performance of financial ratios and hedging towards firm
TRANSCRIPT
Performance of Financial Ratios and Hedging
towards Firm Value of LQ45 companies in
Indonesia
By
Irdian
014201400063
A Skripsi presented to the
Faculty of Business President University
in partial fulfillment of the requirements for
Bachelor Degree in Management
January 2018
i
PANEL EXAMINERS
APPROVAL SHEET
The Panel of Examiners declares that the skripsi entitled
“PERFORMANCE OF FINANCIAL RATIOS AND
HEDGING TOWARDS FIRM VALUE OF LQ45
COMPANIES IN INDONESIA” that was submitted by Irdian
majoring in Management from the Faculty of Business was
assessed and approved to have passed the Oral Examinations on
26th February 2018.
Panel of Examiners
Name and Signature of
Chair – Panel of Examiner
Name and Signature of
Examiner 2
Name and Signature of
Examiner 3
ii
SKRIPSI ADVISER
RECOMMENDATION LETTER
This skripsi entitled “PERFORMANCE OF FINANCIAL
RATIOS AND HEDGING TOWARDS FIRM VALUE OF
LQ45 COMPANIES IN INDONESIA” prepared and
submitted by Irdian in partial fulfilment of the requirements for
the degree of Bachelor in Management. The skripsi has been
reviewed and has been satisfied to accord with the requirement
for further examination. Therefore, I recommend this skripsi to
proceed for the Oral Defence
Cikarang, Indonesia, 24th January 2018
Acknowledged by, Recommended by,
Dr. Dra. Genoveva, M.M. Dr. Drs. Chandra Setiawan, M.M., Ph.D
Head of Management Study Program Advisor
iii
DECLARATION OF ORIGINALITY
I declare that this skripsi, entitled “PERFORMANCE
OF FINANCIAL RATIOS AND HEDGING
TOWARDS FIRM VALUE OF LQ45 COMPANIES
IN INDONESIA” is, to the best of my knowledge and
belief, an original piece of work that has not been
submitted, either in whole or in part, to another
university to obtain a degree.
Cikarang, January 26th, 2018
Irdian
iv
Abstract
This research aims to determine the factors that affect firm value proxy by
Tobin Q ratio. The determinants factors used are financial ratios such as
current ratio, debt to asset ratio and return on equity along with other
factors: tax rate, firm size and hedging dummy. Quantitative method is used
and 16 companies with specific criteria under LQ45 that listed in Indonesia
Stock Exchange are chosen as sample. The results revealed that current ratio
and return on equity have a positive significant affect toward firm value,
meanwhile debt to asset and firm size have a negative significant affect
toward firm value. Hedging and tax rate have no significant affect toward
the firm value. The most significant affect toward firm value from this
research is Return on Equity.
Keywords: firm value, financial ratios, tax rate, firm size, hedging
v
ACKNOWLEDGEMENT
In the name of God, the Most Gracious and the Most Merciful.
First of all, the researcher would like to deliver my highest gratitude to the
One and Only God, for His guidance and reinforcement in all time
especially during the completion of this study.
Researcher would like to convey immeasurable appreciation and deepest
thankfulness to these distinctive people who have given their full support in
making this study possible.
1. Researcher‟s best adviser, Dr. Drs. Chandra Setiawan, M.M., Ph.D., for
his indulgent guidance and constant support. The success of this research is
built within his constructive feedbacks during the research process.
2. Researcher‟s beloved parents, Syaiful Bunardi and Bong Siu Lan
together with the most outstanding brothers, Irwan Saputra, Hengki Setiadi,
Irvin for their support to encourage me until finished.
3. Tommy Saputra, Bayu Surya Dani, Ni Putu Kanilla Wati, Frengki
Wijaya, Aloysius Haryo Nugroho, Yasika Ayudaning Puspita, Laila
Sundari, Riska Ayu Saraswati, Jimmy Tan, Tubagus Achmad Rachmad
Saleh, Kaori Diana Putri, Marlinda and Sir Chandra’s fellow guidance and
all the Zombie squads, thanks for the moral support, sincere friendship and
unforgettable memories.
vi
To those who indirectly contribute in this research, your kindness matters a
lot for the researcher. Thank you very much.
Cikarang, January 26th, 2018
Irdian
vii
Table of Contents
PANEL EXAMINERS .............................................................................. i
SKRIPSI ADVISER ................................................................................. ii
RECOMMENDATION LETTER ............................................................ ii
DECLARATION OF ORIGINALITY .................................................... iii
Abstract .................................................................................................... iv
ACKNOWLEDGEMENT ........................................................................ v
List of Acronym ........................................................................................ x
Chapter I .................................................................................................... 1
INTRODUCTION .................................................................................... 1
1.1 Background of Study ....................................................................... 1
1.1.1 Need for study ............................................................................... 4
1.2 Problem Statement ........................................................................... 5
1.3 Research Questions .......................................................................... 6
1.4 Research Objectives ......................................................................... 6
1.5 Significant of Study ......................................................................... 7
1.6 Limitation ......................................................................................... 7
1.7 Thesis organization .......................................................................... 8
Chapter II .................................................................................................. 9
LITERATURE REVIEW ......................................................................... 9
2.1 Theoretical Review .......................................................................... 9
2.1.1 Firm Value ............................................................................. 9
2.1.2 Theory of Capital Asset Pricing Model ................................. 9
2.1.3 Corporate Risk Management ............................................... 10
2.1.5 Tobin Q Ratio as Measured ................................................. 11
2.2 Previous Research .......................................................................... 15
2.3 Research Gap ................................................................................. 18
viii
2.4 Theoretical Framework .................................................................. 19
2.5 Hypotheses ..................................................................................... 20
Chapter III ............................................................................................... 21
METHODOLOGY .................................................................................. 21
3.1 Research Method ........................................................................... 21
3.2 Research Framework ..................................................................... 22
3.3 Research Instrument ...................................................................... 23
3.4 Sampling Design ............................................................................ 23
3.4.1 Size of Population .................................................................... 23
3.4.2 Size of Sample ......................................................................... 24
3.5 Data Analysis ................................................................................. 26
3.5.1 Descriptive Statistic Analysis .............................................. 26
3.5.2 Panel Data Regression ......................................................... 27
3.5.3 Classical Assumption Test ................................................... 30
3.5.4 Multiple Regression Analysis .............................................. 34
3.6 Testing Hypotheses .................................................................... 36
3.6.1 Significant Level .................................................................. 36
3.6.2 T-test .................................................................................... 36
3.6.3 F-Test ................................................................................... 38
3.6.4 Coefficient of Determination ............................................... 40
Chapter IV ............................................................................................... 41
ANALYSIS OF DATA ........................................................................... 41
4.1 Company Profile ............................................................................ 41
4.2 Descriptive Analysis ...................................................................... 44
4.3 Data Analysis ................................................................................. 46
4.3.1 Classical Assumption Test ................................................... 46
4.3.2 Multiple Regression Analysis .............................................. 50
ix
4.4 T-Test, F-Test, and Coefficient of Determination ......................... 52
Chapter V ................................................................................................ 60
Conclusions and Recommendation ......................................................... 60
5.1 Conclusions .................................................................................... 60
5.2 Recommendations .......................................................................... 61
References ............................................................................................... 62
Journal .................................................................................................. 62
Book ..................................................................................................... 63
Thesis / Dissertation ............................................................................. 64
Website ................................................................................................ 65
Conference ........................................................................................... 65
Lists of Figures ........................................................................................ 66
x
List of Acronym
IFRS: International Financial Reporting Standards
CR: Current Ratio
TR: Tax Rate
DAR: Debt to Asset Ratio
ROE: Return on Equity
FS: Firm Size
Hg: Hedging
FV: Firm Value
LQ45: Top 45 Indonesia Companies
IDX: Indonesia Stock Exchange
BLUE: Best Linear Unbiased Estimator
1
CHAPTER I
INTRODUCTION
The scope of chapter I consist of Background of study, problem statement,
research questions, research objectives, limitations and thesis organization.
Background of study gives brief explanation about reason why the
researcher conducted this research. In the problem statement shows the
findings related to the hedging impact to the firm value. Research questions
contain the question about significant influence independent variables to
dependent variable. Research objectives contain the purpose of this
research. Significant of study explain about the research’s benefit.
Limitations explain several limitations that implemented in this research.
Thesis organization gives brief explanation for each chapter.
1.1 Background of Study
In financial world, growth of company has become consideration to the
people, stakeholder and shareholders. The growth and development of stock
companies overtime led an emergence the increased of stratum capital
owners which is they don’t directly involve to the company management
(Nabavand & Rezaei, 2015). Management is a representative of owner and
monitoring the activities of operations in the company. Company has
become larger overtime and makes people want to invest to the company to
get involved in the ownership. The one who invest in the company called
investor. Company performance is key indicator for the investor to do
investment. Investor judge the company based on their performance. Good
performance indicates that the company has generate more earnings by
utilize the asset that the company has. To determine whether the company
2
has a good or a bad performance, investor uses financial ratios to measure
the capability of the company.
Firm value is the easiest way for the investor to do valuation of the
company. Combination of the firm value and another financial ratio can
assess the financial areas. Based on Murtaqi (2017) firm value cover the
firm’s unique factors and affect the income. Financial ratios represent the
information that the investor need regarding to assess the company value.
Beside shareholder, creditor also has role to assess the firm’s asset because
creditor give loan to the firm. Debt is also a tool to maintain their capital
structure, besides using equity to gain their capital. Fail to pay the debt also
one of the reasons why many companies default. The larger debt of the
company the larger also risk to default. To minimize company become
default, hedging is a tool to reduce it.
Debt also produces risk to the company. Increase in debt also increase the
probability that a company difficult to pay back the debt on time. Figure 1.1
shows overall foreign debt in Indonesia.
Figure 1.1 External Debt Graph
Source: tradingeconomics.com
3
To mitigate business that dealing with potential high risk, the enterprise risk
management is needed. To face uncertainty and reduce the risk while doing
businesses, using financial derivatives may be useful for the company. Most
likely, non-financial firms already using financial derivatives in their daily
business. Risk arise in many ways through global economy and financial
markets, risk become systematic with other units and interact with many
external parties (Sheng, 2010). On the other hand, George S. Oldfield
(1997) started wondering about which is better off transferring the risk to
the other parties or absorb the risk of the financial product and how they
manage the risk. Because of that many companies still used to transferring
their risk to the others rather than take the risk and managed it by
themselves.
Hedging has become a tool for the company to mitigate the risk but there
are still many companies are avoiding hedging itself. The factor that affects
a company hedging is a debt foreign currency. The companies think that
hedging is the same as insurance, when the accident doesn’t occur and they
still need to pay. Hedging is used to prevent any uncertainty in the financial
world and also prevent the financial crisis may occur in any time. There are
many components to prevent financial crisis may bring the companies to
bankruptcy. Another factor that company does hedge is fluctuated currency.
Because company cannot predict the fluctuated currency in financial world,
hedging is tool to anticipate the loss of income from trying to do business
using different currency. Hence, hedging might be a factor that impact to
the firm value. In figure 1.2 shows the fluctuation currency with the lowest
and the highest exchange rate of IDR 12,451 and IDR 14,739 to USD in
2015.
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Figure 1.2 Fluctuation Currency History 2015
Source: usd.fx-exchange.com
Hedging is important to the firms when they do business using other
currency to fixing their currency rate in a length of time. Hedging is needed
for a business that related to export and import, also it needed for aviation
sector. Based on the IFRS accounting hedging rules, companies must
recognize the changes of fair value to the asset or liability or unrecognized
firm commitment or other components that could affect the profit or loss to
the firms.
1.1.1 Need for study
This researched has purpose to determine the relationship between firm
value and the control variables such as ROE, DAR, current ratio, firm size,
tax rate, and hedging dummy. This researched will focusing on the hedging;
therefore, the researcher aims to know “Performance of Financial Ratios
and Hedging towards Firm Value of LQ45 Companies in Indonesia”.
5
1.2 Problem Statement
According to F. Modigliani and Miller (1958) theory shows that risk
management using capital structure will not affect to the firm value;
therefore, hedging won’t affect the firm value. There are some research
theories and still argued that hedging has no impact to the firm value of the
company in Swedish firm (Nguyen, 2015). Meanwhile, Ayturk, et al.
(2016) found the result of the derivative uses doesn’t impact to the firm
value in Turkish market. The similarity on their research is they didn’t
diversify the industry that they chose. The opposite result can be found on
the research made by Dan, et al. (2005) stated hedging on gas with leverage,
profitability and reserves has significant impact to the firm value evidence
oil & gas Canadian companies but the result can be questionable also
because there is a research using oil & gas companies in U.S. Jin & Jorion
(2004) analyze that hedging doesn’t give impact to the market value of the
industry evidence U.S. oil & gas producers. The researches have different
result to other researches. Dan, et al. (2005) used oil & gas Canadian
companies as their sample different from the another researches. Jin and
Jorion (2004) found hedging doesn’t affect to the market value of the
industry although that they used same industry. Another research of Nguyen
(2015) and Ayturk, et al. (2016), they took sample based on derivative uses
of company based on their market. The differences of their results are
questionable whether the hedging does or doesn’t give impact to the firm
value. Investors might be still wondering if the hedging does or doesn’t give
them higher firm value. Firm that used hedging as their tool to mitigate the
risk can be speculated. Speculate the hedging might impact to their profit
loss. Those different results become matrix or references to me to do this
study.
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1.3 Research Questions
The purpose of this study to know whether hedging does impact to the firm
value based on the Indonesia market or not. Because of the different results
to the previous researches, the study aims if hedging affect to the market
value positively.
1. Is there any significant influence on Current Ratio (CR) to Firm
Value?
2. Is there any significant influence on Tax Rate to Firm Value?
3. Is there any significant influence on Debt to Asset Ratio (DAR) to
Firm Value?
4. Is there any significant influence on Return on Equity (ROE) to Firm
Value?
5. Is there any significant influence on Firm Size to Firm Value?
6. Is there any significant influence on Hedging to Firm Value?
7. Is there a simultaneous significant influence on Current Ratio, Tax
Rate, Debt to Asset Ratio, Return on Equity, Firm Size and Hedging
to Firm Value?
1.4 Research Objectives
Based on the questions above, the purpose of this research are:
1. To determine the significant influence on Current Ratio (CR) to
Firm Value
2. To determine the significant influence on Tax Rate to Firm Value
3. To determine the significant influence on Debt to Asset Ratio
(DAR) to Firm Value
4. To determine the significant influence on Return on Equity (ROE)
to Firm Value
5. To determine the significant influence on Firm Size to Firm Value
7
6. To determine the significant influence on Hedging to Firm Value
7. To determine the simultaneous significant influence on Current
Ratio, Tax Rate, Debt to Asset Ratio, Return on Equity, Firm Size
and Hedging to Firm Value
1.5 Significant of Study
This study is given benefit to:
1. The investors
To give information for the investors about determinants to the
company firm value based on the key indicators to know whether
the company overvalue or undervalue and choose the best company
to invest in.
2. The researcher
This study can be as a foundation to create a new research using firm
value with the key indicators and enrich the knowledge deeper for
the researcher.
3. The students and university
To enrich the knowledge regarding firm value of company and apply
key indicators to measuring the performance of the company.
1.6 Limitation
1. This research is only focusing in the company that categorized as
LQ45 and listed in the Indonesia Stock Exchange (IDX).
2. In this research, researcher took seven year’s period of study started
from 2010 to 2016 that observed firms hedging activities. Therefore,
the researcher doesn’t know the requirement how long the hedging
firm’s activities affect firm value.
3. In this research, regression model can only be done using common
effect because of the variable hedging dummy value is 1 and 0.
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1.7 Thesis organization
Thesis organization conducted as follows.
1. Chapter I – Introduction / Background
This chapter represent the research background of study, problem
statement, research questions, research objectives, significant of
study, limitations and organization paper. This chapter provide key
comprehension of this research.
2. Chapter II – Literature review
This chapter represent the theories of previous research. This
chapter consist of theoretical review, previous research, research
gap and hypothesis.
3. Chapter III – Research Methodology
This chapter represent the research data consist of research method,
research framework, research sample and data analysis method.
4. Chapter IV – Analysis and Interpretation
This chapter represent the finding analysis and interpretation of
result and consist of company profile, descriptive analysis, classical
assumption test, regression model analysis and interpretation of
results.
5. Chapter V – Conclusion and Recommendations
This chapter represent the conclusions and recommendations from
result of this paper.
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CHAPTER II
LITERATURE REVIEW
2.1 Theoretical Review
2.1.1 Firm Value
Firm value is measure the asset of the company. It represents the future
business of the company. Fundamental of organization describe the value
of firm which is the manager who are being the representative to optimal
maximization of a firm (Ilaboya, Izevbekhai, & Ohiokha, 2016). The
prosperity of company and shareholder’s wealth can be seen when the firm
has high firm value. Firm value has become one of the indicator to investor
whether the firm has good prosperity in the future.
According to F. Modigliani and Miller (1963) stated that financing method
will not affect the firm value of a company since calculated by earning
power and risk of underlying assets. Therefore, financing method with debt
or equity will not affect the firm value because firm value increment based
on the earning power of the company.
According to Ilaboya, et al. (2016) stated that firm value has two
perspective measurement using profitability ratio such as return on asset
(ROA), return on equity (ROE) also profit margin and stock market
perspective using share price in the stock exchange market.
2.1.2 Theory of Capital Asset Pricing Model
Capital Asset Pricing model has become a famous model in financial
history. This model describes the relationship between expected return and
systematic risk that occurs in the financial world. Basic idea from this model
10
is the investor would like to choose efficient portfolio when the investor are
price takers and expect to have maximum return with the minimum variance
of risk (Nguyen, 2015). The formula to expected return as follows.
(Eq.1)
Where:
rf = risk free rate
βi = beta from industry
rm = expected market return
Researcher include CAPM in the literature review to give understanding
contradictive theories regarding risk management could affect the firm
value. From shareholder’s perspective risk management should contribute
to the firm value. However, it is no entirely obvious why risk management
can increase firm value (Nguyen, 2015).
2.1.3 Corporate Risk Management
Risk is interwoven with the corporate business’s strategy and impact
considerably to the competitive position (Aleš S. Berk, 2009). The risk is a
combination of the likelihood of an occurrence of a hazardous event or
exposures to danger and the damaged might be caused by action or event.
There are several risks according to the type and impact of organization and
its environment. There are written as follows.
1. Strategic risk is risk occurs and affect the strategy of the firm.
11
2. Operation risk is affect the firm capability of producing the goods.
3. Supply risk is affect the flow of resources to enable the operations.
4. Fiscal risk is arising through the taxation.
5. Reputation risk is loss in confidence of the firm.
6. Asset impairment risk is risk due to reduce in capability to increase
net profit.
7. Regulatory risk is risk caused the change of regulatory and affect the
business.
One of the tools to minimize the corporate risk by doing hedging. Hedging
is an investment tool to reduce the risk adverse movement in an asset.
Hedging often used for the company which is the business operations using
foreign currency as their main currency to purchase goods. Shareholder
maximization state that firm do hedging to reduce the cost that involved
with highly volatile cash flows (Jin & Jorion, 2004). There are three type
explanation about hedging with literature. Mayer et al. (1982) stated that
hedging reduces the financial distress and hedging is a way to get tax
incentives. When firm hedging, it will help to reduce the taxes. Leland and
Hayne (1997) stated debt from firm capacity will also increase, therefore
realizing the great leverage with greater taxes advantages. In addition,
hedging might be a signal for the investor to observed the managerial ability
of the firm.
2.1.5 Tobin Q Ratio as Measured
Tobin q ratio popularized by James Tobin of Yale University. The q ratio
defined as the market value of firm divided by replacement cost of firm total
asset (Ilaboya et al. 2016). Most of study used Tobin q ratio as measurement
for firm market value. Tobin q ratio defined market value by number of
share multiple by share price in stock exchange market and divided to book
12
value total asset of firm. Tobin q ratio is one of the tool to assess
performance of company. Tobin q ratio formula can be determined below.
(Eq.2)
Where:
Total market value of firm = share price x number of shares
If the Tobin q ratio is 1 means that the market value has same value with
the replacement cost of the firm. Tobin q ratio with less than 1 means that
the company has lower market value compared to the book value total asset
of firm and categorized as “Undervalued”. Tobin q ratio with greater than 1
means that the company has higher market value compared to the book
value of total asset of firm and categorized as “Overvalued”. Hence, Tobin
q ratio give an information to the investor whether the company has
undervalued or overvalued and the performance of future growth for the
firm.
2.1.5.1 Current Ratio
Current ratio is liquidity ratio to measure the ability of the firm pay its debt
whether its short-term or long-term debt (Murtaqi, 2017). Murtaqi, (2017)
stated that current ratio has positive significant influence to the firm value
using Tobin q ratio even thought, current ratio doesn’t give the highest
significant to the firm value.
If the current ratio value is less than 1 means that the firm has more debt to
pay indicates that obligation of firm to pay would be unable to paid their
13
debt on time. It tells the investor that the firm has liquidity problem and
indicated the firm has financial health issues.
2.1.5.2 Tax Rate
According to Indonesia Regulation Law number 6 year 1983 act 1, tax is
compulsory contributions to a country that is indebted by an individual or a
coercive body under the Act, by not obtaining direct remuneration and used
for the purposes of the state to the greatest possible prosperity of the people.
Corporate tax rates in Indonesia are levied as follows:
Level of Income Tax Rate
Rp. 50,000,000 and below 10%
Rp. 50,000,001 – 100,000,000 15%
Rp. 100,000,001 and above 30%
Tax rate formula is earning before and taxes divided by taxes. Årstad,
(2010) stated that Norwegian companies don’t have indication facing tax
incentives for hedging and tax rate gives significant influence to the firm
value of Norwegian companies.
2.1.5.3 Debt to Asset
Debt to asset represent the leverage ratio indicates that the total amount of
debt from total asset also represent the capital structure of firm (Cuong,
2014). Cuong, (2014) stated that debt to asset gives significant influence to
the firm value even thought, it has negative relationship.
High debt means that firms tendency uses their financing method with debt
rather than equity. That’s why the relationship is negative. When the firms
14
have high debt means the firms have less equity. High debt also give high
probability of firm cannot pay their debt and become default.
2.1.5.4 Return on Equity
Return on equity represent the profitability ratio indicates that how much
the firm generate profit from their shares. Nabavand and Rezaei (2015)
stated that return on equity gives positive significant influence to the firm
value. Return on equity formula is net profit divided by total equity of firm.
High growth company expected to give higher return on equity means that
the company has good profitability. Therefore, investor can measure the
profitability of company using return on equity. Higher the ROE, higher
also the net profit that the company generate.
2.1.5.5 Firm Size
Firm size represent the size of the company indicates that bigger size of
company also has bigger value of asset. (Nguyen, 2015) stated that firm size
gives significant influence to the firm value even thought, it gives negative
relationship.
Based on the Grossman and Hart, (1983) theorem of principal and agency
problem stated that big firm has tendency to hired agent and pay the
incentives to them although the incentive might be expensive. It leads the
company become less efficiency. Big firm size also has tendency using debt
as their financing method. That’s why, it has negative relationship.
2.1.5.6 Hedging Dummy
Hedging dummy represent the company does or doesn’t hedging based on
the annual report and using dummy variable is appropriate for this
15
regression analysis due to it is hard to measure the size of hedging cost for
each company. Hedging doesn’t give significant influence to the firm value
based on the (Nguyen, 2015). It has contradictive result with Dan et al.
(2005) found that hedging has significant influence to the firm value. The
result might be bias because their research environment is different.
In this research, hedging dummy value is 1 if the company does hedge and
0 if the company doesn’t hedge. Hedging also related to the debt and tax.
When company does hedge means that the company has foreign debt. That
explains the tax shield of company. When company does hedge explain that
the company must pay higher interest rate and will impact to lower taxes.
2.2 Previous Research
Previous Research
No. Author, Title Variable Method Result
1. Nadya Marsha and
Isrochmani
Murtaqi (2017);
The Effect of
Financial Ratios
on Firm Value in
The Food and
Beverage Sector of
The IDX
DV: Firm
Value (Tobin
Q Ratio)
IV: ROA,
Current
Ratio, Acid
Test Ratio
Linear
Regress
ion
ROA, Current
Ratio has positive
significant
influence and
Acid Test Ratio
has negative
significant
influence and
simultaneous
significant
influence to the
Firm Value
(Tobin Q Ratio)
2. Yusuf Ayturk, Ali
Osman Gurbuz
and Serhat Yanik
(2016); Corporate
derivatives use and
firm value:
Evidence from
Turkey
DV: Firm
Value (Tobin
Q Ratio)
IV: Ln Total
Asset, ROA,
Dividend
Dummy,
Leverage,
Diversificati
Linear
Regress
ion
Ln Total Asset
has negative
significant
influence and
Dividend Dummy
have positive
significant
influence to the
Firm Value
16
on, Cap.
Ex/Sales,
Foreign
Sales,
Liquidity
and
Derivative
uses
(Tobin Q Ratio);
ROA, Leverage,
Diversification,
Cap. Ex/ Sales,
Foreign Sales,
Liquidity and
Derivatives uses
have no
significant
influence to the
Firm Value
(Tobin Q Ratio)
3. Eirik Haavaldsen
and Hans Fredrik
Ø. Årstad (2010);
Determinants and
Effects of
Corporate
Currency Hedging
DV: Firm
Value (Tobin
Q Ratio)
IV:
Derivatives,
Current
Ratio, Net
Debt, YoY
Revenue
Growth,
ROE% Mean
Tax Rate,
Hedging
Linear
Regress
ion
YoY Growth
Revenue, Tax
Rate, Hedging
have negative
significant
influence and
Current Ratio has
positive
significant
influence to the
Firm Value
(Tobin Q Ratio);
Derivatives, Net
Debt and ROE%
Mean has no
significant
influence to the
Firm Value
(Tobin Q Ratio)
4. Yanbo Jin and
Philippe Jorion
(2004); Firm
Value and
Hedging: Evidence
from U.S. Oil and
Gas Procedurs
DV: Firm
Value (Tobin
Q Ratio)
IV: Firm
Size,
Profitability,
Investment
Growth,
Leverage,
Dividend
Dummy,
Hedging
Linear
Regress
ion
Investment
Growth and
Dividend Dummy
have positive
significant
influence to the
Firm Value
(Tobin Q Ratio);
Firm Size,
Profitability,
Leverage,
Hedging Dummy
17
Dummy and
Production
Cost
and Production
Cost have no
significant
influence to the
Firm Value
(Tobin Q Ratio)
5. Behrooz Nabavand
and Javad Rezaei
(2015); Review
between Tobin's Q
with performance
Evaluation Scale
Based Accounting
and Marketing
Information in
Accepted
Companies in
Tehran Stock
Exchange
DV: Firm
Value (Tobin
Q Ratio)
IV: P/E
Ratio, EPS
Ratio, P/B
Ratio, ROE,
and ROA
Linear
Regress
ion
EPS Ratio, P/B
Ratio and ROE
have significant
influence to the
Firm Value
(Tobin Q Ratio);
P/E Ratio and
ROA have no
significant
influence to the
Firm Value
(Tobin Q Ratio)
6. Ngan Nguyen
(2015); Does
Hedging Increase
Firm Value?
DV: Firm
Value (Tobin
Q Ratio)
IV: Capex,
Diversified
Dummy,
Dividend
Dummy,
Hedging
Dummy,
Leverage,
Profitability,
Firm Size
Linear
Regress
ion
Dividend,
Profitability and
Firm Size have
positive
significant
influence and
Leverage has
negative
significant
influence to the
Firm Value
(Tobin Q Ratio);
Capex,
Diversified and
Hedging have no
significant
influence to the
Firm Value
(Tobin Q Ratio)
7. Chang Dan, Hong
Gu and Kuan Xu;
The Impact of
Hedging on Stock
Return and Firm
DV: Firm
Value (Tobin
Q Ratio)
IV: ROA,
Investment
Linear
Regress
ion
ROA and
Hedging Dgr
have positive
significant
influence to the
18
Value: New
Evidence from
Canadian Oil and
Gas Companies
Growth,
Access to
Financial
Market,
Leverage,
Hedging Dgr
(Delta Gas
Reserves)
and Hedging
Dgp (Delta
Gas
Production)
Firm Value and
Hedging Dgp and
Leverage have
negative
significant
influence to the
Firm Value
(Tobin Q Ratio);
Investment
Growth, Access
to Financial
Market have no
significant
influence to the
Firm Value
(Tobin Q Ratio
2.3 Research Gap
In the previous research, there are still many arguments that hedging doesn’t
affect firm value. However, there was a research showed that hedging
impact to the firm value (Dan et al. 2005). Those researches have many
different type of ways to see whether hedging affects the firm value and
those researches done in their country. Some countries may have different
economic condition. Hedging may impact to those countries that have
fluctuated economic or otherwise. The country may already become a
develop country and less volatility or fluctuated currency. That’s why,
hedging still be argued to many researchers that does hedging impact to firm
value. Uncertainty financial world has become a problem to the developing
country like Indonesia. When crisis impact to the develop country,
economic condition might also impact to the developing country. That’s
why, hedging has role to maintain their risk financial distress and future
cash flow so the investor will calmly invest their money to the company.
19
2.4 Theoretical Framework
Based on the theoretical review from previous research, the researcher had
developed model for theoretical framework of this research as in figure 2
According from this research, the researcher wants to know the relationship
between Current Ratio, Tax Rate, DAR, ROE, Firm Size and Hedging
towards the Firm Value of the company LQ45 and listed in Indonesia Stock
Exchange. In this research, researcher put current ratio as liquidity
measurement; tax rate taken from previous research, debt to asset ratio as
leverage measurement, ROE as profitability measurement, firm size taken
from previous research and hedging taken from previous research.
Figure 2 Research Theoretical Framework Firm Value
Source: Adjusted by Researcher 2017
FV
CR Hg FS ROE DAR TR
H1 H6 H5 H4 H3 H2
H7
20
2.5 Hypotheses
Based on the theoretical framework of this research, research conducted the
hypothesis as follows.
H1: There is a significance influence on Current Ratio towards Firm Value
in LQ45 Companies.
H2: There is a significance influence on Tax Rate towards Firm Value in
LQ45 Companies.
H3: There is a significance influence on Debt to Asset towards Firm Value
in LQ45 Companies.
H4: There is a significance influence on Return on Equity towards Firm
Value in LQ45 Companies.
H5: There is a significance influence on Firm Size towards Firm Value in
LQ45 Companies.
H6: There is a significance influence on Hedging towards Firm Value in
LQ45 Companies.
H7: There is significant simultaneous influence on Current Ratio, Tax Rate,
Debt to Asset, Return on Equity, Firm Size and Hedging towards Firm
Value in LQ45 Companies.
21
CHAPTER III
METHODOLOGY
3.1 Research Method
Quantitative Method
Quantitative research has purpose to explaining and determining the
predicted variable through numerical observation and presentation that
reflect from the independent variable. Quantitative method has two methods
to determine the data. In this research, researcher uses secondary data as
method to gathering the information that related to the research.
In this research, researcher collected the secondary data from audited annual
report of LQ45 companies from period of 2010 - 2016. The secondary data
are current ratio (X1), tax rate (X2), debt to asset ratio (X3), return on equity
(X4), firm size (X5), hedging dummy (X6) as the independent variable and
firm value (Y) as the dependent variable. Using quantitative method,
researcher can determine the result of this study using Eviews (Econometric
Views) 10 to generate the data analysis.
Researcher uses Eviews to produce the result of this study by processing the
raw data. By using Eviews 10, researcher can determine the mean,
minimum, maximum and standard deviation through descriptive statistic. In
order to fulfill the BLUE parameter, researcher uses Eviews to generate
normality test, heteroscedasticity, autocorrelation and multicollienarity.
After this research fulfill the BLUE parameter, researcher continues to
generate regression model using Eviews 10. Hypothesis can be determined
by using F-test and T-test also interpretation of each independent variable
that influenced the dependent variable.
22
3.2 Research Framework
Research framework is framework that has logical argument with the
previous research that has successful created (Suryana, 2010). In figure 3.1
shows the research framework of this research.
Figure 3.1 Research Framework of “Performance of Financial Ratios and
Hedging towards Firm Value of LQ45 Companies in Indonesia"
Figure 3.1 Research Framework
Source: Created by researcher for research purpose, based on Suryana (2010).
Observation
Problem Identification
Findings
Formulate Problem
Formulate Hypothesis
Collecting Data from Annual Report
Data Analysis
Processing Data
Interpretation of Results
Summary and Conclusion
23
3.3 Research Instrument
Research instrument is a tool that chosen by researcher in their research or
study to collect the data into a systematic (Arikunto, 2000). By using
random sampling of LQ45 companies, researcher took randomly 16
companies. These companies are Adaro, Indofood CBP, Indofood, Astra
International, Astra Agro Lestari, Indocement, Lippo Karawaci, Pakuwon
Jati, AKR Corporindo, Bumi Serpong Damai, Kalbe Farma, Adhi Karya,
Telkom, Wijaya Karya, Perusahaan Gas Negara, Gudang Garam.
Researcher collected the data through audited annual report from Indonesia
Stock Exchange period of 2010 until 2016.
To optimize the results of this study, researcher uses Eviews 10 as a tool to
produce the analysis and regression model of this study. Beside using
Eviews 10 as tool to produce the analysis, researcher uses Microsoft Excel
2016 to maintain the raw information from annual report. To make it easier
to calculated, researcher categorize the data annually using table.
3.4 Sampling Design
Sampling is process selecting part of object that taken by researcher to
become an object of observation (Nasution, 2003). If a sampling is correctly
observed, the statistical analysis can conclude the whole population.
3.4.1 Size of Population
Population is a whole of object to be observed (Nasution, 2003).
Population has very large of number to be observed which is not
really proper to be observed. Therefore, a proportion of population
is selected to be observed in this research. In this research,
population is focused on the companies under LQ45 and listed in
Indonesia Stock Exchange.
24
3.4.2 Size of Sample
Sample is a part of population that can be representative to estimate
the whole population (Nasution, 2003). There are two technic of
sampling design. First, probability sample or random sample.
Probability sample has purpose to minimalize the bias of the object
taken by researcher. Second, non-probability sample or non-random
sample. Non-probability sample has characteristic that deviations of
sample value towards population cannot be measure. In this
research, researcher uses non-probability sample using purposive
sample. Purposive sample is sample that taken by researcher with
purpose for this research.
Therefore, probability sample using purposive sampling is chosen
in this research. There are several criteria to choose specific sample
in this research which are:
1. Company that listed in Indonesia Stock Exchange.
2. Company must have positive asset, liabilities and equity.
3. Financial institution and non-financial institution are
excluded due to different nature of business.
4. Company that listed under LQ45 at least 3 years in row or
more.
5. Company that should not be suspended under LQ45 from
2010 – 2016.
Therefore, there are 16 companies chosen as sample in this research
using the several criteria that determined by researcher.
As of 2017, this research takes sixteen companies and categorizes
as LQ45 listed in Stock Exchange Indonesia
25
1. PT. Adaro
2. PT. Adhi Karya
3. PT. Astra International Indonesia
4. PT. Astra Agro Lestari Indonesia
5. PT. AKR Corporindo
6. PT. Bumi Serpong Damai
7. PT. Gudang Garam
8. PT. Indocement
9. PT. Indofood
10. PT. Indofood CBP
11. PT. Kalbe Farma
12. PT. Lippo Karawaci
13. PT. Pakuwon Jati
14. PT. Perusahaan Gas Negara
15. PT. Telkom Indonesia
16. PT. Wijaya Karya
Therefore, there are sixteen companies chosen as sample with
population of LQ45 listed in Indonesia Stock Exchange is 45
companies.
In this research, researcher implement panel data which is consist of
cross-section and time series data. The cross-section data is sixteen
companies listed in Indonesia Stock Exchange as LQ45 and the time
series is seven years of period from 2010 to 2016. This research
decides to choose seven years as availability. Therefore, the number
of observation using panel data by sixteen companies with seven
years of period which are 112 data.
26
3.5 Data Analysis
3.5.1 Descriptive Statistic Analysis
Descriptive Statistic Analysis is analysis of activity that conduct an
assessment towards value, score or sizes of variables and as indicator of
independent variable that being reviewed (Agung, 2000). The information
generate in descriptive statistics are mean, minimum, maximum and
standard deviation.
Mean is average value of data by add up all the number and divide by how
many the numbers are (Nicholas, 2006). Min and Max are the smallest and
highest value of the observation. Mean can be measure as follows (Schwert,
2010).
(Eq.3)
Where:
N = Number of observations
Standard deviation is average of deviation from the mean (Nicholas, 2006).
The smaller standard deviation indicates that the narrower range between
highest and lowest value closely to average value. The formulation can be
measure as follows (Schwert, 2010).
27
(Eq.4)
Where:
N = Number of sample
y = Mean
3.5.2 Panel Data Regression
Panel data is formed between the time series and cross-section data (Endri,
2011). In time series model usually, the model just use many time periods
with one object of observation and cross-section model is the model with
more than one as object but with one-time period. Regression using data
panel usually called pooled data (Endri, 2011). There are two advantages
using pooled data (Endri, 2011). First, pooled data is combination of time
series and cross-section data able to create many degree of freedom with
high value. Second, combination of time series and cross-section able to
solve the issues when there is omitted-variable.
According to Endri (2011) there are three method of estimation parameter
model, which is:
1. Common Effect: Ordinary Least Square
This method is regression model by combination of the time series
and cross-section using OLS method or called common effect. In
common effect, the regression ignored the differences of individual
and time period. This model assumes that there are no differences
between object that observed such as a company has a same
behavior in various period time.
28
2. Fixed Effect.
Different with the common effect that has assumption there are no
differences between company and the period. Fixed effect has
assumption that there are variables that cannot fill the regression
model and allowing intercept inconsistent. This model has
advantages for the research with more time series than cross-section.
3. Random Effect.
In fixed effect, differences of individual and period through
intercept. Random effect shows the differences through the errors.
This model has advantages for the research that using more cross-
section than time series.
Therefore, based on figure 3.2 this research has to followed several steps to
consider the right model of regression that will be used. Below the step to
consider the right model systemically.
Figure 3.2 Panel Data Regression Flow
Source: Syahrial, 2008
29
1. Chow Test
Chow test is a test to determine the right model between fixed effect
and common effect. This model uses F-statistic to test by adding
dummy variable to know the differences intercept between fixed
effect and common effect (Endri, 2011). The formulate stated
below.
(Eq.5)
Where:
RSS = Residual sum of square
The null hypothesis is dummy variable is not significant towards
dependent variable so the research will choose common effect. The
alternate hypothesis is dummy variable is significant towards
dependent variable so the research will choose fixed effect. The
results of chow test can be determined as follows.
1. Ho is accepted and Ha is rejected if Probability value > 0.05,
which means common effect model is accepted.
2. Ho is rejected and Ha is accepted if Probability value < 0.05,
which means fixed effect model is accepted.
2. Hausman test
Hausman test is a test to determine the right model between random
effect and fixed effect. This test formed by hausman has asymptotic
χ2 distribution. The formula stated as follows.
30
(Eq.6)
Where:
𝛽𝐹𝐸−𝛽𝑅𝐸 = Coefficient of fixed effect – coefficient random
effect
𝑉𝑎𝑟(𝛽) = Variance
The null hypothesis is there is no correlation residual between each
independent variable and random effect is chosen. The alternate
hypothesis is there is correlation residual between independent
variable which is fixed effect is chosen. The result of hypothesis can
be determined as below.
1. Ho is accepted and Ha is rejected if probability value > 0.05 and
random effect is chosen.
2. Ho is rejected and Ha is accepted if probability value < 0.05 and
fixed effect is accepted.
3.5.3 Classical Assumption Test
Classical linear regression model has five critical assumptions. The
assumption has required to show technique, ordinary least square, so that
hypothesis can be determined validly. These are five assumptions for
classical assumption test which is (Kreiberg):
1. The errors have zero mean
2. The variance of the errors is constant and finite over all values
of xi
3. The errors are statistically independent of one another
31
4. There is no relationship between the error and the
corresponding x
5. εi is normally distributed
Therefore, the research can be called fulfilled the classical assumption test
if five assumptions above are implemented. To test whether the classical
assumption test fulfilled or not. Researcher uses these steps as follows.
1. Normality Test
Normality test is test to measure whether the data for the research is
normally distributed or not in parametric statistics (Widhiarso).
Normality test can be done through statistical analysis or histogram.
The statistical analysis will show the estimated of distribution for
the regression. Histogram can describe the normality of distribution
data. If the curve of histogram focused on the middle and going
down for the both side or called the bell-shape, histogram can
conclude that the regression model is normally distributed.
However, just looking at the histogram with the bell-shaped. It
doesn’t rule out the possibility of the data is normally distributed.
The best way to check it whether it has normally distributed using
statistical analysis. Jacque-Bera is a method to test whether it is
normal distributed (Dian Christiani Kabasaranga, 2013).
(Eq.7)
Where:
S = Skewness
N = Number of Observation
K = Kurtosis
32
Dian (2013) stated that Jacque-Bera has distribution of chi-square
with X2. Compared to table with two degree of freedom with
significant value of 0.05 (α=5%).
1) Jacque-Bera value > X2, the residual is not normally
distributed.
2) Jacque-Bera value < X2, the residual is normally distributed.
Winarno (2011) stated that distribution of data can be tested by
Jacque-Bera probability. If the probability of Jacque-Bera is greater
than significant value of 0.05 then the data is normally distributed
and vice versa.
1) Jacque-Bera Probability > 0.05, the data is normally
distributed.
2) Jacque-Bera Probability < 0.05, the data is not normally
distributed.
2. Heteroscedasticity
Heteroscedasticity is distribution of same probability in the same
observation of x, and variance each residual is same (BASUKI,
2017). Heteroscedasticity can be detected by the scatterplot. If the
scatterplot created a same form or spread, then it can be concluded
there is a heteroscedasticity in the research. The regression model
called a good regression if there is no heteroscedasticity which
means that there is a singular metric of dependent variable that has
related to the two or more singulars metric of independent variable.
For testing the heteroscedasticity using white test based on the
BASUKI (2017), if the probability of Chi-Square value is greater
than 0.05 then the regression has fulfilled homoscedasticity or there
is no heteroscedasticity in the research vice versa.
33
1) If Prob. Chi-Square > 0.05, there is no heteroscedasticity.
2) If Prob. Chi-Square < 0.05, there is heteroscedasticity.
3. Autocorrelation
Autocorrelation is the correlation between each variances are same
or the variables are related to each other (BASUKI, 2017). If the
autocorrelation occurs the estimation of regression model will bias
or inefficiency. Classical assumption can be fulfilled if there is no
autocorrelation occurs in the research. To determine there is a
correlation in this research, researcher uses Durbin-Watson test. If
the range value of Durbin-Watson 1.5 between 2.5 based on the rule
of thumb still, consider as no autocorrelation. Durbin-Watson
formula as follows.
(Eq.8)
Therefore, the null hypothesis is there is no autocorrelation if the
Durbin-Watson value between range of 2. The alternate hypothesis
is there is a correlation if the Durbin-Watson value less or greater
than 2. The hypothesis can be determined as follows (Stephanie,
2017).
1. There is no autocorrelation if Durbin-Watson value equal to
2. Positive correlation if Durbin-Watson value less than 2.
3. Negative correlation if Durbin-Watson value greater than 2.
34
4. Multicollinearity
Jeeshim (2002) stated that multicollinearity is high degree of
correlation among several independent variables. This commonly
occurs when the large number of independent variables are
incorporate each other. Multicollinearity has consequences to the
estimation of dependent variable. It will affect the results of the
coefficient of determination (R2).
To detecting the multicollienarity problem, multicollinearity can be
assessed by analyzing the matrix of independent variable. The figure
3.5.3 shows the correlation using r value (Heinecke, 2011).
Figure 3.3 Multicollinearity Test
Source: Moore & Flinger, 2013
By analyzing the r value, multicollinearity can be detected. To avoid
the multicollinearity, r value must less than 0.7. If the r value greater
than 0.7, the independent variable can be detected has
multicollinearity. Therefore, to avoid the multicollinearity the r
value must less than 0.7.
3.5.4 Multiple Regression Analysis
Multiple regression is a process that allows researcher to make prediction
about independent variable based on the observation of independent
variable (Jim Higgins, 2005). Multiple regression also a strong statistical
35
and extremely powerful when the researcher develops “model” of the wide
various observation. Multiple regression provides the analysis of
relationship between two variables.
Researcher choose multiple regression to predict or estimate the
relationship between dependent variable based on the independent variable.
Firm value is a dependent variable that chosen by the research and the
independent variables are current ratio, tax rate, debt to asset ratio, return
on equity, firm size and hedging dummy. The multiple regression can be
formulated as follows.
Y = 𝛽0+𝛽1𝑋1+𝛽2𝑋2+𝛽3𝑋3+𝛽4𝑋4+𝛽5𝑋5+ 𝛽6𝑋6+𝜀
(Eq.9)
Where:
Y = firm value
𝛽0 = intercept/constant (value of Y when X1-X6 = 0)
𝛽1 – 𝛽6 = partial regression coefficients
X1 = current ratio
X2 = tax rate
X3 = debt to asset ratio
X4 = return on equity
X5 = firm size
X6 = hedging dummy
𝜀 = random error
36
The partial regression coefficient is really important to predict the
contribution of independent variable to dependent variable. If the partial
regression coefficient has positive value means that the behavior of
dependent variable will follow the independent. An increment of
independent variable value also raising the value of dependent variable, vice
versa. If the partial regression coefficient has negative value means that
dependent and independent has opposite behavior. An increment of
independent variable will impact to the decreasing value of dependent
variable.
3.6 Testing Hypotheses
Testing hypothesis is test that conducted to know whether there is an
influence between independent variable to dependent variable in the
research. There are two type of hypothesis testing. First, null hypothesis (βn
= 0) that represent as H0. Second, alternative hypothesis (βn ≠ 0) that
represent as Ha. Null hypothesis means there is no significant influence
between independent variable to dependent variable meanwhile alternative
hypothesis mean there is significant influence between independent variable
to dependent variable.
3.6.1 Significant Level
This research apply test at significant value of 0.05 or α=5%. If probability
value or P-value greater than significant value, null hypothesis will be
applied which is mean that there is no significant influence.
3.6.2 T-test
T-test in multiple regression is to test whether the parameter estimation of
multiple regression is already right parameter or not. Right parameter means
that the parameter able to explain the dependent variable through
independent variable (Iqbal, 2015).
37
(Eq.10)
Where:
𝛽𝑖 = parameters of the model; the intercept and slope coefficients
𝛽 ̂ = estimator of 𝛽𝑖
se = standard error
The result of hypothesis can conclude to accept or reject hypothesis using
probability value of t-statistic each independent variable with significant
value of 0.05. The hypothesis of t-test conclude as follows.
1) Probability of t-statistics > 0.05 means that there is no significant
influence of independent variable to dependent variable and H0 is
accepted and Ha is rejected.
2) Probability of t-statistics < 0.05 means that there is a significant
influence of independent variable to dependent variable and H0 is
rejected and Ha is accepted.
To help the researcher determine the hypothesis for each independent
variable whether there is significant influence or not. Below is the t-test
hypothesis of this research.
1. 𝐻01: 𝛽1 = 0 or if probability t-statistics > α then there is no significant
partial influence of current ratio towards firm value in LQ45.
𝐻a1: 𝛽1 ≠ 0 or if probability t-statistics < α then there is significant
partial influence of current ratio towards firm value in LQ45.
38
2. 𝐻02: 𝛽2 = 0 or if probability t-statistics > α then there is no significant
partial influence of tax rate towards firm value in LQ45.
𝐻a2: 𝛽2 ≠ 0 or if probability t-statistics > α then there is significant
partial influence of tax rate towards firm value in LQ45.
3. 𝐻03: 𝛽3 = 0 or if probability t-statistics > α then there is no significant
partial influence of debt to asset towards price to firm value in LQ45.
𝐻a3: 𝛽3 ≠ 0 or if probability t-statistics > α then there is significant
partial influence of debt to asset towards price to firm value in LQ45.
4. 𝐻04: 𝛽4 = 0 or if probability t-statistics > α then there is no significant
partial influence of return on equity towards firm value in LQ45.
𝐻a4: 𝛽4 ≠ 0 or if probability t-statistics > α then there is significant
partial influence of return on equity towards firm value in LQ45.
5. 𝐻05: 𝛽5 = 0 or if probability t-statistics > α then there is no significant
partial influence of firm size towards firm value in LQ45.
𝐻a5: 𝛽5 ≠ 0 or if probability t-statistics > α then there is significant
partial influence of firm size towards firm value in LQ45.
6. 𝐻06: 𝛽6 = 0 or if probability t-statistics > α then there is no significant
partial influence of hedging dummy towards firm value in LQ45.
𝐻a6: 𝛽6 ≠ 0 or if probability t-statistics > α then there is significant
partial influence of hedging dummy towards firm value in LQ45.
3.6.3 F-Test
F-test or called test simultaneous model is step to identify the regression
model is feasible to use. Feasible means that the model estimation can
39
explain the dependent variable through independent variable (Iqbal, 2015).
The formula for t-test as follows.
(Eq.11)
Where:
𝑅2 = coefficient of determination
N = samples
k = number of independent variables
The result of hypothesis can conclude to accept or reject hypothesis using
probability value of f-statistic each independent variable with significant
value of 0.05. The hypothesis of t-test conclude as follows.
a) Probability of f-statistics > 0.05 means that there is no significant
influence of independent variable to dependent variable and H0 is
accepted and Ha is rejected.
b) Probability of f-statistics < 0.05 means that there is a significant
influence of independent variable to dependent variable and H0 is
rejected and Ha is accepted.
In this research, f-test will help the researcher to determine the simultaneous
influence of independent variable to dependent variable. The hypothesis for
f-test as follows.
1. H07: β1 = β2 = β3 = β4 = β5 = β6 0 or if probability f-statistics > α then
there is no significant simultaneous influence of current ratio, tax
rate, debt to asset, return on equity, firm size and hedging dummy
towards price to firm value in LQ45.
40
Ha7: at least there is one βi ≠ 0 or if probability f-statistics < α then
there is significant simultaneous influence of current ratio, tax rate,
debt to asset, return on equity, firm size and hedging dummy
towards price to firm value in LQ45.
3.6.4 Coefficient of Determination
Coefficient of Determination explained about variance of independent
variable to dependent variable (Iqbal, 2015). Coefficient determination
value can be measure as R-Square (R2) or Adjusted R-Square. R-Square
normally used when the independent variable just one and Adjuster R-
Square used when the independent more than 1. Normally all the researcher
use R-Square rather than Adjusted R-Square.
Value of R-Square can range from 0 to 1
a) Independent variables have weak capability to explain dependent
variable when R-Square is close to 0.
b) Independent variables have strong capability to explain dependent
variable when R-Square is close to 1.
Value of R-Square also can explain the multicollinearity. When value of R-
Square close to 0, the research might also expose to multicollinearity
Therefore, the research must have strong capability to explain the dependent
variable through independent variable.
41
CHAPTER IV
ANALYSIS OF DATA
4.1 Company Profile
1. Adaro
Adaro was established in 1982. PT. Adaro Energy Tbk. is an energy group
from Indonesia with coal mining through subsidiaries as main business.
Headquarters of Adaro Energy in Tabalong, South Kalimantan and the CEO
is Garibaldi Thohir.
2. Adhi Karya
PT. Adhi Karya (Persero) Tbk. is a company that has construction as main
business and located in Jakarta, Indonesia. PT. Adhi Karya Tbk has founded
on 1960 and the President director is Kiswodarmawan and the main
commissioner is Fadjroel Rachman.
3. Astra International
Astra International is a multinational company that has automotive as main
business. The founders of Astra International are Tjia Kien Tie, William
Soerjadjaja and Liem Peng Hong. Astra International was founded on 1957
and located in Jakarta.
4. Astra Agro Lestari Indonesia
Astra Agro Lestari is a company that located in Jakarta and has main
business on plantation. Astra Agro Lestari was founded on 1997 and
subsidiaries from Astra International. Crude Palm Oil and Kernels is a main
product of Astra Agro Lestari.
42
5. AKR Corporindo
PT. AKR Corporindo is a multinational company that located in Jakarta and
has main business on fuels and natural gas. AKR Corporindo was founded
on 28 November 1977 and the founder is Soegiarto Adikoesoemo
6. Bumi Serpong Damai
PT. Bumi Serpong Damai is an Indonesia real-estate developer company
located in Tangerang. Its business segments are land, industrial building,
house, shop house, hotel, industrial building, office space and educational
centre. The company was founded on 1984 and the founder is Muktar
Widjaja.
7. Gudang Garam
PT. Gudang Garam Tbk. is an Indonesia company that has main business
on cigarettes located in Kediri, East Java. The founder is Surya
Wonowidjojo and founded on 1958.
8. Indocement
PT. Indocement Tunggal Prakarsa Tbk. is an Indonesia company that has
main business on cement. Indocement was founded on 1985 and the
president director is Christian Kartawijaya.
9. Indofood Sukses Makmur
PT. Indofood Sukses Makmur Tbk. is producer of food and beverages and
located in Jakarta. Indofood was founded on 14 August 1990 and the
founder is Sudono Salim.
43
10. Indofood CBP
PT. Indofood CBP Sukses Makmur Tbk. is a subsidiaries company from
Indofood and the company involved in the food industry. The headquarters
located in Jakarta.
11. Kalbe Farma
PT. Kable Farma Tbk. is an international company that produces pharmacy,
supplement, nutrition and health services that located in Jakarta. Kalbe
Farma was founded on 10 September 1966 and the founder are Khouw Lip
Tjoen,Khouw Lip Hiang, Khouw Lip Swan, Boenjamin Setiawan, Maria
Karmila, F. Bing Aryanto.
12.Lippo Karawaci
PT. Lippo Karawaci is a real-estate and developer company in Indonesia
and subsidiaries from Lippo Group. Lippo Karawaci was founded on
October 1990 and the CEO is Gouw Vi Ven.
13. Pakuwon Jati
PT. Pakuwon Jati Tbk. is a real-estate company and located in Surabaya.
PT. Pakuwon Jati Tbk. was founded on 1982. The founder is Alexander
Tedja.
14. Perusahaan Gas Negara
PT. Perusahaan Gas Negara (Persero) Tbk. is state-owned enterprises from
Indonesia and has main business on transmission and distributor of natural
gas. The company was founded on 1859 as I.J.N Eindhoven & Co. On 13
May 1965 change into PGN. The president director is Jobi Triananda
Hasjim.
44
15. Telkom Indonesia
PT. Telekomunikasi Indonesia Tbk. is a state-owned enterprises company
that provide information and communication. Telkom was founded on 23
October 1856 and the founder is Cacuk Sudarijanto.
16. Wijaya Karya
PT. Wijaya Karya Tbk. is an Indonesia company that operates in
construction. The company was founded on 11 Maret 1960. The main
commissioner is Dr. Ir. M. Basuki Hadimuljono, M.Sc and president
director is Bintang Perbowo, SE, MM.
4.2 Descriptive Analysis
Descriptive analysis describes the information for each variable that are
being observed. Using EViews 10, descriptive analysis mostly explained
about mean, median, maximum, minimum, standard deviation, skewness,
kurtosis, Jacque-Bera, probability and sum of observations. In this study,
there are 112 observations using cross-section data with seven years (2010-
2016) and sixteen companies. Summary of descriptive statistic will be
shown on table 4.1 interpret using EViews 10.
Table 4.1 Descriptive Statistic Result
FV_Y CR_X1 TR_X2 DAR_X3 ROE_X4 FS_X5 HD_X6
Mean 1.795 2.326 0.248 0.451 0.187 13.443 0.437
Maximum 5.126 6.985 0.530 0.850 0.475 14.418 1.000
Minimum 0.152 0.450 0.043 0.133 0.045 12.693 0.000
STD 1.224 1.528 0.100 0.176 0.087 0.412 0.498
∑Observ. 112 112 112 112 112 112 112
Source: Eviews10
According to the descriptive statistic result, we can conclude the
explanations as below:
45
1. Firm Value (FV) explained the dependent variable. It shows mean value
of 1.795 along with standard deviation of 1.224 indicates that the data
mostly spread around 1.795 ± 1.224. Standard deviation value has small
gap to the mean due to volatility between firm value of each company.
The maximum value of 5.126 happens to Kalbe Farma in 2012 and
minimum value of 0.152 happens to Adhi Karya in 2011.
2. Current Ratio (CR) explained the independent variable. It shows mean
value of 2.326 along with standard deviation of 1.528 indicates that the
data mostly spread around 2.326 ± 1.528. Standard deviation value still
under to the mean because of value of each company has different
fluctuatuion. The maximum value of 6.985 occurs to Indocement in
2011 and the minimum value of 0.450 occurs to Astra Agro Lestari in
2013.
3. Tax Rate (TR) explained the independent variable. It shows mean value
of 0.248 along with the standard deviation of 0.100 indicates that the
data mostly spread around 0.248 ± 0.100. Smaller standard deviation
indicates that the data has narrow between the lowest value to the
highest value. The maximum value of 0.530 occurs to Adaro in 2010
and the minimum value of 0.043 occurs to Astra Agro Lestari in 2016.
4. Debt to Asset Ratio (DAR) explained the independent variable. It shows
mean value of 0.451 along with the standard deviation of 0.176
explained that the data mostly spread around 0.451 ± 0.176. Standard
deviation has smaller value and narrower spread between the lowest
value to the highest value. The maximum value of 0.850 occurs to Adhi
Karya in 2012 and the minimum value of 0.133 occurs to Indocement
in 2016.
5. Return on Equity (ROE) explained the independent variable. It shows
mean value of 0.187 along with standard deviation of 0.087 explained
that the data mostly spread around 0.187 ± 0.087. Standard deviation
46
still smaller to the mean due to each company has small volatility. The
maximum value of 0.475 occurs to Telkom in 2016 and the minimum
value of 0.045 occurs to Adaro in 2015.
6. Firm Size (FS) explained the independent variable. It shows mean value
of 13.443 along with the standard deviation of 0.412 explained that the
data mostly spread around the 13.433 ± 0.412. Standard deviation has
small value indicates that the steady value of firm size for each
company. The maximum value of 14.418 occurs to Astra in 2016 and
the minimum value of 12.693 occurs to Pakuwon Jati in 2010.
7. Hedging Dummy explained the independent variable. It shows the mean
value of 0.437 along with the standard deviation of 0.498 indicates that
the data mostly spread around 0.437 ± 0.498. Standard deviation has
greater value to the mean value due to the data of hedging just 0 and 1.
When 0 is the company doesn’t do hedging and when 1 is the company
does hedging that explained the minimum and maximum of the value.
4.3 Data Analysis
4.3.1 Classical Assumption Test
Classical assumption test needed to test whether the model has passed the
requirement. The model has to fulfilled the normality test,
heteroscedasticity, multicollinearity, and autocorrelation in order to reach
the valid result using multiple regression.
1. Normality Test
One of the statistical test used to know whether the data normally distributed
or not using normality test (Fallo, Setiawa, & Susanto, 2013). Normality
test generate the statistical and graphic information about distribution each
variable. By looking at the histogram, researcher knows the data normally
distributed or not. This research will show the analysis of the normality test
47
that conducted by the researcher using Jarque-Bera and probability that
shown by table.
Table 4.2 Normality Test Result
0
2
4
6
8
10
12
14
-1.0 -0.5 0.0 0.5 1.0 1.5
Series: Standardized Residuals
Sample 2010 2016
Observations 112
Mean -1.05e-16
Median -0.102699
Maximum 1.553917
Minimum -1.236007
Std. Dev. 0.603921
Skewness 0.354543
Kurtosis 2.715587
Jarque-Bera 2.723907
Probability 0.256160
Source: Eviews 10
To know whether this research normally distributed or not using Jarque-
Bera by comparing the table X2 with two degrees of freedom and significant
value α = 0.05 that is 5.991 (Dian Christiani Kabasaranga, 2013). If the
Jarque-Bera value less than X2 with significant value of 0.05, the data is
normally distributed (Dian Christiani Kabasaranga, 2013). Based on table
4.2 Jarque-Bera value of this research is 2.723907 < 5.991 explained that
the data for each variable are normally distributed. Jarque-Bera probability
also another way to know whether the data is normally distributed or not. If
probability greater than significant value of 0.05 then it proves that the data
is normally distributed. Based on table 4.2 probability of this research is
0.256160 > 0.05 explained the data is normally distributed.
2. Heteroscedasticity.
Heteroscedasticity occurs when there is a constant distribution of proportion
probability in all X observation (BASUKI, Uji Heteroskedastisitas dan
Perbaikan Heteroskedastisitas, 2017). If there is no heteroscedasticity in the
regression model, therefore the regression model of this research accepted.
Using white cross-sectiom coefficient covariance method help the
48
researcher to eliminate the heteroscedasticity that occur in this research.
Table 4.3 shown the heteroscedasticity test result using Eviews 10.
Based on the table 4.3, the result of regression model is homoscedastic
explained that there is no heteroscedasticity occurs in this research.
Table 4.3 Heteroscedasticity Test Result
Source: Eviews 10
3. Autocorrelation Test
Autocorrelation occurs when there is interference of value in certain period
to the value of previous certain period (BASUKI, 2017). Autocorrelation
test can be checked using Durbin-Watson, Berenblutt-Webb, Cocharane-
Ocutt, Two steps Durbin Method and first level differentiation method
(BASUKI, 2017). This research used Durbin-Watson to know whether there
is autocorrelation in the regression model. There are three kinds of
determined the autocorrelation based on Durbin-Watson in which:
1) There is positive correlation if Durbin-Watson value less than 2.
2) There is negative correlation if Durbin-Watson value greater than 2.
3) There is no correlation if Durbin-Watson value equal to 2.
Table 4.4 Durbin Watson Result
Weighted Statistics
Durbin-Watson Stat 0.840459
Source: Eviews 10
Dependent Variable: Y
Method: Panel Least Squares
Date: 01/18/18 Time: 11:36
Sample: 2010 2016
Periods included: 7
Cross-sections included: 16
Total panel (balanced) observations: 112
White cross-section standard errors & covariance (d.f. corrected)
49
Based on the table 4.4 Durbin-Watson result on autocorrelation value of
0.840459 indicates that there is autocorrelation occurs in this research
because of the Durbin-Watson value less than 2. Autocorrelation occurs in
this research because of the independent variable formula using stock price.
Stock price value related to the historical of stock price several days which
is mean that previous stock price will also affect the next stock price.
Autocorrelation of daily stock returns influenced by both the spread and the
information of gradual incorporation (CERQUEIRA, 2006).
4. Multicollinearity Test
Multicollinearity test conducted to test whether there is a correlation
between variable to the another variable that observed in this research.
There are 6 independent variables that used in this research which are
current ratio, tax rate, debt to asset ratio, return on equity, firm size and
hedging dummy. Multicollinearity occurs when the variable value equal or
greater than 0.7 to each other variable. Thus, the value of the variable should
less than 0.7 to reach the valid test of multicollinearity. The result of
multicollinearity will be shown in table 4.5.
Table 4.5 Multicollinearity Test Result
Source: Eviews 10
Based on the table 4.5, the highest value of correlation between tax rate to
the debt to asset ratio that is 0.356 and it still consider as no correlation
CR TR DAR ROE FS HD
CR 1 -0.249 -0.411 -0.051 -0.058 0.361
TR -0.249 1 0.356 -0.090 -0.105 -0.154
DAR -0.411 0.356 1 -0.287 -0.153 -0.018
ROE -0.051 -0.090 -0.287 1 0.039 -0.207
FS -0.058 -0.105 -0.153 0.039 1 0.462
HD 0.361 -0.154 -0.018 -0.207 0.462 1
50
between the variable because the value doesn’t exceed 0.7. Thus, there is
no multicollinearity in this research and the data valid to be continued.
4.3.2 Multiple Regression Analysis
Multiple regression analysis is a statistic analysis used to determine the
value of dependent variable that influenced by independent variable. This
research will show the estimated or predicted value of dependent variable.
Table 4.6 show the result of multiple regression using common effect.
“Coefficient” in the table 4.6 explained the value change in dependent
variable along with the change of independent variable while the coefficient
remains constant (Hoaglin, 2013). Multiple regression formula conducted
based on the coefficient of each variable.
The multiple regression formula shown in the table 4.6 as follows:
Y = 9.115765 + 0.084995 X1 + 1.0875512 X2 – 4.382373 X3 + 5.461090
X4 – 0.496884 X5 – 0.342122 X6
(Eq.12)
51
Table 4.6 Multiple Regression Result
Source: Eviews10
The equations will be described as bellows.
1. Constanta value of 9.115765
Constanta explained if the value of current ratio, tax rate, debt to asset
ratio, return on equity, firm size and hedging dummy are zero, firm size
value still remains 9.115765.
2. Current ratio value of 0.084955
In the multiple regression model explained that there is positive
influence towards current ratio to the firm value. The regression model
tells if there is increasing current ratio of 1%, the firm value will
increase by 0.084955%.
Dependent Variable: Firm Value
Method: Panel Least Squares
Date: 01/10/18 Time: 12:57
Sample: 2010 2016
Periods included: 7
Cross-sections included: 16
Total panel (balanced) observations: 112
White cross-section standard errors & covariance (d.f. corrected)
Variable Coefficient Std. Error t-Statistic Prob.
C 9.115765 2.010042 4.535112 0.0000
CR 0.084995 0.038899 2.185036 0.0311
TR 1.087512 0.714868 1.521278 0.1312
DAR -4.382373 0.422086 -10.38266 0.0000
ROE 5.461090 0.644046 8.479344 0.0000
FS -0.496884 0.141734 -3.505749 0.0007
HD -0.342122 0.188898 -1.811150 0.0730
52
3. Tax rate value of 1.0875512
Multiple regression model explained that there is positive influence
towards tax rate to the firm value. If the tax rate value increase of 1%,
firm value also increased by 1.0875512%.
4. Debt to asset ratio value of -4.382373
This multiple regression model explained there is negative influence of
debt to asset value towards firm value. If debt to asset ratio value
increasing of 1%, firm value will decrease by -4.382373%.
5. Return on equity value of 5.461090
Multiple regression model explained that there is positive influence of
return on equity towards firm value. If return on equity value increase
by 1%, firm value will also increase by 5.461090%.
6. Firm size value of -0.496884
Multiple regression model explained that there is negative influence of
firm size towards firm value. If firm size increased by 1%, firm value
will decrease by -0.496884%.
7. Hedging dummy value of -0.342122
Multiple regression model explained that there is negative influence of
hedging to the firm value. Value of company hedging is 1 and value of
company non-hedging is 0. If the company hedging, the firm value will
decrease by -0.342122.
4.4 T-Test, F-Test, and Coefficient of Determination
T-Test
T-test is a parameter that capable to determine or explained for each
independent variable’s behavior in influencing the dependent variable
(Iqbal, 2015). By looking at the table 4.6 result from t-test can be determine.
T-test will determine the result by comparing the t probability to the
53
significant value of 0.05 (α=5%). If the t probability less than significant
value of 0.05 therefore there is significant influence of independent variable
to the dependent variable and if the t probability greater than significant
value of 0.05 therefore there is no significant influence of independent to
the dependent variable. Based on the table 4.3.2 result of t-test can be
determined as bellows.
1. Current ratio has probability value of 0.0311 less than the significant
value of 0.05. Therefore, H01 is rejected and Ha1 is accepted which
means that current ratio has significant influence towards firm value in
LQ45 companies.
2. Tax rate has probability value of 0.1312 greater than the significant
value of 0.05. Therefore, H02 is accepted and Ha2 is rejected which
means that tax rate has no significant influence towards firm value in
LQ45 companies.
3. Debt to asset ratio has probability of 0.0000 less than significant value
of 0.05. Therefore, H03 is rejected and Ha3 is accepted which means that
debt to asset ratio has significant influence towards firm value in LQ45
companies.
4. Return on equity ratio has probability of 0.0000 less than significant
value of 0.05. Therefore, H04 is rejected and Ha4 is accepted which
means that return on equity ratio has significant influence towards firm
value in LQ45 companies.
5. Firm size has probability of 0.0007 less than significant value of 0.05.
Therefore, H05 is rejected and Ha5 is accepted which means that firm
size has significant influence towards firm value in LQ45 companies.
6. Hedging dummy has probability of 0.0730 greater than significant value
of 0.05. Therefore, H06 is accepted and Ha6 is rejected which means that
hedging dummy has no significant influence towards firm value in
LQ45 companies.
54
Based on the result of t-test, there are two independent variables that has no
significant influence to the dependent variable. Due to there are two
independent variables that has no significant influence which are tax rate
and hedging dummy, regression model has to eliminated two independent
variables. New regression model formula written as bellows.
Y = 9.115765 + 0.084995 CR - 4.382373 DAR + 5.461090 ROE –
0.496884 FS
F-Test
F-test used to indicate the regression model whether the estimation or
prediction of the model valid or not (Iqbal, 2015). The regression model can
be determined valid if the independent variable can explain the influence
towards the dependent variable. if the result of F-statistic probability less
than significant value of 0.05 (α=5%), the regression model can be
determined as valid and vice versa.
Table 4.7 F-Test Result
Weighted Statistics
F-Statistic 54.37384
Prob (F-Statistic) 0.000000
Source: Eviews 10
Based on the table 4.7, Probability F-Statistic has value of 0.000000 less
than the significant value of 0.05. Therefore, Ha is accepted and H0 is
rejected which means there is significant influence of current ratio, tax
rate, debt to asset ratio, return on equity, firm size and hedging dummy
towards firm value in LQ45 companies.
55
Coefficient of Determination
Coefficient of Determination explained the influence of independent
variable towards dependent variable or the influence proportion for all
independent variables towards dependent variable (Iqbal, 2015).
Coefficient of determination value can be measured by R2 value.
Table 4.8 Coefficient of Determination Result
Weighted Statistic
R-squared 0.756533
Adjusted R-squared 0.742621
Source: Eviews 10
Based on the table 4.8, coefficient of determination value is 0.756533 means
that current ratio, tax rate, debt to asset ratio, return on equity ratio, firm
size and hedging explained 75.6533% influence towards firm value and the
rest of 24.3467% explained by the other factor. Coefficient of determination
categorized as good when the value closer to the 1.
Interpretation of Result
1. Interpretation influence current ratio towards firm value.
Based on the table 4.3.2 shows that the significant value of 0.0311 and
Ha1 is accepted. Thus, the first hypothesis is “there is significant
influence of current ratio towards firm value”. Coefficient regression
value of 0.084995 indicates that current ratio has positive influence
towards firm value.
This result supported with the theory Murtaqi (2017) that current ratio
has positive correlation to the firm value. Their research about “The
Effect of Financial Ratios on Firm Value in the Food and Beverage
Sector of the IDX” using 14 firms in Indonesia with period from 2010
56
– 2014. Current ratio and firm value has positive correlation when the
current ratio increase means that lower the debt higher the equity
meanwhile firm value calculation based on the stock multiple with the
number of shares and divided with total asset. Higher the equity higher
also the number of share or stock that investor holds.
2. Interpretation influence tax rate towards firm value.
Based on the table 4.3.2 shows that the significant value of 0.1312 and
H02 is accepted. Thus, the second hypothesis is “there is no significant
influence of current ratio towards firm value”. Coefficient regression
value of 1.087512 indicates that tax rate has positive influence towards
firm value.
This result has contradictive with the theory Årstad (2010) stated the tax
rate has significant influence towards firm value and Norwegian
companies has tendency in hedging due to that the tax rate has influence
the financial performance in their research. Meanwhile, the researcher
found that tax rate has no significant influence toward firm value in
Indonesia and low tendency of hedging might cause insignificant result.
Contradictive result might be found the research done using Indonesia’s
company.
3. Interpretation influence debt to asset ratio towards firm value.
Based on the table 4.3.2 shows that the significant value of 0.0000 and
Ha3 is accepted. Thus, the third hypothesis is “there is significant
influence of debt to asset towards firm value”. Coefficient regression
value of -4.382373 indicates that debt to asset has negative influence
towards firm value.
57
This result supported by Cuong, (2014) stated that TA/TD ratio has
significant influence to the firm value with negative coefficient.
Negative coefficient indicates that if the debt to asset ratio higher and
firm value will decrease. Nguyen (2015) also stated that previous
studies had shown that leverage increase the level of distress of
company. Based on the company’s annual report that debt mostly comes
from public and private bank to financing the infrastructure of
Indonesia.
4. Interpretation influence return on equity towards firm value.
Based on the table 4.3.2 shows that the significant value of 0.0000 and
Ha4 is accepted. Thus, the fourth hypothesis is “there is significant
influence of return on equity towards firm value”. Coefficient regression
value of 5.461090 indicates that return on equity has positive and the
most significant influence towards firm value.
This results also supported by Nabavand and Rezaei (2015) stated that
return on equity has significant influence towards firm value. Return on
equity can be used to measuring profitability of company. Profitability
is one of the reason increasing in firm value related to the pecking theory
order suggests that company with less debt avoiding financing with debt
because external fund might costly for the company (Myers, 1984).
Based on the company’s annual report that big companies have
tendency to do financing using their equity.
5. Interpretation influence firm size towards firm value.
Based on the table 4.3.2 shows that the significant value of 0.0007 and
Ha5 is accepted. Thus, the fifth hypothesis is “there is significant
influence of firm size towards firm value”. Coefficient regression value
58
of -0.496884 indicates that firm size has negative influence towards firm
value.
This results also supported by Nguyen (2015) found that firm size has
significant influence towards the firm value with positive relationship.
Meanwhile, this research has negative influence to the firm value.
Ayturk et al. (2016) also found that firm size has negative influence to
the firm value. According to the theory principal and agency problem
when bigger company hired an agent and pay incentives cause
inefficiency and less effectiveness of company (Grossman & Hart,
1983). This theory will lead less firm value for the bigger firm size.
6. Interpretation influence hedging towards firm value.
Based on the table 4.3.2 shows that the significant value of 0.0730 and
H06 is accepted. Thus, the sixth hypothesis is “there is no significant
influence of hedging towards firm value”. Coefficient regression value
of -0.342122 indicates that hedging has negative influence towards firm
value.
This result line with theory Nguyen (2015) stated there is no significant
influence of hedging towards firm value and contradictive with Dan et
al. (2005) and Årstad (2010) stated hedging has significant influence
towards firm value. This result might be different from the researcher
result. Dan et al. (2005) researched= about the oil & gas in Canadian
meanwhile researcher done the result using random sampling based on
LQ45 in Indonesia.
59
7. Simultaneous influence of current ratio, tax rate, debt to asset, return on
equity, firm size and hedging towards firm value.
Based on the hypothesis states that “there is significant simultaneous
influence current ratio, tax rate, debt to asset, return on equity, firm size
and hedging towards firm value” is accepted. F-statistic has proved the
hypothesis which is f-statistic value 0.000000 < 0.05. Current ratio, tax
rate, debt to asset, return on equity, firm size, and hedging has explained
the variance towards firm value which is 75.633% meanwhile
24.3467% explained by the other variables or factor which are the
variable doesn’t state in this research.
60
CHAPTER V
CONCLUSIONS AND RECOMMENDATION
5.1 Conclusions
After the researcher passed the classical assumption test and interpret the
regression model, the conclusion can be summarizing as follows.
1. There is positive significant influence of current ratio and return on
equity towards firm value. Positive significant influence explains that
when increment of current ratio and return on equity will impact to the
increment of firm value.
2. There is negative significant influence of debt to asset and firm size
towards firm value. Negative significant influence explains that
increment of debt to asset and firm size of company impact to the lower
firm value.
3. There is no significant influence of tax rate and hedging towards firm
value. Tax rate doesn’t really impact to the firm value whether the tax
rate increase or decrease. Hedging has p-value of 0.0730 and will
significant in this research if the researcher uses significant value of
10%. So, hedging and tax rate has weak impact to the firm value of
company.
4. ROE gives the most significant or impact to the firm value.
5. There is a simultaneous significant influence of current ratio, tax rate,
debt to asset, return on equity, firm size and hedging towards firm value.
Overall within 7 years in this research, researcher found that debt to asset
and ROE give most significant impact to the firm value. Enhancement of
infrastructure in Indonesia impact to the increment of debt in Infrastructure
companies. Source of debt of infrastructure companies mostly come from
private and public banks in Indonesia. Meanwhile, ROE gives significant
impact to the other company beside infrastructure company that using their
equity to financing their company. Hedging gives weak impact to the firm
61
value because public and private bank Indonesia directly give the loan to
the company so the company with high debt will not hedge.
5.2 Recommendations Based on the results from regression model and conclusion of this research,
researcher could conduct the recommendations based on the research. The
recommendations are:
1. For investors
Debt and ROE give signal for the investor to choose the right company
to invest in. Investor should be careful to the company with high debt
because of the company might be default but the firm value of the
company will be undervalued and investor could buy the stock with
affordable price. Otherwise, ROE give signal to the investor that high
ROE means high firm value and the company will be overvalued with
the high price of stock. So to consider which the best company to invest
in, investor should evaluate the company by using their management
and their financial ratios.
2. For future research.
In order to get more relevance result, the researcher encourage to uses
longer time series for the future research. The usage of another financial
ratio might give different impact to the firm value.
3. For students and university.
Enrich their knowledge of how financial performance and hedging
impact to the firm value.
62
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Lists of Figures
Figure 1.1 External Debt Graph
Source: tradingeconomics.com
Figure 1.2 Fluctuation Currency History 2015
Source: usd.fx-exchange.com
67
Company Year X1 X2 X3 X4 X5 X6 Y
ICBP 2010 2.59804346 0.2647 0.299306812 0.195243929 13.1258491 0 2.04019694
ICBP 2011 2.87107116 0.2472 0.296467608 0.192941998 13.1824962 0 1.99180488
ICBP 2012 2.76252994 0.2461 0.32481981 0.190407063 13.2492835 0 2.66036447
ICBP 2013 2.41062811 0.2473 0.376243107 0.168482234 13.3277158 0 2.79655882
ICBP 2014 2.1832023 0.2529 0.396233657 0.168330447 13.3963774 0 3.06643317
ICBP 2015 2.32600797 0.271 0.383037424 0.178383101 13.4242383 0 2.95821759
ICBP 2016 2.40678199 0.2722 0.359876262 0.196277809 13.4609271 0 3.46000419
KLBF 2010 4.3886548 0.2508 0.179219628 0.232816751 12.8471095 0 4.33257644
KLBF 2011 3.65274448 0.2459 0.212533392 0.233728054 12.9177446 0 3.85218135
KLBF 2012 3.40539741 0.2309 0.217277858 0.240800958 12.9739567 0 5.12652318
KLBF 2013 2.83925917 0.234 0.248792582 0.231819082 13.0536569 0 4.26700082
KLBF 2014 3.40363666 0.2326 0.209863171 0.216052543 13.0942975 0 4.71579477
KLBF 2015 3.69649506 0.2438 0.20137612 0.18811853 13.136607 0 4.5176165
KLBF 2016 4.13114433 0.2394 0.181410771 0.188616316 13.1825861 0 4.66411185
GGRM 2010 2.700834 0.2515 0.306470021 0.197689233 13.4877276 0 2.50355617
GGRM 2011 2.2447937 0.2505 0.371917591 0.201951714 13.5920513 0 3.05432632
GGRM 2012 2.17021686 0.2643 0.359042504 0.152926216 13.6181457 0 2.59577644
GGRM 2013 1.72207934 0.2615 0.420600245 0.149030854 13.7056093 0 1.59171354
GGRM 2014 1.62016495 0.2513 0.429261808 0.162368367 13.7650767 0 2.00602779
GGRM 2015 1.77035886 0.2527 0.40150127 0.169776085 13.8028107 0 1.66639086
GGRM 2016 1.93789066 0.2529 0.371513883 0.168654422 13.799007 0 1.95307437
AALI 2010 1.93169764 0.2903 0.151793962 0.282094609 12.9440778 0 4.47143232
AALI 2011 1.30967174 0.2503 0.174269966 0.296524822 13.0087915 0 3.19069465
AALI 2012 0.68462512 0.285 0.209386287 0.26910362 13.0941153 0 2.30750399
AALI 2013 0.45000658 0.2695 0.313792113 0.185344189 13.1750242 0 2.51695002
AALI 2014 0.58468531 0.2896 0.362146991 0.221438488 13.2685389 0 1.96063223
AALI 2015 0.79898261 0.4082 0.456183282 0.059466336 13.3326883 0 1.10549409
AALI 2016 1.02753688 0.043 0.273780508 0.120175131 13.3842839 0 1.22348101
BSDE 2010 2.17247127 0.159 0.365931702 0.070085654 13.0679909 0 1.34652727
BSDE 2011 2.11857326 0.135 0.354267505 0.122563433 13.1067815 0 1.3409362
BSDE 2012 2.90204087 0.1285 0.371493607 0.140419701 13.224189 0 1.14859582
BSDE 2013 2.66712184 0.1137 0.405670588 0.216592165 13.3535731 0 0.9999542
BSDE 2014 2.18107792 0.072 0.343393982 0.21633578 13.4492427 0 1.17865671
BSDE 2015 2.73160732 0.1341 0.724055306 0.106413223 13.2840377 0 1.80132032
BSDE 2016 2.93583865 0.1596 0.635026087 0.083667124 13.3414494 0 1.53880626
ADHI 2010 1.19282333 0.4049 0.823902502 0.219178879 12.6926439 0 0.29553266
ADHI 2011 1.10299929 0.4385 0.837988662 0.184469572 12.7862511 0 0.1518389
ADHI 2012 1.24442635 0.4748 0.84998629 0.180636892 12.8960891 0 0.35971145
68
ADHI 2013 1.39100332 0.4282 0.84070889 0.263769924 12.9877092 0 0.27980701
ADHI 2014 1.30185917 0.4506 0.843120843 0.199086076 13.0194853 0 0.59935601
ADHI 2015 1.56048773 0.3767 0.692016453 0.090084013 13.2243016 0 0.45463808
ADHI 2016 1.29062643 0.4856 0.729153427 0.057894639 13.3030974 0 0.3685696
TLKM 2010 0.91481463 0.259 0.438662302 0.281308163 14.0021704 0 1.59473034
TLKM 2011 0.95804227 0.2583 0.408261688 0.253685574 14.0130649 0 1.37916039
TLKM 2012 1.0416443 0.245 0.303336141 0.358528016 13.798768 0 2.86777808
TLKM 2013 1.01164021 0.2552 0.352664322 0.362422698 13.8688794 0 2.93102505
TLKM 2014 1.06201349 0.2565 0.351282891 0.376692503 13.8995579 0 3.63937884
TLKM 2015 1.28171828 0.256 0.387508027 0.434313231 13.9247237 0 3.72218905
TLKM 2016 1.31643141 0.2361 0.338724229 0.47490315 13.9531844 0 4.46847313
PAKU 2010 1.14952714 0.1792 0.588805478 0.156187977 12.6927157 0 1.76597863
PAKU 2011 1.38242511 0.1944 0.586900811 0.159506885 12.7592682 0 1.57186765
PAKU 2012 1.34236051 0.1494 0.585697838 0.244532529 12.878856 0 1.36856476
PAKU 2013 1.30192592 0.1462 0.558430056 0.276813392 12.968401 0 1.39844584
PAKU 2014 1.40730483 0.091 0.506486905 0.313998256 13.2245523 0 1.47889667
PAKU 2015 1.22263914 0.1957 0.496485552 0.148127533 13.2736522 0 1.27207408
PAKU 2016 1.32665766 0.1344 0.466981799 0.161552128 13.3154275 0 1.31614535
WIKA 2010 1.36031046 0.3424 0.695088295 0.162378159 12.7983954 0 0.60120375
WIKA 2011 1.13879731 0.379 0.733343587 0.176151548 12.9202788 0 0.40915709
WIKA 2012 1.13703587 0.3716 0.734745656 0.235544708 12.9229888 0 1.08633753
WIKA 2013 1.09057614 0.3859 0.750725842 0.198869899 13.1001969 0 0.70931168
WIKA 2014 1.11858918 0.3471 0.693463613 0.152513125 13.2016489 0 1.31525645
WIKA 2015 1.18520826 0.3598 0.722579904 0.129273989 13.2923094 0 0.76693696
WIKA 2016 1.47557571 0.3644 0.598067325 0.091781027 13.4927121 0 0.68075373
PGAS 2010 3.43395554 0.1984 0.529381007 0.428012664 13.5063349 1 3.34275709
PGAS 2011 5.49921556 0.2007 0.445232933 0.356026314 13.4910316 1 2.48449791
PGAS 2012 4.19634152 0.203 0.397468225 0.388678176 13.5765899 1 2.87560761
PGAS 2013 2.01008134 0.2053 0.374944501 0.32776321 13.7250932 1 2.04280222
PGAS 2014 2.59282311 0.236 0.433269944 0.260017134 13.9054767 1 1.80801108
PGAS 2015 2.58126591 0.08 0.534596814 0.133240163 13.9520662 1 0.74302371
PGAS 2016 2.60577322 0.1985 0.536124908 0.097339147 13.9641329 1 0.74772
LPKR 2010 4.49145187 0.1738 0.493703347 0.072684996 13.2083173 1 0.91033603
LPKR 2011 6.42370612 0.1736 0.484696322 0.086522773 13.2614811 1 0.83417121
LPKR 2012 5.59881841 0.1611 0.538784431 0.115329969 13.3956635 1 0.92795911
LPKR 2013 4.9597876 0.1725 0.547047631 0.112324668 13.4955494 1 0.67094103
LPKR 2014 5.23329922 0.1515 0.5326833 0.177668374 13.577046 1 0.62337083
LPKR 2015 6.91326723 0.2029 0.542261323 0.054138277 13.6162292 1 0.57796753
LPKR 2016 5.45466407 0.2121 0.515935171 0.055599831 13.6589999 1 0.38206685
69
ASII 2010 1.28400307 0.1915 0.479970228 0.289730614 14.0525285 1 1.95679295
ASII 2011 1.36399909 0.1822 0.50600895 0.277921359 14.1861678 1 1.95138315
ASII 2012 1.39907342 0.1848 0.353096179 0.253212194 14.2607247 1 1.16725221
ASII 2013 1.24196292 0.1899 0.411701132 0.209976645 14.3304016 1 1.0513
ASII 2014 1.32259293 0.1911 0.441866682 0.183878528 14.3729654 1 1.14792684
ASII 2015 1.37930537 0.2046 0.454075729 0.123390736 14.3899365 1 0.92761765
ASII 2016 1.23938302 0.1776 0.46571194 0.130816405 14.4180609 1 1.27933934
INDF 2010 2.03648988 0.2757 0.474302782 0.158324293 13.6746403 1 0.90541966
INDF 2011 1.90952798 0.23 0.410102181 0.154749705 13.7290508 1 0.75374188
INDF 2012 2.04885437 0.2424 0.425146 0.139994517 13.773709 1 0.86489324
INDF 2013 1.66729915 0.2683 0.508621353 0.089037175 13.8926109 1 0.74207639
INDF 2014 1.81007219 0.1662 0.532115657 0.129847129 13.9348884 1 0.68854289
INDF 2015 1.70533427 0.1478 0.530427132 0.086024211 13.9629918 1 0.4948051
INDF 2016 1.50813143 0.0854 0.465267023 0.119861981 13.9147371 1 0.84679392
INDC 2010 5.55373872 0.241 0.146326511 0.246147619 13.1859993 1 3.82608413
INDC 2011 6.98536771 0.235 0.133179214 0.228900929 13.2589085 1 3.45787317
INDC 2012 6.02762901 0.2366 0.146622656 0.245298536 13.3570799 1 3.66421937
INDC 2013 6.14806599 0.24 0.136412265 0.218137448 13.4249998 1 2.76709013
INDC 2014 4.93374694 0.2232 0.141948272 0.212792066 13.460672 1 3.18611316
INDC 2015 4.8865736 0.2282 0.136491818 0.182547144 13.4415123 1 2.97353018
INDC 2016 4.52502806 0.0664 0.133061354 0.148068517 13.4792957 1 1.88026128
AKRC 2010 1.20885123 0.1737 0.627056358 0.083876592 12.8845456 1 0.85600443
AKRC 2011 1.35734308 0.197 0.569740138 0.166204872 12.9195092 1 1.39157059
AKRC 2012 0.69354852 0.2357 0.642864807 0.147000371 13.0714226 1 1.32329273
AKRC 2013 1.17131728 0.1602 0.633492168 0.114788031 13.1653376 1 1.1602555
AKRC 2014 1.0866769 0.2041 0.597070492 0.132618506 13.1699712 1 1.09020108
AKRC 2015 1.4955856 0.214 0.520745034 0.145308199 13.181933 1 1.86371443
AKRC 2016 1.27093375 0.1646 0.489959411 0.129652038 13.1995012 1 1.5129227
Adaro 2010 1.72192578 0.53 0.54537877 0.121471639 13.6050439 1 2.02514312
Adaro 2011 1.66519935 0.45 0.568432615 0.226065761 13.7102003 1 1.10339844
Adaro 2012 1.57232967 0.463 0.566859294 0.127979996 13.7990152 1 0.81295064
Adaro 2013 1.77189635 0.4532 0.539960017 0.073088498 13.8996885 1 0.4392355
Adaro 2014 1.64167339 0.4303 0.483804916 0.056332616 13.9072939 1 0.41181185
Adaro 2015 2.40392499 0.4606 0.399491464 0.045034615 13.9538834 1 0.18318218
Adaro 2016 2.4710304 0.3766 0.419544185 0.089988542 13.9438461 1 0.61699695