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FINANCIAL LEVERAGE AND ITS EFFECT ON
RETURN ON EQUITY (ROE) AND EARNINGS PER SHARE (EPS)
(An Empirical Analysis of Mining Industry Listed in Indonesia Stock Exchange)
By
Andal Pradipta
604081000002 f)itcrin~.
<11>ri Tgl. 1'o. lnduk ~ · ,ffikasi
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INTERNATIONAL PROGRAM
MANAGEMENT MAJOR
FACULTY OF ECONOMICS AND SOCIAL SCIENCES
STATE ISLAMIC UNIVERSITY SY ARIF HIDAYATULLAH
JAKARTA
1430 H/2009 M
PEHPUST!\KAt.\N tH/\MA UIN S'(_:J,_i1_;;J .J_·\i---,J:..F~ f_:}\
FINANCIAL LEVERAGE AND ITS EFFECT ON
RETURN ON EQUITY (ROE) AND EARNINGS PER SHARE (EPS)
(An Empirical Analysis of Mining Industry Listed in Indonesia Stock Exchange)
Thesis
Submitted to Faculty of Economics and Social Sciences
To Meet the Requirements in Achieving Degree of Bachelor of Economics
By
f\ndal Pradipta
604081000002
Under Supervision of
Academic Supervisor I
Pro[ Dr. Ahmad Rodoni, MM NIP. 150 3 I 7 955
rwr""°"/'~mi'~ / ~?-J
-------·-Prof. Dr. Azzam Jasin, MBA
MANAGEMENT MAJOR
Academic Supervisor II
Dr. Rofikoh Rokhim
FACULTY OF ECONOMICS AND SOCIAL SCJENCES
We have administered comprehensive examination to i\ndal Pradipta student
If) 604081000002 on Wednesday. 6 August 2008 with the title
"FlNANCIAL LEVERAGE AND ITS EFFECT ON RETURN ON
EQUITY AND li:ARNINGS PER SHARE (AN ~~MPIRICAL ANALYS1S
OF MINING INDUSTRY LISTED IN ll'ilOONESIA STOCK
EXCHANGE)". After proper examination of the student. we decided that this
thesis is accepted as partial requirements for the title of Bachelor of
Economics on the field of Management, State Islamic University Syarif
H idayatul lah Jakarta.
Ciputat, 6 August 2008
Comprehensive Examination Team
Prof. Dr. Abdul Hamid, MS
Deputy Dean Prof. Dr. Ahmad Rodoni, MM
Head of Management Department
Biography
Abstract
Abstrak
Preface
CONTENTS
CHAPTER I. INTRODUCTION
A. Background
B. Problem Identification
C. Purpose and Significance
CHAPTER II. LITERATURE REVIEW
A. Company Sources of Fund
B. Financial Leverage
c. Liquidity
D. Return on Equity
E. Earnings per Share
F. Previous Research
G. Theoretical Framework
H. Consideration Framework
Page
11
12
14
16
19
22
25
27
28
29
CHAPTER III. RESEARCH METHODOLOGY
A. Research Scope
B. Sampling Method
C. Data Collection Method
D. Analysis Method
E. Research Variable Operational
CHAPTER IV. FINDINGS AND ANALYSIS
A. General View on Research Object
B. Findings and Analysis
CHAPTER V. CONCLUSION
A. Conclusion
B. Implication
REFERENCES
APPENDIX
30
31
32
33
40
44
60
76
77
79
List of Tables
Page
Table 1.1 Company's Debt and Debt to Equity Ratio 7
Table 2.2 List of Previous Research 27
Table 3.3 Research's Variables and Its Indicators 40
List of Figures
Page
Figure 1.1 ROE Change Sensitivity upon EBIT Change 8
Figure 1.2 Directional Factor of Return on Equity Line 9
Figure 2.3 Corporation's Capital Scheme Philosophical Framework 28
Figure 2.4 Return on Equity 29
Figure 2.5 Earnings per Share 29
Figure 4.6 Elnusa 44
Figure 4.7 Aneka Tambang 46
Figure 4.8 Petrosea 47
Figure 4.9 International Nickel Indonesia 49
Figure 4.10 Radiant Utama Interinsco 50
Figure 4.11 Ti mah 53
Figure 4.12 Central Korporindo Internasional 56
Figure 4.13 Apexindo Pratama Duta 57
I. PERSONAL IDENTITY
1. Name
2. Place and date of birth
3. Address
4. Phone
5. Email
II. EDUCATION
I. Elementary school
2. Junior high school
3. Senior high school
BIOGRAPHY
: Anda! Pradipta
: Jakarta, February 17, 1987
: JI. Karya Pemuda No.9 Beji Timur, Depok
: 0818998628
: SD Baiturrahmah Padang
: SMPN 2 Padang
: SMA Pribadi Depok
III. ORGANIZATIONAL EXPERIENCE
1. Jakarta Debating Competition committee
2. Organizing committee on MoU between Intel and UIN
IV. FAMILY BACKGROUND
1. Father
2. Place and date of birth
3. Address
4. Phone
5. Mother
6. Place and date of birth
7. Address
8. Phone
: Riya:ntoko
: Sragen, August 2, 1955
: JI. Karya Pemuda No.9 Beji Timur, Depok
: 08121033941
: S. Neni Budi Ratni
: Yogyakarta, Febrnary 17, 1960
: JI. Karya Pemuda No.9 Beji Timur, Depok
: 08561108902
ABSTRACT
This study is an empirical work that investigates the effect of a firm's leverage on returns and earnings. Writer undertakes the tests based on the explicit valuation model of some researcher and tested in the mining industry in Indonesia. This is consistent with the findings of Modigliani and Miller (1958). Results are robust to other factors.
Using several measures for debt capacity, writer finds that the negative effect is stronger for firms with limited debt capacity. Moreover, firms with an increase in leverage ratio tend to have less futme investment, controlling for the potential negative effect of growth option on leverage ratio.
These findings are consistent with a dynamic view of the pecking-order theory that an increase in leverage reduces finns' safe debt capacity and may lead to future underinvestment. Writer finds that the observed patterns are stronger for changes in the long-term debt than that in the short-term debt and remain significant among financially healthy firms.
Further, while leverage seems to be working well for few Cf~tegories of companies, it is affecting some others negatively. Companies that are moderately geared i.e. in the range of gearing ratio of 50 percent to 85 percent have been able to generate a good ROE. In a nutshell, it is the management who take the lead and responsible for the usage of company's external somce of fund to leverage their company as to maximize the practice.
Keywords: financial leverage, liquidity, return on equity, earnings per share, mining industry, Indonesia
ABSTRAK
Penelitian ini adalah studi empiris yang menyelidiki efek dari hutang perusaharu1 terhadap pengembalian modal dan pendapatan. Penulis menjalankan tes berdasarkan model penghitungan dari beberapa peneliti yang selanjutnya di tes pada industri pertambangan di Indonesia. Ini konsisten dengan penemuan dari Modigliru1i dan Miller (1958). Hasilnya adalah bagus terhadap faktor-faktor lain.
Menggunakan beberapa pengukuran untuk hutang, penulis menemukan bahwa perusahaan dengan kapasitas hutang yang terbatas mempunyai efek negatif yang lebih kuat. Selebihnya, perusahaan dengan peningkatan dalam rasio hutang cenderung untuk memiliki investasi masa depan yang lebih kecil, mengendalikan pertumbuhru1 yang potensial terhadap efek negatif pada rasio huta11g.
Penemuan ini konsisten denga11 pru1da11gan dinamis dari teori pecking-order yang menyatakan bahwa peningkata11 hutang menurunkan kapasitas aman huta11g dari perusahaa11 itu dru1 dapat berujtmg pada investasi yru1g kecil pada masa depan. Penulis menemukan bahwa pola yang diteliti lebih kuat untuk perubahan pada huta11g jangka pru1jang daripada lrntang jangka pendek dan tetap signifikan diantru·a perusahaan denga11 finansial yang sehat.
Lebih jauh lagi, ketika hutang bekerja dengan baik untuk beberapa perusahaan, temyata hutru1g memberika11 efek negatif pada perusahaa11 lain. Perusahaan denga11 rasio hutang yang moderat yang berada dalrun rasio a11tara 50% dan 85% bisa menghasilkan pengembalian modal yffi1g baik. Selanjutnya, adalah ma11ajemen perusahaan yang memimpin dan bertanggung jawab terhadap penggunaan dana ekstemal untuk operasional perusahaa11 dan memaksimalkarmya.
Kata kunci: hutang, likuiditas, pengembalia11 modal, laba bersih per sahrun, industri pertrunbangan, Indonesia
PREFACE
In the name of Allah SWT, writer would like to say thank you for the
completion of this paper with the title "Financial Leverage and its Effect on Return
on Equity and Earnings per Share". This paper is a framework that aims to shed some
light on the financial structure of mining industry and further analyzes the impact of
leverage on it. The writer would like to say thank you to the following that have
given their support in making this paper possible:
1. My beloved parents for their great support and endless courage to help
me finishing this paper.
2. Dean Faculty of Economics and Social Sciences Drs. Faisal Badroen,
MBA.
3. Academic supervisor I Prof. Dr. Ahmad Rodoni, MM and academic
supervisor II Dr. Rofikoh Rokhim for their extremely helpful
assistance along with the consent to finish this paper.
4. Bisnis Indonesia for providing data to meet my data requirement for
my thesis.
5. All my friends in International Program as we have shared good times
together: Mamat, Abdul, Donal, Hendry, Iqbal, Savirul, Faqih, Fitry,
Tia, Erika, Aysa, Khairiyah, Kiki, Nada and everyone in the class that
could not be mentioned one by one.
6. All the staff in International Program office for handling the required
document especially Mrs. Fitri, and Mr. Syamsudin.
7. My tennis mates Bono, Sofyan, Rama; you guys rock.
8. Asus WSF for limitless contribution; my dependent partner; hail on
you.
9. My handy 2 gigabyte HP thumb drive for maximum mobile data
transfer.
l 0. Bignet for internet connection 111 KP9 to let me connect to virtual
world along with surfing for international journals.
11. Canadian corner in main library for providing cozy place to write this
paper.
12. The legendarily powerful yet efficient l ZZ-FE for making me
conveniently move from one place to another.
13. 'Attack' package to power up my energy to run the day; 3 packages at
a time is considered normal.
14. And to everyone that helped me to go through this paper.
A. Background
CHAPTER I
INTRODUCTION
Facing an open competition in the globalization age is one of the
toughest tasks for either state own enterprise or private company.
Corporations are highly obliged to offer excellent products or services
throughout the competition. Consumer will have more variety of choices to
products or services available in the market. Subsequently, it is just the matter
of time to survive in the competition; consumer will judge which product is
the best. Along with increasing market share, all firms aim to expand their
operation.
Competing in a tight competition needs accurate calculation where
every pace is vital and as important as corporation's current undertaking in
addition to future achievement. Most of the time, capital support is playing
important role to expand or survive in such rivalry (Padron, 2005:61). As
such, there are various ways for a corporation to get financing for their
operation, either internally or externally. At times, internally generated funds
will not be sufficient to finance all of the firm's proposed expenditures. In
these situations, the corporation may find it necessary to attract large amount
of financial capital externally or otherwise forgo the projects that are forecast
to be profitable (Ross, 2006:389).
At the same time, corporation is trying its best to create value added,
which is at the end will attract investors to put and invest their money in it.
From many indicators for investor, some of them are return on equity (ROE)
and earnings per share (EPS) of that particular corporation (Sunarto, 2001).
Investors are looking for the best performance stock, which is represented by
constantly increasing price or at least maintain its price at the particular range.
That point, later, indicates the profitability, as well as its return and earnings.
By doing so, they will be able to draw large amount of capital to develop their
size in terms of market share.
Information needed by investors in capital market is not only
fundamentally precise, but also technically defined (Sunarto, 2001 ).
Fundamental inf01mation is internally derived while technical information is
acquired externally, such as economy, politic, finance, and other factors.
Information obtained internally is usually taking form in financial report.
Fundamental and technical information can be used as a basis for investor to
predict return, risk, amount, time, and other factors related to activity in
cap ital market.
One of the most imp01tant concepts in all of finance deals with risk
and return. As it goes with one of the financial management principles, it says
that the risk return trade off-we will not take additional risk unless we expect
to be compensated with additional return (Keown, 2005:26). Risk is the
prospect of an unfavorable outcome. This concept has been measured
operationally as the standard deviation or beta (Keown, 2005:190). The
practice of corporate risk management has changed dramatically over the past
two decades. Originally, risk management was implemented on an
uncoordinated basis across different units of the firm. The primary focus of
these ad hoe risk management programs was to minimize costs. Today,
however, risk management of currency exposure has, in many cases, evolved
into a firmwide exercise that addresses both short-te1m and long-term
exposures and encompasses financial as well as operational hedges.
The ultimate goal of firmwide risk management is to reduce risk while
placing the firm in a position to benefit from opportunities that arise from
exchange rate changes. For example, Davis and Militello (1995) describe how
Union Carbide employs a firmwide perspective in risk management. The
company uses a one-year horizon for financial hedges (e.g., foreign-exchange
derivatives), whereas for longer horizons, operational adjustments are made in
sourcing, utilization of different plant locations, and pricing.
Firmwide risk management for multinational corporations (MNCs) is
the combination of both financial and operational hedges as part of an
integrated risk management strategy aiming at reducing exposure to foreign
exchange risk (Cartera, 2001). Changes in exchange rates can influence
MNCs' current and future expected cash flows and ultimately, stock prices.
The direction and the magnitude of changes in the exchange rate on firm value
are a function of the firm's corporate hedging policy and the structure of its
foreign currency cash flows. The latter depends on the firm's competitive
position in the industries in which it operates. The fo1mer indicates whether
the MNC utilizes operational hedges and financial hedges to manage currency
exposure.
Leverage is traditionally viewed as arising from financing activities:
finns boITow to raise cash for operations. The standard measure of leverage is
total liabilities to equity. However, while some liabilities-like bank loans and
bonds issued are due to financing, other liabilities--like trade payables,
deferred revenues, and pension liabilities-result from transactions with
customers and suppliers in conducting operations. Financing liabilities are
typically traded in well-functioning capital markets where issuers are price
takers.
Compared to many areas of corporate finance, relatively little is known
about the fundamental detenninants of the expected rates of return of
individual firms-that is, how the characteristics of a paiticular firm affect '
expected returns earned by security-holders.
Miller-Modigliani (1958; henceforth MM) argued rigorously that the
value of a firm is independent of its capital structure. The immediate
implication of Proposition I was that the return on equity capital is an
increasing function of leverage. This is because debt increases the riskiness of
the stock and hence equity shareholders will demand a higher return on their
stocks.
MM's Proposition II stated that the rate of return on common stock of
companies whose capital structure includes some debt is equal to the
appropriate capitalization rate for a pure equity stream plus a premium related
to financial risk (Sivaprasad, 2007). The impact of these propositions on
corporate finance is immense but the original sample they used is very limited.
Further empirical work uses much larger samples but results are mixed. Some
authors (Hamada, 1972; Bhandari, 1988) show that returns increase in
leverage, others show that they decrease in leverage (Dimitrov and Jain, 2005;
Penman 2007).
Financial leverage, in turn, results from a company resorting to debts.
Their role in the structure ensures a greater return on equity in the case of
prosperity, but in the case of a slump brings about greater losses, as it
increases liabilities. Each of these decisions has advantages and involves risk
at the same time. Analysis of leverage aims at providing infonnation al;>out the
advantages and risk resulting from it. By putting emphasize into financial
leverage, it is a step closer to reduce risk as well as predicting the return.
In the theory of firm's capital structure and financing decisions, the
Pecking Order Theory or Pecking Order Model was developed by Stewart C.
Myers and Nicolas Majluf in 1984. It states that companies prioritize their
sources of financing (from internal financing to equity) according to the law of
least effort, or of least resistance, preferring to raise equity as a financing
means of last resort. Hence, internal funds are used first, and when that is
depleted, debt is issued, and when it is not sensible to issue any more debt,
equity is issued. This theory maintains that businesses adhere to hierarchy of
financing sources and prefer internal financing when available, and debt is
preferred over equity if external financing is required.
Mining industry has been the main source of income for Indonesia for
a long time. Given the abundant resources, Indonesia is one of the most
attractive places for foreign investors. According to PricewaterhouseCoopers'
research, the contribution of mining industry to the economy of Indonesia in
2003 reached around Rp. 19.5 trillion, which is mostly in the form of
government income (Rp. 9.3 trillion) and purchases from local suppliers (Rp.
7.1 trillion).
Expenditure for the public interest is quite large such as for regional
I
and social development, reached around Rp. 604 billion (2003), staff training
(Rp 164 billion), research and development (US$ 1.04 million). Meanwhile,
expenditures for the reclamation, mine closure and environmental control
reached US$ 83.6 million.
The numbers of Indonesian workers absorbed by the mining industry
in 2003 came out at 33,112 people. These numbers are resulted from research
involving 33 companies that have been operating and 35 exploration
companies during 1999-2003 and do not reflect all gold and coal producers in
Indonesia. Some of them are already go public while some others have yet to
neither list their shares in stock market nor offer their shares to public. If all
the data is entered for various types of minerals industry, the numbers will
cettainly increase and affect the entire figures.
Below is the list of go public mining company in Indonesia (figures in
Rupiah expressed in thousands, data of2007):
Table I.I Comoanv's Debt and Debt to Equitv Ratio
No Company Short term debt Long term debt DER
1 A TPK Resources 18,995,251,422 1,350,972,348 0.11
2 Bumi Resources 7,984,974,395,526 5,338,504,954,160 1.26
3 Indo Tambangraya Megah 2,245,990,428,000 755,213,882,000 0.68
4 Resource Alam Indonesia 60,762,715,000 29, 708,899,000 1.09
5 Perdana Karya Perkasa 76,675,215,684 76,785,475,234 0.88
6 Tambang Batubara Bukit 695,010,000,000 421,789,000,000 0.40
7 Asam 496,740,688,000 182,048,658,000 0.95
8 Petrosea 463,854,200,814 1,933,740,064,186 1.09
9 Apexindo Pratama Dula 918,095,000,000 277,169,000,000 1.26
10 Elnusa 3,688,450,000,000 2,337,494,000,000 1.80
11 Energi Mega Persada 3,548,668,475,674 10,505,938,001,544 2.85
12 Medco Energi Internasional 121,440,162,625 111,038,745,798 l.29
13 Radiant Utama Interinsco 1,798,816,747,000 1,474,300, 753,000 0.37
14 Aneka Tambang I 94,792,000,000 114,063,000,000 1.69
15 Cita Mineral Investindo 2,366,059,276,000 2,339,218,588,000 0.36
16 International Nickel Indonesia 1,350,230,000,000 323,163,000,000 0.50
17 Timah 50,837,822,000 54,437,767,000 0.16
18 Central Korporindo Int' I 117, 166,000,000 21,315,000,000 3.29
19 Citatah Industri 102,024,224,000 2,680,594,000 5.33 Mitra Jnvestindo
Source: Bi.mis Indonesia
An alternative measure of overall company performance is return on
equity (ROE). A company's ROE is affected by the same income statement
items that affect ROA as well as by the company's degree of financial
leverage, which is shown as follows (Ross, 2006:467):
ROE ROA x Leverage measure
Net income Net income Total assets x
Equity capital Total assets Equity capital
Figure 1.1 ROE change sensitivity upon EBIT change
(Source: Tadeusz Dudycz, "The Different Faces of Leverage")
Figure 1.2 shows two variants of financing the same investment.
Variant B involves a greater contribution of debts in the capital structure, and
variant A smaller. The total capital is identical in both variants. We can see
that variant B, with respect to the size of EBIT, initially ensures a smaller
return on equity, which grows after crossing a certain value of EBIT. Variant
B also leads to a higher sensitivity of changes in return on equity to changes in
EBIT. The sensitivity depends on changes in the J3 angle.
If we assume that, just as in the case of operating leverage, the
sensitivity of changes in return on equity (ROE) to EBIT change is a measure
of financial leverage in the static approach (independent ofEBIT value), then
on the basis of Figure 1.3 we can derive that the financial leverage FL equals:
}{_()
!!.ROE FL=wfJ· =--
-· f!.EBIT
Figure 1.2
/ .dROE
Directional factor (slope) ofretum on equity line (Source: Tadeusz Dudyez, "The Different Faces of Leverage")
The leverage measure is simply the inverse of the capital ratio (when
only equity counts as capital). The higher the capital ratio, the lower the
leverage measure and degree of financial leverage.
It is also important to look for a financial capital to extend the
capacity. Thus, due to the increasing of credit by commercial banks, it is
natural that financial leverage plays important role on the development of a
firm. While exercising financial leverage, the cost of that leverage is also
increasing. This is also affecting net income, ROE, and BPS of that particular
firm.
This paper contributes to the existing literature on the relationship of
returns and capital structure in three directions. First, this is a study which
expands the limited work carried out on leverage and returns by examining
leverage as an independent variable and its impact on returns. Second, the
paper tests for linearity of leverage and return. This is an important test to I
enable the better understanding of the traditionalist theory of capital structure
and optimal capital structure. Thirdly, the writer undertakes robustness checks
with several factors.
The definitions of financial leverage used in corporate finance
textbooks vary, but most textbooks discuss this result, and the explanation
offered for a positive relationship between financial leverage and the expected
rate of return is usually along the same lines. All else equal, a firm with high
financial leverage has high external financial capital and low internal capital,
so that the additional revenue from one additional unit of production is offset
by a relatively small increase in leverage cost.
B. Problem Identification
Attracting and convincing investors have never been easy for it ties
with company's value, performance, goodwill, as well as its reputation. It is
also important to know the relation between financial leverage and ROE along
with EPS. Therefore, corporation can consider how large in taking external
financing to can-yon their operation.
In fact, each and every year, under certain condition, there is always
corporation with the closest relation on the topic chosen for the research: the
higher the financial leverage, the higher the ROE and EPS (Pamela, 2006).
Based on the background explained previously, the writer wants to focus on
the problem needs to be addressed which is impact of leverage in terms of its
effects on ROE and EPS in mining industry. Aside from main focus of the
problem, there are also other problems:
1. Does financial leverage and liquidity affect ROE for listed mining
company in Indonesia Stock Exchange from year 2005-2007?
2. Does financial leverage and liquidity affect EPS for listed mining
company in Indonesia Stock Exchange from year 2005-2007?
C. Purpose and Significance
I. Purpose
This research seeks to analyze relation between financial leverage
and ROE as well as EPS. The aims of the paper are:
a. To examine the significance of financial leverage and liquidity towards
ROE for listed mining company in Indonesia Stock Exchange from
year 2005-2007.
b. To examine the significance of financial leverage and liquidity towards
EPS for listed mining company in Indonesia Stock Exchange from
year 2005-2007.
This paper also presents a twofold approach to the measurement of
leverage: static and dynamic, which give two separate parameters
providing the company board with different information. By this research,
expectantly it will bring benefit to those who needed it especially for
mining industry in this country.
2. Significance
Separately, there are various conditions that later influence the
significance of the research:
a. Academically, this research expects the contribution for the
development of lmowledge and general information on effect of
leverage in mining industry, and parties related to the problem of
research.
b. Practically, this research offers initiative for corporation, particularly
mining company, related to the problem ofresearch.
c. Socially, this research provides idea that keenly brings changes in the
way people think, act, as well as providing general knowledge for
society.
d. Technically, this research tries to make a good study of both the
concept and methodology on top of the entire research activity. On the
other hand, it is also possible that this research is used as a basis
reference for next research with related topic.
CHAPTER II
LITERATURE REVIEW
A. Company Sources of Fund
Sources of fund for a company in general include the following
(Husnan, 1994):
I. Internal
a. Retained earning, influenced by:
l) Amount of earning in that particular period
2) Dividend policy
The higher the dividend, the lower the retained earnings. Vice
versa.
3) Reinvestment
b. Depreciation accumulation
2. External
a. Debt, classified as follows:
I) Short term debt(< I year)
2) Mid term debt (I-JO years)
3) Long term debt(> I 0 years)
b. Owners' equity
Equity from owners of the company for limitless time. Accordingly, it
is an equity being staked for all business risk as well as other risk.
Sources of financing from owners' equity:
I) Common stock
It represents the ownership in a company. Common stock does not
have a maturity date, but exists as long as the firm does. Nor does
common stock have an upper limit on its dividend payment. The
common shareholders have the right to the residual income after
bondholders and preferred stockholders have been paid.
2) Preferred stock
Security with characteristics of both common stock and bonds. It is
similar to common stock because it has no fixed maturity date, the
nonpayment of dividends does not bring on bankruptcy, and
dividends are not deductible for tax purposes. Preferred stock is
similar to bonds in that dividends are limited in amount.
3) Cumulative preferred stock
Basically, cumulative preferred stock is the same as preferred
stock. What makes them different is that the earlier has cumulative
right, wherein the company does not generate profit for some
period, dividend will be suspended until the company generates
profit. It requires all past unpaid preferred stock dividends to be
paid before any common stock dividends are declared.
B. Financial Leverage
The study of the combination of internal and external financial
resources in company liability has generated controversy over the years.
Especially significant is Miller and Modigliani's (Ml\i!) important contribuuion
to capital structure theory of 195 8, which showed that, given a company's
investment policy, and not taking taxes and transaction costs into account, the
choice of financial policy does not affect the current market value of the
company. However, because real markets are far from the so-called "perfect
capital markets" on which MM based their work, numerous studies have
shown the interdependence among investment decisions, financing decisions,
and fitm value. Continued interest in this topic justifies further study of a
company's financial decisions that determine its level of debt.
In MM tests of proposition II, returns to shareholders are approximated
by actual shareholder net income and estimations are made in the cross section
of all firms in a risk class for a single year. As the authors discuss amongst
themselves, this is very crude. The writer uses panel data that contains
information for three consecutive years and combines the cross section with
the time series. The writer represents returns to shareholders as stock returns
in excess of risk-free rate.
MM defined leverage as ratio of the market value of bonds and
preferred debt to the market value of all securities; the writer measures
leverage as the ratio of the book values of total debt to total capital. Leverage
based on book values is associated with lower average returns, whereas
leverage based on market is associated with higher returns (Fama and French,
1992; henceforth FF). He concluded that this variation in their findings is
explained and absorbed by the book-to-market effect. In MM, the only
independent variable is the leverage ratio to test for the linearity of the
relationship. In this study, on top of the leverage ratio, the writer uses
variables that reflect average leverage in every risk class and idiosyncratic
risk, including the FF risk factors.
Investigation has been done on the relationship between leverage and
returns (Arditti, 1967). He defined returns as the geometric mean of returns
and leverage was defined as the ratio of debt measured in book value to equity
measured at market value. He found leverage to have a negative sign in the
regressions-though insignificant-to returns. He argued that the negative
relationship is because there are inter-firms risks which are not accounted for
by the probability distribution variables. Others also examined the relationship
between leverage and returns (Hall, 1967). He defined returns as profits after
tax and ratio of equity to assets as an indicator for leverage. His results show
that equity to assets was positively related to returns, indicating that returns
have an inverse relationship with returns. This was because large amounts of
leverage (i.e. low equity to assets) imply high risks, and hence profitable firms
take some of the exceptional returns in the form of reduced risks. Another
researcher also undertook an examination of the effect of leverage on industry
returns (Baker, 1973). He measured leverage inversdy as the ratio of equity to
total assets for the leading firms in an industry over the he year period. He too
found that relatively large amounts of leverage tend to raise industry profit
rates, more leverage implying greater risks. In this study, in addition to the I
leverage ratio and its square, the writer uses three variables that reflect
leverage, including its ratio and the particular return and earnings.
From various financial ratios, there are some ratios and corporate
financial information that can be used to predict return. Financial ratio is
divided into five categories (Ang, 1997): (I) liquidity ratio; (2) activity ratio;
(3) profitability ratio; (4) leverage ratio; and (5) market ratio. Profitability
ratio consists of seven ratios: gross profit margin (GPM), net profit margin
(NPM), operating return on assets (OPROA), return on asset (ROA) or return
on investment (ROI), return on equity (ROE), and operating ratio (OPR).
Leverage ratio is divided into eight different ratios: debt ratio, debt to
equity ratio, long-tenn debt to equity ratio, long-term debt to capitalization
ratio, times interest earned, cash flow interest coverage, cash flow to net
income, and cash return on sales (Ang, 1997: 18). Leverage ratio shows how
large debt ratio which is ratio from total debt to total assets.
C. Liquidity
Liquidity is the speed and ease at which an asset can be converted into
cash. The more cun-ent assets that a firm has relative to its current liabilities,
the greater the firm's liquidity (Keown, 2005:41). Or else, the ability of a firm
to pay its bills on time. Measuring a firm's liquidity is not an easy task, for it
has two approaches. The first approach compares cash and the assets that
should be converted into cash within the year. The assets here are the current
assets, and the debt is the current liabilities in the balance sheet. The current
ratio measured by its current assets relative to its current liabilities.
Furthermore, remembering that the three primary current assets include cash,
accounts receivable, and inventories, it is possible to make the measure of
liquidity more restrictive by excluding inventories, the least liquid of the
current assets, in the numerator. This revised ratio is called acid-test ratio.
The second approach of liquidity examines the firm's ability to convert
accounts receivable and inventory into cash on a timely basis (Keown,
2005:75). The conversion of accounts receivable into cash may be measured
by computing how long it takes to collect the firm's receivables; that is, how
many days of sales are outstanding in the form of accounts receivable. It can
be answered by computing the average collection period, which indicates how
rapidly a firm is collecting its credit, measured by the average number of days
it takes to collect its accounts receivable. It could be the same conclusion by
measuring how many times accounts receivable are "rolled over" during a
year, or the accounts receivable turnover ratio, measured by the number of
times its accounts receivable are collected during a year.
As a general rule, management would want to collect receivables
sooner rather than later-that is, reduce collection period and increase
inventory turnover. However, it may be that a company's management would
intentionally extend longer credit terms as a policy for reasons it deems
justifiable. Alternatively, slower collection could mean that management is
simply not being as careful at enforcing its collection policies. In other words,
it may not be managing receivables effectively. Some people may want to
know the same thing for inventories that is just determined for accounts
receivable: How many times are a firm turning over inventories during the
year? In this manner, some insight can be gain into the liquidity of inventories.
The inventory turnover ratio indicates the relative liquidity of inventories, as
measured by the number of times a firm's inventories are replaced during the
year.
The impact of the liquidity of a firm's assets on optimal leverage has
been a source of debate for many years. Williamson (1988) and Shleifer and
Vishny (1992) predict that asset liquidity increases optimal leverage, while
Morellec (200 l) and Myers and Rajan (1998) argue that its effect is negative
or curvilinear. The rationale for a positive effect of asset liquidity on leverage
relies on the idea that less liquid assets sell at higher costs, which increases the
costs of liquidation, bankruptcy, and debt. Lower asset liquidity therefore
creates the need to reduce the probability of costly default by lowering the
leverage. Yet models that predict a non-positive effect argue that lower asset
liquidity makes it more costly for managers to expropriate value from
bondholders. Thus, lower asset liquidity reduces the costs of debt, and as a
result, companies use more debt. Despite substantial progress in modeling the
relation between asset liquidity and leverage, limited empirical evidence
pertains to this effect because of the difficulty of obtaining a measure of asset
liquidity. In turn, existing studies that examine the relation between asset
liquidity and leverage - such as Alderson and Betker (l 995), Kim (1998), and
Benmelech, Garmaisc, and Moskowitz (2005) - tend to limit their attention to
narrow and specific samples offinns or assets.
Merton (1987) states that relatively larger firms will have a greater
trading interest since more individuals and institutions will have positions in
the firm. Larger films will also be more carefully scrutinized by both
professional and unsophisticated analysts. The increased information
gathering makes prices more efficient, thereby reducing adverse selection
costs. By providing a larger pool of trades to offset adverse selection costs,
increases in the breadth of ownership, paiticularly smaller shareholders, can
offset additional adverse selection costs and lead to reduced spreads. We
therefore expect increases in finn size, volume, and the number of
shareholders to be negatively related to changes in spreads.
The expected impact of corporate diversification on liquidity is
unclear. There are arguments and evidence supporting both a positive and
negative relationship between diversification and liquidity. Chang and Yu
(2004) suggest diversified firms have reduced adverse selection costs since
firm-level prices are less sensitive to information asymmetries arising in
individual divisions. In addition, Benston and Hagerman (1974) point out that
since market makers hold undiversified portfolios, corporate diversification
reduces inventory holding costs by reducing volatility. The reduction in either
adverse selection costs or inventory holding costs would reduce spreads for
diversifying firms. Alternatively, Nanda and Narayanan (1999) suggest that
allowing each of a diversified firm's divisions to trade independently
facilitates price discovery by investors. More accurate market prices would
mitigate adverse selection problems and improve liquidity. Note that price
discovery arguments do not preclude offsetting diversification effects.
D. Return on Equity
There are only three ratios from profitability ratio that have
significance on the corporation's profit forecast for a year ahead (Machfoedz,
1994). Those three ratios are: GPS, NIS, and NINW. GPS is usually stated as
gross profit margin (GPM), NIS also acknowledged as net profit margin
(NPM), and net income to net w01th (NINW) as return on equity (ROE).
Furthermore, from the three ratios, the only ratio that has significant relation
(1%) with earning prediction is net income to net worth (NINW) or retnrn on
equity (ROE) (Machfoedz, 1994).
The financial structure of a corporation provides the market with
information about the firm, with the market value of the firm increasing with
the level of debt (Ross, 1977). This can be taken to mean that, if managers
raise the level of debt, then it is because their expectations for the future of the
company permit it to meet its obligations, making it clear that the risk of
insolvency is not relevant. The value of a company and the size of its debt are
positively correlated (Raviv, 1990). Variations in the company's level of debt
will affect its market value, since the firm's change in capital structure
transmits information about the future expectations of the company. For
example, an announcement of the reduction of the number of common stocks
in exchange for a debt offering has a positive effect on the market, which
becomes a negative effect when the reverse happens (Copeland and Lee,
1991).
There is a positive relationship between stock retnrns and leverrge
(Hamada, 1972; Bhandari, 1988). Both test the relationship in the cross
section of all firms. Calculation on returns as profits after taxes and interest
which is the earnings the equity and preferred shareholders receive on their
investment for the period (Hamada, 1972). The \'<Titer calculates returns as
stock returns in excess of risk-free rate. He tests the relationship in the cross
section of all firms. He uses industry as a proxy for business risk since his
sample lacks sufficient firms to yield statistically significant coefficients.
Industry classification is indeed a good proxy for business-risk across
industries (Bradley, 1984), the writer undertakes cross-sectional analysis
separately for each firm. Stock returns is inflation adjusted (Bhandari, 1988)
whereas the writer defines stock returns in excess of the risk free rate that
encompasses the inflation adjustment. He conducts his tests in the cross
section of all firms without assuming different risk classes. On the other hand,
the writer defines leverage as the ratio of book value of debt to the book value
of equity. Nevertheless, the writer accounts for the difference between the two
measures by using book-to-market ratio as a risk factor (FF, 1992).
Analysis on the relationship between operating leverage and the
expected rate of return is defined by fixing the output price and the variable
cost per unit of output and allowing exogenous random output (Lev, 1974). He
defines operating leverage as the ratio of fixed to variable operating costs and
shows that the CAPM beta is higher for firms with greater operating leverage
(actually, with lower variable cost per unit of output). A firm's asset beta can
be decomposed into the product of the degree of operating leverage, the
degree of financial leverage, and the amount of "intrinsic business risk"
(Mandelker and Rhee, 1984). They define the degree of operating leverage to
be the elasticity of earnings with respect to changes in production.
E. Earnings per Share
Firm size has been one of the variables most commonly used in
explaining a company's level of debt. Study has made it clear that the size of a
firm is positively related to its use of debt as a source of financing (Requejo,
1999). The larger a firm is, the more information is expected to be available
about it, which reduces the level of information asymmetries in the market,
making it possible to obtain financial resources from lenders.
The tangible assets of a firm can be considered representative of the
real guarantees that it can offer its creditors. Therefore, the importance of
those assets among total assets influences its level of debt, which rises with
the increase of warranties offered by the firm to satisfy its obligations arising
from contracted debt (Requejo, 1999).
The reputation of a finn may affect its leverage capability, since it
reduces the conflicts between the company and its lenders. By fulfilling its
payment obligations, a company enjoys a good reputation, which may be
sufficient to eliminate conflicts with its creditors. Reputation can be measured
by the age of the company (Datta, 1999; Andre's Alonso, 1999) or the firm's
rating (Crabbe and Post, 1994), among other means, and it can be expected to
have a positive relationship with debt. This is because companies with better
reputations are more mature and better known in the market, since the
companies that are most concerned about having a reputation for being honest
are those that expect to remain in the market for a long time (Myers, 1977).
For them, honesty is the best policy.
The EPS formula does not include preferred dividends for categories
outside of continued operations and net income. Earnings per share for
continuing operations and net income are more complicated in that any
preferred dividends are removed from net income before calculating EPS.
Remember that preferred stock rights have precedence over common stock. If
preferred dividends total Rp. 10,000,000,000 then that is money not available
to distribute to each share of common stock.
On the other hand, companies with greater opportunities for growth
have a lower leverage ratio than those with lower growth opportunities, since
financing through shares is a mechanism that reduces the problem of under
investment associated by financing through debt. Faced with high debt levels
and good growth opportunities, and acting to protect shareholders, directors
would prefer not to carry out some positive investment projects if the profits
find their way into the hands of bondholders (Myers, 1977). Similar results are
obtained by some researchers (Requejo, 1999; Alonso, 2000).
F. Previous Research
The following is list of previous literature study or research conducted in the
similar extent.
Table 2.2 L' t f 1s o previous researc 1
No. Name Research Result
I Jie Cai and Zhe Zhang Leverage change, debt • Leverage leads to future capacity, and stock prices underinvestment (2008)
• Leverage has positive effect on stock price
2 Sheeja Sivaprasad and Empirical test on leverage • Leverage alone to have negative Gulnur Muradoglu and stock returns (2007) relationship with returns
• Return increase with leverage comprising other issues such as factors of size and risk.
3 Graeme Guthrie A note on operating leverage • Operating leverage and
and expected rates of return expected return an~ negatively
(2006) correlated
4 Espen Eckbo and Liquidity risk, leverage, and • Lower liquidity and lower
Oyvind Norli long-run IPO returns (2004) leverage both contribute to a lower expected return
5 Doron Nissim and Financial statement analysis • Profitability is differentially Stephen Penman ofleverage (200 I) related to the amount of
financing leverage
r:), J ·3-r LJif\l :f' ,-1(;
G. Theoretical Framework
In order to expand the business, a corporation needs capital support
wherein one of the alternatives is debt. When using debt, a corporation is also
executing financial leverage. Due to the financial leverage, the output of the
corporation varies, caused by leverage factor itself. Besides, with the existence
of financial leverage, corporation also bears the cost of using debt that will
eventually reduce its earnings.
Philosophical Framework
Company Sources of Fund
Internal
+
~] ~---D~ebt I Financial Leverage
LCost ofDeht
EPS
Figure 2.3 Corporation's capital scheme philosophical framework
With the purpose of knowing the effect of financial leverage towards
earnings, therefore the writer will calculate financial leverage executed by
each corporation from its financial report while the earnings of that pai1icular
corporation is expressed both in ROE and EPS.
As for knowing the effect of financial leverage towards earnings,
whichever both measured by ROE and EPS, the mining industry wit! be
selected for it has the one of the highest development in recent years yet the
most supporting pillar for the economy of the country.
H. Consideration Framework
Return on equity
X1 Financial leverage
1 ~~-x~2-L-i-qu_i_d-it-y~__,f ____!
Earnings per share
Figure 2.4 Return on equity
IJ-r--------il X2 Liquidity .
X 1 Financial leverage
Figure 2.5 Earnings per share
[
[
ROE
EPS
A. Research Scope
CHAPTER Ill
RESEARCH METHODOLOGY
This research aims at knowing firm's financial leverage measured by
financial leverage, as well as its impact in terms of its effects on return on
equity (ROE) and earnings per share (EPS) in mining industry. Financial
leverage describes the using of financial capital in order to finance the
corporation while financial leverage ratio shows how assets are financed,
whether using debt or equity. Financial leverage (FL) is used to measure how
large assets are financed by creditor. The bigger financial leverage, the higher
financial capital used to make profit.
This research limits the time-span for three years, starting from 2005
until 2007. Financial report of all mining firms that have been listing (go
public) in Indonesia Stock Exchange (!DX) will be presented as of 31
December of each year. Mining industry is chosen because it is one of the
most important pillars in supporting the economy of country. Mining industry
has been attracting numerous foreign investors while at the same time
contributes large income to state's finance as well as to sustain the economy ..
According to PricewaterhouseCoopers' research, the contribution of mining
industry to the economy oflndonesia in 2003 reached around Rp. 19.5 trillion,
which is mostly in the form of government income (Rp. 9.3 trillion) and
purchases from local suppliers (Rp. 7.1 trillion).
B. Sampling Method
Sampling is part of statistical practice concerned with the selection of
individual observations intended to yield some knowledge about a population
of concern, especially for the purposes of statistical inference. Each
observation measures one or more properties (weight, location, etc.) of an
observable entity enumerated to distinguish objects or individuals. Survey
weights often need to be applied to the data to adjust for the sample design.
Results from probability theory and statistical theory are employed to guide
practice (Kish, 1995).
In this study, population selected as research object is mining firms
that have been listing (go public) in Indonesia Stock Exchange (IDX).
Criterion in choosing the population is because only firms that have been
listing can be monitored constantly. There are total 19 go public mining firms
listed in Indonesia Stock Exchange (IDX) with the exception of stock that is
started to listing and delisting in addition to the availability of information
during the research.
Pooling data method is used as the sampling method. It combines
numerous events happening at numerous times. Pooling data method is a data
set containing observations on multiple phenomena observed over multiple
time periods. Alternatively, the second dimension of data may be some entity
other than time. Pooling data sets are two-dimensional (Arellano, 2003).
C. Data Collection Method
Data used in this research is secondary data acquired from
Documentation Center of Bisnis Indonesia together with literature study by
reading, intensifying, and scmtinizing literature related to the research. In
research, Secondary data is collected and possibly processed by people other
than the researcher in question (Schutt, 2006). Common sources of secondary
data for social science include censuses, large surveys, and organizational
records.
The rationale behind literature study is to get theoretical information as
a comparison in the description. Data obtained from Bisnis Indonesia is
sourced from Indonesia Stock Exchange (!DX). Data acquired from Capital
Market Directory, Indonesia Stock Exchange for three consecutive years,
consist of financial report as of 31 December 2005, 2006, and 2007, taking
form in the profitability ratio (ROE and EPS), liquidity (current assets to
current liabilities), and leverage ratio (debt to total assets).
D. Analysis Method
1. Descriptive
In this research, descriptive analysis will also be used as an
approach, by interpreting obtained data which is based on the emerging
facts within the research time-span hence there will come into view the
description on the research object.
Descriptive Statistics are used to describe the basic features of the
data gathered from an experimental study in various ways. They provide
simple summaries about the sample and the measures. Together with
simple graphics analysis, they form the basis of virtually every
quantitative analysis of data. It is necessary to be familiar with primary
methods of describing data in order to understand phenomena and make
intelligent decisions.
In general, statistical data can be briefly described as a list of
subjects or units and the data associated with each of them. Although most
research uses many data types for each unit, this introduction treats only
the simplest case. Meanwhile, the data is processed using SPSS version
16, using corporations' financial data from the year 2005 until 2007.
2. Quantitative
Quantitative approach is applied for this research. Method used for
this research is verification analysis, by using hypothesis experiment
through data processing and statistical test. Quantitative research is the
systematic scientific investigation of quantitative properties and
phenomena and their relationship. The objective of quantitative research is
to develop and employ mathematical models, theories and/or hypothesis
pertaining to natural phenomena. The process of measurement is central to
quantitative research because it provides the fundamental connection
between empirical observation and mathematical expression of
quantitative relationships. Some tests include in this quantitative research
are as follows:
a. Assumption Classic Test
1.) Autocorrelation
Autocorrelation is a mathematical tool for finding repeating
patterns, such as the presence of a periodic signal which has been
buried under noise, or identifying the missing fundamental
frequency in a signal implied by its hannonic frequencies. It is used
frequently in signal processing for analyzing functions or series of
values, such as time domain signals. Infonnally, it is the similarity
between observations as a function of the time separation between •
them. More precisely, it is the cross-correlation of a signal with
itself. In statistics, the autocorrelation of a random process describes
the correlation between the processes at different points in time.
2.) Multicollinearity
Multicollinearity is a statistical phenomenon in which two or
more predictor variables in a multiple regression model are highly
correlated. In this situation the coefficient estimates may change
erratically in response to small changes in the model or the data.
Multicollinearity does not reduce the predictive power or reliability
of the model as a whole; it only affects calculations regarding
individual predictors. That is, a multiple regression model with
correlated predictors can indicate how well the entire bundle of
predictors predicts the outcome variable, but it may not give valid
results about any individual predictor, or about which predictors are
redundant with others.
3.) Heteroskedasticity
In statistics, a sequence or a vector of random variables is
heteroskedastic, if the random variables have different variances.
The complementary concept is called homoskedastiity. The term
means "differing variance" and comes from the Greek "hetero"
('different') and "skedasis" ('dispersion').
When using some statistical techniques, such as ordinary
least squares (OLS), a number of assumptions are typically made.
One of these is that the error term has a constant variance. This will
be true if the observations of the error term are assumed to be drawn
from identical distributions. Heteroscedasticity is a violation of this
assumption.
b. Multiple Linear Regressions
I.) Coefficient determination (R2)
Coefficient of detem1ination, f·t2, is the proportion of
variability in a data set that is accounted for by a statistical model.
There is no consensus about the exact definition of R2• Only in the
case of linear regression are all definitions equivalent. In this case,
R2 is simply the square of a correlation coefficient. R2 is a statistic
that will give some information about the goodness of fit of a
model. In regression, the R2 coefficient of determination is a
statistical measure of how well the regression line approximates the
real data points. An R2 of 1.0 indicates that the regression line
perfectly fits the data.
2.) F test
An F-test is any statistical test in which the test statistic has
an F-distribution if the null hypothesis is true. The formula for an F
test in multiple-comparison ANOVA problems is: F = (between
group variability) I (within-group variability)
In many cases, the F-test statistic can be calculated through
a straightforward process. Jn the case of regression: consider two
models, 1 and 2, where model I is nested within model 2. That is,
model 1 has p 1 parameters, and model 2 has p 2 parameters, where
p2 > p1. (Any constant parameter in the model is included when
counting the parameters. For instance, the simple linear model y =
mx + b hasp= 2 under this convention.) If there are n data points to
estimate parameters of both models from, then calculate Fas
where RSS; is the residual sum of squares of model i. If the
regression model has been calculated with weights, then replace
RSS; with x2, the weighted sum of squared residuals. F here is
distributed as an F-distribution, with (jJ2 - pi, n - p2) degrees of
freedom; the probability that the decrease in x2 associated with the
addition of p2 - p 1 parameters is solely due to chance is given by the
probability associated with the F distribution at that point. The null
hypothesis, that none of the additional p 2 ·- p 1 parameters differs
from zero, is rejected if the calculated Fis greater than the F given
by the critical value of F for some desired r"jection probability (e.g.
0.1).
3.) t test
A t-test is any statistical hypothesis test in which the test
statistic has a student's t distribution if the null hypothesis is true. It
is applied when the population is assumed to be normally
distributed but the sample sizes are small enough that the statistic on
which inference is based is not normally distributed because it relies
on an uncettain estimate of standard deviation rather than on a
precisely known value. In testing the null hypothesis that the
population mean is equal to a specified value po, one uses the
statistic. The t statistic to test whether the means are different can be
calculated as follows:
where s is the sample standard deviation of the sample. n is the
sample size. The degrees of freedom used in this test is n - 1.
3. Hypothesis
A hypothesis consists either of a suggested explanation for an
observable phenomenon or of a reasoned proposal predicting a possible
causal correlation among multiple phenomena. The scientific method
requires that one can test a scientific hypothesis. Scientists generally base
such hypotheses on previous observation or on extensions of scientific
theories. A Hypothesis is never to be stated as a question, but always as a
statement with an explanation following it. It is not to be a question
because it states what he/she thinks or believes will occur.
Any useful hypothesis will enable prediction by reasoning
(including deductive reasoning). It might predict the outcome of an
experiment in a laboratory setting or the observation of a phenomenon in
nature. The prediction may also invoke statistics and only talk about
probabilities. Karl Popper, following others, has argued that a hypothesis
must be falsifiable, and that one cannot regard a proposition or theory as
scientific if it does not admit the possibility of being shown false. Other
philosophers of science have rejected the criterion of falsifiability or
supplemented it with other criteria, such as verifiability (e.g.,
verificationism) or coherence (e.g., confirmation holism). The scientific
method involves experimentation on the basis of hypothesis in order to
answer questions and explore observations.
This research uses the following hypothesis:
Hoa= Financial leverage and liquidity do not affect ROE
H1a =Financial leverage and liquidity affect ROE
Hob= Financial leverage and liquidity do not affect EPS
H1b =Financial leverage and liquidity affect EPS
E. Research Operational Variable
There are four variables used in this research as a foundation for
further analysis of research. Those variables are selected from the closest
relation on each element, which represent every measurement of paiticular
item. Each element represented by indicator that makes it easier to read and
understand as to proceed to the research. Some variabks are already available
in the annual report of the company, while some other needs to be calculated
by using the standard formula.
Operational variables used in this research are derived from items
registered in financial report of each company. Those are financial leverage
(total debt to total assets), liquidity (current assets to current liabilities), return
on equity (net income to common equity) and earnings per share, which is
readily available in financial statement.
Variables chosen for the research object are:
Table 3.3 Research's variables and its indicators
Variable Indicator
Financial Leverage Financial Leverage (FL)
Earnings Per Share (EPS) EPS
Return On Equity (ROE) ROE
Liquidity Current Ratio
Explanation on variables used in this research:
1. Financial Leverage (FL)
Calculating financial leverage from each firm for three consecutive years,
and calculating average financial leverage for mining industry. Financial
leverage measures ratio between total debt to total assets with the
following formula:
FL= Total Debt
Total Assets
2. Return on Equity (ROE)
ROE analyzes the accounting rate of return on stockholders' investment,
as measured by net income related to common equity:
ROE= Net Income
Common Equity
3. Earnings Per Share (EPS)
EPS aims at knowing net income on a per share basis, which is determined
by net income related to shares outstanding:
EPS = Net Income
Total Shares Outstading
4. Liquidity
Liquidity is the speed and ease at which an asset can be conve1ted into
cash. Current ratio measured by firm's liquid assets (current assets)
relative to its liquid debt (current liabilities).
C . Current Assets
urrent ratio = -------Current Liabilities
5. Analysis on financial leverage effect towards ROE and EPS
This research uses multiple linear regression analysis, an analysis in which
the relationship between one or more independent variables and another
variable, called dependent variable, is modeled by a least squares function,
called linear regression equation. It uses two variables which is variable X
and Y, where changes in variable X are causing changes in variable Y.
However, changes in variable Y do not cause changes in variable X.
Regression analysis intends to know how far one variable affects other
variables.
Regression analysis equation uses the following Jonnula:
Where:
Y =ROE and EPS
a= Constant
X 1 =Financial leverage
X2 = Liquidity
S = Error margin
To test the significance of variable X and variable Y, regression
coefficient is applied with t-statistic test, a = 10% with the following
formula:
t= rJ(n- k-1)
~
The statistical test is conducted by comparing the t calculation and t table
in certain consideration level. If statistical test showed t calculation is
lower than t table, then Ho is rejected. It means variable X (independent)
affects variable Y (dependent).
To test the significance of variable X and variable Y, therefore the writer
conducts F test, a= 10% with the following formula:
F= (~) {
(l-r 2)}
(n-k-1)
The result of calculation is compared with F table, with a = l 0%. If the
calculation of F is lower than F table, then I-10 is rejected. That means
variable X affects variable Y.
CHAPTER IV
FINDINGS AND ANALYSIS
A. General View on Research Object
l. Company profile
a. Elnusa
2.500.000.000,000
2.000.000.000.000
1, 500.000,000,000
1.000.000.000.000
500,000,000.000
0
Elnusa
2005 2006 2007
Figure 4.6
~=,,,Net lncon)e
~Con1n1on Cquity
Totd! Assets
""""<i>'"U·Total Debt
_,,,,~=Currenl Assc1ts
~~"""""'"'-Current Li<tbilities
Elnusa is present amidst Indonesia's flourish oil and gas business
development. Born as the Indonesia's oil and gas pioneers' idea, in
1969, Elnusa was initially a subsidiary of PT Pertamina. Elnusa started
its operation as a provider of electronic communications equipment,
ship navigation and radar system for oil and gas carriers in Indonesia.
Elnusa's long business journey has encountered various
challenges, yet it has managed to stand still surfing the oil and gas
business turmoil in 1980s and survived the J 998's economic crisis as
well. Elnusa, along with its subsidiaries has tumed into an integrated oil
and gas service provider which has received customer's remarkable
trusts. Not only has trust been given by PT Pertamina, the parent
company, it has also been delivered by a couple of prominent multi
national partner companies such as Chevron, Total EP, Shell,
ConocoPhillips, BP and so on.
The demand for business development has made Elnusa undertake
necessary reposition. On October 2007, four of its subsidiaries which
were the back-bone of the company's oil and gas service business were
merged into the parent company along with a horizontal merge which
was intended to strengthen the main business. This is a reposition which
was meant to materialize Elnusa as a business player which focuses on
upstream integrated oil & gas services.
The new position has made it certain for Elnusa fully confidence
into the upstream integrated oil and gas service business both in
domestic and overseas markets. The trust it has earned and its
commitment to putting quality excellence first priority along with its
extensive experience in oil and gas business has become Elnusa's major
capital in running the growing integrated oil and gas business, in
response to the stronger price of oil and gas in the international market.
b. Antam
14 ,000,000,000,000
12,000,000,000.000
10,000.000,000,000
3,000,000,000,000
6,000,000,000,000
4,000.000,000,000
2,000,000,000,000
0
Aneka Tambang
2005 2006 2007
Figure 4.7
""""iF'=Nct lncoine
~Co1nn1on Equity
, Total Asscls
~TotalDcbt
"''"""~''"''Current Assets
==\il"'"'"Currcnt liab!litic:.
With four decades of experience sinee 1968, Antam is an
Indonesian limited liability state corporation that is vertically integrated
to undertake all stages of the mining process from exploration, mining,
smelting, and refining through to marketing. Antam's main products are
ferronickel, nickel ore, gold, silver and bauxite.
Antam is 35% held by the public, the majority of which is by
foreign institutions, who have held Antam for a number of years. Listed
on the Indonesian and Australian Stock Exchanges, Antam is one of
very few Indonesian companies to be fully listed on a stock exchange
outside of Indonesia and therefore, must meet international standards of
governance and transparency. While Antam is 65% held by the
government, Antam is not run by bureaucrats. Antam's main goal is
creating shareholder value, not following the directives of the state. In
general, Antam's approach to increase shareholder value is by lowering
costs while profitably expanding operations in a sustainable manner.
Antam offers highly viable opportunities to potential investors and
joint venture partners. It has strong, focused, future-oriented
management and skilled people; high quality products and long-term
loyal customers; a proven record of profitable performance while
upholding international standards of community development and
environmental management; and good future prospects with vast quality
mineral reserves yet to be developed.
c. Petrosea
16.000.000.000,000
14.000,000,000,000
12.000,000,000,000
10.000,000,000,000
8,000,000,000,000
6,000.000,000,000
4,000.000,000,000
2,000,000.000,000
0
2005
Pete rose a
2006 2007
Figure 4.8
=<v-- Ncl l!1con1c
~-Con1n1on [quity
Totdl Assets
~"""' T ot.:il Debt
.rn"'"'"'""-Currcnt Assets
~"~~"'~Current Li.Jbilities
PT Petrosea Tbk is a multidisciplinary engineering, construction
and mining company with a track record of achievement in Indonesia
since 1972. Today, Petrosea is recognized as one oflndonesia's leading
engineering and construction contractors.
Petrosea has been listed on the Jakaiia and Surabaya Stock
Exchanges since 1990 and was the first publicly-listed Indonesian
engineering and construction company in Indonesia. Our strategic
shareholder and partner, Clough Ltd (ASX:CLO) provides Petrqsea
with access and support to world-class management and project
delivery systems which allows Petrosea to deliver international
excellence to the Indonesian market.
Petrosea has been involved in the development of Indonesia's
considerable oil & gas, mining and infrastructure industries, providing a
wide array of services and support throughout the Indonesian
Archipelago. We possess an enviable reputation for expertise in
engineering and management, quality in construction and the ability to
complete difficult and high-risk projects on time. Our successful track
record for project delivery is based on an approach that demonstrates a
total commitment to health and safety, quality, community, value and
business integrity.
d. International Nickel Indonesia
International Nickel Indonesia 25,000,000.000,000
20.000,000,000,000
15,000,000,000,000
10,000,000.000,000
5,000,000,000,000
0
2005 2006 2007
Figure 4.9
·1= Ncl lnco1nc
··~-=C01Y!n)Ot1 Equity
''""''""Total Assets
·--Total Debt
""'-'"''"'"'Current 1\sscts
""''4lli=~Currcnt li.:ibi!ilics
PT Inco is one of the world's premier producers of nickel, a
versatile metal, which is important in improving living standards and
fostering economic growth. For more than three decades, since the
signing of its Contract of Work with the Indonesian Government in
1968, the Company has provided skilled jobs, shown concern for the
needs of the communities in which it operates, benefited shareholders
and contributed positively to the Indonesian ec()nomy.
PT Inco produces nickel in matte, an intermediate product, from
lateritic ores at its integrated mining and processing facilities near
Sorowako on the island of Sulawesi. Its entire production is sold in US
Dollars under long-term contracts for refining in Japan. PT Inco's
competitive strengths include abundant ore reserves, a skilled, well-
trained workforce, low-cost hydroelectric power, modern production
facilities and an assured market for its product.
The Company is owned 60.8 percent by Vale lnco of Canada, one
of the world's leading nickel producers, and 20.1 percent by Sumitomo
Metal Mining Co., Ltd. of Japan, a premier mining and smelting
company. In addition, 20.0 percent of PT Jnco's shares are owned by
public shareholders and the balance by four other Japanese companies.
e. Radiant Utama Interinsco
Radiant Utama lnterinsco 450.000,000.000
400,000.000,000
350,000,000,000
300,000,000.000
250,000,000,000
200,000,000,000
150.000,000,000
100,000.000.000
so.000.000.000
0
2005 2006 2007
Figure 4.10
"~~Net !neon)(.'
~Con1rnon [quily
,,_, Toli'il Assets
--Tot~1! Debt
~"='~"'"'~Current Assets
""""AJW'"""Current liubi!itics
Radiant Utama as a nucleus activity of today's PT. Radiant Utama
Interinsco was incorporated in 1975 by two co-founders aiming,
initially, to provide NOT, Inspection and local services to oil and gas
industries.
r----
PERPUSTAKAAN UTAMA UIN SYAHID JAKARTA l
In 1979, PT. Supraco Indonesia was formed, and soon followed
by new identity of PT. Radiant Utama Interinsco in 1984 to differentiate
activities from well-diversified Radiant Utama Group of Companies.
Through the years, the Company has grown to become one of the most
successful companies in Indonesian oil and gas area, carries more than
30 years of experiences.
Today, PT. Radiant Utama Interinsco and its affiliate company,
PT. Supraco Indonesia has been actively engaged in 4 major areas:
Operation Support Services, Inspection & Certification, Operation &
Maintenance, and Provision of Offshore Services & Trading. Our
commitment towards fine quality and excellence in service was
endorsed further when we obtained ISO 9001: 2000 in year 2002. We
offer our Partner the expertise and lmowledge of Indonesian Energy
sector where our unique ability to combine overseas and domestic
capabilities will continue to offer high value business opportunities.
Radiant Utama provides on-land drilling and work over services. On-
land drilling activities consist of integrated operation management to
drill new wells. It can also provide work over services to maintain
production levels on the existing wells.
To support its activities and deliver excellent service, Radiant
Utama is supported with auxiliary equipments, complete heavy-duty
transportation fleet, more than 42,000 square meter of base camp and I
maintenance facilities (completed with several workshops, office,
warehouse, training center and camp that able to accommodate 80
packs), integrated management system and skillful an experienced
manpower.
Currently, Radiant Utama operations are spreading throughout
Indonesia, working for number of clients. Apart from own-operation,
Radiant Utama can also supply and manage drilling experts from
various level of expertise. Many foreign companies that are operating in
Indonesia outsource all their drilling crew to Radiant Utama. Also,
Radiant Utama can provide rental services for selection of drilling
equipments, such as: BOP (Blow-Out Preventer), Tubular, Mud Pumps,
Engines, Generator sets and other rig accessories. These well
maintained equipments remain as important Radiant Utama' s assets
besides the Rig unit themselves.
Radiant Utama is continuously developing cooperation with some
Drilling Services companies to work on several integrated drilling
projects or Integrated Project Management (IPM), which is becoming a
trend in oil and gas industry nowadays. With capabilities that can
stretch from well and drilling engineering preparation, H&SE
evaluation, drilling operation (including to coordinate various se1vice
companies which involve in drilling process such as: electric logging,
mud engineering, mud logging, directional, cementing, etc) and well
completion, Radiant Utama could be both the IPM holder or the
suppmtive !PM holder.
f. Timah
6,000.000.000.000
5' 000. 000, 000,000
4,000,000,000.000
3.000,000,000,000
2,000,000,000.000
l,000.000,000,000
0
2005
Tin1ah
2006 2007
Figure 4.11
"'"'"4r-Nct lncon1c
"""'8--Co1n111on Cquity
, Totul Assets
-TotolDcbt
'"''"1"''"''Currcnt Assets
'""'"if!J''"""Currcnt Li<1bilities
PT Timah (Persero) Tbk inherited a long history of tin mining in
Indonesia, which has been continued for more than 200 years. The tin
mineral resources in Indonesia were found in the land and the
surrounding waters of the islands of Bangka, Belitung, Singkep,
Karimun, and Kundur.
During the colonial era, tin mining in Bangka was controlled by
colonial government enterprise "Banka Tin Winning Bedrifj" (BTW),
while in Belitung and Singkep they were managed by Dutch private
companies, Gemeenschappelijke Mijnbouw Maatschappi Biliton
(GMB) and NV Singkep Tin Exploitatie Maatschappij (NV SITEM). A
decade after the Declaration of Independent of the Republic of
Indonesia, these three entities were nationalized on 1953-1958 to
become three State Enterprises. The State Tin Enterprises Coordinating
Board (BPU PN Tambang Timah) was established in 1961 to oversee
the three State Tin Mining Enterprises, and in 1968 these four entities,
together with a smelter unit, were consolidated into the state company
to become Perusahaan Negara (PN) Tambang Timah.
With the enactment of Law Number 9 of 1969 and issuance of
Government Regulation Number 19of1969, the status of PN Tambang
Timah and Mentor Tin Smelter Project were changed in 1976 to
become limited liability company, wholly owned by the Government of
Indonesia (Perusahaan Perseroan - Persero) and the name was changed
to PT Tam hang Timah (Persero ).
The world tin crisis, as the result of liquidation of International
Tin Council (ITC) in 1985, forced the Company to undertake
fundamental restructuring in order to survive. The restructuring, which
took place between 1991 and 1995, focused on reorganization,
relocation of the Head Office from Jakarta to Pangkal Pinang,
reconstruction of the main and supporting production facilities, and the
divestment of non-core assets and functions.
The successful restructuring was able to restore company's health
and competitiveness, and made it possible for the Government to
partially privatize PT Timah through Initial Public Offering of its shares
in Indonesia and in the international stock exchange. PT Timah was
listed in the Jakarta Stock Exchange, the Surabaya Stock Exchange, and
The London Stock Exchange in 19 October 1995. Since then, 35% of
the Company's shares were belonging to the public, both domestic and
international, while the remaining 65% remained with the Government
of the Republic oflndonesia.
To facilitate the growth sh·ategy through business diversification,
in 1998 PT Timah Tbk reorganized the business group by splitting-up
the Company's operations into three subsidiaries, which practically
placed PT Timah Tbk as the holding company and correspondingly
expand the business scope into mining field, industry, engineering, and
trading.
Currently, PT Timah Tbk is known as the largest producing
company of tin metal worldwide and is in the process to diversify into
businesses other than thin based on its current and to be developed I
competence.
g. C~entral Koq1orindo lntcrnasional
Central l<orporindo lnternasional 800,000,000,000
700,000,000,000
600,000,000.000
500,000,000,000
400,000,000,000
300,000,000,000
200,000,000,000
100,000,000,000
0
2005 2006
-~ Ncl lncon1c
-~Con1n1on Cquity
Totul ASS('tS
-Tot.:ilDcbt
2007
Figure 4.12
PT. Central Korporindo !nternasional Tbk was esiabiished in
September 1999. This company is specializing itself in coal based
industry. It started the business as a coal merchant. Through the
development, it has its own concession and port nowadays.
The board of commissioners and directors based the company
1T:anagerner.t ca the professionalis1n with a target focused on the
product quality and services !!Jr the custorr;>.":r
the company keeps on looking for some further development in areas
which can optimize its specialist in coal based industry. Power Plants
industries are the ones that can serve the goal.
h. ,i\pexindo Pratatna Duta
Apexindo Pratama Outa 5.000,000,000,000
4,000,000,000,000
3.000,000,000,000
2.000,000,000,000
1,000,000.000,000
0
I i.000.000.000.000) 2005 2006 2007
Figure 4.13
~=Net lnco111c
~Cornrnon Equity
,_--- -"Totill Assets
~ T ot.11 Debt
'"~'"""""(Urrenl AS5CtS
"'"'"21~-""Cuncnt liubi!itics
Apeexindo Pratama Duta is Indonesia's largest national onshore &
offshore drilling contractor that has been serving both prominent local
and international clients domestically as well as abroad for the last two
decades. Throughout our services, we have consistently put health and
safety of our employees and other stakeholders. in addition to
protection of the surrounding environment. as our number one priority.
On September 2008, PT Mitra Rajasa Tbk through its subsidiary
Mira International Holdings had successfully acquired majority stake at
Apexindo to become the new majority shareholders. Thus, since then
Mira Holdings International become the controlling shareholders of
Apexindo with 98.14% of shares stake.
PT Apexindo Pratama Duta Tbk (Apexindo) offers onshore and
offshore drilling services to oil and gas as well as geothermal
exploration and production companies that cover wide range of area
throughout the country. In July 2002, the company became the first
drilling contractor to be listed on the stock exchange with APEX as its
ticker symbol.
With a fleet of 1 jack-up, 4 submersible swamp barge and 1 newly
build super premium jack-up; as well as 8 onshore rigs with most
massive horsepower in Indonesia, Apexindo has undeniably become a
leading national drilling company in Indonesia and possibly in
Southeast Asia.
2. Limitation on data
There are total 19 mining companies listed in Indonesia Stock
Exchange. All companies have been listing for at least four years, except
six newly listed companies, which began listing in 2006, such as A TPK
Resources, Inda Tambangraya Megah, Perdana Karya Perkasa, Elnusa,
Mitra Investindo, and Cita Mineral Investindo. Due to this reason, these
six companies do not have their financial report for year 2005. Therefore,
it does not include in the analysis for year 2005, whereas in 2006 and
2007 all companies had published their financial report.
Consequently, the writer only takes into account the 13 companies
for 2005. While for year 2006 and 2007, all 19 companies are included in
the analysis given that they had their financial report published.
B. Findings and Analysis
I. Financial leverage and liquidity toward ROE
a. Assumption classic test
1) Autocorrelation
Model Surnrnary1>
Adjusted R Std. Error of the
Model R R Square Square Estimate Durbin-Watson
1 .380' .144 .098 .82589 1.708 -a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: ROE
There are numerous numbers shown here. In this matter,
Durbin-Watson is the only figure needs to be watched closely. The
Durbin-Watson statistic is a test statistic used to detect the presence
of autocorrelation in the residuals from a regression analysis. Its
value always lies between zero and four.
The test statistic shows that the Durbin-Watson value is 1.708.
If the Durbin-Watson statistic is substantially less than two, there is
evidence of positive serial correlation. Small value indicates
successive error te1ms are, on average, close in value to one another,
or positively correlated. Thus, by looking at the figure shown above,
financial leverage and liquidity are positively correlated with return
on equity.
Correlations -ROE FL Liquidity
Pearson Correlation ROE 1.000 .310 -.163
FL .310 1.000 -.697
Liquidity -.163 -.697 1.000
Sig. (1-tailed) ROE .009 .112
FL .009 .000
Liquidity .112 .000
N ROE 57 57 57
FL 57 57 57
Liquidity 57 57 57
Above table is correlation matrix between ROE, financial
lev.erage and liquidity. N is 57, along with the using of Pearson
Correlation. From the test table, it can be concluded using
following terms; Correlation coefficient between ROE and financial
leverage is 0.310 with significance of 0.009, which is lower than
0.1. Subsequently, lower calculation means Bo is rejected in favor of
H1; correlation coefficient between ROE and liquidity is -0.163 with
significance of 0.112, which is higher than 0.1. This implies that H-0
is accpeted.
2) Multicollinearity
Coefficients a
Correlations Collinearity Statistics
Model Zero-order Partial Part Tolerance VIF -1 FL .378 .312 .304 .554 1.804
Liquidity -.228 .036 .034 .554 1.804 -a. Dependent Variable: ROE
I
ticollinearity is a statistical phenomenon in which two or more
predictor variables in a multiple regression model are highly
correlated. In this situation the coefficient estimates may change
erratically in response to small changes in the model or the data.
Multicollinearity does not reduce the predictive power or reliability
of the model as a whole; it only affects calculations regarding
individual predictors.
Perfect multicollinearity takes place if the correlation between
two independent variables is equal to 1 or -1. A tolerance of less
than 0.20 and/or a VIF of 5 and above indicates a multicollinearity
problem. The test statistic shows that the tolerance and VIF value
are 0.554 and 1.804 respectively. That means it has positive
correlation. Thus, financial leverage and liquidity are positively
correlated with return on equity.
3) Heteroskedasticity
Scatterplot
Dependent Variable: ROE
G-., 0 :I .,, 'iii 3! 4-.,, .. N . ., i:: .. 2-'ti 0 :I ... (/)
0 i::
0 Oo 0 0 0 ·u; o- o® ot!b o "' oo G1J oocf'(fb 000 .. ... 00 oO 0 "" 0 .. 0:
' ' ' ' ' ' -3 -2 -1 0 1 2 3
Regression Standardized Predicted Value
A sequence or a vector of random variables is heteroskedastic
if the random variables have different variances. A number of
assumptions are typically made. One of these is that the error term
has a constant variance. This will be true if the observations of the
error term are assumed to be drawn from identical distributions.
Thus, the model plot above shows that it still subjects to
heteroskedasticity.
b. Multiple linear regression
Model Summary"
Adjusted R Std. Error of the
Model R R Square Square Es!imare
1 .319" .101 .068 .70831
a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: ROE
The multiple linear regression intends to know the relationship
between one or more independent variables and another variable,
called dependent variable. The coefficient of correlation describes the
strength of the relationship between two sets of interval-scaled or ratio-
scaled variables, which is designated as R square. R square is a statistic
that will give some information about the goodness of fit of a model.
In regression, the R square coefficient of determination is a statistical
measure of how well the regression line approximates the real data
points. An R square of 1.0 indicates that the regression line perfectly
fits the data. Later, F-test, B and t-test are also important in explaining
the result.
Adjusted R square is a modification of R square that adjusts for
the number of explanatory terms in a model. Unlike R square, the
adjusted R square increases only if the new term improves the model
more than would be expected by chance. The adjusted R square can be
negative, and will always be less than or equal to R square.
Adjusted R square does not have the same interpretation as R
square. As such, care must be taken in interpreting and reporting this.
statistic. Adjusted R square is particularly useful in the feature
selection stage of model building.
The test statistic shows that the R square value is 0.101, while
adjusted R square is 0.098. R square value explains that only 10.1 % of
independent variables affect dependent variable. The other 89.9% is
affected by other variables. The figures also indicate that the
regression line perfectly fits the data.
ANOVA'
Model Sum of Squares df Me?!.._Square F Sig.
1 Regression 3.059 2 1.529 3.048 .056'
Residual 27.092 54 .502
Total 30.151 56 -a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: ROE
test is any statistical test in which the test statistic has an F-distribution
if the null hypothesis is true. The value of the test statistic used in an F
test consists of the ratio two different estimates of quantities which are
the same according to the null hypothesis being tested. In the usual
applications, statistical modeling assumptions are made founded on
using the normal distribution to describe random errors and the
Model
1 (Constant)
FL
Liquidity
estimates used in the ratio are statistically independent but are typically
derived from the same data set.
The test statistics shows that F value is 3.048 with significance of
0.056. Afterward, it is compared with the F t'lble with a= l 0%. Since
the statistical test showed F calculation is lower than F table, then Ho is
rejected. Therefore, it concludes that variable X affect variable Y.
Coefficients a
Unstandardized Standardized
Coefficients Coefficients 90% Confidence Interval for B
B Std. Error Beta T Sig. Lower Bound Upper Bound
-.323 .466 -.694 .490 -1.257 .611
1.169 .552 .381 2.119 .039 .063 2.276
.053 .093 .102 .569 .572 -.13:l .239 -a. Dependent Variable: ROE
A t-test is any statistical hypothesis test in which the test statistic
has a student's t distribution if the null hypothesis is true. It is applied
when the population is assumed to be normally distributed but the
sample sizes are small enough that the statistic on which inference is
based is not normally distributed because it relies on an uncertain
estimate of standard deviation rather than on a precisely known value.
The test statistic shows that B value is -0.323, while financial
leverage's t is 2.119, with significance of 0.039. Since the statistical
test showed t calculation is lower than t table, then H0 is rejected. It
means that the effect of financial leverage towards ROE is significant.
Liquidity's t coefficient calculation is 0.569 with significance of0.572.
Since the statistical test showed t calculation is higher than t table, then
H0 is accepted. It means that the effect of liquidity towards ROE is not
significant.
2. Financial leverage and liquidity toward EPS
a. Assumption classic test
I) Autocorrelation
Model Summaryb
Adjusted R Std. Error of the
Model R R Square Square Estimate Durbin-Watson
1 .333' .111 .063 291.30226 2.235 -a. Predictors: (Constant). Liquidity, FL
b. Dependent Variable: EPS
There are numerous numbers shown here. In this matter,
Durbin-Watson is the only figure needs to be watched closely. The
Durbin-Watson statistic is a test statistic used to detect the presence
of autocorrelation in the residuals from a regression analysis. Its
value always lies between zero and four.
The test statistic shows that the Durbin-Watson value is 2.235.
If the Durbin-Watson statistic is substantially less than two, there is
evidence of positive serial correlation. Small value indicates
successive error te1ms are, on average, close in value to one another,
or positively correlated. Thus, by looking at the figure shown above,
financial leverage and liquidity are negatively correlated with
earnings per share.
Correlations
EPS FL Liquidity -Pearson Correlation EPS 1.000 -.231 -.005
FL -.231 1.000 -.697
Liquidity -.005 -.697 1.000
Sig. (1-tailed) EPS .042 .486
FL .042 .000
Liquidity .486 .000
N EPS 57 57 57
FL 57 57 57
Liquidity 57 57 57 -
Above table is correlation matrix between ROE, financial
leverage and liquidity. N is 57, along with the using of Pearson
Correlation. From the test table, it can be concluded using
following terms; Correlation coefficient between EPS and financial
leverage is -0.231 with significance of 0.042, which is lower than
0.1. Subsequently, lower calculation result as compared to the table
denotes that Ho is rejected; Correlation coefficient between EPS and
liquidity is -0.005 with significance of 0.486, which is higher than
0.1. Higher calculation result as compared to the table indicates that
Ho is accepted.
2) Multicollinearity
Coefficients a
Correlations Collinearity Statistics
Model Zero-order Partial Part Tolerance VIF
1 FL -.274 -.331 -.330 .567 1.764
Liquidity .039 -.196 -.188 .567 1.764
a. Dependent Variable: EPS
Multicollinearity is a statistical phenomenon in which two or
more predictor variables in a multiple regression model are highly
correlated. In this situation the coefficient estimates may change
erratically in response to small changes in the model or the data.
Multicollinearity does not reduce the predictive power or reliability
of the model as a whole; it only affects calculations regarding
individual predictors.
Perfect multicollinearity takes place if the correlation between
two independent variables is equal to I or -1. A tolerance of less
than 0.20 and/or a VIP of 5 and above indicates a multicollinearity
problem. The test statistic shows that the tolerance and VIP value
are 0.567 and 1.764 respectively. That means it has positive
correlation. Thus, financial leverage and liquidity are positively
correlated with earnings per share.
3-
;; :i ..,
"iii 2-di er: .., di N
1-. ., i:: .. ""' :i ... (j) o-i:: 0
"iii "' ~ -1-0) di er:
-2-
PERPUST AKAAN UT AMD UIN SYAHID JAKARTA
3) Heteroskedasticity
Scatterplot
Dependent Variable: .EPS
0 0
0
0
0 0 0
80 0 0 0
6' 0
0 oo 0 0 0 0 0 <e> 0 0
oeO cPo
0
' ' ' ' . . -3 -2 -1 0 1 2
Regression Standardized Predicted Value
A sequence or a vector of random variables is heteroskedastic
if the random variables have different variances. A number of
assumptions are typically made. One of these is that the error term
has a constant variance. This will be true if the observations of the
error term are assumed to be drawn from identical distributions.
Thus, the model plot above shows that it still subjects to
heteroskedasticity.
0
' 3
b. Multiple linear regression
Model Summaryb
Adjusted R Std. Error of the
Model R R Square Square Estimate
1 .327' .107 .074 273.85588
a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: EPS
The multiple linear regression intends to !mow the relationship
between one or more independent variables and another variable,
called dependent variable. The coefficient of correlation describes the
strength of the relationship between two sets of interval-scaled or ratio-
scaled variables, which is designated as R square. R square is a statistic
that will give some information about the goodness of fit of a model.
In regression, the R square coefficient of determination is a statistical
measure of how well the regression line approximates the real data
points. An R square of 1.0 indicates that the regression line perfectly
fits the data. Later, F-test, B and t-test are also important in explaining
the result.
Adjusted R square is a modification of R square that adjusts for
the number of explanatory terms in a model. Unlike R square, the
adjusted R square increases only if the new term improves the model
more than would be expected by chance. The adjusted R square can be '
negative, and will always be less than or equal to R square.
Adjusted R square does not have the same interpretation as R
square. As such, care must be taken in interpreting and reporting this
statistic. Adjusted R square is particularly useful in the feature
selection stage of model building.
The test statistic shows that the R square value is 0.107, while
adjusted R square is 0.063. R square value explains that only 10.7% of
independent variables affect dependent variable. The other 89.3% is
affected by other variables. The figures also indicate that the
regression line perfectly fits the data, since the value of R square is
above 0.05.
ANOVA0
-Model Sum of Squares di Mean Square F Sig.
1 Regression 485507.617 2 242753.808 3.237 .047'
Residual 4049840.415 54 74997.045
Total 4535348.032 56 -a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: EPS
F test is any statistical test in which the test statistic has an F
distribution if the null hypothesis is true. The value of the test statistic
used in an F-test consists of the ratio two different estimates of
quantities which are the same according to the null hypothesis being
tested. In the usual applications, statistical modeling assumptions are
made founded on using the normal distribution to describe random
Model
1 (Constant)
FL
Liquidity
errors and the estimates used in the ratio are statistically independent
but are typically derived from the same data set.
The test statistics shows that F value is 3.237 with significance of
0.047. Afterward, it is compared with the F table with a= 10%. Since
the statistical test showed F calculation is lower than F table, then Ho is
rejected. Therefore, it concludes that variable X affect variable Y.
Coefficients a
Unstandardized Standardized
Coefficients Coefficients 90o/o Confidence Interval for B
B Std. Error Beta t Sig. Lower Bound Upper Bound
674.120 180.106 3.743 .000 313.029 1035.211
-542.750 213.339 -.456 -2.544 .014 -970.469 -115.030
-64.590 35.878 -.323 -1.800 .077 -136.520 7.340
a. Dependent Variable: EPS
At-test is any statistical hypothesis test in which the test statistic
has a student's t distribution if the null hypothesis is true. It is applied
when the population is assumed to be normally distributed but the
sample sizes are small enough that the statistic on which inference is
based is not normally distributed because it relies on an uncertain
estimate of standard deviation rather than on a precisely known value.
The test statistic shows that B value is 674.120, while financial
leverage's t is -2.544, with significance of 0.014. Since the statistical
test showed t calculation is lower than t table, then H0 is rejected. It
means that the effect of financial leverage towards BPS is signific~nt.
Liquidity's t coefficient calculation is -1.800 with significance of
0.077. Since the statistical test showed t calculation is lower than t
table, then Ho is rejected. It means that the effect of liquidity towards
BPS is significant.
A. Conclusion
CHAPTERV
CONCLUSION
This paper demonstrates that increases in the financial leverage of a firm
can be associated with increases in the firm's expected rate of return, a result
that is in accordance with the claim made in standard corporate finance
textbooks that financial leverage and the expected rate of return should be
positively related. All that is required to be in compliance with the result is to
allow the firm to cease operations if it becomes sufficiently unprofitable.
The expected rate of return of a firm with an abandonment option is a
weighted average of (i) the expected rate of return of the firm without the
abandonment option and (ii) the expected rate of return of the abandonment
option (which is typically less than the risk-free interest rate). As fixed costs
grow, the first component increases, as the textbooks argue, leading to a
higher overall expected rate of return. However, the abandonment option also
becomes more valuable, shifting more of the weight onto the latter
component, and leading to a lower overall expected rate of return. In general,
increases in fixed costs have an ambiguous impact on the expected rate of
return of a firm. Writer analysis of a particular example shows that the second
effect can dominate the first when financial leverage is high.
The essence of this research can be summed in point as follows:
1. Increases in the financial leverage of a firm can be associated with increases
in the firm's expected rate ofretum.
2. The expected rate of return of a firm is weighted average of the expected
rate of returns either with or without abandonment options, depends on the
case.
This paper demonstrates the dangers in drawing inferences from static
financial models or, more specifically, those that ignore the flexibility that is
embedded in typical investment projects. It shows that when these real options
are considered, supposedly 'standard' results can be overturned.
The evidence presented here has clear implications that leverage has an
important role to play in explaining returns. However, the relationship is not
necessarily positive. The empirical findings of MM in a couple of risk classes,
namely mining sector cannot be generalized into all risk dasses, bearing in
mind that these two sector employed by MM are highly regulated and capital
intensive.
B. Implication
After looking at the explanation of data for each consecutive year, it can
be concluded that financial leverage and liquidity affect ROE as well as EPS,
though there is exception in which financial leverage does not affect EPS. The
study besides rating the mining industry has also highlighted the growth
strategy each of these companies should adopt. The recommendations for
growth strategy are based on the current financial structure and the
performance of these companies as discussed above.
Hopefully with this study, company can adopt the way they operate their
business with some concern following the study. The findings of this study
should be useful to researchers and investors:
l . The approach used here also provides a model for future research on the
earnings response coefficient as well as other are,as, e.g., the choice of
financing method by managers.
2. Knowledge of factors influencing return-earnings relationship helps
investors to direct their information acquisition efforts to firms known
to or predicted to have the above characteristics, e.g., earnings
persistence or high book value per share.
3. Future research designed to determine the value relevance of specific
financial and nonfinancial information should control for the effects of
size, earnings predictability, earnings persistence, and returns.
It can be concluded that while leverage seems to be working well for few
category of companies, it is affecting some others negatively. Companies that
are moderately geared i.e. in the range of gearing ratio of 50 percent to 85
percent have been able to generate a good ROE. In a nutshell, it is the
management who take the lead and responsible for the usage of company's
external source of fund to leverage their company as to maximize the practice.
REFERENCES
Alam, Pervaiz. "Disaggregated Earnings and The Prediction of ROE and Stock Prices", Kent State University, Ohio, 2006.
Cartera, David. et al. "Firmwide Risk Management of Foreign Exch'lnge Exposure by US. Multinational Corporations", Oklahoma State University, Oklahoma, 200!.
Cai, Jie. et al. "Leverage Change, Debt Capacity, and Stock Prices", LeBow College of Business, Drexel University, 2008.
Dudycz, Tadeusz. "The Different Faces of Leverage", Wroclaw University of Technology, 2006.
Guthrie, Graeme. "A Note on Operating Leverage and Expected Rates of Return", Victoria University of Wellington, 2006.
Jelinek, Kate. "The Effect of Leverage Increases on Earnings Management", The University of Rhode Island, 2007.
Keown, A.J. et al. "Financial Management: Principles and Applications", Tenth Edition, Prentice Hall, New Jersey, 2005.
Lipson, Marc et al. "Liquidity and Firm Characteristics", University of Virginia, 2006.
' Madura, Jeff. "Financial Institutions and Markets", Seventh Edition, Thomson, New York, 2006.
Padron, et al. "Determinant Factors of Leverage", University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, 2006.
Ross, Westerfield, Jordan. "Corporate Finance Fundamentals", Seventh Edition, McGraw Hill, New York, 2006.
Ricciardi, Victor. "Risk Perception Primer: Nan-alive Research", Golden Gate University, 2004.
Schutt, R. "Investigating the Social World", Sage Publications, 2006.
Sekaran, Uma. "Research Methods for Business", Forth Edition, John Wiley, New York, 2003.
Sibilkov, Valeriy. "Asset Liquidity and Capital Structure", University of Wisconsin, 2007.
Sunarto. "Pengaruh Rasio Probabilitas dan Leverage Terhadap Return Saham Perusalwan Manufaktur", Semarang, 2001.
Sivaprasad, Muradoglu, "An Empirical Test on Leverage and Stock Returns", Cass Business School, London, 2007.
APPENDIX
Harian
lli!tni!t Jndont!tia Diterbitkan Oleh PT. Jurnalindo Aksara Grafika
www.blsnls.com Wisma Blsnis .Indonesia Lt.'5-8 JI. K.H.Mas Mansyur No.12AJakarta 10220 Tele 021-57901023
Fax 021-57901024 (Pemasaran), 021-57901025 (Redaksl), 021-57901028 (Perusahaan)
Nomor: 004/JAG/BIIU/X/08 Hal : Studi Kepustakaan
Dengan !format,
Dengan ini kami beritahukan bahwa mahasiswa Ekonomi Manajemen Universitas Islam Negri SyarifHidayatullah Jakarta dibawah ini:
Nama NIM Jurusan Fakultas Alamat
: Anda! Pradipta : 604081000002 : Manajemen : Ekonomi : JI. Karya Pemuda No. 9 Depok
Telah melaksanakan Studi Kepustakaan di Perpustakaan dan Dokumentasi Harian Ekonomi Bisnis Indonesia pada tanggal 21 Oktober 2008 berupa LKP {Laporan Keuangan Go Public Tahun 2005 - 2007) untuk keperluan penyusunan skripsi yang berjudul:
"Financial Leverage and its Effect on REO and EPS (an empirical analysis of mining company listed in indonesia stock exchange".
Demikian surat keterangan ini kami buat dengan sebenamya agar dapat dipergunakan sebagaimana mestinya.
Fd§Of ;,:{.AAN OKUME~!T ASI
J
~~1rtl~1th'··· l;~.t,,,1esw
Iin Solihin Asmen Pusdok Bisnis Indonesia
Descriptive Statistics
Mean Std. Deviation N
ROE .3977 .73376 57
FL .5142 .23923 57
Liquidity 2.2700 1.42255 57
Correlations
ROE FL Liquidity
Pearson Correlation ROE 1.000 .310 -.163
FL .310 1.000 -.697
Liquidity -.163 -.697 1.000
Sig. (1-tailed) ROE .009 .1 '12
FL .009 .000
Liquidity .112 .ODO
N ROE 57 57 57
FL 57 57 57
Liquidity 57 57 57
Variables Entered/Removed"
Variables Variables
Model Entered Removed Method
1 Liquidity, Fla . Enter
a. All requested variables entered.
b. Dependent Variable: ROE
Model Summaryb
Adjusted R Std. Error of the
Model R R Square Square Estimate
1 .319a .101 .068 .70831
a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: ROE
ANOVAb
Model Sum of Squares df Mean Square F Sig .
1 Regression 3.059 2 1.529 3.048 . 056'
Residual 27.092 54 .502
Total 30.151 56
a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: ROE
Coefficients a
Standardized
Unstandardized Coefficients Coefficients 90% Confidence Interval for B
1del B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) -.323 .466 -.694 .490 -1.257 .611
FL 1.169 .552 .381 2.119 .039 .063 2.276
Liquidity .053 .093 .102 .569 .572 -.133 .239
Dependent Variable: ROE
Residuals Statistics•
Minimum Maximum Mean Std. Deviation
Predicted Value -.1747 .8989 .3977 .23371
Residual -.60478 4.50105 .00000 .69554
Std. Predicted Value -2.449 2.145 .000 1.000
Std. Residual -.854 6.355 .000 .982
a. Dependent Variable: ROE
""' (.)
c: <I> ::J
40
30
Cl" ?Q <I> -....
LL
-2
Histogram
Dependent Variable: ROE
0 2 4 6
Regression Standardized Residual
N
8
57
57
57
57
r·Jte;J.n =8.15E-17 std. Dev. =0.982
M=57
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: ROE 1.0 ··-·····-······-··--········-·· ··-·····-····-· ........... .
.0 0 ... a.
0.8
E o.6 :J
<.> "t:J
21 () 0.4 Q) a. x w
0.2
0
o.o~------r---,.---,.---,.---,.---,.------~
0.0 0.2 0.4 0.6 0.8 1 .0
Observed Cum Prob
Scatterplot
Dependent Variable: ROIE
13.00
0
5.00
4.00
w ~ 3.00
2.00 0
1.00
0.00 ~~
0
(ili> 0 ~--.~~~~-,...~~~~~~~~~-.·~~~~~~~~~~~
-2 0 2 4 •l 8
Regression Standardized Residual
Descriptive Statistics
Mean Std. Deviation N
EPS 248.4132 284.58454 57
FL .5142 .23923 57
Liquidity 2.2700 1.42255 57
Correlations
EPS FL Liquidity
Pearson Correlation EPS 1.000 -.231 -.005
FL -.231 1.000 -.697
Liquidity -.005 -.697 1.000
Sig. ( 1-tailed) EPS .042 .486
FL .042 .000
Liquidity .486 .000
N EPS 57 57 57
FL 57 57 57
Liquidity 57 57 57
Variables Entered/Removed"
Variables Variables
Model Entered Removed Method
1 Liquidity, Fla . Enter
a. All requested variables entered.
b. Dependent Variable: EPS
Model Summary"
Adjusted R Std. Error of the
Model R R Square Square Estimate
1 .327' .107 .074 273.85588
a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: EPS
ANOVA"
Model Sum of Squares df Mean Square F Sig.
1 Regression 485507.617 2 242753.808 3.237 .047'
Residual 4049840.415 54 74997.045
Total 4535348.032 56
a. Predictors: (Constant), Liquidity, FL
b. Dependent Variable: EPS
Coefficients'
Standardized
Unstandardized Coefficients Coefficients 90% Confidence Interval for B
el B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) 674.120 180.106 3.743 .000 313.029 1035.211
FL -542.750 213.339 -.456 -2.544 .014 -970.469 -115.030
Liquidity -64.590 35.878 -.323 -1.800 .077 -136.520 7.340
ependent Variable: EPS
Residuals Statistics'
Minimum Maximum Mean Std. Deviation
Predicted Value 64.0812 536.5336 248.4132 93.11165
Residual -400.78555 782.62524 .00000 268.92114
Std. Predicted Value -1.980 3.094 .000 1.000
Std. Residual -1.463 2.858 .000 .982
a. Dependent Variable: EPS
Histogram
Dependent Variable: EPS
12
10
,.. 8 (.)
c: Ql ::s tr
6 Ql ... u..
4
2
0 -2 -1 0 2 3
Regression Standardized Residual
N
57
57
57
57
Me~n =-7 .98E-17 Std. Dev. =0.982
M =57
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: EPS
1.0 ·-·-···---··-·-···-···-·-·-··· .. ······················-········-·----······· ··········---·~7·-. o~ .... ······-··--················
00 0.8
~ ~ ~ ~ E O.G :I u .., 2 <> 0.4 Ql 0.. >< w
0.2
0.0 0.0 0.2 0.4 0.6 0.8
Observed Cum Prob
1.0
1200.00
1000.00
800.00
(/) a. 600.00 w
400.00
200.00
0.00
-2 -1
Scatterplot
Dependent Variable: EPS
0
0
0
Oo co 00
co
0 0
0
Regression Standardized Residual
()
0 0 0
2 3
iM.trqu:M~
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"'"' ......... U.PA' DARI A!"JMTAS UOf!MAL • oom 51.ll30 &1>1.11~nga!alnanlkljlllmg1111 Mo<lal d.uar fDJQii darl 449,000.000.noo ~m pllf · ,HiduJ>O:tln~ tt20S 1.625 31Oc1Gri>11i2.:lll7datl1.150.ll00.000 iahtlmpf!:. i>OSLUAR llt.\SA : '·. {ill.SH) Aklivnla!Milln; " 31 llllum001200IJ delli3llohloonm&1Rp 100·~., U.eA Si:aauw 1Wc M!NOmrAS ll5.i42 .(ll.oj80
· .llmral)Aldi\'i,Tldik lnnur 328.31$ 2~7.S<S :=~f~~~=a!~e:~ ttAK·u~OMis Ai'i1S LABA llEnSl!l
1s:WS· 31 llll~ 2001 dan2M.C07.(XXl seham pllf l'ERIJS/llfMff AtlAK 1.005 31Dll1.01J1l>Df200ll. 211.810 Tllll'lblhan Modal Disolor· BtM • LABA BfRSIH U.7711 25J7' Sardo~ "" 1.ABA P{i!t SAHAM .fmlbJ,i&li!as l.nha Ur.ahapuS11>1mOaw 177.72 310,SI JIJMl.AH KEWWAN OAN El«ifTAS tnbli Bmlll plll' Salwn Daw "" 81,7~
~taran: ltiporan koWlllgan lm1m1lk1a1lan uri!uk latiurHafuin \'1111!1
~D~~~°:~~r~e~!t2~a~s~~~~: Oadalll), dengan pandap11! wajar lanJ>a pangawlllian.
SAUIO PER 31 DESE.ljBER 1005 {0Ji3Fbn Kombalij ..... ... '·"' J.a~lleM 25.218 · J~rarta,.J1 1.1~ro12oos SAUIO PER lf llESEMBER MGB 2!.BHI •. ''" ~.77•
,I.WM~ ""' ""' ·S.E& o
_ln~Bonlh .64.176 ""' Dfrekal
SAL!lO PER lf DESEMllE/l 2007 uusa • 70.137 142.553· PT CJTA MfUE~ IUVESTif"DO Tbk
twriw- )\ (il "; (efi,
sarRp317,2Juta pada tahun 2006
arRp2.613,5juta n2006
~tan sebesar Rp 39.4 millar pada f:ahun 2006
asl-sebesar Rp 6 m!liar oada tahun·2oos
amortisasi '07
asl sebesar Rp 127 jut.a
6.383.809 2227.500
15.544.154 335.no
128.064.116 194.540 74.026
2.294.691
155.118,606
1.323.075
194.117.952 393.344.980
12.773.040 48.143
601.607.190
756.725.796 =
18.437.909 495.sn· 655.800 922.390
33.782
34.964 30.256.000
50.837.822
52.sn.so3 1.~60.2Q4 '
54·43z.1§z-:. ,_-1os:2i5.sa9· •
-.-
6.741.149
40.026,559 553.380
79.604.938 840.427 109.741
2.589.130
130.465.324
1.084.767
200.388.152 387 .822.455
857.147
567.000
12.ns.040 47.843
603.540.404
734.pOS.728
25.420,059 1.282.326
455,938 3.271
255.631
56.582 14-.828.000
42.301.807
34.964 67.042.969
1.156.216
6B"2J.4,j~9 110.535:956,
:~J 19q-~rsati::iin ~:s.,~~-' ·:.- _ .·., .¢.an ~ .. ~4--m~!~r;~~ham~~ri. a- '. ..
-~~~~??o:~~-~~ ·:~ --~J.:C:O~~~?~.~ .--
s
-~., ... __ ,.
..... ;~·o •;.;'
---~ 41~:-1S~~057~\· .. ;.;322:000_.ooo,· · ·< .. ··'- ,1,~- '> __ ,_:. __ 6:4<570.QOO: ·
.·.·4;000.oocf :_ ..... - "A.0.00.000· ~ 4,2S5.150" 2.799,772.
6s~_·:; _::~23:469.772: 7s5~~i9t .134.0o5.7za ·
,j~END_~PATAN_BERSIH 267,:401.141 262.696.694 '
'BeaAN-;Ok"oK PENDAPATAN {238.159._075) (221.230.764)
iABA KoTofi 29.232.066 41.465.930 ; --~'·. . -.
~E~'USAHA·
P0nju8ran 6.543.870 16.967.B41 Umum dan administrasi 8.368.383 15.256.178
Jumlah Beban Usaha 14.912.253 32.224.019
LABA USAHA 14.319.813 9.241,.911 '
PENDAPATAN (BEBAN) LAIN.JAIN Penghasilan-bunga · 169.721 42.876 Laba (rug!) sells!h kurs-bersih 1.961 - (4.Tf5~ Beban bunga (12.271.536) (7.277.606 Provis! bank 1•9.1001 Danda keterl~mbal;?;n 44.126 (16-789l ~ak dan· administrasl 1s;o3s !'·760
minlstrasl Bank . (9.577 4.773 Ke~!an penghapusan Jamlnan (60.620 Lai ln · 505.976 2.'692
Beban Laln.l..aln ~ Bersih (11.751.716) (7.328.753)
LABA SEBELUM BEBAN PAJAK PENGHASILAN •, ~ 2.~8.097 1.9~3.158
Manfaat (BEiban) Pajak P~nghasllan ---
Kini {1.311.027) {1.179.422) Tangguhan 238:308 532.621
Jumlah Beban'Pajak Penghaslla~n Bersih . . 11._oz2.11s) {546.80.1)
LABABERSlH • •. · ;,49:5~;8 1.266.357 .
LABA PER SAHAM ~ :.0;35 0,32
. Modal·D.iternpatkan; --:, -·., dan Dise-IDr Penuh ~ . -~
Saham- • ___ ; , - _; Tambahlin : ... _ . _' . _ . ,_ . Sari A ··, S&ri 9; - M~I· Oisefur .Ag!Q Saham SaldQ 1..abac "Ekutta$. ¥ Bersih
Saldo 1 Janueri 2005
Penambahan modal .dlseior ssham sari B yang berasal dari exercised waran serl I! Laba ~rslh tahun berjalan
saido 31 Desember 2005 ?eotlrr.!ia!lan ~1-disetoi -~. seliB yang·berasal Pari-exe.-cis&d waran serl II
---- 322.000.000" :.s.rso.ooo 4.ooo.ooo ' 1.533:414 , 564.263:414 230.000.uw-: •. r·
¥ .. , 57.920._900, 1.256:358
230,QOO_.tJOO 3~00~,900-::·iij':6iQ,1JQo· '4.000.000' 2.799.772 .-; '".-,
·-~s.4_-as:osr:· ~ .,.
.- -
. ··~. s1'J5s.:001 (91'.iss.057)
~.9;w_'.ooo 1:206.358
.623,-:4fi9.77i
. 26.4~:057" • KO"rlverSifambahan modal ·.cr1seWtke ffipdaJ di tempatkan · $n di~t_or pen[ffl__ _. . -Latia oo_raiii· tahun berja!an
-~-~t ~1-~~;,:z001--·~-::-"'i:s!l:·:ooo:ooo· · _· _-__ ' _ -1.495.378"·"'·._ ~;-1:<:"s5.'37-B , ·
11 -,
nyfuinan
~-1.2004)
l isllmim'<l
""" ' n 2Q04)
J: k';;'tiga
rbsi
84;0-15
353.237
4(Y,1S1
26214 ·Jl.903 26.516
i3~15!l 9:7.866 'fi:324 t3.837
6S!t2.W
235;380
27.504
13.84;2 25,5:00
13'.418 64-.105
3.S'tr i2..3$0
539A9S
rafu'i~nJaWh "'·'·r-· · .... ;,-·-,·v·c-·-.-.. ·--· 1w~.b!Jt1~h:lsa-~a:~ .. ;,;;
-~~~ifri·erfuair._konti;ak.~erja Hwang iain-1alti ·
KEW.(Wis~ :\f.tl._~~PANJAN.G Kewa;t-minjal)~~.g:,s:~-d)kur.irtW
b<1:s1en· yrulg_~ruf{1cii'li:i0:dai?fu' S:<1tlf:taf100 - S:ewa gups::usaha eilmbi-eya£!'.n
Kewajlban imba-!nn kmja
EKUITAS
1Q~:~-~--' .. '12;$$4
?;~~ ~,009
".;2$;4~'\ <' '4ft-~:8S:
9;{[16
~Ti'.59~
.. 1·57:}}~9
c '9S'f
19.93$ ,3.413
2.wa-:
'fi!EMN' USAHA LANG.SUNG
I· fA~.K?101*.
6E6-AN PENJUALAN DAN ADMIN!STRASI
LABA-US-AHA
fil'ti>'a !et~p 1)1·tld:a)> lerlagfh
[' :}'2.674" 1~;$6Q ', ,._ ' ' - -;.;w;.- - ',, ,' - ''. i04J327 _. ,. . ., JUt41.AH.PSN:l?ff~~t~ _LAlN-LAl.N :<;·'.~::::.
tiv:~;t~:. _. .. ; · +t-~q~- , ·: ;,~-~~.ifo~.~A£~~!!:{ P~~Sf.l:!AA~ ASOSIASI
LABA SEBELUM PAJAK PENGHAS!LAN
4®:' {4,335} 16:-925 6.003 7.050 ~:1;;1
,':sf411
867
11'3.'}Q9
7.11a
32:669: 5.226 •t.794-
4fl.W-8
71.804
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A\ i
:/ 11 I, ii
1 lahun 2DD5 153,;00_1
l.35&'; 01:ey,.142
MOOa!.:saharr.MOO::iJciasa:t-*10AOO;oOD bh:imf.de'ii -(f?J!;i_l'P.l'n~
'BEBAN PAJAK PENGHASILAN
jl
11 ll 322 .. 104"
2NSS·. 19-,SSS
344,157 17~:4,51, j L~A •. ~EksiH
st~00'\!···1·· ·\.\ . . ' . ·.·· . • . ,,. Ar',,.·: .. .,: .:RATA~RA,Ti(l'.ERTl.ff<~<;;:o:ptJJ~fLt;:E:tJ!:BAR' - ' I
., ·-:, :-.~'Afif~~B!;\S.\'{Y~.~:~-:s~:F6~Jt1:~:::~~'fr"_;;~.:L--." -u;·2.sop;o..~~'.:\ 10_2.600.DQQ \
LABA-US-AHA PER SA'.HAM (NILA! PENOHj 1lZ,_ '" 77'1 214 I , , I LABA BERSIH PER SAHAM DASAR {NILA°fpEfi:l£JlJ 815 j 5'32
!l Jfifo!'CT"..asi kc1Jillnglln dt:.aJois_'91~b11~il'·Cariliipor:aq:·htl\l-?rt-S::m ~.~r:;,:o!id_a:;f;>.~, ".1fll>Jk Whl,n 1nn9 ; i b-erakhir pa do tc~nnaarZ'l:·J 9.9:iember2('05 a:"v.i l<l.ii6.~ .. !:_cwang:in i::r.\ukt::;t.u::i }'Zng be:<:Bhi: l 1 psda. ~;:mg gal 31 De:icinber 200-4 rang telah ·ijimJi:jli-Clch' K8-ntt<,r A.!\tm~;_fou-l)lik H;ry<1nW \ Sa_~ri & Re-k<in {Pricaw·merhouseCoopers) vang tel ah m&n9ellJatkan pill'.\tla;;iitw;zjar tanpa.
.~ilerygecua!h111 <l.!ik1m. !aporann)'u ierbr.ggai 27 f;.fa;it,-f 2GOB .
·-·-----.- . i __ .. \4t1.l:r;:!'?/ l~·~·'tl/ 51.300 I 450 I 10.260 I 53~\695 ! I --- -
. . L'~~arnn \if'~(· P:Ji,n :;:ihun ZCG·~ rnl:!rupakun JBpOmn k:euungan flcr1i~ahnan yang h~(&ri. i;,endili.
as;~.17 \1Q.250} (30.7130)
-0:>':1\/-05 .,"'
83,617~,ltf~:l'. "•~Q.260) " . ,{30.r.l:lO)
1':!.24i , I 12 .242
Jakarta, 1S Matot 20-0G
S.E&O
Dircks!
51.300 ! 4$0 ! 10.260 ! 589:212 I 12.242 ! 654.524 \ PT PETROSEA Tbk
I
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i I I ' I
c
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·,¥~"' 51 /16 Joo · '· . v: . -'
T
:lL; A• ·P .0 R A.• !\I ; .K: E;. U; A;.,N,-·G A-· ti!: 2 ,O . O 7 1 ·rO p.:. !\I . ;2 o-:- 0-.6]
.. - ,_. .. . i.~t~4 .. t~J.tJL.i~t.Y.C_ .. §lfS
KEWAJ!BAN'LANCAR -_ :~- ..,_r-.- - PENoAPATAN USAHA ___ ~- · .. 98.642 . ·as.sss· 1.661 1.141 HUtang bimkjangka pe;ldek.J 5.158. '4.973 BEBAN'POKOK PENJUALAN 78:953 ':' ·a221s
Htl!ang usal)a-plhak ketlga' • 122.593 ,_ 30.612 -.·
Piut2n~ usaha - plhek ka~g?- beralh - 16:4~4 1$.555 Hutarig lain-lain- 767. .. · r•;··.1.5.47 LABAKOTOR 19.689. - 7~740.-
Piut.apg i~l;;.lein . '179 126 Hu~1:fpaj?J;, . 1.62~ 927 -Saban usatia · '
.-... ; . ' Biaya masih harus ·dlbayii.r 12.212 . 13.265 Persediaan- be~ih 7?.611 76.641 Uiing'muka:dlte;ima·- plhak keUga 3.347' 7.296· . \ Behan p,emasaran'dan Panjua!an-. •.• ~11.489 :10.360, Pajak dibayar dimuka 726 607• Bagien-kewa~Qan tklak tancar_jatuh tempo
·57.9'43 Beban umum dao admlnistrasl
' 11.768 10.507.
Aktiva lancar l~innya . · I '" '1 Hutang ]angka. pSnjang · 55.356
, • .Kewajiban sewa guna u~ha .1.654 2.270 JUMLAH Bl;BAN USA!iA 23.257 '-, 20.857 m · Hutang konversl 11.6~3 .· . 59.032
JUMLAH AKTIVALANCAf!. 52.640 . 96,101 JL!Ml.AH \(EWAJISAN LANCAA
RUGIUSAHA·· 3.668 13:121 11.7.166" 1176.280 · ... .pengtiasi!an {beban) !eln-lain
" I II KEWAJlBAN TIO AK LAN CAR· ' Keuntungan penju11hm akUva tetae 93' AKTIVA TIOAK LAHCAR Hutang kepad;i pihiilcyang mempunyal
Plulang deli pihakya'ng mempiiny"ai 22°14 Pendap.atan bunga 77. • . ·91:
hubung~n· lstimewa · · . 2.690 Beban bunga· .(t001)· · ftss1r: Kewaµoan sewa-guna· t1Sah<i. • · 907 .93 hubungiih istimewa Kaunfungan. (keNglan) selisih ki,trs 94
Aktivi:talap Yang tii:lakQ.igunakaii ,. '
, da!am ~P~rasi • b~i:$ih· '1.965
~va teb.ip·. s&te!ah O!'"Wran91 · .akumuia&J _penY1.;1sUiOO'. ...
2001: Rp-139.914 Juia 2005_:.Rp 12s:1~SJu~· 59.~.95
. ': ': Bis.ya dl~ngguh~n - bersih 14.3U\ '
I Dana yang dlbatasl pencalrannYa 1.131
Akliva tidak !3~r 1.iirmya. .662 ..
J~Ml.AHA~VA TIDAK LAN CAR . · -S7.9ZS
173. Ke',yajiban P.aJ~k tan9gul/an, _ 2,987 5.602 (5.646) 12.164
Cadangari !mbalan p.astipascir-kerja , 13.754 10.471 . Penghapus."an peraediaan - - ·_(12.790)
< 2:012. P~ndapatan dltangguhk.an ~ari. { ; : PeoyisJhiin ·persedfaan usang dan (a.31j) .restrukhlrlsasl hutang 1.453. ,6.322 lambat·gerak -
JUi.1LAH ~!BAt·fTIDAK LANCAR .. ,. . ' 21.315 :25.178 Lain-lain barsih ~ (22) , 19
~UMLA,H J:<£WAJl8AN . 138.461 , 1.00.468 JUMl.AH BEBAN LA!N·LAIN 16.559\ . (10291 EKUITAS (Dl;Fl"s!E.NSl'MOOAI..) · Pos lua~ bias a"
,_ 1' .18'.011 . '-.· - {4.627) Modalsaham
Modal dasar Rp 1.2so.ooo.ooo.-ocio' . . ' RUG! SEBl;:LUM PAJAK {14.9641 / {2.3".4-18' H.S}J~ ~40 jula saham sari~ nominal Rp. 500
.·
8.400 juta saham sari B nominal RP 100 PENGHAslLAN PAJAK
7B2 · _Ditempatkan den disetoc penuh i;eriA. , . : Klnl 840 jUtasiaham pada tahun2007.dm'i2006[' . Tangguhan ! 2.615. 1 2.837
i 665 sari B 300.839.821_ sahampada'tahun.2007 . 459.084 420.000 ' - Tambahanmodaldisetor-bersih·. · . 72.305 . ,53.154
- $8,S~&: Dafisit .' -.' ~-, . ,, {489.302) .. , (476,~63)! .RUG18ERSIH JUMLAH EKµITAS (DEflSlENSI MO(}P,-t)' , 42.067 . 3.60911 . RUG! PER SA HAM {da! -. h h
.,i;:· • _ .J . _ am rup1a penu ) , Rugjber.slh.(terma!lukposluerbiasa) ,_ . · (13.50) j (24.50)
-... R!Jglberslh(tidakte;masukpos!u.ir~)- - (8,28) . (24.50}
Caf<ilan;.'
-Neraca. Laporan Laba-Rug! dan Lapor~n Paii.lbahan Ekuitas PT. Citatah Tbk un!uk tai}un_yang berakhlr 31 Desembar2007 dlsusun berdesarkan Laporan
•. 1<£iuangan yang telah dlaudlt oleh Kantor Akuntan Pub-Uk MU!yamin Sensi Suryanto (an-independent·-mambet, pf Moore Stephens lniemalionaJ Umited) del)gan
J !ah 1 ·pen·d.•p•.t Wajar Ta.op• .P•.nga:cualioin d~gan paragraf·penje!asan, sedangkan . Modat Sahim Oise tor. Barslh . Def!-"· 1 ""~-~·~·~ ~eraca·, Laporan Laba-Rug\ dan.lapoian parubahan EkUitas PT. Ci!atah Thk
Saldo P~1 Jenuarl200S .· 420 000 53154 {456 382) j 16 772 ! •w""'!'¥.,.' 1"''~ ,.,,y,, "'"""''I~""' NUUlll t\11.t.m~ rouw1:1uw11"~;;><>1 .. uow <><:''' . . • • . \ • , , ' I • • ,. __ .J ••• -1---1.---""-·'---'"- • ,.,_, '''··-" ~ -' __ ., ___ ,,.,_, __ Run! bersih tahun berialan' · ·• · • · 120
f?-aldO par31 Oenmbw2006 P_en!ngketan modal iwm de.n lainbahan
'modal dlf.el.ot dari ~versJ hultmg
420.000
SS.084
(3.SP9)
.58.235 !12.339)
.. Jakarta, 31 Maret 2008
Olreks\
