (IDX).
Figure 1.1 Growth of IDX Composite
Source: Google Finance, 2023
1
Based on figure 1.1, the stock market in Indonesia has experienced significant growth in
the past few decades. The growth means that the overall stock market in Indonesia has
experienced a positive trend and has gained value over time. This also can indicate that the
economy is performing well and investors have confidence in the market. The positive
trend shown by IDX Composite has made investors even more interested in investing in
stocks in Indonesia. This is in line with data from KSEI in 2022 which states that the
number of capital market investors in Indonesia has increased and by the end of 2022 there
have been 10 million investors.
The banking sector is one of those that has a lot of traction on the stock market. Banks play
a vital role in any economy because they facilitate economic activity by providing financial
services. Banks serve as financial intermediaries, mobilizing depositors' funds and
transferring cash to borrowers in the form of loans and credit. The banking industry in
Indonesia is no exception. The banking industry in Indonesia plays a significant role and
contributes to the rise and expansion of the Indonesian economy, beginning with small,
medium, and big commercial loans, and even as a location to guarantee public deposits
(Simatupang, 2019).
Figure 1.2 Growth of Total Assets of Banks in Indonesia
Source: Otoritas Jasa Keuangan (OJK), 2022, adjusted by Researcher
2
The banking industry in Indonesia has grown significantly over the years. According to
data figure 1. 2 from the Financial Services Authority (OJK), the banking sector's total
assets grew from IDR 6,729 trillion in 2016 to IDR 10,487 trillion in 2022, representing an
average annual growth rate of 7.7 % in the past seven years. These banks include stateowned banks, regional banks, private banks, and international banks, and they all provide
an extensive variety of financial services and products to consumers throughout the
country. Rising deposits and loans from both the corporate and retail divisions, along with
the development of digital banking products and services. In addition, referring to data
from the Financial Services Authority (OJK), the banking industry throughout 2022 has
also managed to record positive performance, in which the national banking net profit has
reached IDR 200 trillion, reaching IDR 201.82 trillion to be precise. The acquisition of net
profit increased by 43.94% compared to the 2021 period (Sahara, 2023).
Because the development of banks in Indonesia continues to occur, in 2021, OJK redefined
the classification of banks from grouped based on Commercial Banks based on Business
Activities (BUKU), to Bank Groups based on Core Capital (KBMI). This is stated in OJK
Regulation (POJK) Number 12/POJK.03/2021 concerning Commercial Banks which aims
to make business activities at banks not limited to capital anymore. There are four
categories of KBMI, namely the KBMI 1 group has a core capital of up to IDR 6 trillion,
KBMI 2 has a core capital of over IDR 6 trillion to IDR 14 trillion, KBMI 3 core capital is
from IDR 14 trillion to IDR 70 trillion, and KBMI 4 core capital is above IDR 70 trillion.
Until 2022, there are only 4 banks classified as banks in the KBMI 4 category and 10 banks
classified as banks in the KBMI 3 category. The net profit of the bank category based on
the core capital of KBMI 4 and KBMI 3 is still the basis for national banking performance.
Throughout 2022 the net profit of the KBMI 4 banks is IDR 143.34 trillion, growing high
by 45.7% (yoy). In addition, banks in the KBMI 3 category also recorded net profit growth
of IDR 34.42 trillion, growing 30.97% (yoy) (Sahara, 2023). Banks are able to enhance
their lending operations when they are profitable, which can boost investment and generate
employment. Profitable banks can also draw in additional deposits, increasing the amount
of credit available to the economy. This may result in more spending and investment, which
3
would fuel economic expansion. Additionally, successful banks typically have higher
capital buffers, which can increase their resistance to economic shocks and financial crises.
In investing activities in the capital market, an important factor to be considered by
investors is the company's stock price. Stock price is a fundamental indicator for investors
to evaluate investments, manage portfolios, generate profits, and gauge market trends and
sentiment. The price of a stock is formed from the sale and demand for stocks, the high
demand and also the sale of stocks very quickly affect the price of a stock (Ginting &
Erward, 2013). By analyzing stock prices, investors can make informed decisions and
navigate the dynamic and ever-changing landscape of the stock market. As a result, before
making an investment decision, investors must understand the elements that might impact
stock values, including internal and external factors.
Before making an investment selection, the investor should investigate and comprehend
the firm's performance. (Purwanto & Agustin, 2017). Investors can acquire insights into a
company's financial state by evaluating financial performance indicators, often known as
financial ratios. These ratios may be used to evaluate a company's financial health, identify
its strongest points and shortcomings, and even forecast stock market returns (Ayem &
Wahyuni, 2017). Investors might have greater trust in a firm that is in solid financial shape.
As a result, stock demand will grow, potentially benefiting the company's stock price
(Dewi, 2022). The influence of these financial performance metrics on bank stock prices
in Indonesia is critical for a number of reasons. First, investors and financial analysts may
make wise investment judgments by comprehending the connection among financial
performance and stock returns. Second, policymakers may utilize the study's results to
make educated policy decisions that will help the banking industry and the economy as a
whole develop. Finally, the research can give insights into the general health of the banking
system, indicating areas that need to be improved as well as those that are operating well.
As a consideration in making an investment decision, investors also must be mindful of the
outside variables that could might influence stock prices. Stock prices, according to
Sudirman (2015), are heavily impacted by macroeconomic factors. Macroeconomics aims
to quantify the economy's performance and forecast how it may improve. It is also
4
concerned with how an economy functions as a whole, as well as how various sectors of
the economy interact with each other in order to comprehend how aggregate functions
(Utami, 2021). Waris (2014) asserts that a number of external variables, including inflation,
interest rates, foreign exchange rates, transaction volume, and environmental
circumstances, such as political and economic stability, have an impact on stock prices. As
a result, prior to deciding stock investing selections, potential investors should consider
these external aspects as relevant for study.
Financial ratio and macroeconomic factors exert an impact on stock prices have been
studied by several previous researchers. For instance, a study by Ilham (2022) found that
BOPO and NPL ratio have significant positive influence toward stock price. Study by
Kusuma (2017) found that CAR, LDR, NPL and BOPO have significant impact toward
stock price. Similarly, a study by D. Putri (2017) found that CAR and LDR have significant
impact toward stock price. Apart from financial performance, the influence of
macroeconomics toward stock prices has also been studied before. According to Herdini
et al. (2021), Inflation rates, BI rate and exchange rate have a significant effect toward
stock price.
As a result of the aforementioned explanation, investors should consider financial
performance and macroeconomics before making an investment decision. Therefore, the
goal of this research is to examine how financial performance and macroeconomics affect
stock price in order to identify which indication investors may use to anticipate their
investment decision. The financial performance ratios used in this study are BOPO, NPL,
LDR, and CAR, while the macroeconomic parameters used include inflation, exchange
rate and BI rate. Furthermore, this research will concentrate on examining the Bank
category KBMI 3 and KBMI 4 listed in IDX, considering the importance of bank category
KBMI 3 and KBMI 4 in economic growth.
5
1.2 Problem Statement
Stock market are known with their market volatility and high fluctuations. Therefore,
understanding the factors that affect stock prices empowers investors to make informed
decisions, assess risk, identify value opportunities, adopt a long-term perspective, manage
risk effectively, time their entry and exit, and monitor their investment portfolios. Before
make investment decision, financial performance ratios and macroeconomic conditions are
crucial indicators that can affect stock price, which can help investors to gain more
information before decide their investment decision.
Several previous studies have found a relationship between financial performance ratios
and macroeconomic factors toward stock prices, with the financial performance ratios used
in this study being BOPO, CAR, LDR, and NPL, and the macroeconomic factors used in
this study being inflation, exchange rate, BI rate. However not all prior research found that
independent variables exert significant impact on the dependent variable. As found by
Nurhayati & Pertiwi (2021) NPL and LDR has no significant influence toward stock price.
According to Fordian (2017), there is no impact by LDR and CAR toward stock price.
Study by Vilia & Coline (2021) found that NPL and CAR have no significant effect toward
stock price. Yudistira & Adiputra (2020) found that BOPO, inflation and BI rate have no
impact on stock price. A. Saptiani (2018) found that Inflation rate and exchange rates does
not exert significant impact toward stock price. Previous research has yielded inconsistent
findings on the effect of financial performance and macroeconomic factors toward stock
price. As a consequence, additional study is needed to obtain another empirical result and
learn more about the factors that drive stock returns. According to the problem statement,
the purpose of this study is to discover the impact of financial ratio and macroeconomics
factors on stock price, particularly in the bank category KBMI 3 & 4 in Indonesia, in order
to obtain more empirical results.
6
1.3 Research Questions
In this study, researcher use Return on Asset ratio, Loan to Deposit ratio, Gross NonPerforming Loan Ratio, Capital Adequacy ratio, Inflation Rate and Exchange Rate as
independent variable to analyze the impact of macroeconomics and financial performance
toward stock return in Indonesia banking industry. The researcher has specified the
problem to be examined in this study in order to attain the study's objectives.
1) Does BOPO significantly influence toward the stock price of bank category KBMI
3& KBMI 4?
2) Does LDR significantly influence toward the stock price of bank category KBMI
3& KBMI 4?
3) Does NPL significantly influence toward the stock price of bank category KBMI
3& KBMI 4?
4) Does CAR significantly influence toward the stock price of bank category KBMI
3& KBMI 4?
5) Does Inflation Rate significantly influence toward the stock price of bank category
KBMI 3& KBMI 4?
6) Does Exchange Rate significantly influence toward the stock price of bank category
KBMI 3& KBMI 4?
7) Does BI Rate significantly influence toward the stock price of bank category KBMI
3& KBMI 4?
8) Do BOPO, LDR, NPL, CAR, inflation rate, exchange rate, and BI rate
simultaneously have significant influence toward the stock price of bank category
KBMI 3 & 4?
1.4 Research Outline
The substance of this proposal might be split into five chapters, whose will be explained in
the order listed below:
7
CHAPTER I INTRODUCTION
Chapter I will primarily focus on explaining why researcher chose the topic for this
research, it will also cover problem statement regarding the influenced of macroeconomics
and financial performance toward stock return in Indonesia banking industry, research
question identification, and the organization that describes this research.
CHAPTER II LITERATURE REVIEW
The second chapter of this thesis is critical to the research since it draws on relevant
literature, theories, and earlier research to support the study. It serves to identify and
comprehensively analyze the issues and objectives outlined in Chapter 1. This chapter also
describes the process through which researchers come up with hypotheses.
CHAPTER III RESEARCH METHODS
This chapter focuses on the research methodology, encompassing the methods, steps, and
overall framework employed to gather quantitative data for the study. Chapter III will
address the chosen research methodology, outlining the approach and techniques used to
collect and analyze data for this study, such as research design, framework, sampling and
data analysis design.
CHAPTER IV RESULTS, ANALYSIS AND DISCUSSION
This chapter is the main and most important part of this entire study, where this chapter
will discuss Data Analysis, explain the details of the data that has been proceeds, discuss
known problems and answers to problems.
CHAPTER V CONCLUSIONS AND RECOMMENDATIONS
This is the final component of the study, and it contains findings and the summarized of
the data proceeds in Chapter IV. This chapter will also give suggestions based on the
outcomes from the research.
8
CHAPTER II
LITERATURE REVIEW
2.1 Review of Literature
2.1.1 Bank
Banks have an essential intermediate function as a financial institution that links money
from parties with surplus funds to others that are underfunded. (Harahap & Hairunnisah,
2017). Banks' role as intermediary institutions places them in a very important position, as
they promote the smooth operation of the financial system, implement monetary policy,
gather and channel funds to the public, increasing the flow of funds for investment (Bilian
& Purwanto, 2015). According to Law No. 10 of 1998, a bank is described as an institution
that gathers cash for savings accounts and then generates them back into loans or other
bank products to improve people's living circumstances. Banks are one of the instruments
that have become engines of economic growth. This is in line with Article 4 of the Law of
the Republic of Indonesia No. 7 of 1992 on Banking, which states that Indonesian banking
strives to promote the realization of national development by enhancing fairness, economic
expansion, and national stability in order to improve people's well-being.
2.1.2 KBMI
KBMI is a bank classification determined by OJK, where banks in Indonesia are classified
based on their core capital. Capital adequacy is an important aspect to strengthen bank
institutions and back up credit risk (Rustendi, 2019). Bank capital consists of core capital
and supplementary capital. Core capital in banking can be understood as paid-up capital by
bank owners and capital originating from reserves formed and added to retained earnings.
So, the bank's core capital is an accumulation of paid-up capital, reserves formed, and
retained earnings.
Based on POJK Number 12 /POJK.03/2021 the grouping of Commercial Banks based on
bank core capital (KBMI) can be grouped into 4, namely,
9
a) KBMI 1 with a core capital of up to 6 trillion Rupiah,
b) KBMI 2 with a core capital of more than 6 trillion Rupiah to 14 trillion Rupiah,
c) KBMI 3 with a core capital of more than 14 trillion Rupiah up to 70 trillion Rupiah,
d) KBMI 4 with a core capital of more than 70 trillion Rupiah. KBMI is the result of
an update of the previous type of bank classification.
Previously, bank categories were classified as commercial bank groupings based on
business activities known as Commercial Banks by Business Activities (BUKU).
According to NOMOR 12/POJK.03/2021, grouping based on BUKU if related to KBMI,
can be explain by:
a) BUKU 1 can be equated with KBMI 1;
b) BUKU 2 can be equated with KBMI 1;
c) BUKU 3 can be equated with KBMI 2 or KBMI 3;
d) BUKU 4 can be equated with KBMI 3 or KBMI 4.
In the past, bank groups were formed on the basis of BUKU in order to encourage
consolidation. However, the OJK's aims were not met throughout its creation. Instead,
BUKU classification make many banks have good risk management but are unable to
develop due to constraints on capital regulations. These small banks do not issue new
products and are hampered from becoming big banks because of these capital regulations.
As a result, OJK decided to make improvements with adjusted BUKU classification to
KBMI with the goal of making the bank cluster more accurate and not limiting the
development of banks in Indonesia.
2.1.3 Capital Market
The capital market is a place where short-term and long-term financial instruments, such
as debt and stock issued by the government, public entities, and private businesses, may be
traded (Azis et al., 2015). The capital market's purpose is to offer finance resources for
businesses and governmental organizations as well as for individuals to engage in investing
activities (Bitar, 2019). It facilitates the flow of capital between investors seeking
opportunities to grow their wealth and entities (such as corporations and governments) in
10
need of financing for various purposes, such as expansion, infrastructure development, or
debt management.
According to Stosic-Mihajlovic (2016) in Mujib & Candraningrat (2021), The capital
market in an economy country serves two primary functions: the economic function and
the financial function. With the goal to fulfill its economic purpose, the capital market
offers tools or methods for bringing together two parties with an interest in one another. In
carrying out its financial role, the capital market gives investors the opportunity or
possibility to get a return on money that has been distributed in line with the characteristics
of the chosen investment. The capital market provides finance for the operations of the
issuer or other party in need of cash, whereas the investor seeks to make money by
investing their assets in the capital market.
According to OJK, the capital market comprises two distinct segments: the primary market
and the secondary market. The primary market is where freshly issued securities are
exchanged for the first time in public before they are listed on the stock exchange. It is also
where newly issued securities are traded. The trading of securities that have been listed on
the Stock Exchange takes place in the secondary market, which is a continuation of the
primary market. To accommodate the wide range of demands of investors and issuers,
many types of financial instruments are exchanged on the capital market. Stocks, bonds,
derivatives, and commodities are examples of these instruments. The Indonesia Stock
Exchange (IDX) is the primarily exchange in Indonesia that provides a platform for
exchanging various financial products such as stocks, bonds, and mutual funds.
2.1.4 Stock
2.1.4.1 Stock Definition
According to Fahmi (2013) as cited by Nurutami (2019), Stock is a formal evidence of
capital/funds ownership in a corporation that clearly shows the nominal value, firm name,
and is accompanied by unambiguous rights and duties to each holder. Stock is a type of
security that denotes ownership of a portion of the issuing business. "Shares" are units of
stock that provide its owner access to a portion of the company's assets and income based
on how many stocks they possess (Hayes, 2023). Stocks are units of ownership that grant
11
shareholders certain rights, such as voting rights and a share of the company's profits. They
serve as a vehicle for investors to participate in the financial performance and growth
prospects of publicly traded companies. Investing in stocks can provide investors with
opportunities to get profit through capital appreciation from rising prices and income
through dividends (Ery Yanto et al., 2021). But it also carries risks inherent in the volatility
and uncertainty of the stock market. They are one of the primary financial instruments
traded in the capital market.
According to Hayes (2023), There are two form of stock, which is common stock and
preferred stock:
a) Common stocks are securities that provide as proof of a company's ownership
(Amadeo, 2021). Shareholders are entitled to a part of the business's earnings
(dividends), and they are willing to bear the risk of losses suffered by the business.
Those who own shares in the company have the ability to vote on its management.
b) Preferred stock is a type of investment that gives the shareholder priority over the
company's assets and profits. Preferred stock has the following features: a focus
over common stockholders for dividends; no or limited voting rights; an
opportunity to influence company management, especially regarding the selection
of the board of directors; and the right to get the greatest payment at par value of
share first after creditors in the event of a company liquidation (N. Putri, 2019).
2.1.4.2 Stock Price
One of the factors, consideration of an investor before investing their funds in a stock
market is the stock price. Stock price could be explained as the market value that is
determined when a share of a company's stock is exchanged. Stock price can described as
the price established as a result of the interaction between sellers and buyers of stock with
the goal of collecting profits from the firm, which included both capital gains and dividends
(Hidayat, 2018). The concept of a stock price, according to Jogiyanto (2014), The notion
of stock price refers to the price of a share that occurs on the stock market at a specific
point established by investors and determined by the supply and demand for shares in the
capital market.
12
It is important to note that stock prices are subject to fluctuations and can experience
volatility. Stock prices will always be fluctuating in response to the company's future
prospects as well as the amount of demand and supply for those stocks (Ardiyanto et al.,
2020). Stock price is influenced by several external and internal factors. Internal factors
are those that occur within the organization. External factors are those that originate outside
of the firm. According to Otoritas Jasa Keuangan (2022), there are several external and
internal factors that can affect stock prices. Fundamental conditions of macroeconomics,
Rupiah exchange rate fluctuations against foreign currencies, government policies, certain
news that can trigger panic factors, and market manipulation factors are several external
factors that could influence stock prices. While for the internal factors, company
fundamental factors, corporate actions and projection of company performance in the
future are several internal factors that could influence the stock prices of a company.
Understanding factors that influenced stock price movements is crucial for investors and
market participants, as it enables them to make informed decisions regarding buying,
selling, or holding stocks in their investment portfolios
2.1.4.3 Stock Analysis
Investors must conduct extensive research before investing in stocks. Stock analysis is
essential for making informed investing decisions, reducing risks, and reaching financial
goals. It gives investors useful insights and data-driven analyses to help them manage the
complexity of the stock market and maximize their investment returns. Therefore,
understand the performance of the company by doing some analysis are really important
for investors. Commonly, there are two kinds of analysis that can be used to analyze stock
prices in the future, namely fundamental and technical analysis (Fathinah & Setiawan,
2021).
a) Technical Analysis
The objective of technical analysis is to predict future pricing and volume trends
by analyzing patterns found in stock charts. Technical analysis makes the
assumption that patterns and movements frequently repeat themselves in the future.
(Frederick, 2019). This analysis projects stock values using resources such as charts
13
and historical data of stock price, with the goal of providing significant insights into
the future direction of stock prices. Technical analysis is frequently used alongside
with fundamental analysis to build a full picture of a stock's prospective market
performance.
b) Fundamental Analysis
Fundamental analysis is a way of assessing a stock's intrinsic value using economic
basic information and financial data from the firm. The fundamental analysis
compares the intrinsic value to the market price to see if the stock price has
represented the intrinsic value. (Zerodha, 2018). According to Husnan (2015) in
(Hilmi et al., 2019), fundamental analysis is the process of estimating future stock
prices by calculating the value of fundamental factors that influence future stock
prices, such as sales, sales growth, costs, and dividend policies, and then applying
the relationship between these variables to arrive at an estimated stock price.
Financial ratio analysis will be utilized to study the firm together with trend or
growth identification, operational effectiveness assessment, understanding of the
company's features, and health assessment.
In conclusion, both technical analysis and fundamental analysis play crucial roles in
understanding and predicting stock prices. Technical analysis provides insights into shortterm price movements and market trends, while fundamental analysis focuses on assessing
the long-term value and financial health of a company. By employing these analytical
methods, investors can make informed decisions about buying, selling, or holding stocks,
ultimately aiming to maximize their returns in the stock market.
2.1.5 Financial Ratio Analysis
The financial ratio analysis describes as a tool in evaluating the financial performance and
condition of companies. According to (Kasmir, 2017) The technique of dividing one
number by another to compare statistics in financial records is known as a financial ratio.
Comparisons can be conducted within one element and the elements in one financial report
or among elements in the financial statements, and the similar numbers can be in the data
from one or more periods. Financial ratio provides insights into a company's financial
health, performance, and risk profile. It helps investors make informed decisions, manage
14
their portfolios effectively, and assess the value and potential of their investments. The
greater a company's financial performance, the higher expectations investors have for it
(Widayanti & Colline, 2017).
According to Amalia (2020) in Amalia & Nugraha (2021), financial ratios play a crucial
role in evaluating a company's financial performance and predicting its business continuity,
whether it remains healthy or faces challenges. These ratios are essential in measuring the
performance and soundness of banks as well. A trustworthy bank maintains its soundness
through normal banking operations and fulfills its obligations in accordance with banking
regulations (Medyawicesar et al., 2018). In this study, researcher will focus on key
financial ratios that provide insights into the financial performance and soundness of a
bank, including Operational Efficiency Ratio, Loan to Deposit Ratio, Non-Performing
Loan Ratio, and Capital Adequacy Ratio. By analyzing these ratios, researcher aim to
assess the relationship between the overall health and stability of the banks toward stock
price under study.
2.1.6 Operational Efficiency Ratio (BOPO)
A bank's capacity to manage operating expenditures in relation to operating revenue is
evaluated using the operational efficiency ratio, often known as the BOPO ratio. This ratio
shows how a company may make the operational expenditures it employs to fund all of its
operational operations as efficient as possible (Rahmani, 2023). The BOPO ratio
demonstrates the effectiveness and capability of a bank in managing its operations. When
the costs of making a profit are lower than the income generated by using these assets,
there is excellent operational efficiency. Banks that are unable to increase their level of
business efficiency will lose their ability to compete when it comes to raising public money
and using those funds to channel capital for businesses. (N. Nurhayati, 2020).
When the expenses incurred to create profits are less than the profits gained from the use
of these assets, this is referred to as good operational efficiency. The lower BOPO ratio
indicates the more efficient the banks operate, which make the higher bank’s profit
(Wismaryanto, 2013).This is due to the fact that a small BOPO ratio indicates that the
15
company uses a small operational cost to obtain its business profit. BOPO ratio is
formulated as follow:
𝐵𝑂𝑃𝑂 =
𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐶𝑜𝑠𝑡
𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐼𝑛𝑐𝑜𝑚𝑒
2.1.7 Loan to Deposit Ratio (LDR)
The Loan to Deposit Ratio measures the relationship among the quantity of credit granted
by a bank and the money it receives (Fadila, 2015). According to Kasmir (2019) in Aryanti
et al. (2022), LDR (Loan to Deposit Ratio) is the ratio used to assess the structure of the
amount of credit issued in comparison to the total of public funds and own capital utilized.
The Loan to Deposit Ratio (LDR) reflects the bank's ability to return withdrawn funds that
have been invested that have been made to rely on loans that have been given as a source
of liquidity (Situmeang, 2021). In addition, LDR ratio also used to identify a bank's ability
and capacity to meet its short-term obligations.
The greater the LDR ratio, the less liquidity the bank has to address any unanticipated
financial requirements. In contrast, if the LDR ratio was excessively poor, could not be
making as much money as it might (Murphy, 2020). Banks must balance their ability to
repay the money they borrow with the quantity of credit they give to the general people.
As a result, bank liquidity must be kept under control to fulfill demands when clients
withdraw money and distribute loans to debtors (borrowers). Understanding and managing
the LDR effectively is crucial for banks in maintaining financial stability, managing risks,
and optimizing profitability. Below is the formula to calculated LDR:
𝐿𝐷𝑅 =
𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠
16
2.1.8 Non-Performing Loan (NPL)
NPL is a condition in which the debtor is unable to pay all of his obligations in the form of
funds or interest to the creditor (Saladin et al., 2022). NPL is a payment error where the
bank has not received debt payments from the borrower for more than 3 months or during
the agreed maturity date as stated in the loan agreement (Aryanti et al., 2022). According
to Kasmir (2013) as cited by Nugroho & Rachmaniyah (2020), NPLs can develop as a
result of two factors: the first is a mistake on the part of the bank in assessing the credit
provision, and the second is a fault on the part of the creditor who intentionally or
unintentionally fails to fulfill his obligations. NPL ratio is one of the metrics used to
examine the credit risk, asset quality and efficiency in the allocation of resources to
productive sectors, which become critical indicators for analyzing a bank (Setiawan et al.,
2017).
An appropriate NPL value in a bank is 5% of its credit, according to Bank Indonesia
Regulation No 6/10/PBI/2004 regarding the Soundness Rating System for Commercial
Banks. The rise in NPLs will have a detrimental impact on the bank. A higher NPL ratio
increases the chance of a bank experiencing financial difficulties since poor credit quality
results in more non-performing loans. A greater NPL ratio also shows that a bank's loan
portfolio has a higher level of credit risk. Therefore, in this instance, a bank's profitability
is inversely correlated with its NPL ratio (KUSUMA, 2017). On the other hand, a low NonPerforming Loan ratio can reflect a better level of soundness of the bank, which can lead
the increase in investor attractiveness to invest their money into the company (Hariyani et
al., 2021). Below is the formula for Non-Performing Loan (NPL):
𝑁𝑃𝐿 =
𝑁𝑜𝑛 − 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠
2.1.9 Capital Adequacy Ratio (CAR)
According to Irham (2013) in Aryanti et al. (2022), Capital Adequacy Ratio (CAR) is a
critical indicator for assessing a bank's financial health and capacity to finance its
operations with its capital ownership. It serves as a buffer to safeguard the bank's
17
operational activities and protect against potential losses. CAR reflects the adequacy of a
bank's equity capital in managing unforeseen circumstances and financial risks. The CAR
limit is set by the government of the respective state where the bank operates, signifying
the importance of capital in evaluating the overall health of the banking system (Irawati et
al., 2019). By adhering to the provisions set forth by regulatory authorities and maintaining
a sufficient CAR, banks can demonstrate their capacity to manage risks and protect the
interests of depositors and stakeholders alike.
As a solvency ratio, CAR is instrumental in evaluating a bank's ability to absorb potential
losses and withstand adverse economic conditions. It assesses a bank's financial health and
compliance with regulatory standards by calculating the percentage of capital to riskweighted assets. Notably, higher CAR values generally indicate a bank's greater resilience
in challenging economic environments. A high CAR reflects the company's ability to
guarantee its capital based on its assets. Investors will have more confidence in investing
their shares in healthy companies. The larger the number of investors that are interested in
investing in their shares, the higher the stock price of the firm (Fahlevi et al., 2018). In
accordance with Bank Indonesia's regulations, a healthy bank must maintain a CAR of at
least 8%. The formula for calculating CAR is as follows:
𝐶𝐴𝑅 =
(𝑇𝑖𝑒𝑟 1 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 + 𝑇𝑖𝑒𝑟 2 𝐶𝑎𝑝𝑖𝑡𝑎𝑙)
𝑅𝑖𝑠𝑘 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠
2.1.10 Macroeconomic
Macroeconomic factors encompass a broad range of economic variables that reflect the
overall health and stability of an economy. Macroeconomics is the science of the causes
and patterns that impact the entire economy, such as inflation, unemployment, and
economic growth (Mankiw, 2016). According to Putong (2013) in Pardede (2021),
macroeconomic study purpose to gain insight into the economic system's complex
dynamics and interrelationships. The goal of macroeconomics is to better understand
economic events, enhance policymaking, and promote stability and long-term growth.
18
Macroeconomic conditions influence the performance of a business, which may affect the
market price of the stock of the business. Investors use macroeconomics to foresee and
plan their moves in a variety of asset markets, hoping to take advantage of opportunities
and reduce risks (Rasure, 2023). Understand macroeconomic can make businesses and
individual investors gain insights into the motivations of market participants, enabling
them to make more informed decisions that optimize the utilization of scarce resources and
enhance overall value creation (Cerdasco, 2023).
2.1.11 Inflation Rate
The inflation rate is a crucial macroeconomic metric that measures the pace of increase in
general prices for goods and services and, therefore, the decline in the buying power of
currencies as time passes. It is a significant component that has an impact on many
economic aspects, including financial markets and banking activities. According to Natsir
(2014) in Herdini et al. (2021), Inflation is the widespread and continuing increase in the
price of goods and services. Price increases in a few commodities are not termed inflation
unless they expand to (or result in) price increases in a large number of other commodities.
Several effects of inflation may be seen in society. High and unstable inflation can cause a
decline in purchasing power, an unpredictability that makes it challenging for the people
to decide how to allocate their cash, and a decline in the competitiveness of national
products (Utari et al., 2015). Because excessive inflation places different costs on society,
governments all over the world strive to manage inflation at a moderate level(Mankiw,
2016). Inflation that is low and stable will stimulate economic growth. A managed inflation
rate will improve entrepreneur earnings, which will promote future investment and,
eventually, will speed the formation of economic growth. A high inflation rate, on the other
hand, will have a negative influence on the economy, which might destabilize social and
political stability.
In a perfect scenario, the stock market is keen to see annual price increases of 1% to 3%,
which is known as low-to-moderate inflation. When annual inflation surpasses this degree,
the stock market is dominated by ambiguity, fluctuation, and decreased consumer
expenditure. As a result, economic growth slows down, which is undesirable for investors
19
and increases valuation concerns, perhaps leading to poor performance in the stock market
(Duggan, 2023).
2.1.12 Exchange rate
Due to its role as a means of exchange for the purchase and sale of goods and services,
currency plays a significant role in a nation. When compared to another country's money,
a country's currency has a distinct value. According to Sukirno (2015) in Clarensia (2021)
The exchange rate or foreign exchange rate is the total amount of units of local currency
necessary to get one unit of foreign currency. On the other hand, the exchange rate might
refer to the cost of a foreign currency in terms of home currency. Various variables,
including shifts in societal consumption patterns, changes in export and import prices, price
increases brought on by inflation, and economic expansion, among others, can have an
impact on currency fluctuations (Pardede, 2021).
A country's currency can represent the state of its economy. When a country's economy
develops, its currency tends to strengthen versus other nations' currencies (Dwijayanti,
2021). The fall in Rupiah exchange rate vs the USD reflects a drop in public appetite for
the Rupiah as a result of the national economy's deterioration. On the other side, the rise of
the Rupiah versus the US dollar suggests that economic circumstances are favorable.
According to Ahadiyatun (2018) in Pardede (2021), the strengthening of the rupiah (IDR)
is excellent news for investors since it indicates that Indonesia's economy is doing well,
which will encourage investors to buy shares. On the other hand, if the USD value rises
against the IDR, it indicates that Indonesia's economy is failing, and investors would keep
their funds rather than investing in shares.
2.1.13 Bank Indonesia Rate (BI Rate)
The BI Rate, released during each monthly meeting of the Bank Indonesia Board of
Governors (RDG), serves as a key interest rate benchmark. It signifies the official monetary
policy stance of Bank Indonesia (Bank Indonesia, 2016). Following ratification, the BI
Rate value is distributed to people in general as an indicator for the reference lending rate.
The BI Rate determines the interest rates charged by banks or leasing businesses in credit
transactions (Aini, 2018). The BI Rate is an interest rate that expresses monetary policy in
20
response to future odds of attaining the inflation target through regulating liquidity in the
money market. The BI rate influences deposit rates as well as bank lending rates. In general,
if expected future inflation exceeds the established target, Bank Indonesia will raise the BI
rate. Bank Indonesia, on the other hand, would drop the BI rate if future inflation
expectations were lower than the objective (Sunardi & Ula, 2017).
The Bank Indonesia Rate (BI Rate) exert significant impact on various aspects of the
economy. Being the bank's reference interest rate, the BI Rate is an important instrument
for influencing economic activity, inflation, and financial market dynamics. This does not
rule out the possibility of stock prices. According to Dithania & Suci (2022),The BI Rate
has a favorable impact on the profitability of banks, which implies that rising BI rates result
in increased earnings for banks. Bank profitability increases when the BI Rate rises can be
caused by increase of loan interest that will also cause banks to get greater loan repayments.
For banks that already have solid fundamentals. The increase in interest rates is a
momentum that can be used to increase income from interest rates (Haknuh, 2023). With
the condition of rising bank profits, this can also attract investors. This is supported by
research by Rachmawati (2019) and Herdini et al. (2021) who found that there is a
significant relationship between the BI rate and stock prices.
2.2 Previous Research
Table 2.1 Previous Research
No.
1.
Author/ Title/ Year
Ilham, (2022)
Title:
Method
Multiple Linear Regression
Results
This study found that
simultaneously, NIM,
Independent Variable:
Analysis Of Influence
Of Net Interest Margin NIM
Ratio (NIM), Costs
Operational
To
BOPO
Operational
Income
(BOPO),
Non
Performing Loan (NPL) NPL
And Loan To Deposit
Ratio (LDR) To Stock
BOPO, NPL and LDR
exert
significant
impact toward stock
prices.
Partially,
BOPO and NPL have
negative
significant
impact, while LDR
21
2.
Price In Registered Soe LDR
Banks
On
The
Indonesian
Stock
Dependent Variable:
Exchange Year 20162020
Stock Price
and NIM have no
Yudistira & Adiputra Multiple Linear Regression
(2020)
This study found that
Title:
Independent Variable:
The
Influence
of ROA
Internal and External
Factors on Stock Prices
BOPO
significant effect.
simultaneously,
the
all
independent
variable
exert
significant influence
toward stock price.
When it comes to
NIM
ROE
partial
influence,
ROA and ROE have
significant
Inflation Rate
effect
toward stock price,
while BOPO, NIM,
BI Rate
Dependent Variable:
Inflation rate and BI
rate
have
significant
3.
D. A. Putri (2017)
Title:
no
effect
Stock Price
toward stock price.
Multiple Linear Regression
This research found
that Partially Return
Use 23 Banking Companies on
“Effect Of ROA, CAR, the IDX in 2012 - 2015
NPM, And LDR On
Stock Price Commercial
Independent Variable:
Banks. “
ROA
On Assets, Capital
Adequacy Ratio, Net
Profit Margin, and
Loan to Deposit Ratio
give
positive
significant influence
CAR
to the share price
22
NPM
LDR
Dependent Variable:
Stock Price
4.
Aryanti et al. (2022)
Title:
Multiple Linear Regression
This study found that
Partially, ROA, LDR,
Independent Variable:
“The Influence of ROA,
ROE, LDR, CAR, and ROA
NPL on Banking Stock
Price Registered on
LDR
IDX”
and CAR have no
significant effect to
stock price. NPL and
ROE have negative
significant
CAR
NPL
effect
toward stock price.
Simultaneously,
ROA, ROE, LDR,
ROE
CAR NPL have effect
to stock price
Dependent Variable:
Stock Price
5.
E. Nurhayati & Pertiwi Multiple Linear Regression
(2021)
Title:
Use 4 Banks listed on the IDX
period 2009-2018
“Measurement Analysis
of the Most Dominant
Independent Variable:
Factors Affecting Prices
Indonesian
BUMN
Banking Shares Period CAR
2009 – 2018”
NPL
This study found that
partially, CAR, NIM
and
ROA
positive
have
significant
effect toward stock
price. NPL and LDR
partially
significant
have
no
effect
toward stock price.
23
LDR
ROA
NIM
Dependent Variable:
Stock Price
6.
Wijono et al., (2023)
Title:
“Effect of ROA, NIM,
and BOPO on Prices
LQ20 Banking Shares
on the Indonesia Stock
Exchange Period 20162022 Using Panel Data
Analysis”
Multiple Linear Regression
This study found that
ROA,
Independent Variable:
ROA
BOPO,
and
NIM simultaneously
exert
significant
impact toward stock
BOPO
price. When it comes
to
NIM
Dependent Variable:
partial
impact,
ROA and NIM have
significant
effect
toward stock price,
Stock Price
while BOPO have no
significant
effect
toward stock price.
7.
KUSUMA (2017)
Title:
Multiple Linear Regression
This study found that
simultaneously
Use 31 Banks listed on the IDX
“Effect Of Financial period 2010-2014
Performance
And
Company
Values
Independent Variable:
Banking On The Price
Of The Banking Share
Listing On Indonesia CAR
Stock Exchange (IDX)
all
independent variables
have significant effect
toward stock price.
Partially, CAR and
PER have positive
significant
effect
24
NPL
toward stock price,
while NPL, BOPO,
LDR
BOPO
and
LDR
negative
have
significant
effect toward stock
PER
price.
Dependent Variable:
Stock Price
8.
Fordian (2017)
Title:
Multiple Linear Regression
This study found that
simultaneously, CAR,
Use BUMN Banks listed on the
“Effect Of CAR, LDR, IDX period 2012-2016
AND EPS On Share
Price (Study on Soe
Independent Variable:
Banks Which Listings
On The IDX Period
2012 – 2016)
CAR
LDR and EPS give
significant
impact
toward stock price.
Partially, EPS have
significant
effect
toward stock price,
LDR
EPS
while CAR and LDR
have no significant
effect.
Dependent Variable:
Stock Price
9.
Vilia & Colline (2021)
Title:
Multiple Linear Regression
This study found that
simultaneously CAR,
Use 4 Banks listed on the IDX
“The Effect of Camel on period 2016-2019
Stock Price At Bank
BUKU 4 Which Are
Independent Variable:
Listed On The Indonesia
Stock Exchange For The
2016-2019 Period”
CAR
NPL, LDR, ROA and
NPM exert significant
influence
toward
stock price. Partially,
ROA and LDR have
25
NPL
negative
significant
effect, while NPM
LDR
ROA
have
positive
significant
NPL
NPM
effect.
and
partially
CAR
have
significant
Dependent Variable:
no
effect
toward stock price.
Stock Price
10.
Hariyani et al. (2021)
Title:
Multiple Linear Regression
Independent Variable:
“Analysis of Financial
Performance on Stock ROA
Price
(In
Banking
Companies Registered
NPL
on the Indonesia Stock
Exchange Period 20152019)
PER
Dependent Variable:
This study found that
partially,
NPL
negative
significant
effect toward stock
price,
PER
positive
have
significant
effect toward stock
price.
Meanwhile,
ROA
has
no
significant
11.
Nurliandini et al. (2021)
Title:
has
effect
Stock Price
toward stock price
Multiple Linear Regression
This study found that
partially, ROE have
Independent Variable:
“Analysis
of
the
Influence
of ROE
Fundamental, Technical
and
Macroeconomic
CR
Factors on Share Prices
in Chemical Subsector
Companies Listed on the DER
Sharia Stock Index”
Trading Volume
positive
significant
effect toward stock
price, meanwhile, CR,
DER,
Trading
volume, Inflation rate
and
exchange
partially
have
rate
no
26
Inflation Rate
significant
effect
toward stock price.
Exchange rate
Dependent Variable:
Stock Price
12.
Herdini et al. (2021)
Multiple Linear Regression
This study found that
BI rate, exchange rate,
Title:
Independent Variable:
“Analysis Of Bank
Indonesia
Certificate
(Sbi) Interest Rate,
Inflation And US Dollar
Exchange Rate On
Stock Price (Case Study
of Companies StateOwned Banks Listing on
the Indonesia Stock
Exchange Period 20142018)”
BI Rate
and
inflation
simultaneously have
significant
Exchange Rate (USD/IDR)
rate
effect
toward stock price.
Partially, BI rate and
Inflation Rate
Dependent Variable:
Exchange rate have
positive
significant
effect toward stock
Stock Price
price, while inflation
rate
partially
negative
have
significant
effect toward stock
price.
13.
Safuridar
Asyuratama, (2018)
Title:
& Multiple Linear Regression
This study found that
Inflation
Use 9 Banks listed on the IDX
period 2012-2017
“Analysis
of
Macroeconomic
Independent Variable:
Indicators on Banking
Sector Stock Prices “
Inflation Rate
exchange rate, and
interest
rate
simultaneously have
significant
effect
toward stock price.
Interest
Exchange Rate
rate,
exchange
rate
and
rate
27
Interest Rate
partially have positive
significant
Dependent Variable:
Stock price
effect
toward stock price,
while inflation rate
have
negative
significant effect
14.
SAPTIANI (2018)
Title:
Multiple Linear Regression
This study found that
partially, GDP and
Independent Variable:
“Effect Of Changes In
Economic Indicators On Inflation Rate
Banking Share Price In
Indonesia
Through
Exchange Rate
Changes In Book Value
Of Share”
BI Rate
GDP
Currency
in
Circulation
have
significant
effect
toward stock price,
while Inflation rate,
exchange rate and BI
rate
have
significant
Currency in circulation
no
effect
toward stock price.
Dependent Variable:
Stock Price
15.
Rachmawati (2019)
Title:
Multiple Linear Regression
This study found that
partially, oil price and
Independent Variable:
“Influence Of World Oil
Prices, Dollar Exchange Oil Price
and BI Rate On Share
Prices Of Transportation
Exchange Rate
Companies Which Are
Listed On The IDX For
The 2014 - 2018 Period” BI Rate
Dependent Variable:
exchange rate have
significant
negative
effect toward stock
price, while BI rate
have
significant
positive
effect
toward stock price.
28
Stock Price
2.3 Research Gap
There have been several studies examining the relationship between financial ratio or
macroeconomics factors toward stock prices before this thesis. Yet, the researcher
discovered some shortcomings in past studies, including scope, variable, sample period,
and contradictory outcomes. Based on the gap that has been found, this research uses
financial ratio and macroeconomic factor as independent variable toward stock price of
Bank category KBMI 3 & KBMI 4, since there is a scarcity of studies that simultaneously
consider financial ratios and macroeconomic factors and as independent variables.
Financial ratios used in this research are BOPO, CAR, LDR and NPL while
macroeconomics factor used in this research are inflation rate, exchange rate and BI rate.
In addition, this research uses the updated data from 2017 – 2022 and use quarterly report,
which is different from majority previous research that used annual report. The researcher
also found there are inconsistent or even contradiction in the results of some previous
studies. Therefore, this research purpose to fill the gap between previous research and give
result that could be used by companies to assess their performance and by other interested
parties, including investors, to assess and assist in making the optimal investment decision.
Differences and gaps in previous studies can be seen from the results obtained from each
study. Several studies were conducted to see how the influence of the company's financial
performance and macroeconomics on banking stock prices in Indonesia. In research by
Ilham, (2022) and KUSUMA (2017) found that BOPO have negative significant influence
toward stock prices, while Yudistira & Adiputra (2020) and Wijono et al., (2023) found
that BOPO have no significant effect toward stock price. Research by D. Putri (2017),
KUSUMA (2017), Vilia & Coline (2021) has found that LDR have significant effect
toward stock price. Meanwhile, Aryanti et al, (2022), Nurhayati & Pertiwi (2021), Fordian
(2017) have found LDR have no significant effect. In research by Ilham, (2022), Aryanti
et al, (2022), KUSUMA (2017) and C. Hariyani et. al (2021)found that NPL have
significant effect toward stock price, while Nurhayati & Pertiwi (2021), Vilia & Coline
29
(2021) have found that NPL have no significant effect toward stock price. KUSUMA
(2017), Nurhayati & Pertiwi (2021) have found that CAR have positive significant effect
toward stock price, while Vilia & Coline (2021), Fordian (2017) have found that CAR have
no significant influence. In research by Rachmawati (2019) have found that exchange rate
have negative significant effect toward stock price. Meanwhile, N. Nurliandini et al.
(2021), A. Saptiani (2018) have found there is no significant effect. Herdini et al. (2021),
Safuridar & Asyuratama, (2018) have found that inflation rate have significant effect
toward stock price, while N. Nurliandini et al. (2021), A. Saptiani (2018) have found there
is no significant effect. In research by Rachmawati (2019) and Herdini et al. (2021), it is
found that BI rate have significant effect toward stock prie, while Yudistira & Adiputra
(2020) have found that BI rate have no significant effect.
2.4 Theoretical Framework
BOPO (X1)
H1
LDR (X2)
H2
NPL (X3)
H3
CAR (X4)
H4
Banking Companies
Stock Prices (Y)
H5
Inflation Rate (X5)
H6
Exchange Rate (X6)
H7
BI Rate (X7)
H8
Figure 2.1 Theoretical Framework
Source: Adjusted by researcher, 2023
30
2.5 Hypothesis
Following the theoretical framework, the research hypothesis may be phrased thereby:
H1: There is significant influence of BOPO toward Bank category KBMI 3 & KBMI 4
stock prices
H2: There is significant influence of LDR toward Bank category KBMI 3 & KBMI 4 stock
prices
H3: There is significant influence of NPL toward Bank category KBMI 3 & KBMI 4 stock
prices
H4: There is significant influence of CAR toward Bank category KBMI 3 & KBMI 4 stock
prices
H5: There is significant influence of Inflation Rate toward Bank category KBMI 3 &
KBMI 4 stock prices
H6: There is significant influence of Exchange Rate toward Bank category KBMI 3 &
KBMI 4 stock prices
H7: There is significant influence of BI Rate toward Bank category KBMI 3 & KBMI 4
stock prices
H8: There are simultaneous significant influence of BOPO, LDR, NPL, CAR, inflation
rate, exchange rate and BI rate toward Bank category KBMI 3 & KBMI 4 stock prices
31
CHAPTER III
METHODS
3.1 Research Design
A research design is a strategy or framework that directs the acquisition, measurement, and
analysis of data in a study, allowing researchers to gather and evaluate data that is relevant
to their research questions or hypotheses in an effective and efficient manner. There are
three research approaches that commonly used: quantitative, qualitative, and hybrid or
mixed methodologies. The qualitative research evaluated the social issue by analyzing the
data's interpretations, descriptions, ideas, and characteristics. The outcome of qualitative
research takes the shape of a story and employs data gathering approaches such as
interviews, observations, documents, or audio and visual resources. Quantitative research
uses statistics to examine data to study the relationship among variables in order to evaluate
objective ideas. Quantitative research involves utilizing data that is quantifiable and can be
subject to statistical analysis, including performance metrics, observations, attitudes, and
demographic information. In the mixed-method research approach, both quantitative and
qualitative data are combined, along with the incorporation of a theoretical framework and
underlying philosophical perspectives (Creswell & Creswell, 2022).
The researcher will be applied quantitative research methodologies in this study. This
decision was based on the research questions and the need to examine relationships
between variables using statistical analysis. It might be employed to explore averages and
trends, to make forecasts, to establish causal linkages, and to extrapolate results to wider
populations.
3.2 Research Framework
Research framework explained researcher working framework and provides an overview
of the conceptual structure and design of the study. It explained step by step from the
32
beginning until the conclusion of the study. Below is the research framework for this
research:
Figure 3.1 Research Framework
Source: Adjusted by researcher, 2023
Figure 3.1 shows the whole process and steps of this research. Start from make the research
background, the researcher has to collect the information and identify the problem related
to the topic. Then, the researcher made the research question that would be study in this
research. These steps allow the researcher to state what has become the objective and the
curiosity of the researcher on conducting this study. After completed the research
background, the researcher continues to find and collect the literature, publications and
previous research to support the study. This helps the researcher to get deeper knowledge
33
about the theory, variables and all information needed for the study. After collecting and
reviewing the literature, the researcher starts to make the theoretical framework and make
the development of the hypothesis. As the research background and literature review
already finish, the researcher begins to gather and organize data. The data obtain within
this study obtained from quantitative method which in this research collected through
reliable sources. After obtain all the data, the researcher starts to process the data and run
the analysis using Microsoft Excel 2019 and EViews version 12. The researcher will use
the analysis to interpret the results and answer the research questions posed in research
background. The researcher will draw a conclusion and provide recommendations based
on the interpretation of the results.
3.3 Sampling Design
Sampling design refers to the plan or strategy for selecting a sample from a population for
a research study. The sampling design is an important aspect of the research study because
it determines the representativeness of the sample and the study's capacity to derive reliable
findings regarding the population. The sampling design described the criteria and
procedure used by researcher in order to select the samples
3.3.1 Size of Population
As defined by Sugiyono (2020), the population is a generalization region composed of:
items / individuals with specific attributes and characteristics that prompt researchers to
learn and subsequently form conclusions. In this research, the population are all banking
companies listed in Indonesia Stock Exchange. The selected population aims to support the
analysis in research in order to get decent results.
3.3.2 Size of Sample
A sample is a portion of the population chosen for study in research. There are two ways
to determine the sample that researchers can use, which is probability sampling and nonprobability sampling (Sugiyono, 2020). The researcher will use non-probability sampling
in this study, which includes selecting a sample in a method that does not provide each
34
member of the population an equivalent opportunity of getting involved in the sample; this
strategy is solely based on particular evaluations or criteria. Non-probability sampling has
several types, and the one will be used in this study is purposive technique. Purposive
sampling provides quite strict requirements or criteria, with the aim that the selected sample
will later be in accordance with the desired characteristics in the study.
The purposive sampling criteria used in this study are as follows:
1. Bank Listed in Indonesia Stock Exchange from 2017 until 2022
2. Bank are categorized as KBMI 3 and KBMI 4
3. Banks have quarterly financial reports that can be accessed by public for the period
2017 – 2022
4. The bank has the data needed for research
Based on the criteria given, below are the data samples for this research:
Table 3.1 Sample Proportion
No
Code
Name of Company
Years
2017
2018
2019
2020
2021
2022
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
PT. Bank Rakyat
1.
BBRI
Indonesia (Persero)
Tbk.
PT. Bank Negara
2.
BBNI
Indonesia (Persero)
Tbk.
3.
BMRI
4.
BBCA
PT. Bank Mandiri
(Persero) Tbk.
PT. Bank Central
Asia Tbk.
35
5.
BBTN
6.
BNGA
7.
NISP
8.
BNLI
9.
PNBN
10.
BTPN
11. BDMN
12.
BNII
PT. Bank Tabungan
Negara Tbk
PT. Bank CIMB
Niaga Tbk
PT. Bank OCBC
NISP Tbk
PT. Bank Permata
Tbk
PT. Bank Pan
Indonesia Tbk
PT. Bank BTPN Tbk
PT. Bank Danamon
Indonesia Tbk
PT. Bank Maybank
Indonesia Tbk
13. MEGA PT. Bank Mega Tbk
Total
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
52
52
52
52
52
52
312 observation data
36
3.4 Data Collection Design
In research, data collection design refers to the strategy for collecting data that will be
utilized to answer a study question or purpose. There are two types of data that can be used
in a study, namely primary data and secondary data. Primary data is information gained
directly from first-hand sources or collected directly by researcher through surveys and
interviews with sources. Secondary data refers to data gathered by an individual rather from
the researcher who is performing the present study. It refers to data that has been previously
collected and made available for use by others.
The researcher used secondary data acquired from multiple official online sources in this
study, such as Yahoo Finance website, company official website, OJK website, Bank
Indonesia website, government website and several other related sources that relevant to
this study.
3.5 Operational Definition
Table 3.2 Operational Definition
Variables
Definition
Formula
Scale
𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐶𝑜𝑠𝑡
𝑇𝑜𝑡𝑎𝑙 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝐼𝑛𝑐𝑜𝑚𝑒
Ratio
Independent Variables
Operational efficiency
(BOPO) is a ratio that
Operational
Efficiency
Ratio (X1)
demonstrates how a
corporation may optimize
the operational expenses
it uses to finance all of its
operational operations.
(Rahmani, 2023).
Loan to
Deposit
Ratio (X2)
Loan to Deposit Ratio
(LDR) is a liquidity ratio
that calculates the ratio
𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠
Ratio
between the amount of
37
credit extended by a bank
and the funds received by
a bank (Fadila, 2015)
Non-Performing Loan
(NPL) is defined as a ratio
that reflects a condition in
Non-
which the debtor is unable
Performing
to pay all of his
Loans (X3)
obligations in the form of
𝑁𝑜𝑛−𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝐿𝑜𝑎𝑛𝑠
𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠
Ratio
funds or interest to the
creditor (Saladin et al.,
2022).
Capital Adequacy Ratio
(CAR) is an important
Capital
measure for evaluating
Adequacy
bank's financial health
Ratio (X4)
and ability to finance its
(𝑇𝑖𝑒𝑟 1 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 + 𝑇𝑖𝑒𝑟 2 𝐶𝑎𝑝𝑖𝑡𝑎𝑙)
𝑅𝑖𝑠𝑘 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠
Ratio
𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑃𝑟𝑖𝑐𝑒 𝐼𝑛𝑑𝑒𝑥 − 𝑃𝑟𝑒𝑣𝑖𝑜𝑢𝑠
𝑃𝑟𝑖𝑐𝑒 𝐼𝑛𝑑𝑒𝑥
𝑃𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑃𝑟𝑖𝑐𝑒 𝐼𝑛𝑑𝑒𝑥
Ratio
Closing Price
Amount
operations with its capital
ownership (Irham, 2013).
Inflation is a economy
condition where there is a
Inflation
Rate (X5)
tendency to increase
prices for goods and
services in general and
continuously (Natsir,
2014).
Exchange
Rate USD –
Rupiah (X6)
Exchange Rate is the
amount of domestic
money that individual
needed to obtain one unit
38
of foreign currency
(Sukirno, 2015, as cited
by Clarensia, 2021)
The BI Rate is a policy
interest rate that signifies
BI Rate
(X7)
the official monetary
policy stance of Bank
The data Issued from Bank
Indonesia
Indonesia and used as
Ratio
benchmark of interest rate
(Bank Indonesia, 2016)
Dependent Variable
Stock price can described
as the price established as
a result of the interaction
between sellers and
Stock Price
buyers of stock with the
(Y)
goal of collecting profits
Closing Price
Amount
from the firm, which
included both capital
gains and dividends
(Hidayat, 2018)
3.6 Data Analysis Design
3.6.1 Descriptive Analysis
Descriptive statistical analysis is a type of statistic that is used to analyze data by explaining
or exhibiting the collected data without the goal of drawing broad conclusions (Sugiyono,
2020). It entails calculating metrics like maximum, minimum, median, mean, standard
deviation, and number of observations.
Maximum and Minimum describe the highest and the lowest value of all sample of data.
39
Mean is a measure of central tendency for one variable that represents the arithmetic
average, or the sum of all values divided by the entire amount of observations (Neuman,
2014). The calculation of the mean can be described as stated below:
𝑋=
∑ 𝑋𝑖
𝑛
(E.q.3.1)
Where:
X
: mean
∑
: sum of all the data (sigma)
Xi
: data i until n
n
: number of observations
Median according to Neuman (2014) is an assessment of central tendency for a single
variable that reflects a level or score at which half of the instances are higher and half of
the cases are lower.
Standard Deviations is a statistic used to calculate the dispersion of collected relative data
toward average and calculated as square root of the variance (Neuman, 2014). The formula
of standard deviation of sample is calculated as follows:
𝑆= √
∑ 𝑓𝑖 (𝑥𝑖 − 𝑥̅ )2
𝑛−1
(E.q.3.2)
Where:
S
: standard deviation
40
∑ fi
: total frequency data-I, i=1, 2, 3, …
𝑥𝑖
: data-i
𝑥̅𝑙
: mean
n
: amount of data
3.6.2 Panel Data Analysis
Panel data is defined as data that have multiple observation objects in period of more than
a year, which is a combination of time series data and cross-section data. Panel data is
classified into two form which is balanced panel and unbalanced panel. According to
Hidayat (2014), A balanced data panel has the same total observation time as the data or
there are no missing observations, and data is available for every entity in every time
period, whereas an unbalanced data panel has a different total observation time with the
data or contains missing observations for some entities in certain time periods. This
research thus will focus on balance panel since there are no missing observations, and data
is available for every entity in every time period.
There are three approaches to emphasize the estimation on panel data regression. The
following three approaches or method to analyze the right model to use:
1. Common Effect Model
Due to the fact that it just mixes time series and cross section data, this is the most
straightforward panel data model technique. Since the common effect model does
not account for either the historical or individual dimensions, it is believed that
individual data behavior is constant throughout time. It is utilized with the OLS
(Ordinary Least Squares) method (Basuki & Prawoto, 2016).
2.
Fixed Effect Model
In this approach, the regression model's intercept might vary between individuals
in order to represent the distinctive characteristics of various units. The least
squares dummy variable model (LSDV), commonly known as the dummy variable
41
approach, is used to estimate the Fixed Effects model in panel data by capturing
intercept differences across individuals. When the individual-specific intercept may
be linked with one or more regressors, FEM is applicable (Gujarati, 2015).
3. Random Effect Model
This model will compute panel data with associated disturbance factors over time
and across people. The error terms of every individual in the Random Effect model
manage the variance in intercepts. The Random Effects model has the added benefit
of removing heteroscedasticity. This paradigm is also known as the Error
Component model (ECM) or the Generalized Least Squares (GLS) method (Basuki
& Prawoto, 2016). REM is suitable when the starting points for each unit aren't
connected to the factors we're studying. Another advantage of REM is that we can
include factors that don't change over time (Gujarati, 2015)
In choosing which model is better for research, it is necessary to do some testing to see
which model is suitable for use. According to Basuki & Prawoto (2016) the following tests
can be carried out:
a. Chow Test
This test decides if the common effect or fixed effect model will be employed with
the research data. The sum of squares of the estimated errors is compared in this
model. It is determined using the following criteria:
•
The common effect model will be chosen when probability value from the
test results is > 0.05
•
The fixed effect model will be chosen when probability value of the test
results is < 0.05
b. Hausman Test
In the Hausman test, it compares between two possible models for research, namely
random effects or fixed effects. The Hausman test refers to the chi-square statistical
distribution, there are two criteria for the test:
42
•
The random effect model will be chosen when probability value from the
test results is > 0.05
•
The fixed effect model will be chosen when probability value of the test
results is < 0.05
c. Lagrange Multiplier Test
As an extra test to contrast Common effects and Random effects, the Lagrange
multiplier test is utilized. The Lagrange Multiplier Test can aid in a more thorough
study of the coefficients between Common Effects and Random Effects. It is
determined using the following criteria:
•
The common effect model will be chosen when probability value from the
test results is > 0.05
•
The random effect model will be chosen when probability value of the test
results is < 0.05
3.6.3 Classical Assumption Test
It is critical in empirical study to guarantee that the statistical models' underlying
assumptions are met. Violation of these assumptions can result in biased or inconsistent
parameter estimates, reducing the results' reliability. As a result, performing classical
assumption tests is critical for determining the validity of the statistical analysis. The
classical assumption tests evaluate the main assumptions of linear regression models, such
as normality, heteroscedasticity, the lack of multicollinearity, and the absence of
autocorrelation. These assumptions lay the groundwork for accurate and valid inference.
1. Normality Test
Normality test is employed to figure out if the residual data distribution falls into
the category of a normal or abnormal distribution (Sugiyono, 2020). A decent
regression model has residuals that are normally or nearly normally distributed.
There are several strategies or methods that may be used to perform a normality
test. In this study, researcher used the Jarque-Bera method.
43
Jarque-Bera test measure the normality distribution of a series or residual based on
the skewness and kurtosis measures (Gujarati, 2015). It computes a test statistic
based on the sample skewness and kurtosis and compares it to a chi-squared
distribution critical value. If the test statistic reaches the critical value, the data
deviates considerably from a normal distribution. Researcher determine the
significance level of 5% or 0.05, then there are two conclusions:
a) When the probability value of the Jarque-Bera test is greater than 0.05, it
suggests that the data follows a normal distribution.
b) When the probability value of the Jarque-Bera test is less than 0.05, it
suggests that the data deviates from a normal distribution.
2. Multicollinearity Test
The multicollinearity test is performed to see if the regression model revealed any
correlation among independent variables. Multicollinearity happens when the
correlation within independent variables is strong. There should be no correlation
across independent variables in a decent regression model (Ghozali & Ratmono,
2017). To identify multicollinearity, there are several tests could apply, one of them
are through review the correlation matrix for the predictor variables. The
relationship between independent variables should have correlation coefficient with
an absolute value less than 0.7, if the coefficient correlation value of a variable
higher than 0.7, typically indicates a strong correlation between predictor variables,
which indicates there are the multicollinearity issue (Moore and Flinger, 2013, as
cited by Pardede, 2021).
3. Heteroscedasticity Test
The heteroscedasticity test is used to determine if there is an inequality in variance
from the residual in one observation to the residual in the other data in the
regression model. If the residual difference in research is constant with regard to
other observations, the condition is said to be homoscedastic, whereas it is said to
be heteroscedastic if the variances are different. Ghozali & Ratmono (2017) stated
a decent regression model is not heteroscedasticity or homoscedasticity.
44
There are several methods to find heteroscedasticity like scatter plot, Glejser test,
White test and Park Test. This research will examine Park test with the hypotheses
test are follows
a) H0
: no heteroscedasticity
b) H1
: there
is heteroscedasticity
Where:
a) If probability value < 0,05, Ho is rejected, so there is heteroscedasticity
b) If probability value > 0,05, H0 is accepted, so there is no heteroscedasticity
4. Autocorrelation Test
Autocorrelation test is used to examine the correlation between the residuals
(errors) of a linear regression model and the corresponding observations in a time
series. It helps to determine if there is any systematic pattern or relationship
between the errors and the order of the observations over time. In panel data
analysis, the Durbin-Watson test serves as a tool to assess autocorrelation. As
outlined by Santoso (2018), the Durbin-Watson value should fall within the range
of -2 to 2. This range is interpreted as follows:
a) When the Durbin-Watson statistic (DW) is less than -2, it indicates the
presence of positive autocorrelation.
b) When the Durbin-Watson statistic falls between -2 and 2, it suggests no
autocorrelation
c) When the Durbin-Watson statistic (DW) exceeds 2, it implies the existence
of negative autocorrelation.
3.6.4 Multiple Regression Analysis
Multiple regression analysis is a method of statistics for examining the link across a
dependent variable and a multitude of independent variable (Sugiyono, 2020). Its objective
is to discern the nature of the link among alterations in the independent variables and
changes in the dependent variable. Since there are several independent variables in this
study, the researchers decided to employ multiple regression analysis. The independent
45
variables in this study are BOPO, LDR, NPL, CAR, Inflation Rate, Exchange Rate and BI
Rate, while the dependent variable is Stock Price. The formula for multiple regression
analysis can be written as follows:
𝑌 = 𝛽0 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + 𝛽3 𝑋3 + 𝛽4 𝑋4 + 𝛽5 𝑋5 + 𝛽6 𝑋6 + 𝛽7 𝑋7 +∈
(E.q.3.3)
Where:
Y
: Stock Price
𝛽0
: Constant
𝛽1 − 𝛽6
: Regression coefficient
𝑋1
: BOPO
𝑋2
: LDR
𝑋3
: NPL
𝑋4
: CAR
𝑋5
: Inflation Rate
𝑋6
: Exchange Rate
𝑋7
: BI Rate
∈
: Random error
E.q.3.3 equation reflects how all variables relate to each other, the results of the equation
can determine whether the independent variable affects positively or negatively, both
values affect how the stock price variable moves.
3.7 Hypothesis Testing
Hypothesis testing is a fundamental statistical tool for drawing conclusions and making
sound judgments based on sample data. In the context of this study, hypothesis testing will
allow for the analysis of variable correlations and the assessment of statistical significance.
46
The validity of research questions may be examined through the design and testing of
hypotheses, and meaningful conclusions can be drawn from the findings.
To begin hypothesis testing, two opposing hypotheses are defined: the null hypothesis (H0)
and the alternative hypothesis (Ha). The null hypothesis states that independent factors
have no substantial impact on the dependent variable, whereas the alternative hypothesis
states that independent variables have a significant influence on the dependent variable.
3.7.1 Significant Level
The significance level is a statistical measure used by academics to make judgments about
whether the researcher should accept or reject a null hypothesis. It allows researchers to
identify statistical associations resulting from true links between variables. Significance
levels are usually known as significant p-values and marginal levels. Significance level of
5% will be used in the test of this research. The null hypothesis with significance level
below 5 % should be rejected, while if its value exceeds 0.05, null hypotheses should be
accepted.
3.7.2 T-test
According to Sarwono (2016), the T-test is critical in multiple linear regression for
evaluating individual hypotheses and deciding their acceptance or rejection. To make this
assessment, the T-statistics probability value is compared to the significance level and the
T-table. The interpretation of t-statistics results in this study, which uses a 5% significant
threshold, is as follows:
a) When the probability value is < 0.05, it leads to the rejection of H0 and the
acceptance of HA, signifying the presence of an independent variable's effect on
the dependent variable.
b) When the probability value is > 0.05, it results in the acceptance of H0 and the
rejection of HA, suggesting the absence of an independent variable's impact on the
dependent variable.
The T-test equation in this study can be formulated as follows:
47
1) Null Hypothesis (H01): β1 = 0 or, when the probability of t-statistics > 0.05, it
implies that BOPO does not exert a significant influence on the stock prices of
banks categorized as KBMI 3 & 4 during the period from 2017 to 2022.
Alternative Hypothesis (HA1): β1 ≠ 0 or, when the probability of t-statistics < 0.05,
it implies that BOPO exert a significant influence on the stock prices of banks
categorized as KBMI 3 & 4 during the period from 2017 to 2022.
2) Null Hypothesis (H02): β2 = 0 or, when the probability of t-statistics > 0.05, it
implies that LDR does not exert a significant influence on the stock prices of banks
categorized as KBMI 3 & 4 during the period from 2017 to 2022.
Alternative Hypothesis (HA2): β2 ≠ 0 or, when the probability of t-statistics < 0.05,
it implies that LDR exert a significant influence on the stock prices of banks
categorized as KBMI 3 & 4 during the period from 2017 to 2022.
3) Null Hypothesis (H03): β3 = 0 or, when the probability of t-statistics > 0.05, it
implies that NPL does not exert a significant influence on the stock prices of banks
categorized as KBMI 3 & 4 during the period from 2017 to 2022.
Alternative Hypothesis (HA3): β3 ≠ 0 or, when the probability of t-statistics < 0.05,
it implies that NPL exert a significant influence on the stock prices of banks
categorized as KBMI 3 & 4 during the period from 2017 to 2022.
4) Null Hypothesis (H04): β4 = 0 or, when the probability of t-statistics > 0.05, it
implies that CAR does not exert a significant influence on the stock prices of banks
categorized as KBMI 3 & 4 during the period from 2017 to 2022.
Alternative Hypothesis (HA4): β4 ≠ 0 or, when the probability of t-statistics < 0.05,
it implies that CAR exert a significant influence on the stock prices of banks
categorized as KBMI 3 & 4 during the period from 2017 to 2022.
48
5) Null Hypothesis (H05): β5 = 0 or, when the probability of t-statistics > 0.05, it
implies that Inflation Rate does not exert a significant influence on the stock prices
of banks categorized as KBMI 3 & 4 during the period from 2017 to 2022.
Alternative Hypothesis (HA5): β5 ≠ 0 or, when the probability of t-statistics < 0.05,
it implies that Inflation Rate exert a significant influence on the stock prices of
banks categorized as KBMI 3 & 4 during the period from 2017 to 2022.
6) Null Hypothesis (H06): β6 = 0 or, when the probability of t-statistics > 0.05, it
implies that Exchange Rate does not exert a significant influence on the stock prices
of banks categorized as KBMI 3 & 4 during the period from 2017 to 2022.
Alternative Hypothesis (HA6): β6 ≠ 0 or, when the probability of t-statistics < 0.05,
it implies that Exchange Rate exert a significant influence on the stock prices of
banks categorized as KBMI 3 & 4 during the period from 2017 to 2022.
7) Null Hypothesis (H07): β7 = 0 or, when the probability of t-statistics > 0.05, it
implies that BI Rate does not exert a significant influence on the stock prices of
banks categorized as KBMI 3 & 4 during the period from 2017 to 2022.
Alternative Hypothesis (HA7): β7 ≠ 0 or, when the probability of t-statistics < 0.05,
it implies that BI Rate exert a significant influence on the stock prices of banks
categorized as KBMI 3 & 4 during the period from 2017 to 2022.
3.7.3 F-test
The F-test aims to determine the significance influence caused by the independent variable
toward the dependent variable simultaneously or as a whole (Sarwono, 2016). There are
two criteria for the test, as follows:
a) When the probability value is less than 0.05, it leads to the rejection of H0 and the
acceptance of HA, indicating the presence of an effect from all independent
variables on the dependent variable.
49
b) When the probability value exceeds 0.05, it results in the acceptance of H0 and the
rejection of HA, signifying the absence of an effect from all independent variables
on the dependent variable.
The F-test equation in this study can be formulated as follows:
1) Null Hypothesis (H08): β1 = β2 = β3 = β4 = β5 = β6 = β7= 0 or, when the probability
of F-statistics > 0.05, it implies that BOPO, LDR, NPL, CAR, Inflation Rate,
Exchange Rate and BI Rate does not exert simultaneous significant influence on
the stock prices of banks categorized as KBMI 3 & 4 during the period from 2017
to 2022.
2) Alternative Hypothesis (HA8): at least there is one βi ≠ 0 or, when the probability
of F-statistics < 0.05, it implies that BOPO, LDR, NPL, CAR, Inflation Rate,
Exchange Rate and BI Rate exert simultaneous significant influence on the stock
prices of banks categorized as KBMI 3 & 4 during the period from 2017 to 2022
3.7.4 Coefficient of Determination (R2)
According to Sarwono (2016), R Square (R2), also known as the Determination Coefficient
Test, effectively assesses how much an independent variable may impact a dependent
variable. The potential range of the coefficient of determination falls between 1 and 0,
denoted as 0 < R2 < 1. The explanation of the R2 value can be expressed as follows:
a) When R2 is near to zero, the independent variables have little explanatory power
over the variance of the dependent variable.
b) When R2 is near to one, the independent variables have a high level of explanatory
power over the variance of the dependent variable.
50
CHAPTER IV
RESULTS, ANALYSIS AND DISCUSSION
4.1 Descriptive Analysis
Descriptive statistics are critical in providing a summary and broad information about all
variables in this study. It helps to gain more understanding about the characteristics from
all variables used by providing number of observations, minimum, maximum, median
mean and standard deviation for each variable. In this study, there are total of eight
variables, where there are 312 observation units for each variable.
Table 4.1 Descriptive Statistics Result
SP
BOPO
LDR
NPL
CAR
IR
EXR
BIR
Mean
3044.370 77.3011
90.0500
2.7551 21.8453
2.9729 14280.50
4.5104
Median
2562.578 77.6350
89.0850
2.9200 21.6100
3.1150 14301.33
4.4583
Maximum
9738.115 98.3100 171.3200
6.4100 35.4300
5.5467 15670.00
6.0000
Minimum
152.328 46.5400
56.0600
0.7300 15.8300
1.4267 13322.33
3.5000
Std. Dev.
2364.890 10.0848
17.7288
0.9972
3.5485
1.1067
552.53
0.8320
312
312
312
312
Source: Proceed Data by EViews 12 SV
312
312
312
Observations
312
According to table 4.1, below are the elaborations of the information of variable descriptive
statistic result:
1. SP (Stock Price) is the dependent variable in this study. The mean value of this
variable is 3044.370 with standard deviation of 2562.578. SP has the maximum
value in 9738.115 that occurred on PT. Bank Mega Tbk in 2021Q1 and the
minimum value is 152.328 occurred on PT. Bank Maybank Indonesia Tbk in
2020Q2
2. BOPO (Operational Efficiency Ratio) is the independent variable in this study. The
mean value of this variable is 77.3011 with standard deviation of 10.0848. BOPO
has the maximum value in 98.3100 that occurred on PT. Bank Permata Tbk in
51
2018Q2 and the minimum value is 46.5400 occurred on PT. Bank Central Asia
Tbk.in 2022Q4
3. LDR (Loan to Deposit Ratio) is the independent variable in this study. The mean
value of this variable is 90.0500 with standard deviation of 17.7288. LDR has the
maximum value in 171.3200 that occurred on PT. Bank BTPN Tbk in 2021Q4 and
the minimum value is 56.0600 occurred on PT. Bank Mega Tbk in 2017Q1
4. NPL (Non-Performing Loan) is the independent variable in this study. The mean
value of this variable is 2.7551 with standard deviation of 0.9972. NPL has the
maximum value in 6.4100 that occurred on PT. Bank Permata Tbk in 2017Q1 and
the minimum value is 0.7300 occurred on PT. Bank BTPN Tbk in 2019Q1
5. CAR (Capital Adequacy Ratio) is the independent variable in this study. The mean
value of this variable is 21.8453 with standard deviation of 3.5485. CAR has the
maximum value in 35.4300 that occurred on PT. Bank Permata Tbk in 2021Q2 and
the minimum value is 15.8300 occurred on PT. Bank Negara Indonesia (Persero)
Tbk in 2017Q4
6. IR (Inflation Rate) is the independent variable in this study. The mean value of this
variable is 2.9729 with standard deviation of 1.1067. IR has the maximum value of
5.5467 that occurred in 2022Q4 and the minimum value is 1.4267 that occurred in
2020Q3
7. EXR (Exchange Rate) is the independent variable in this study. The mean value of
this variable is 14280.50 with standard deviation of 552.53. EXR has the maximum
value of 15670.00 that occurred in 2022Q4 and the minimum value is 13322.33
that occurred in 2017Q2
8. BIR (BI Rate) is the independent variable in this study. The mean value of this
variable is 4.5104 with standard deviation of 0.8320. BIR has the maximum value
of 6.0000 that occurred in 2019Q1 and the minimum value is 3.5000 that occurred
in 2021Q4
52
4.2 Data Analysis
4.2.1 Panel Data Regression
When examined data panel regression, the first step that researcher do is determine the
model of panel data regression. There are three prevalent models are employed in panel
data regression, namely CEM (Common Effect Model), FEM (Fixed Effect Model) and
REM (Random Effect Model). The selection of the best model for panel data analysis needs
the execution of several tests. The Chow Test, the Hausman Test, and the Lagrange
Multiplier Test are among the important examinations. These tests, taken together, aid in
the cautious selection of the suitable model for properly analyzing panel data.
1. Chow Test
To decide whether Common Effect Model or Fixed Effect Model is better for the
research, Chow Test is examined. As a result of the Chow test results, there are two
hypothesis provisions, which are as follows:
•
H0: Common effect model is more suitable to use than fixed-effect model
•
H1: Fixed effect model is more suitable to use than common effect model
H0 is accepted and H1 is denied when the probability value exceeds the significant
value (0.05), and vice versa.
Table 4. 2 Chow Test
Redundant Fixed Effects Tests
Equation: Untitled
Test cross-section fixed effects
Effects Test
Statistic
Cross-section F
Cross-section Chi-square
232.743213
735.548340
d.f.
Prob.
(12,292)
12
0.0000
0.0000
Source: Proceed data by EViews 12 SV
Based on table 4.2, the probability value in chow test is less than significance value
of 0.05, therefore Chow Test reveal that the fixed effect is the best model because
H1 is accepted and H0 is rejected
2. Hausman Test
53
The Hausman Test is employed to determine the suitability of either the Fixed
Effect Model or the Random Effect Model for the research. Based on the outcomes
of the Hausman test, it yields two distinct hypotheses, outlined as follows:
•
H0: Random effect model is more suitable to use than fixed effect model
•
H1: Fixed effect model is more suitable to use than random effect model
H0 is accepted and H1 is denied when the probability value exceeds the significant
value (0.05), and vice versa.
Table 4.3 Hausman Test
Correlated Random Effects - Hausman Test
Equation: Untitled
Test cross-section random effects
Test Summary
Cross-section random
Chi-Sq. Statistic
Chi-Sq. d.f.
Prob.
0.000000
7
1.0000
Source: Proceed data by EViews 12 SV
Table 4.3 reveals that the probability value produced from the Hausman Test
surpasses the significance criterion of 0.05. Consequently, the conclusion of the
Hausman Test supports the selection of the random effects model, as it leads to the
acceptance of H0 and the rejection of H1. Notably, the two tests undertaken thus
far provide contradictory results: the Chow test shows that the fixed effects model
is preferred, while the Hausman Test suggests that the random effects model is
preferred. As a result, the Lagrange Multiplier Test, a third test, is deemed required.
3. Lagrange Multiplier Test
Lagrange Multiplier Test is used to decide whether Common Effect Model or
Random Effect Model is better for the research. As a result of the Lagrange
Multiplier Test results, there are two hypothesis provisions, which are as follows:
•
H0: Common effect model is more suitable to use than random effect model
•
H1: Random effect model is more suitable to use than common effect model
54
H0 is accepted and H1 is denied when the probability value exceeds the significant
value (0.05), and vice versa.
Table 4.4 Lagrange Multiplier Test
Lagrange Multiplier Tests for Random Effects
Null hypotheses: No effects
Alternative hypotheses: Two-sided (Breusch-Pagan) and one-sided
(all others) alternatives
Test Hypothesis
Cross-section
Time
Both
Breusch-Pagan
2579.708
(0.0000)
8.705339
(0.0032)
2588.413
(0.0000)
Honda
50.79083
(0.0000)
-2.950481
(0.9984)
33.82823
(0.0000)
King-Wu
50.79083
(0.0000)
-2.950481
(0.9984)
39.44563
(0.0000)
Standardized Honda
61.55963
(0.0000)
-2.576827
(0.9950)
34.97460
(0.0000)
Standardized King-Wu
61.55963
(0.0000)
-2.576827
(0.9950)
42.35362
(0.0000)
--
--
2579.708
(0.0000)
Gourieroux, et al.
Source: Proceed data by EViews 12 SV
Per data presented in table 4.4, the probability value obtained from the Breusch-Pagan test
falls below the significance level of 0.05. Consequently, test results favor the selection of
the random effects model, as it leads to the acceptance of H1 and the rejection of H0. Based
on the results of the three tests, it is concluded that the Random Effects Model is the best
fit for this research since from the three tests being carried out, Hausman Test and Lagrange
Multiplier Test show the preferred model to choose is Random Effect Model.
4.2.2 Classical Assumption Test
1. Normality Test
The normality test purpose is to determine if the residual data distribution falls into
the category of a normal or abnormal distribution. In this research, the researcher
uses Jarque-Bera test. The criteria for Jarque-Bera test are if the prob. value is more
than 0.05 then it is said to be normal.
55
Figure 4.1 Normality Test
Source: Proceed data by EViews 12 SV
Based on figure 4.1, the value of probability is 0.072647, which is more than
significance level 0.05. Thus, it can be stated that the distribution of the independent
and dependent variables in this study is normally distributed.
2. Multicollinearity Test
The multicollinearity test is performed to find out if the regression model revealed
any correlation across independent variables. In this research, researcher used the
correlation matrix to identify multicollinearity.
Table 4.5 Correlation Matrix
Source: Proceed data by EViews 12 SV
56
The relationship between independent variables should have correlation coefficient
with an absolute value less than 0.7, which indicates there is no multicollinearity
issue. Based on table 4.5, the correlation matrix result the highest correlation value
is found between LLDR and LBOPO with 0.466477. Due to each independent
variables correlation value being no higher than 0.7 and the models indicate there
is no multicollinearity, the entire matrix value result may still be viewed as a good
regression model.
3. Heteroscedasticity Test
The heteroscedasticity test serves the purpose of assessing whether there is
variability inequality among the residuals in the regression model across different
observations. In this research, the Park test will be employed to investigate this,
with the following hypotheses:
•
H0: No heteroscedasticity.
•
H1: Heteroscedasticity exists.
If the probability value exceeds the significance level (0.05), then H0 is embraced,
and H1 is dismissed, and conversely.
Table 4.6 Heteroscedasticity Test
Dependent Variable: LOG(RESID*RESID)
Method: Panel EGLS (Cross-section random effects)
Date: 08/19/23 Time: 19:27
Sample: 2017Q1 2022Q4
Periods included: 24
Cross-sections included: 13
Total panel (balanced) observations: 312
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOGBOPO
LOGLDR
NPL
CAR
IR
EXR
BIR
1.296188
-0.661606
-0.871956
0.077932
0.039829
0.025969
8.12E-05
0.034955
9.092520
1.693295
1.186586
0.207571
0.049868
0.121774
0.000221
0.169415
0.142555
-0.390721
-0.734845
0.375449
0.798693
0.213259
0.367655
0.206328
0.8867
0.6963
0.4630
0.7076
0.4251
0.8313
0.7134
0.8367
Source: Proceed data by EViews 12 SV
57
According to table 4.6, the heteroscedasticity test result shows the probability
significance value of LOGBOPO (0.6963), LOGLDR (0.4630), NPL (0.7076),
CAR (0.4251), Inflation Rate (0.8313), Exchange Rate (0.7134), BI Rate (0.8367).
All independent variables significance values are greater than significance value
0.05, which describe all of independent variables in this research avoids symptoms
of heteroscedasticity.
4. Autocorrelation Test
Autocorrelation test is used to examine the correlation between the residuals
(errors) of a linear regression model and the corresponding observations in a time
series. The value of Durbin Watson should be more than -2 and less than 2.
Table 4.7 Autocorrelation Test
Source: Proceed data by EViews 12 SV
The result from Durbin-Watson stat in table 4.7 is 0.51409, which implies the value
greater than -2 and less than +2. Therefore, no autocorrelation occurs in this test.
4.2.3 Multiple Linear Regression Analysis
Multiple regression analysis represents a statistical methodology employed to explore the
interplay across a dependent variable and an array of independent variables. In this
particular study, multiple linear regression analysis is applied to find out the relationship
between BOPO, LDR, NPL, CAR, Inflation Rate, Exchange Rate and BI Rate toward stock
price of Bank Category KBMI 3 & 4. The outcomes of the multiple regression analysis are
showcased using the random effect model within Table 4.8:
58
Table 4.8 Multiple Linear Regression Result
Dependent Variable: LOGSP
Method: Panel EGLS (Cross-section random effects)
Date: 09/10/23 Time: 15:33
Sample: 2017Q1 2022Q4
Periods included: 24
Cross-sections included: 13
Total panel (balanced) observations: 312
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOGBOPO
LOGLDR
NPL
CAR
IR
EXR
BIR
15.73055
-1.900233
0.168395
-0.060209
0.016360
-0.049538
-0.000061
0.051596
1.321296
0.226635
0.159987
0.027215
0.006407
0.014913
2.67E-05
0.020832
11.90539
-8.384561
1.052554
-2.212376
2.553575
-3.321893
-2.296128
2.476836
0.0000
0.0000
0.2934
0.0277
0.0112
0.0010
0.0223
0.0138
Source: Proceed data by EViews 12 SV
Based on table 4.8, the equation of multiple linear regression can be generated as follows:
LOGSP = 15.73055 – 1.900233 LOGBOPO + 0.168395 LOGLDR – 0.060209 NPL
+ 0.016360 CAR – 0.049538 IR – 0.000061 EXR + 0.051596 BIR
The explanation of the equation above can be described as follows:
1. The constanta value is 15.73055
This value means if the output of all the independent variables is zero, then the
value of the log of stock price is 15.73055.
2. Regression coefficient of LOG BOPO is -1.900233
The regression coefficient signifies that the logarithm of operational efficiency ratio
(BOPO) has a negative impact on the logarithm of stock price. This implies that a
1% increase in the logarithm of BOPO, while keeping other variables constant,
leads to a 1.900233% decrease in the logarithm of stock price.
3. Regression coefficient of LOG LDR is 0.168395
The regression coefficient signifies that the logarithm of loan to deposit ratio (LDR)
has a positive impact on the logarithm of stock price. This implies that a 1%
59
increase in the logarithm of LDR, while keeping other variables constant, leads to
a 0.168395 % increase in the logarithm of stock price.
4. Regression coefficient of NPL is -0.060209
The regression coefficient signifies that the non-performing loan ratio (NPL) has a
negative impact on the logarithm of stock price. This implies that a 1% increase in
the logarithm of NPL, while keeping other variables constant, leads to a 0.060209
% decrease in the logarithm of stock price.
5. Regression coefficient of CAR is 0.016360
The regression coefficient signifies that the capital adequacy ratio (CAR) has a
positive impact on the logarithm of stock price. This implies that a 1% increase in
the logarithm of CAR, while keeping other variables constant, leads to a 0.168395%
increase in the logarithm of stock price.
6. Regression coefficient of Inflation Rate (IR) is -0.049538
The regression coefficient signifies that the inflation rate has a negative impact on
the logarithm of stock price. This implies that a 1% increase in the logarithm of
NPL, while keeping other variables constant, leads to a 0.049538 % decrease in the
logarithm of stock price.
7. Regression coefficient of Exchange Rate (EXR) is -0.000061
The regression coefficient signifies that the exchange rate has a negative impact on
the logarithm of stock price. This implies that a 1% increase in the logarithm of
NPL, while keeping other variables constant, leads to a -0.000061% decrease in the
logarithm of stock price
8. Regression coefficient of BI Rate (BIR) is 0.051596
The regression coefficient signifies that the capital adequacy ratio (CAR) has a
positive impact on the logarithm of stock price. This implies that a 1% increase in
60
the logarithm of CAR, while keeping other variables constant, leads to a 0.051596
% increase in the logarithm of stock price.
4.3 Hypothesis Testing
Hypothesis testing purpose is to analyze variable correlations and the assessment of
statistical significance. Below are three core tests in hypothesis testing:
4.3.1 T-test
The purpose of this test is to find out if certain independent variables have a statistically
significant influence on the dependent variable. This type of test compares the probability
value associated with each variable to a level of significance. When the probability value
for an independent variable is less than 0.05, it suggests acceptance of the alternative
hypothesis, indicating that the independent variable has a significant influence on the
dependent variable. Below is the detailed explanation of the T-test based on table 4. 8
which contain the multiple linear regression results:
1. LOGBOPO has probability value less than 0.05 (0.0000 < 0.05). As a result of this
outcome, H01 is rejected and HA1 is accepted. This result implies that BOPO exert
a significant influence toward stock prices of bank category KBMI 3 & 4 in 2017
– 2022
2. LOGLDR has probability value greater than 0.05 (0.2934 > 0.05). As a result of
this outcome, H03 is rejected and HA3 is accepted. This result implies that LDR does
not exert significant influence toward stock prices of bank category KBMI 3 & 4
in 2017 – 2022
3. NPL has probability value less than 0.05 (0.0277 < 0.05). As a result of this
outcome, H03 is rejected and HA3 is accepted. This result implies that NPL exert a
significant influence toward stock prices of bank category KBMI 3 & 4 in 2017 –
2022
4. CAR has probability value less than 0.05 (0. 0112 < 0.05). As a result of this
outcome, H04 is rejected and HA4 is accepted. This result implies that CAR exert a
61
significant influence toward stock prices of bank category KBMI 3 & 4 in 2017 –
2022
5. IR has probability value less than 0.05 (0.0010 < 0.05). As a result of this outcome,
H05 is rejected and HA5 is accepted. This result implies that Inflation Rate exert a
significant influence toward stock prices of bank category KBMI 3 & 4 in 2017 –
2022
6. EXR has probability value less than 0.05 (0.0223 < 0.05). As a result of this
outcome, H06 is rejected and HA6 is accepted. This result implies that Exchange
Rate exert a significant influence toward stock prices of bank category KBMI 3 &
4 in 2017 – 2022
7. BIR has probability value less than 0.05 (0.0138 < 0.05). As a result of this
outcome, H07 is rejected and HA7 is accepted. This result implies that BI Rate exert
a significant influence toward stock prices of bank category KBMI 3 & 4 in 2017
– 2022
4.3.2 F -test
Through examined the F-test, the researcher informed whether or not the independent
variable simultaneously has significant effect to the dependent variable. The F-test
compares the probability value associated with the F-statistic to a level of significance of
0.05. When the probability value of the F-statistic falls below 0.05, it signifies the
acceptance of the alternative hypothesis, meaning that all independent factors
simultaneously have a significant influence on the dependent variable.
Table 4. 9 F-Test
Weighted Statistics
F-statistic
18.01583
Prob(F-statistic)
0.000000
Source: Proceed data by EViews 12 SV
The result of F- test in table 4.9 revealed that the probability value of F-statistic is 0.000000,
this describe the probability of F-statistic is lower than 0.05. This result exert that H08 is
rejected and HA8 is accepted, means BOPO, LDR, NPL, CAR, Inflation Rate, Exchange
62
Rate and BI Rate exert simultaneous significant influence on stock price of banks category
KBMI 3 & 4 in 2017 – 2022.
4.3.3 Coefficient of Determination (R2)
Through the coefficient of determination, it is informed the researcher about how much the
potential percentages of independent variables characterizing the dependent variable. The
possible value of the coefficient of determination is between 1 and 0 (0 < R2 < 1). Below
is the outcome of the coefficient of determination (R2).
Table 4.10 Coefficient of Determination
Weighted Statistics
R-squared
0.293205
Adjusted R-squared
0.276930
Source: Proceed data by EViews 12 SV
According to the data presented in table 4.10, the adjusted R-squared value is 0.276930,
signifying that the independent variables, including BOPO, LDR, NPL, CAR, Inflation
Rate, Exchange Rate, and BI Rate, collectively account for 27.69% of the variation in the
dependent variable. The remaining 72.31% of the variation is influenced by unexamined
factors in this research.
4.4 Interpretation of the Result
1. The Influence of BOPO Toward Stock Prices of Bank Category KBMI 3 & 4
From the T -test and coefficient regression result, it is showing the probability value is
0.0000 and the coefficient regression value is -1.900233. This result described that
BOPO partially has a negative significant effect toward bank category KBMI 3 & 4
stock prices. BOPO (operational efficiency ratio) The ratio measures the efficiency
and capacity of banks to effectively carry out their daily operations. The lower BOPO
ratio indicates the more efficient the banks operate, which make the higher bank’s
profit. On the other hand, the higher BOPO ratio indicates bank operational activities
63
are less efficient because the bank needs higher operational cost to obtain its business
profit. Therefore, the lower BOPO ratio of the bank will increase investor interest in
investing to the companies because it indicates the bank could maximize their
operational efficiency which lead to the higher profit. This study's findings are backed
by prior research by Ilham (2022) and Kusuma (2017), which found that BOPO has a
negative significant impact on stock price.
2. The Influence of LDR Toward Stock Prices of Bank Category KBMI 3 & 4
From the T-test and coefficient regression result, it is showing the probability value is
0.2934 and the coefficient regression value is 0.168395. This result described that LDR
partially has a positive, but not significant effect toward bank category KBMI 3 & 4
stock prices. In the theory, the higher the LDR ratio can cause a threat to liquidity risk
in a bank. But a ratio that is too low can also indicate that banks have not used their
loans properly, which can cause the bank to not be earning as much as it could be
because they are still not as effective in utilizing their credit. From the results of this
study, LDR has a positive effect because investors pay more attention to how the bank's
performance is in terms of credit efficiency and also the profits it generates.
Furthermore, the findings of this study reveal that investors don't give much
consideration to the LDR ratio as a factor to consider when investing, implying that the
LDR ratio has no major influence on banking stock prices. This is also supported by
research from Aryanti et al, (2022), Nurhayati & Pertiwi (2021), Fordian (2017) which
state that LDR have no significant effect toward stock price.
3. The Influence of NPL Toward Stock Prices of Bank Category KBMI 3 & 4
From the T-test and coefficient regression result, it is showing the probability value is
0.0277 and the coefficient regression value is -0.060209. This result described that NPL
partially has a negative significant effect toward bank category KBMI 3 & 4 stock
prices. NPL ratio shows how many bad loans a bank has. According to Bank Indonesia
Regulation, a reasonable NPL value in a bank is 5% of its credit, higher NPL ratio
indicates the bank has more bad loans or higher credit risk in a bank’s portfolio which
can affect the profitability and the soundness of the bank. This can cause doubts and
64
even reduce investor interest in investing to the Company's stocks which can also cause
a decrease in the stock price. The result of this research is supported by previous
research by Ilham, (2022), Aryanti et al, (2022), and C. Hariyani et. al (2021) that also
found NPL has a negative significant effect toward stock price.
4. The Influence of CAR Toward Stock Prices of Bank Category KBMI 3 & 4
From the T-test and coefficient regression result, it is showing the probability value is
0.0112 and the coefficient regression value is 0.016360. This result described that CAR
partially has a positive significant effect toward bank category KBMI 3 & 4 stock
prices. A high value of CAR ratio generally indicates a bank's greater resilience in
challenging economic environments because it is showing company's ability to
guarantee its capital based on its assets. Thus, it can also describe that the bank is
healthy and will increase investor trust to invest their money to the stock, which can
lead into the higher company price. The result of this research is in line with research
by D. Putri (2017), Kusuma (2017) and Nurhayati & Pertiwi (2021) which state that
CAR has a positive significant effect toward stock price.
5. The Influence of Inflation Rate Toward Stock Prices of Bank Category KBMI 3
&4
From the T -test and coefficient regression result, it is showing the probability value is
0.0010 and the coefficient regression value is -0.049538. This result described that
inflation rate partially has a negative significant effect toward bank category KBMI 3
& 4 stock prices. The inflation rate is a crucial macroeconomic statistic that quantifies
the rate at which the overall level of prices for goods and services rises continuously.
Because the rises of the overall level prices for goods and services, it could make the
operational expenses also rise and could affect the profitability of a company. A high
inflation rate, on the other hand, will have a negative influence on the economy, which
might destabilize social and political stability, which could lead to lowering investor
excitement to invest their money in stock and choose the other type of investment that
have lower risk since inflation rate could influences the volatility of the stock market.
The result of this research in line with Herdini et al. (2021) and Safuridar &
65
Asyuratama, (2018) which state inflation rate has a negative significant effect toward
stock price.
6. The Influence of Exchange Rate Toward Stock Prices of Bank Category KBMI 3
&4
From the t -test and coefficient regression result, it is showing the probability value is
0.0223 and the coefficient regression value is -0.000061. This result described that
exchange rate partially has a negative significant effect toward bank category KBMI 3
& 4 stock prices. The rise in the dollar exchange rate versus the rupiah signifies a
weakening of the rupiah. Weakening currency rates can indicate that a country's
economic condition is not performing well, which can lead domestic investor interest
to fall owing to concern about economic circumstances that could influence the
condition of Indonesian banks, as well as fluctuations in the Indonesian stock market.
This may induce investors to choose not to invest in stocks or to sell their shares,
causing stock prices to decrease as investors seek a safer investment choice. The result
is supported by previous research by Rachmawati (2019) which state that Exchange
rate USD/IDR has a negative significant influence toward stock price.
7. The Influence of BI Rate Toward Stock Prices of Bank Category KBMI 3 & 4
From the t -test and coefficient regression result, it is showing the probability value is
0.0138 and the coefficient regression value is 0.051596. This result described that BI
rate partially has a positive significant effect toward bank category KBMI 3 & 4 stock
prices. BI Rate is an interest rate that represents monetary policy, changes in the BI rate
affect deposit rates and bank lending rates. The increase in BI rate has possibility to
increase the profitability of the bank. In addition, increase in BI Rate make the interest
rate of bank also increase, which could make the inflation decrease. In this situation, it
can increase the interest of investor to invest their money into bank stock. The result of
this research in line with Rachmawati (2019) and Herdini et. al (2021) which state there
is a positive significant influence of BI Rate toward stock price.
66
8. Simultaneous Influence of All Independent Variables toward Stock Prices of Bank
Category KBMI 3 & 4
From the result of F-test, the probability value of f-statistic is 0.000000, which is
smaller than the significance value 0.05. Therefore, this means HA8 is accepted that
BOPO, LDR, NPL, CAR, Inflation Rate, Exchange Rate and BI Rate simultaneously
have significant effect toward stock price of bank category KBMI 3 and KBMI 4 in the
period 2017 – 2022. The value of the adjusted R square is 27.69%, this result means
that the independent variables BOPO, LDR, NPL, CAR, Inflation Rate, Exchange Rate
and BI Rate simultaneously have an effect of 27.69% toward stock price and the
remaining 72.31% is explained by other factors which are not examined in this
research.
67
CHAPTER V
CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
This research has objective to identify the influence of financial ratio and macroeconomics
factors toward bank category KBMI 3 and KBMI 4 stock price. There are seven
independent variables use in this study, namely BOPO, LDR, NPL, CAR, Inflation Rate,
Exchange Rate and BI Rate. The purposive sampling method applied to select the
observation data and resulting 312 observation data from 13 banks within 2017 – 2022
using quarterly data. Below are the conclusions obtained from this study:
1. BOPO has a negative significant influence toward bank KBMI 3 & 4 stock price.
This mean increase in BOPO can influence the decrease on stock price. The lower
BOPO ratio indicates the more efficient the banks operate because the banks could
maximize the utilization of their operational expenses. This result could lead into
the higher profit that bank could obtained, which also resulted to the increase
investor interest in investing to the companies.
2. LDR has a positive insignificant influence toward bank KBMI 3 & 4 stock price.
Theoretically, the higher LDR ratio could bring negative impact since it also
increases the liquidity risk. On the other hand, it also could increase the bank's
profits from the loan's interest payments. Because of these two possibilities, it might
cause the difference analysis by investors and contra in stock market, which make
LDR does not have a significant effect. From that result, it makes investors do not
consider this ratio in choosing stocks.
3. NPL has a negative significant influence toward bank KBMI 3 & 4 stock price.
This mean increase in NPL can influence the decrease on stock price. The greater
NPL ratio indicates the bank has more bad loans and higher level of credit risk in a
bank's loan portfolio. The result of worse credit quality might increase possibilities
of a bank experienced financial difficulties and led to decrease in bank stock price.
68
4. CAR has a positive significant influence toward bank KBMI 3 & 4 stock price. This
mean increase in CAR can influence the increase in stock price. A high value of
CAR ratio often implies a bank's stronger resilience in difficult economic
circumstances. It is also described that the bank is healthy. The healthy bank could
increase interest of investor and impact on the increase of stock price.
5. Inflation rate has a negative significant influence toward bank KBMI 3 & 4 stock
price. This mean increase in inflation rate can influence the decrease in stock price.
A high inflation rate will have a negative influence on the economy and make
economic growth slows. This situation could lead to lowering investor excitement
to invest their money in stock and make stock price decreasing.
6. Exchange rate has a negative significant influence toward bank KBMI 3 & 4 stock
price. This mean increase in USD/IDR exchange rate can influence the decrease in
stock price. The rise in the dollar exchange rate versus the rupiah signifies a
weakening of the rupiah and indicate that a country's economic condition is not
performing well. This situation could decrease investor interest in investing in stock
market and make the stock price decreasing.
7. BI rate has a positive significant influence toward bank KBMI 3 & 4 stock price.
This mean increase in BI rate can influence the increase in stock price. An increase
in the BI rate implies an increase in the bank's interest rate, which might lead to an
increase in the bank's profitability due to an increase in loan interest. This condition
may pique investor interest in investing in bank stocks, causing the stock price to
rise.
8. Simultaneously, BOPO, LDR, NPL, CAR, inflation rate, exchange rate, and BI rate
exert a significant influence toward bank KBMI 3 & 4 stock price. All independent
variables have an effect by 27.69% toward stock price and the remaining 72.31%
is explained by other factors which are not examined in this research.
69
5.2 Recommendations
1. Investors
Before do investment in a stock company, it is really important for investor to do the
stock analysis. Investor can pay attention to financial ratio as internal factor to gain
insight of the financial performance of the company and for external factors, investor
can pay attention to macroeconomics factors. In accordance with the variables used in
this study, investor can consider to analyze BOPO, NPL and CAR for financial ratio
and inflation rate, exchange rate and BI rate for macroeconomics factor before invest
their money into stock, especially in banking stocks.
2. Banking Company
To attract investor to invest their money in the company, Bank should pay more
attention and manage their financial ratio, especially BOPO, NPL and CAR ratio. A
good financial ratio could achieve by keep maintaining a good financial performance
in the company. With a good financial performance, it could make bank more profitable
and could run their operational smoothly. In addition, bank company also must pay
attention to the macroeconomics factor that could affect the performance and
profitability of bank, such as inflation rate, exchange rate and BI rate. By keep attention
to the several point above, it could result to increase of investor interest to invest their
money into the stock of company.
3. Future Researcher
It is recommended that further researcher add other variables that have not been used
in this study. Beside add other variables, the researcher also suggests that future
researcher could use other type of observation period like annually or monthly and can
extend the observation period. With add other variable, use the other type and extend
the time period of the data, it is hoped that it can complement the findings of this study
and create better research which can be useful for third parties. In addition, future
researcher could also consider to increase the number of sample or even change the
sampling method.
70
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APPENDICES
Appendix 1 Common Effect Model
Dependent Variable: LOGSP
Method: Panel Least Squares
Date: 09/10/23 Time: 12:52
Sample: 2017Q1 2022Q4
Periods included: 24
Cross-sections included: 13
Total panel (balanced) observations: 312
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOGBOPO
LOGLDR
NPL
CAR
IR
EXR
BIR
24.99164
-4.057571
0.527809
-0.165267
0.000662
-0.110657
-0.000108
0.044633
2.070804
0.431973
0.297211
0.051790
0.013558
0.044352
8.21E-05
0.060391
12.06857
-9.393105
1.775876
-3.191059
0.048792
-2.494984
-1.314680
0.739075
0.0000
0.0000
0.0768
0.0016
0.9611
0.0131
0.1896
0.4604
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.371158
0.356678
0.772820
181.5643
-358.2515
25.63261
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
7.643753
0.963528
2.347766
2.443740
2.386124
0.110252
81
Appendix 2 Fixed Effect Model
Dependent Variable: LOGSP
Method: Panel Least Squares
Date: 09/10/23 Time: 12:53
Sample: 2017Q1 2022Q4
Periods included: 24
Cross-sections included: 13
Total panel (balanced) observations: 312
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOGBOPO
LOGLDR
NPL
CAR
IR
EXR
BIR
15.57983
-1.875337
0.174018
-0.058834
0.016601
-0.048781
-6.07E-05
0.051301
1.307406
0.227604
0.160717
0.027298
0.006422
0.014927
2.67E-05
0.020854
11.91660
-8.239487
1.082760
-2.155226
2.584769
-3.268039
-2.275759
2.460021
0.0000
0.0000
0.2798
0.0320
0.0102
0.0012
0.0236
0.0145
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.940478
0.936605
0.242601
17.18580
9.522693
242.8270
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
7.643753
0.963528
0.067162
0.307098
0.163057
0.528891
82
Appendix 3 Random Effect Model
Dependent Variable: LOGSP
Method: Panel EGLS (Cross-section random effects)
Date: 09/10/23 Time: 15:33
Sample: 2017Q1 2022Q4
Periods included: 24
Cross-sections included: 13
Total panel (balanced) observations: 312
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LOGBOPO
LOGLDR
NPL
CAR
IR
EXR
BIR
15.73055
-1.900233
0.168395
-0.060209
0.016360
-0.049538
-0.000061
0.051596
1.321296
0.226635
0.159987
0.027215
0.006407
0.014913
2.67E-05
0.020832
11.90539
-8.384561
1.052554
-2.212376
2.553575
-3.321893
-2.296128
2.476836
0.0000
0.0000
0.2934
0.0277
0.0112
0.0010
0.0223
0.0138
Effects Specification
S.D.
Cross-section random
Idiosyncratic random
0.880805
0.242601
Rho
0.9295
0.0705
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.293205
0.276930
0.241987
18.01583
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.429071
0.284579
17.80157
0.514009
Unweighted Statistics
R-squared
Sum squared resid
0.265010
212.2123
Mean dependent var
Durbin-Watson stat
7.643753
0.043118
83
Appendix 4 Raw Data
No
1
2
Company
BBCA
BBNI
Period
(Quarterly)
SP
BOPO
CAR
LDR
NPL
IR
EXR
BIR
2017.1
3120.78
65.20
23.10
75.05
1.47
3.64
13337.00
4.75
2017.2
2017.3
2017.4
2018.1
2018.2
2018.3
3529.58
3772.31
4179.31
4622.23
4499.46
4717.38
61.83
59.86
58.65
63.29
62.12
60.17
22.10
23.62
23.06
23.65
22.81
23.19
74.49
74.74
78.22
77.85
77.02
80.88
1.47
1.53
1.49
1.54
1.43
1.44
4.29
3.81
3.50
3.28
3.25
3.09
13322.33
13388.67
13544.67
13625.33
14077.33
14684.33
4.75
4.50
4.25
4.25
4.75
5.50
2018.4
2019.1
2019.2
2019.3
2019.4
2020.1
4928.86
5446.17
5666.23
6051.14
6305.87
6309.76
58.24
65.20
62.59
59.84
59.09
77.09
23.39
24.49
23.58
23.79
23.80
22.50
81.58
81.03
78.97
80.58
80.47
77.64
1.41
1.47
1.41
1.62
1.34
1.60
3.17
2.62
3.14
3.40
2.95
2.87
14682.33
14126.00
14247.00
14145.67
14003.67
14754.33
5.92
6.00
6.00
5.50
5.00
4.75
2020.2
2020.3
2020.4
2021.1
2021.2
2021.3
5379.31
6082.42
6268.98
6823.69
6325.85
6342.62
66.59
65.57
63.45
63.27
60.28
54.29
22.93
24.72
25.83
24.53
25.33
26.15
73.28
69.55
65.77
65.24
62.35
61.97
2.08
1.93
1.79
1.83
2.39
2.36
2.27
1.43
1.57
1.43
1.48
1.57
14730.67
14708.33
14307.67
14295.00
14424.67
14390.67
4.42
4.00
3.83
3.58
3.50
3.50
2021.4
2022.1
2022.2
2022.3
2022.4
2017.1
7389.38
7824.59
7601.36
7859.23
8654.62
6072.66
54.15
56.73
52.38
48.55
46.54
70.49
25.66
23.86
24.72
25.36
25.77
19.00
61.96
60.54
63.47
63.34
65.23
89.33
2.16
2.30
2.21
2.16
1.71
3.04
1.76
2.29
3.79
5.19
5.55
3.64
14269.33
14367.00
14603.33
15026.67
15670.00
13337.00
3.50
3.50
3.50
3.83
5.17
4.75
2017.2
2017.3
2017.4
2018.1
2018.2
2018.3
6503.33
7208.46
8240.77
9435.77
8078.85
7426.54
71.02
70.30
70.99
70.54
71.19
70.30
18.99
19.01
15.83
17.92
17.46
17.80
88.93
87.86
85.58
90.13
87.28
89.04
2.83
2.75
2.26
2.26
2.10
2.01
4.29
3.81
3.50
3.28
3.25
3.09
13322.33
13388.67
13544.67
13625.33
14077.33
14684.33
4.75
4.50
4.25
4.25
4.75
5.50
2018.4
2019.1
2019.2
2019.3
2019.4
2020.1
7940.91
9001.95
8977.34
8149.62
7482.54
6655.48
70.15
70.54
72.60
71.76
73.16
73.15
18.51
19.18
18.68
19.33
19.73
16.07
88.76
91.26
92.30
96.57
91.54
92.26
1.90
1.88
1.75
1.80
2.27
2.38
3.17
2.62
3.14
3.40
2.95
2.87
14682.33
14126.00
14247.00
14145.67
14003.67
14754.33
5.92
6.00
6.00
5.50
5.00
4.75
2020.2
4074.66
82.81
16.71
87.79
3.03
2.27
14730.67
4.42
84
3
4
BBRI
BBTN
2020.3
2020.4
2021.1
2021.2
4800.97
5574.83
6153.28
5500.08
88.99
93.31
81.57
81.21
16.75
16.78
18.07
18.18
83.11
87.28
87.24
87.83
3.56
4.25
4.12
3.94
1.43
1.57
1.43
1.48
14708.33
14307.67
14295.00
14424.67
4.00
3.83
3.58
3.50
2021.3
2021.4
2022.1
2022.2
2022.3
2022.4
5099.21
6800.00
7623.36
8612.73
8283.85
9237.31
80.47
81.18
70.20
68.59
68.05
68.63
19.90
19.74
19.29
18.42
18.90
19.27
85.14
79.71
85.24
90.06
91.18
84.25
3.81
3.70
3.46
3.16
3.04
2.81
1.57
1.76
2.29
3.79
5.19
5.55
14390.67
14269.33
14367.00
14603.33
15026.67
15670.00
3.50
3.50
3.50
3.50
3.83
5.17
2017.1
2017.2
2017.3
2017.4
2018.1
2018.2
2420.78
2808.17
3002.15
3259.85
3705.69
3216.62
71.73
72.55
72.32
69.14
70.43
70.50
20.86
21.67
22.17
22.96
20.74
20.12
93.15
89.76
90.39
88.13
92.26
95.27
2.16
2.23
2.23
2.10
2.39
2.33
3.64
4.29
3.81
3.50
3.28
3.25
13337.00
13322.33
13388.67
13544.67
13625.33
14077.33
4.75
4.75
4.50
4.25
4.25
4.75
2018.3
2018.4
2019.1
2019.2
2019.3
2019.4
3081.08
3360.76
3851.72
4185.94
4298.64
4144.76
69.12
68.40
70.21
71.12
70.50
70.10
21.02
21.21
21.68
20.77
21.62
22.55
93.15
88.96
91.43
93.90
93.84
88.64
2.46
2.16
2.31
2.33
2.94
2.62
3.09
3.17
2.62
3.14
3.40
2.95
14684.33
14682.33
14126.00
14247.00
14145.67
14003.67
5.50
5.92
6.00
6.00
5.50
5.00
2020.1
2020.2
2020.3
2020.4
2021.1
2021.2
4157.30
2820.86
3255.32
3811.02
4620.98
4130.00
72.97
77.49
80.64
81.22
76.83
78.30
18.23
19.83
20.38
20.61
19.40
19.63
90.39
85.78
82.58
83.66
86.77
84.52
2.81
2.98
3.02
2.94
3.12
3.27
2.87
2.27
1.43
1.57
1.43
1.48
14754.33
14730.67
14708.33
14307.67
14295.00
14424.67
4.75
4.42
4.00
3.83
3.58
3.50
2021.3
2021.4
2022.1
2022.2
2022.3
2022.4
3761.92
4187.19
4389.18
4504.55
4347.69
4677.54
76.37
74.30
64.26
63.98
62.59
64.20
24.37
25.28
22.39
22.97
24.00
23.30
83.05
83.67
87.14
88.95
88.92
79.17
3.29
3.08
3.15
3.32
3.14
2.82
1.57
1.76
2.29
3.79
5.19
5.55
14390.67
14269.33
14367.00
14603.33
15026.67
15670.00
3.50
3.50
3.50
3.50
3.83
5.17
2017.1
2017.2
2017.3
2017.4
2018.1
2018.2
2028.83
2444.17
2800.00
3133.69
3680.46
3134.77
84.13
83.82
83.46
82.06
85.58
90.27
18.90
18.38
16.97
18.87
17.92
17.42
107.79
111.49
109.79
103.13
104.12
111.46
3.34
3.23
3.07
2.66
2.78
2.78
3.64
4.29
3.81
3.50
3.28
3.25
13337.00
13322.33
13388.67
13544.67
13625.33
14077.33
4.75
4.75
4.50
4.25
4.25
4.75
2018.3
2018.4
2019.1
2528.62
2460.76
2562.34
90.27
85.58
98.12
17.96
18.21
17.62
112.83
103.49
112.19
3.17
2.81
2.92
3.09
3.17
2.62
14684.33
14682.33
14126.00
5.50
5.92
6.00
85
5
6
BDMN
BMRI
2019.2
2019.3
2019.4
2020.1
2468.13
2276.82
2002.22
1714.44
95.08
95.08
98.12
91.61
16.99
16.88
17.32
18.73
114.24
111.54
113.50
114.22
3.32
3.54
4.78
4.91
3.14
3.40
2.95
2.87
14247.00
14145.67
14003.67
14754.33
6.00
5.50
5.00
4.75
2020.2
2020.3
2020.4
2021.1
2021.2
2021.3
951.98
1354.03
1567.63
1872.87
1582.97
1370.00
93.02
93.02
91.61
89.28
89.85
89.85
19.10
18.95
18.21
17.65
17.80
17.97
111.27
93.26
103.49
88.62
89.12
92.79
4.71
4.56
2.81
4.25
4.10
3.94
2.27
1.43
1.57
1.43
1.48
1.57
14730.67
14708.33
14307.67
14295.00
14424.67
14390.67
4.42
4.00
3.83
3.58
3.50
3.50
2021.4
2022.1
2022.2
2022.3
2022.4
2017.1
1733.52
1719.75
1633.09
1501.38
1456.74
4457.27
89.28
86.00
85.61
85.61
86.00
69.80
17.32
18.15
17.36
17.32
20.17
23.24
113.50
95.39
93.12
92.60
92.65
92.80
4.78
3.60
3.54
3.45
3.38
3.55
1.76
2.29
3.79
5.19
5.55
3.64
14269.33
14367.00
14603.33
15026.67
15670.00
13337.00
3.50
3.50
3.50
3.83
5.17
4.75
2017.2
2017.3
2017.4
2018.1
2018.2
2018.3
5001.58
5278.85
5501.38
6853.85
6250.38
6779.62
70.60
70.89
72.11
69.30
70.86
70.98
23.19
23.81
23.24
22.46
22.52
23.08
89.57
93.78
93.29
93.52
94.11
98.45
3.47
3.58
2.92
3.47
3.55
3.25
4.29
3.81
3.50
3.28
3.25
3.09
13322.33
13388.67
13544.67
13625.33
14077.33
14684.33
4.75
4.50
4.25
4.25
4.75
5.50
2018.4
2019.1
2019.2
2019.3
2019.4
2020.1
7298.86
8569.14
6422.11
4849.77
4192.54
3232.38
70.85
73.34
75.09
76.78
82.71
68.18
22.79
22.83
22.24
23.04
24.59
23.21
94.95
94.30
95.66
96.48
98.85
95.08
2.94
2.96
3.35
3.52
3.21
3.68
3.17
2.62
3.14
3.40
2.95
2.87
14682.33
14126.00
14247.00
14145.67
14003.67
14754.33
5.92
6.00
6.00
5.50
5.00
4.75
2020.2
2020.3
2020.4
2021.1
2021.2
2021.3
2486.03
2620.32
2800.17
3153.61
2462.03
2352.86
86.58
83.46
88.87
80.81
82.47
82.76
24.47
25.93
22.79
26.23
26.54
26.56
94.34
88.70
94.95
85.33
85.51
87.82
4.24
3.45
2.94
3.26
2.94
3.03
2.27
1.43
1.57
1.43
1.48
1.57
14730.67
14708.33
14307.67
14295.00
14424.67
14390.67
4.42
4.00
3.83
3.58
3.50
3.50
2021.4
2022.1
2022.2
2022.3
2022.4
2017.1
2562.81
2372.79
2405.27
2570.31
2994.15
2829.30
86.62
70.13
70.89
72.00
72.91
75.98
24.59
25.69
25.45
25.33
25.34
21.11
98.85
84.68
89.88
93.97
90.97
89.22
3.21
2.96
2.96
2.89
2.86
3.95
1.76
2.29
3.79
5.19
5.55
3.64
14269.33
14367.00
14603.33
15026.67
15670.00
13337.00
3.50
3.50
3.50
3.83
5.17
4.75
2017.2
2017.3
2017.4
3045.63
3312.31
3609.42
73.17
71.85
71.78
21.55
21.98
21.64
88.61
89.05
88.11
3.79
3.74
3.45
4.29
3.81
3.50
13322.33
13388.67
13544.67
4.75
4.50
4.25
86
7
BNGA
2018.1
2018.2
2018.3
2018.4
4072.88
3615.58
3339.81
3527.65
66.01
67.09
67.62
66.48
20.94
20.64
21.38
20.96
90.67
94.17
92.48
96.74
3.35
3.13
3.04
2.79
3.28
3.25
3.09
3.17
13625.33
14077.33
14684.33
14682.33
4.25
4.75
5.50
5.92
2019.1
2019.2
2019.3
2019.4
2020.1
2020.2
3666.41
3821.35
3720.64
3513.29
3542.06
2271.25
63.01
66.58
67.46
67.44
63.01
74.18
22.47
21.01
22.50
21.39
17.65
19.20
93.82
97.94
92.52
96.37
94.91
87.65
2.73
2.64
2.61
2.39
2.40
3.42
2.62
3.14
3.40
2.95
2.87
2.27
14126.00
14247.00
14145.67
14003.67
14754.33
14730.67
6.00
6.00
5.50
5.00
4.75
4.42
2020.3
2020.4
2021.1
2021.2
2021.3
2021.4
2802.22
3074.36
3310.66
3042.37
2974.80
3540.82
76.35
80.03
71.38
69.11
68.82
67.26
19.83
19.90
18.51
18.94
19.40
19.60
83.03
82.95
81.15
86.00
83.29
80.04
3.50
3.29
3.30
3.19
3.06
2.81
1.43
1.57
1.43
1.48
1.57
1.76
14708.33
14307.67
14295.00
14424.67
14390.67
14269.33
4.00
3.83
3.58
3.50
3.50
3.50
2022.1
2022.2
2022.3
2022.4
2017.1
2017.2
3780.12
4028.86
4216.35
5004.81
978.52
1222.00
56.37
55.30
55.59
57.35
85.42
84.29
18.20
18.41
19.32
19.46
18.21
18.14
83.66
84.79
83.18
77.61
95.65
99.14
2.74
2.47
2.26
1.88
3.95
3.92
2.29
3.79
5.19
5.55
3.64
4.29
14367.00
14603.33
15026.67
15670.00
13337.00
13322.33
3.50
3.50
3.83
5.17
4.75
4.75
2017.3
2017.4
2018.1
2018.2
2018.3
2018.4
1321.38
1251.92
1348.08
1075.08
946.31
886.06
83.89
83.27
82.22
82.00
81.64
81.49
18.60
18.22
18.66
18.13
19.00
17.63
91.99
94.67
90.66
94.82
91.00
93.51
3.98
3.78
3.54
3.40
3.00
1.73
3.81
3.50
3.28
3.25
3.09
3.17
13388.67
13544.67
13625.33
14077.33
14684.33
14682.33
4.50
4.25
4.25
4.75
5.50
5.92
2019.1
2019.2
2019.3
2019.4
2020.1
2020.2
1149.30
1048.44
1063.71
964.05
824.44
675.43
82.02
81.00
82.79
82.44
81.65
84.41
19.90
20.00
21.00
19.10
18.79
19.00
95.89
93.00
97.00
94.00
92.67
88.00
3.05
3.00
3.00
1.72
3.06
4.00
2.62
3.14
3.40
2.95
2.87
2.27
14126.00
14247.00
14145.67
14003.67
14754.33
14730.67
6.00
6.00
5.50
5.00
4.75
4.42
2020.3
2020.4
2021.1
2021.2
2021.3
2021.4
778.87
855.76
1019.26
983.73
964.52
1020.39
87.82
89.63
81.41
78.71
78.83
79.36
20.24
17.63
21.39
21.35
21.92
19.10
82.32
93.51
83.69
76.78
75.06
94.00
3.93
1.73
3.85
3.25
3.39
1.72
1.43
1.57
1.43
1.48
1.57
1.76
14708.33
14307.67
14295.00
14424.67
14390.67
14269.33
4.00
3.83
3.58
3.50
3.50
3.50
2022.1
2022.2
2022.3
1007.30
1051.27
1076.62
76.49
74.67
74.10
22.82
20.77
20.61
74.19
78.62
84.47
3.68
3.60
3.61
2.29
3.79
5.19
14367.00
14603.33
15026.67
3.50
3.50
3.83
87
8
9
BNII
BNLI
2022.4
2017.1
2017.2
2017.3
1151.08
349.61
321.23
305.30
74.44
85.57
84.93
85.43
21.86
16.98
16.91
17.71
83.19
88.40
86.66
87.63
2.84
3.93
3.78
4.15
5.55
3.64
4.29
3.81
15670.00
13337.00
13322.33
13388.67
5.17
4.75
4.75
4.50
2017.4
2018.1
2018.2
2018.3
2018.4
2019.1
288.32
281.52
248.33
210.40
204.67
261.13
86.97
84.92
85.37
84.61
83.85
90.72
17.63
17.63
19.20
19.04
19.09
18.74
88.12
85.62
94.00
100.64
96.46
90.13
3.00
3.25
2.93
2.83
2.66
3.01
3.50
3.28
3.25
3.09
3.17
2.62
13544.67
13625.33
14077.33
14684.33
14682.33
14126.00
4.25
4.25
4.75
5.50
5.92
6.00
2019.2
2019.3
2019.4
2020.1
2020.2
2020.3
248.69
242.00
212.73
182.89
152.33
201.29
91.65
92.67
89.28
82.27
87.52
88.59
19.06
20.06
21.42
20.64
21.97
23.36
92.26
96.25
94.13
102.12
94.20
80.74
3.22
2.78
3.62
3.65
5.23
4.51
3.14
3.40
2.95
2.87
2.27
1.43
14247.00
14145.67
14003.67
14754.33
14730.67
14708.33
6.00
5.50
5.00
4.75
4.42
4.00
2020.4
2021.1
2021.2
2021.3
2021.4
2022.1
245.42
388.82
375.32
342.41
352.06
309.61
88.98
84.87
90.95
86.49
84.94
82.87
19.09
25.27
25.41
26.01
21.42
25.58
96.46
84.39
80.09
84.51
94.13
82.05
2.66
4.16
4.66
4.99
3.62
4.28
1.57
1.43
1.48
1.57
1.76
2.29
14307.67
14295.00
14424.67
14390.67
14269.33
14367.00
3.83
3.58
3.50
3.50
3.50
3.50
2022.2
2022.3
2022.4
2017.1
2017.2
2017.3
279.02
265.91
243.08
651.43
661.65
700.85
87.06
86.69
87.08
87.29
90.78
93.10
24.81
23.40
25.66
16.99
18.89
18.84
84.02
90.21
86.92
74.58
86.70
82.75
3.79
3.73
3.81
6.41
4.72
4.70
3.79
5.19
5.55
3.64
4.29
3.81
14603.33
15026.67
15670.00
13337.00
13322.33
13388.67
3.50
3.83
5.17
4.75
4.75
4.50
2017.4
2018.1
2018.2
2018.3
2018.4
2019.1
642.23
632.38
539.49
519.48
496.62
960.47
94.83
94.72
98.31
96.45
93.36
87.98
18.12
17.73
19.59
19.19
19.44
19.90
87.54
88.99
86.11
90.61
90.08
86.91
4.60
4.59
4.26
4.78
4.36
3.78
3.50
3.28
3.25
3.09
3.17
2.62
13544.67
13625.33
14077.33
14684.33
14682.33
14126.00
4.25
4.25
4.75
5.50
5.92
6.00
2019.2
2019.3
2019.4
2020.1
2020.2
2020.3
863.83
965.38
1199.13
1205.24
1233.28
1282.02
87.71
87.21
87.04
92.60
89.35
92.04
19.81
19.84
19.89
19.61
21.26
21.60
92.69
87.99
86.32
79.94
80.69
74.53
3.58
3.33
2.77
3.18
3.74
3.78
3.14
3.40
2.95
2.87
2.27
1.43
14247.00
14145.67
14003.67
14754.33
14730.67
14708.33
6.00
5.50
5.00
4.75
4.42
4.00
2020.4
2021.1
2021.2
2325.34
2451.48
2063.00
88.76
82.25
86.01
19.44
35.21
35.43
90.08
76.57
75.44
4.36
2.93
3.31
1.57
1.43
1.48
14307.67
14295.00
14424.67
3.83
3.58
3.50
88
10
11
BTPN
MEGA
2021.3
2021.4
2022.1
2022.2
1964.85
1714.69
1403.11
1243.36
88.31
90.07
72.46
74.20
34.04
19.89
33.12
32.96
74.05
86.32
69.87
77.66
3.30
2.77
3.17
3.11
1.57
1.76
2.29
3.79
14390.67
14269.33
14367.00
14603.33
3.50
3.50
3.50
3.50
2022.3
2022.4
2017.1
2017.2
2017.3
2017.4
1194.38
1128.46
2650.94
2537.00
2532.00
2484.00
73.43
82.44
82.10
83.58
84.23
90.86
33.17
34.19
23.93
24.52
25.23
24.91
83.28
68.93
94.32
95.41
94.59
96.62
3.09
3.13
0.79
0.81
0.83
0.82
5.19
5.55
3.64
4.29
3.81
3.50
15026.67
15670.00
13337.00
13322.33
13388.67
13544.67
3.83
5.17
4.75
4.75
4.50
4.25
2018.1
2018.2
2018.3
2018.4
2019.1
2019.2
3128.77
3407.54
3866.15
3664.09
3682.66
3644.06
81.94
80.70
81.09
85.40
92.39
91.14
25.44
23.62
24.30
23.69
22.68
22.88
96.17
93.72
96.63
96.25
137.38
151.77
0.94
1.07
1.19
1.22
0.73
0.78
3.28
3.25
3.09
3.17
2.62
3.14
13625.33
14077.33
14684.33
14682.33
14126.00
14247.00
4.25
4.75
5.50
5.92
6.00
6.00
2019.3
2019.4
2020.1
2020.2
2020.3
2020.4
3345.45
3205.71
2737.14
1950.69
2233.87
2561.53
89.83
90.56
92.76
89.33
89.57
91.72
23.91
23.51
21.95
22.52
24.34
23.69
147.46
171.32
169.09
153.49
151.89
96.25
0.81
0.78
0.94
1.08
1.05
1.22
3.40
2.95
2.87
2.27
1.43
1.57
14145.67
14003.67
14754.33
14730.67
14708.33
14307.67
5.50
5.00
4.75
4.42
4.00
3.83
2021.1
2021.2
2021.3
2021.4
2022.1
2022.2
2925.57
2806.78
2799.52
2740.47
2625.25
2538.91
81.52
81.96
85.25
85.60
90.22
86.33
26.81
26.46
24.52
23.51
24.41
24.09
138.01
144.77
136.61
171.32
136.68
149.92
1.36
1.39
1.49
0.78
1.32
1.25
1.43
1.48
1.57
1.76
2.29
3.79
14295.00
14424.67
14390.67
14269.33
14367.00
14603.33
3.58
3.50
3.50
3.50
3.50
3.50
2022.3
2022.4
2017.1
2017.2
2017.3
2017.4
2477.23
2590.00
2483.13
2996.83
2984.92
2957.38
88.61
80.02
80.92
82.98
81.41
81.28
23.81
25.94
24.50
24.02
25.00
24.11
155.90
130.29
56.06
57.02
56.41
56.47
1.34
1.32
3.57
3.15
2.83
2.01
5.19
5.55
3.64
4.29
3.81
3.50
15026.67
15670.00
13337.00
13322.33
13388.67
13544.67
3.83
5.17
4.75
4.75
4.50
4.25
2018.1
2018.2
2018.3
2018.4
2019.1
2019.2
3132.15
3710.00
4358.31
4727.42
5191.09
5855.38
80.21
81.17
79.10
77.78
72.23
74.98
21.40
21.06
21.03
22.79
24.25
23.26
59.83
58.66
68.40
67.23
71.31
71.85
2.34
2.28
2.19
1.60
1.75
1.65
3.28
3.25
3.09
3.17
2.62
3.14
13625.33
14077.33
14684.33
14682.33
14126.00
14247.00
4.25
4.75
5.50
5.92
6.00
6.00
2019.3
2019.4
2020.1
5771.21
5912.30
5697.22
74.79
74.10
69.71
24.42
23.68
24.70
71.00
69.67
67.48
1.37
2.46
1.55
3.40
2.95
2.87
14145.67
14003.67
14754.33
5.50
5.00
4.75
89
12
13
NISP
PNBN
2020.2
2020.3
2020.4
2021.1
6337.93
6882.26
7108.47
9738.11
70.18
70.98
65.94
62.17
25.34
26.01
22.79
26.60
67.67
64.03
67.23
61.71
1.56
1.40
1.60
1.30
2.27
1.43
1.57
1.43
14730.67
14708.33
14307.67
14295.00
4.42
4.00
3.83
3.58
2021.2
2021.3
2021.4
2022.1
2022.2
2022.3
8439.41
7862.70
8429.69
8430.05
5863.64
5248.92
62.05
60.09
56.06
63.18
62.73
58.78
27.31
28.20
23.68
22.93
22.51
22.59
61.46
62.20
69.67
69.82
70.52
78.44
1.26
1.25
2.46
1.14
1.16
1.27
1.48
1.57
1.76
2.29
3.79
5.19
14424.67
14390.67
14269.33
14367.00
14603.33
15026.67
3.50
3.50
3.50
3.50
3.50
3.83
2022.4
2017.1
2017.2
2017.3
2017.4
2018.1
5448.08
896.60
893.67
918.69
966.27
941.12
56.76
75.38
75.78
76.29
77.07
73.19
25.41
18.23
17.55
17.71
17.51
17.01
68.04
85.89
94.34
89.78
93.42
91.13
1.23
1.89
1.88
1.90
1.79
1.72
5.55
3.64
4.29
3.81
3.50
3.28
15670.00
13337.00
13322.33
13388.67
13544.67
13625.33
5.17
4.75
4.75
4.50
4.25
4.25
2018.2
2018.3
2018.4
2019.1
2019.2
2019.3
923.58
856.23
845.98
903.20
887.34
868.64
72.84
73.06
74.43
73.52
73.87
74.78
16.74
17.03
17.63
17.74
18.53
18.61
96.70
100.91
93.51
89.69
91.12
90.50
1.77
1.75
1.73
1.85
1.82
1.84
3.25
3.09
3.17
2.62
3.14
3.40
14077.33
14684.33
14682.33
14126.00
14247.00
14145.67
4.75
5.50
5.92
6.00
6.00
5.50
2019.4
2020.1
2020.2
2020.3
2020.4
2021.1
843.25
835.32
724.74
728.87
768.98
870.08
74.77
71.88
71.95
76.76
81.13
81.53
19.10
18.71
20.64
20.92
17.63
22.03
94.00
89.84
86.57
77.28
93.51
73.87
1.72
1.80
1.82
1.81
1.73
1.96
2.95
2.87
2.27
1.43
1.57
1.43
14003.67
14754.33
14730.67
14708.33
14307.67
14295.00
5.00
4.75
4.42
4.00
3.83
3.58
2021.2
2021.3
2021.4
2022.1
2022.2
2022.3
818.81
717.14
691.95
649.26
641.09
699.85
72.08
73.66
76.49
77.14
69.52
69.10
22.73
22.41
19.10
22.33
21.98
20.81
76.58
72.67
94.00
70.31
73.94
81.24
2.53
2.39
1.72
2.25
2.42
2.32
1.48
1.57
1.76
2.29
3.79
5.19
14424.67
14390.67
14269.33
14367.00
14603.33
15026.67
3.50
3.50
3.50
3.50
3.50
3.83
2022.4
2017.1
2017.2
2017.3
2017.4
2018.1
735.92
839.84
931.92
1080.85
1165.31
1230.69
71.08
79.41
80.03
79.25
85.04
80.24
21.39
21.03
22.43
23.57
21.99
22.35
77.20
86.58
93.30
91.20
96.39
90.25
2.42
2.94
2.88
2.94
2.84
2.65
5.55
3.64
4.29
3.81
3.50
3.28
15670.00
13337.00
13322.33
13388.67
13544.67
13625.33
5.17
4.75
4.75
4.50
4.25
4.25
2018.2
2018.3
2018.4
932.15
831.85
1131.97
80.46
78.48
75.54
21.70
23.04
23.49
100.01
102.60
104.15
2.78
3.08
2.97
3.25
3.09
3.17
14077.33
14684.33
14682.33
4.75
5.50
5.92
90
2019.1
2019.2
2019.3
1392.73
1256.25
1381.21
77.82
76.99
77.17
23.89
23.81
23.80
104.10
102.45
104.80
3.03
2.94
2.95
2.62
3.14
3.40
14126.00
14247.00
14145.67
6.00
6.00
5.50
2019.4
2020.1
2020.2
2020.3
2020.4
2021.1
1258.17
1100.48
756.21
793.87
915.59
1116.80
77.04
78.93
79.40
76.31
76.50
80.68
24.07
24.48
26.70
28.14
29.55
28.15
107.92
103.26
90.82
84.23
83.26
86.12
3.02
2.89
2.90
2.96
2.93
3.52
2.95
2.87
2.27
1.43
1.57
1.43
14003.67
14754.33
14730.67
14708.33
14307.67
14295.00
5.00
4.75
4.42
4.00
3.83
3.58
2021.2
2021.3
2021.4
2022.1
2022.2
2022.3
911.61
775.08
777.81
780.74
1201.36
1926.62
78.50
75.92
78.60
78.09
72.81
71.46
28.83
29.75
29.66
28.52
27.49
28.30
83.52
86.14
88.05
84.45
91.75
92.17
3.18
3.42
3.73
3.31
3.37
3.55
1.48
1.57
1.76
2.29
3.79
5.19
14424.67
14390.67
14269.33
14367.00
14603.33
15026.67
3.50
3.50
3.50
3.50
3.50
3.83
2022.4
2062.54
74.76
29.81
91.67
3.58
5.55
15670.00
5.17
91