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EFFECTS OF EVA (ECONOMIC VALUE ADDED), EVA
SPREAD, EVA MOMENTUM AND RETURN ON
ASSETS ON STOCK RETURN
(Empirical Study in Indonesian Stock Market)
THESIS
By
Dranantya Ikhsan Wirawan
04/181027/EK/15701
Faculty of Economics and Business
Universitas Gadjah Mada
2011
ii
APPROVAL STATEMENT
iii
ADVISOR STATEMENT
iv
STATEMENT OF ORIGINALITY
v
ACKNOWLEDGEMENTS
Praises are to Allah, The Almighty, The Most Beneficent, and the Most
Merciful. Without His blessings, love, guidance, and miracles, the researcher would
have never finished this thesis entitled Effects of Eva (Economic Value Added), Eva
Spread, Eva Momentum and Return on Assets on Stock Return.
The researcher would also like to dedicate his gratitude to the following:
1. Mamduh Mahmadah Hanafi, Dr., MBA who acted as the thesis advisor for his
invaluable insights and advices which makes the completion of this thesis
possible.
2. Nurul Indarti, Sivilokonom. Cand. Merc.,Ph.D. and Gugup Kismono, Drs.,
M.B.A. who acted as examiner of this thesis which granted comprehensive
criticism and inputs to this thesis
3. All lecturers and officials of Gadjah Mada University Faculty of Economics and
Business for their guidance and learning experience.
4. My colleagues for immense support and inspiration
5. My family for their endless motivation and support
The researcher humbly acknowledges that this thesis is imperfect in nature
therefore any reviews, critics, and suggestions are welcome. The researcher hopes
that this thesis will be useful for managers, investors and academicians.
Yogyakarta, April 2011
Dranantya Ikhsan Wirawan
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TABLE OF CONTENTS
APPROVAL STATEMENT ......................................................................................... ii
ADVISOR STATEMENT ........................................................................................... iii
STATEMENT OF ORIGINALITY ............................................................................. iv
ACKNOWLEDGEMENTS .......................................................................................... v
TABLE OF CONTENTS ............................................................................................. vi
LIST OF TABLES ..................................................................................................... viii
LIST OF EQUATIONS ............................................................................................... ix
LIST OF APPENDICES ............................................................................................... x
ABSTRACT ................................................................................................................. xi
ABSTRAK .................................................................................................................. xii
CHAPTER I: INTRODUCTION .................................................................................. 1
1.1 Problem Background ........................................................................................... 1
1.2 Research Question ............................................................................................... 5
1.3 Limitation of The Problem .................................................................................. 6
1.4 Objectives of The Study ...................................................................................... 6
1.5 Benefits of The Study .......................................................................................... 6
1.6 Report Outline ..................................................................................................... 7
CHAPTER II: LITERATURE REVIEW AND HIPOTHESIS DEVELOPMENT ..... 9
2.1 Variable Identification ......................................................................................... 9
2.1.1 Economic Value Added ................................................................................ 9
2.1.2 EVA Spread ................................................................................................ 12
2.1.3 EVA Momentum......................................................................................... 14
2.1.4 Return On Asset .......................................................................................... 16
2.1.5 Stock Return ............................................................................................... 17
2.2 Hypotheses Development .................................................................................. 19
2.2.1 Effect of EVA on Stock Return .................................................................. 19
2.2.2 Effect of EVA Spread on Stock Return ...................................................... 20
2.2.3 Effect of EVA Momentum on Stock Return .............................................. 21
2.2.4 Effect of Return on Asset on Stock Return................................................. 21
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CHAPTER III: Research method ................................................................................ 23
3.1 Research Data and Sample ................................................................................ 23
3.2 Calculating Research Variables ......................................................................... 25
3.2.1 Calculating EVA, EVA Spread, and EVA Momentum .............................. 25
3.2.2 Calculating Return on Asset ....................................................................... 31
3.2.3 Calculating Stock Return ............................................................................ 32
3.3 Research Model ................................................................................................. 33
3.4 Goodness of Fit ................................................................................................. 34
3.5 Assessing Classical Assumptions ...................................................................... 35
3.5.1 No Autocorrelation ..................................................................................... 36
3.5.2 Homoscedasticity ........................................................................................ 36
CHAPTER IV: Data analysis ...................................................................................... 37
4.1 Descriptive Statistics ......................................................................................... 37
4.2 Classical Assumption Tests ............................................................................... 38
4.2.1 No Autocorrelation ..................................................................................... 38
4.3 Hypotheses Testing ........................................................................................... 39
4.3.1 Effect of EVA on Stock Return .................................................................. 39
4.3.2 Effect of EVA Spread on Stock Return ...................................................... 40
4.3.3 Effect of EVA Momentum on Stock Return .............................................. 41
4.3.4 Effect of Return on Asset on Stock Return................................................. 42
4.4 Goodness of Fit ................................................................................................. 43
CHAPTER V: Conclusion .......................................................................................... 45
5.1 Conclusion ......................................................................................................... 45
5.2 Implication ......................................................................................................... 45
5.3 Limitation .......................................................................................................... 45
5.4 Suggestions ........................................................................................................ 46
REFERENCES ............................................................................................................ 48
APPENDIX ................................................................................................................. 51
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LIST OF TABLES
Table 1: Company List ................................................................................................ 23
Table 2: Descriptive Statistics ..................................................................................... 37
Table 2: Durbin Watson Test ...................................................................................... 38
Table 3: EVA Coefficient ........................................................................................... 40
Table 4: EVA Spread Coefficient ............................................................................... 41
Table 5: EVA Momentum Coefficient ........................................................................ 42
Table 6: ROA Coefficient ........................................................................................... 43
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LIST OF EQUATIONS
Equation 1: Economics Value Added ........................................................................... 9
Equation 2: Economics Value Added ......................................................................... 10
Equation 3: EVA Spread ............................................................................................. 12
Equation 4: EVA Momentum ..................................................................................... 14
Equation 5: EVA Momentum Explanation ................................................................. 15
Equation 6: Return on Asset ....................................................................................... 16
Equation 7: Capital Gain ............................................................................................. 19
Equation 8: Total Stock Return ................................................................................... 19
Equation 9: Cost of Debt ............................................................................................. 26
Equation 10: Cost of Equity ........................................................................................ 27
Equation 11: Periodic Stock Return ............................................................................ 28
Equation 12: Market Return ........................................................................................ 29
Equation 13: Beta Coefficient ..................................................................................... 29
Equation 14: Weighted Average Cost of Capital ........................................................ 30
Equation 15: EVA of Period t ..................................................................................... 30
Equation 16: EVA Spread of Period t ......................................................................... 31
Equation 17: EVA of Period t ..................................................................................... 31
Equation 18: ROA of Period t ..................................................................................... 31
Equation 19: Stock Return of Period t ........................................................................ 32
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LIST OF APPENDICES
Appendix 1: Summary of EVA Regression on Return ............................................... 51
Appendix 2: Summary of EVA Spread Regression on Return ................................... 52
Appendix 3: Summary of EVA Momentum Regression on Return ........................... 53
Appendix 4: Summary of ROA on Return .................................................................. 54
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ABSTRACT
This study examines the effects of Economic Value Added (EVA), EVA
Spread, EVA Momentum, and Return on Asset (ROA) on Stock Return as a measure
of investors‟ wealth creation. These methods are then compared in terms of its ability
in explaining the Stock Return. EVA Spread and EVA Momentum are new methods
of firms‟ performance measurement constructed on the basis of EVA. EVA and ROA
are the traditional method of firms‟ performance widely used currently. EVA Spread
and EVA Momentum are argued to have better ability in explaining the variance of
investors‟ wealth creation compared to the two traditional methods.
This study employs simple linear regression to examine the effect of the
measures on Stock Return. To compare the ability of the methods in explaining the
stock return, this study uses the Adjusted R Squared method. The data used in this
study is the Adjusted Closing Price and financial data of firms listed in the LQ45
Index from 2004 to 2008 period in the Indonesia Stock Exchange. Data for Adjusted
Closing Price is acquired from Yahoo Finance. The Financial data is acquired from
OSIRIS database.
The result of the study shows that EVA Spread and ROA have significant
effects on stock return while EVA and EVA Momentum do not have significant
effects on stock return. ROA is found to have the highest ability in explaining the
variance of Stock Return compared to the other three methods.
Keywords: Economic Value Added, EVA Spread, EVA Momentum,
Return on Asset, Stock Return, Performance Evaluation
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ABSTRAK
Penelitian ini mengkaji pengaruh Economics Value Added (EVA), EVA
Spread, EVA Momentum, dan Return on Asset (ROA) terhadap Pengembalian
Saham sebagai alat pengukuran penciptaan kekayaan bagi investor. Metoda-metoda
tersebut dibandingkan dalam hal kemampuannya menjelaskan pengembalian saham.
EVA Spread dan EVA Momentum adalah metoda-metoda baru yang dibangun
berdasarkan EVA untuk mengukur kinerja perusahaan. EVA dan ROA adalah metoda
tradisional yang banyak digunakan dalam pengukuran saat ini. EVA Spread dan EVA
Momentum diunggulkan untuk memiliki kemampuan yang lebih baik dalam
menjelaskan varians pada penciptaan kekayaan bagi investor dibanding dengan kedua
metoda tradisional yang lain.
Penelitian ini menggunakan regresi linear sederhana untuk mengkaji pengaruh
metoda-metoda pengukuran tersebut pada pengembalian saham. Untuk
membandingkan kemampuan dari metoda-metoda dalam menjelaskan pengembalian
saham, penelitian ini menggunakan metode Adj-R2 Tersesuaikan. Data yang digunaka
dalam penelitian ini adalah data harga saham penutupan harian tersesuaikan (adjusted
closing price) dan data keuangan dari perusahaan perusahaan Indeks LQ45 di Bursa
Efek Indonesia dari periode tahun 2004 hingga 2008. Data untuk harga penutupan
tersesuaikan didapat dari Yahoo Finance. Data keuangan perusahaan didapatkan dari
data OSIRIS.
Hasil dari penelitian ini menunjukkan EVA Spread dan ROA memiliki
pengaruh yang signifikan terhadap pengembalian saham sedangkan EVA dan EVA
Momentum tidak memiliki pengaruh signifikan terhadap pengembalian saham. ROA
ditemukan memiliki kemampuan yang paling baik dalam menjelaskan varians dari
pengembalian saham dibanding ketiga metoda yang lain.
Kata kunci: Economic Value Added, EVA Spread, EVA Momentum, Return on
Asset, Pengembalian Saham, Penilaian Kinerja.
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CHAPTER I: INTRODUCTION
1.1 Problem Background
Finding the best fundamental measurement to evaluate the performance of
managers and the profitability of projects and subsidiary companies is still one of the
greatest challenges of corporate finance. A number of ratios and indicators have been
utilized to evaluate a firm‟s management performance. Numerous authors like
Finegan (1991), Stern (1993), O‟Byrne (1996), Uyemura, Kantor, and Pettit (1996),
Dodd and Chen (1996), Milunovich and Tsuei (1996), Kramer and Pushner (1997),
Makelainen (1998), Biddle, Bowen, and Wallace (1999) (de Wet & du Toit, 2007),
and de Wet and du Toit (2007) have studied to find the best internal indicator to
evaluate firms performance. A number of authors such as Hartono and Chendrawati
(1999), Pangabean (2005), and Nugrahanto (2007) studied to find it in Indonesian
markets. These authors employed a method where they test the effect of an indicator
of their choice (e.g. Return on Asset) towards an indicator of shareholders wealth
creation (e.g. Stock Return) using regressions.
Measuring firms‟ performance is a fundamental duty of a financial manager in
their role of making decisions in the area of capital expenditure, investment, and
financing (Damodaran, 2002). Measuring the performance of firms and projects are
mainly intended to measure their profitability. The outcome of the performance
measurement are helpful information for managers to make decisions regarding
financial structures, fair value of subsidiaries divestments, acquisition values,
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stopping or continuing certain projects, and setting the proper managers and
employees incentives.
Finding the best or simply right method of measuring performance is a great
challenge. Publicly traded companies may use the price of their stocks to measure the
company‟s performance although it is not without its weaknesses. Damodaran (2002)
argued that market stock prices are up to date and observable but they are also noisy
where they tend to fluctuate around the true value of the firm. These weaknesses are
caused by sudden capital inflow or outflow from the market, market liquidity issues,
and misleading rumors which often show values that do not reflect the fundamental
condition of the firm. Furthermore there are far more business units and projects that
managers cannot refer to stock prices simply because they do not issue their own
stocks or their stocks are not traded often enough to see the reaction when the firm‟s
performance change. Stock prices can only reflect the performance of the firm as a
whole and not to the levels of individual projects, subsidiaries, and divisions.
A tool for performance method should ideally also able to be projected before
a specific project is launched. This characteristic is useful in determining whether an
execution of a project is coherent the interest of shareholders in maximizing their
wealth. A manager should only launch a project when the [project creates value to the
shareholders. This means that the measure should be able to tell managers whether a
certain proposed project will increase Stock Returns in the future and also able to
forecast the magnitude of such increase.
Many managers and analysts have been using ratios to set goals, measure
performance, and determine success or failure of a certain firm or project. Despite the
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commonality, using ratios to measure performance can be detrimental for the firm
and lead managers to make decisions that injure the interest of shareholders. This
occurs for one of two or both reasons (Stewart, 2009). First is that these traditional
measures only measure a few indicators out of many that are in the minds of
stakeholder of the firm. Shareholders do not only consider net income and asset. They
also worry about bankruptcy risks, firm‟s investment opportunity, etc. The second
reason is that ratios are denominated by a single variable that is not free from
managers‟ power to change them to fit their desire and might injure shareholders‟
interest. For instance in traditional measures using simple ratios, even though many
companies use Return on Equity (ROE), it is susceptible to manipulation when
managers have rights to make decisions over the level of investment (Jensen &
Meckling, 2009).
When pressured to increase ROE for example, the board of directors may
issue more corporate bond to buy back its outstanding stocks. This action increases
firm‟s Return on Equity given the firm is making profit simply because the value of
equity which is a denominator in this ratio is smaller therefore the ratio increases.
However this action also increases its leverage away from optimal leverage level
since different leverage level. Increase in leverage will increase the risk bared by the
shareholders in the forms of probable bankruptcy costs and other disadvantages such
as staff leaving, suppliers demanding disadvantageous payment terms,
bondholder/stockholder infighting, etc (Kraus & Litzenberg, 1973). Managers may
also invest only in projects that produce high return on equity and divest from
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projects that produce smaller return on equity although it shows positive Net Present
Value thus eliminating the possibility to gain more economic benefit.
EVA Momentum was developed by Stewart (2009), which was also one of
the developers of Economic Value Added (EVA) decades earlier, after it was first
lightly mentioned by Abate, Grant, and Stewart III (2004) as a “scaled recent change
in EVA.” EVA and EVA Momentum are instruments to measure the financial
performance of firms. A number of authors such as de Wet and du Toit (2007) have
supported EVA the better measure of firms‟ wealth creation through empirical
studies. Others such as Dodd and Chen (1996) and Tsuji (2006) proven otherwise.
A number of modifications have been performed because the use of EVA is
still highly controversial in terms of its merit of effectively measure the wealth
creation of firms. Among the modification were engineering EVA to become a
standardized unit so it can be regressed with ratios which what Stock Return
fundamentally is. De Wet and du Toit (2007) standardized EVA by dividing it with
firm‟s asset and calling it EVA Spread. Stewart (2008) which was one of the co-
developer of EVA standardized EVA by subtracting it with the previous year‟s EVA
and dividing it with the firm‟s sales for which period EVA is measured and called it
EVA Momentum. EVA Spread has been empirically tested against Return on Asset
in the Johannesburg Stock Exchange to examine which of either EVA Spread or
Return on Equity is better. The study shows that EVA Spread is better in explaining
Stock Return than Return on Equity. The EVA Momentum on the other hand has
never publicized of being tested in terms of explaining Stock Return. The proposing
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paper of EVA Momentum had only calculated the EVA Momentum of a number of
firms in the New York Stock Exchange.
This study tests the merit of EVA, EVA Spread, EVA Momentum, and Return
on Asset in determining the Stock Return of firms in the Indonesian Stock Exchange
(IDX) prior to comparing their ability in explaining Stock Return to find which
measures actually has an effect on Stock Return. Finding the ability of these measures
to explain Stock Return is important for managers and investors to find which
measures of investments that needed to be maximized. For the test of the effect of
EVA Momentum on Stock Return, this study is likely to be the first in the world
since this is a fairly new concept and there has been no publicized study which tests
the effect of EVA Momentum in determining the stock state of return. These
measures will then be compared against one another using coefficient of
determination adjusted R2.
1.2 Research Question
This study formulates the problems into the following questions:
1. Does EVA affect Stock Return?
2. Does EVA Spread affect Stock Return?
3. Does EVA Momentum affect Stock Return?
4. Does ROA affect Stock Return?
5. Which of these measures best explain Stock Return?
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1.3 Limitation of The Problem
This research is limited to:
1. Corporations included the LQ45 Index of the Indonesian Stock Exchange. The
corporations are not mainly operating in the financial industry.
2. Corporations performance evaluated from 2004 to 2008
1.4 Objectives of The Study
This study aims to:
1. Find whether EVA affect Stock Return
2. Find whether EVA Spread affect Stock Return
3. Find whether EVA Momentum affect Stock Return
4. Find whether ROA affect Stock Return
5. Find which among EVA, EVA Spread, EVA Momentum, and ROA that has
the best ability in explaining Stock Return
1.5 Benefits of The Study
Benefits expected to be obtained from this study are:
1. This study serves as an input on how to evaluate the performance of managers
in a certain period.
2. This study contributes evidence in the discussion of using economic ratios in
managers‟ performance evaluation.
3. This study gives input whether EVA Momentum or EVA Spread can replace
EVA and ROA in a number of studies such as agency theory.
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4. This study will be used as a reference for other researchers to study further
about this topic and other related topics.
5. This study will help investors in determining which tools should be used in
forecasting the market share price based on firms‟ predicted performance.
1.6 Report Outline
This report is systematically divided into five chapters. The chapters are
introduction, literature review and hypothesis development, research method, data
analysis, and conclusion. The chapters will be outlined as follows:
BAB I: INTRODUCTION
This chapter consists of background problem, problem formulation, research
objectives, research benefits, and report outline.
BAB II: LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
This chapter explains the relevant concepts and theories to this research topic.
This chapter also lists empirical evidence from previous researches. This chapter will
explain the development of hypotheses which will be tested in this study.
BAB III: RESEARCH METHOD
The third chapter consists of explanations about the description of the
research, data, population, sample, variable measurement, research model, ad data
analysis method.
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BAB IV: DATA ANALYSIS
This chapter explains descriptive statistics, classical assumption testing,
hypotheses testing and discussion, and research findings.
BAB V: CONCLUSION
This final chapter discusses the conclusion from the research result, research
limitation, and suggestions for future researches
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CHAPTER II: LITERATURE REVIEW AND HIPOTHESIS
DEVELOPMENT
2.1 Variable Identification
This study employs four models in order to be able to examine the
significance of the independent variables separately and compare the ability of the
variables in explaining the independent variables. Each model has one independent
variables and one dependent variable. The independent variables for the four models
are Economic Value Added (EVA), EVA Spread, EVA Momentum, and Return on
Assets (ROA).
2.1.1 Economic Value Added
Economic Value Added (EVA) is a measure of the dollar surplus value
created by an investment or a portfolio of investments (Damodaran, 2002). Surplus
value is a residual income that is obtained by subtracting cost of capital from the
operating profit. It is calculated by subtracting the monetary cost of capital value
from the monetary return of capital invested in the mentioned investment or portfolio
of investment.
Equation 1: Economics Value Added
Economic Value Added = (return on capital – Cost of Capital) x (Capital Invested in
Project)
EVA focuses on managerial effectiveness in a given year. The theory of
Economic Value Added rests on two fundamental assertions. First, is that a firm,
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division, or project is not principally profitable until it earns a return on its invested
capital that exceeds its opportunity cost of the invested capital. Second, is that wealth
is created when managers make positive Net Present Value (NPV) investment
decisions for the shareholders (Grant, 2003). Grant (2003) and Stewart (1991) argued
that in principle, EVA is directly related to wealth creation in the context that Net
Present Value can be expressed as the present value of future EVA. The operational
formula of EVA is as follows:
Equation 2: Economics Value Added
EVA = NOPAT – Monetary Cost of Capital
NOPAT = EBIT x (1 – Tax Rate) = ROC x Capital
Monetary Cost of Capital = WACC x Capital
WACC = wdkd x (1 – Tax Rate) +weke
where:
EVA : Economic Value Added
NOPAT : Net Operating Profit After Tax
EBIT : Earnings before Interest and Tax
WACC : Weighted Average Cost of Capital
ROC : Return on Capital
wd : Weight of Debt (%)
kd : Cost of Debt (%)
we : weight of Equity (%)
ke : Cost of Equity (%)
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Many major companies, such as Coca-Cola, AT&T, Quaker Oats, Briggs &
Stratton, CSX, and even not for profits like US Postal Services, have implemented
EVA (Tully, 1993). These companies have been using EVA for various purposes like
as a measure of corporate and divisional performance, as a compensation base, to
increase manager awareness of stockholder interests, to emphasize long-term
importance and benefits of research and development and employee training, to
increase firm value (Burkette & Hedley, 1997).
By using EVA, firms are given incentives to employ the optimum financial
leverage. This is because EVA incorporates risk bared by the company shown by the
inclusion of cost of capital in the calculation. High risk firms will be penalized by
their investors with high cost of capital. Risky companies in term of their financial
leverage are also penalized with high risk premium when the cost of capital is
analyzed with the CAPM. In CAPM, risk premium is determined by the firm‟s equity
beta market. Firms‟ equity beta market is determined by its capital structure where
equity beta market is an increasing function of financial leverage (Mensah, 1992;
Hong & Sarkar, 2007). Optimum financial leverages will result in the lowest cost of
capital (Douglas, 1970; Kraus & Litzenberg, 1973; Damodaran, 2002). Hong and
Sarkar (2007) also found that firms‟ equity beta market is determined by a function of
growth opportunities, leverage ratio, earnings volatility, market price of risk, and
correlation of the firm's earnings with the market portfolio; a decreasing function of
earnings level, earnings growth rate, and corporate tax rate; virtually independent of
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bankruptcy costs; and an increasingly (slightly decreasing) function of the risk-free
interest rate for high (low and moderate) leverage ratios.
2.1.2 EVA Spread
EVA Spread is a standardized version of EVA. It is acquired by dividing EVA
of a certain year with total invested capital of the firm in the beginning of that year.
EVA Spread was used by de Wet and du Toit (2007) to examine the Johannesburg
Stock Exchange. EVA spread can also be defined as the difference between after-tax
Return on Capital (ROC) and Weighted Average Cost of Capital (WACC) (Abate,
Grant, & Stewart III, 2004). The following algebra explains that the two definitions
are the equivalent.
Equation 3: EVA Spread
As a ratio, EVA Spread is size neutral. EVA Spread has the capability to
compare the residual economic profitability of companies regardless of the difference
of the size of the company in term of their invested capital. EVA Spread is then more
related to Stock Return than simple EVA because Stock Return is also a measure that
is neutral to the size of companies. Bigger size companies are naturally expected to
have greater EVA because it employs larger capital.
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Being size neutral, EVA Spread is more applicable in evaluating stocks to
formulate a portfolio investment. Simple EVA cannot be compared to other
companies if it has different size of capital employed and surely there are no two
companies that employ the same exact capital at any point. In order to compare
companies of different sizes, the sizes of companies need to be controlled. Hence
while simple EVA can be used for firms to select projects or divisions to invest in
because the investing firms will get the monetary benefit of the profit obtained from
the project or division in a more direct manner than investors in securities exchange,
EVA Spread is a better tool for individual or institutional investors in supplying
capital in securities exchange.
EVA Spread is a better tool for investors due to at least two reasons. The first
reason is that the condition where while firms investing in projects or divisions will
directly acquire free cash flow from the investment, stock investors do not. They will
acquire return from their investment through dividends which are less significant and
capital gain. Stock investors are more interested in the increase price (capital gain) of
the stocks which are measured by percentage instead of monetary units.
The second reason is that in increasing the value of firms, managers are
interested to make all investments that result in positive EVA. As long as the Return
of Capital is larger than the Weighted Average Cost of Capital for that specific
investment, the firm is increasing in value. This is because firms acquire capital from
elsewhere such as through debt and stocks issued. Different condition applies with
14
investors. Investors are the suppliers of capital; they do not acquire capital from
elsewhere hence they will use their limited capital to gain as much return as possible.
2.1.3 EVA Momentum
EVA Momentum is a further development of EVA. It is the change in firms‟
EVA over a period divided by the sales of the prior period (Stewart, 2009). EVA is
claimed by its developer, Stewart (2009), as the ratio that fits the demand of the
market for a measure that cannot be increased without creating value. The formula of
EVA Momentum is as follows:
Equation 4: EVA Momentum
Aside from the attributes which are also possessed by EVA and EVA Spread,
such as risk adjusted, EVA Momentum also focuses on the change of the creation that
firms make. In order for a firm to have positive EVA Momentum, the firm needs to
create more EVA than before. Stagnant EVA is considered as a condition where
managers „do not screw it‟. The only role management has in the period is to
maintain the condition of the firm. Positive EVA Momentum shows how much
benefit did the efforts of managers to improve the firms yield. Negative EVA
Momentum shows how much the damage of actions the managers cause to the firm in
that period.
Stewart (2009) stated that EVA Momentum is formed from EVA Margin.
EVA Margin was described as EVA-to-Sales Ratio (Abate, Grant, & Stewart III,
2004) for a certain number of sales. It is acquired by dividing EVA to the certain
15
number of sales it made in a certain period. It is how much of the certain sales end up
becoming EVA after deducting all operating and financial costs. EVA Momentum
goes further than EVA and EVA Spread. EVA and EVA Spread state that in order for
a company to achieve economic profit, managers need to make investment decisions
that generate more Net Operating Profit After Tax than Cost of Capital while taking
all costs of operations for granted. EVA Momentum takes a further step where firms
make economic profit if managers are able to make more sales that exceed cost of
sales, operations, and capital than the sales made from the previous year. EVA
Momentum will be achieved when firms produce efficiency gain, which is
optimization of operations and financing so the cost of operations and cost of capital
decreases for the same level of sales revenue as the preceding period. The firm can
also produce profitable growth, which is an increase of sales revenue at a positive
EVA Margin (marginal sales are higher than its marginal costs, which consists of
operation and capital costs).
Equation 5: EVA Momentum Explanation
16
where
EVAt1 = EVA as a result of efficiency gain from previous period
Salest - Salest-1 = increased sales from preceding period
EVAt2 = EVA of the increased sales
The merit of EVA Momentum in determining Stock Return is yet to be tested.
No study has been published to response the arguments proposed by Stewart (2009).
This study will be the first study that tests the merit of EVA Momentum and
including it in the search for the best determinant of Stock Return.
2.1.4 Return On Asset
Return on Asset is a measure of how efficiently a firm is using its assets to
produce net income. It is the proportion of Net Income a firm generated relative to the
total asset the firm employs. In the Du Pont analysis, ROA is broken down into the
product of Net Profit Margin and Total Asset Turnover. Net Profit Margin is the
Profit (Net Income) yielded by each monetary unit of turnover (sales) makes and
Total Asset Turnover is the number of turnovers (sales) yielded by each monetary
unit of total assets which consists of fixed assets and current assets.
Equation 6: Return on Asset
17
ROA, along with Return on Equity, is the most widely used ratio in measuring
the financial performance of firms (Rappaport, 1998). It is widely used due to its
convenience. The data needed to find the value of ROA are all available in financial
report of firms and data needed to find ROA are all collected within a specific period
only as opposed to measurements requiring the availability of cost of capital which
require data of stock prices from a longer period of time to find the sound basis for
the measurement. Aside from that, ROA can also be calculated for firms that do not
issue stock to the public or those whose stock are not traded frequently.
An empirical study conducted by Dodd and Chen (1996) show that despite its
simplicity, ROA performs superior relative to other profitability ratios in explaining
the variations of Stock Returns. Hence, ROA is not only useful for firms in evaluating
their financial performance but also useful for investors in analyzing the performance
of stocks available for investment.
2.1.5 Stock Return
Return is the objective of investments. It is what investors want to get from
investing their assets to firms. It is what firms are trying to deliver to investors in
return of assets being invested into them. Return is the compensation given by firms
to their investors for the time of delayed consumption and risk related to the
investment (Tandelilin, 2001; Jones, 2010). Equity assets (specifically common
stocks) are no different from other assets. Investors put their money in them to
acquire return and firms are collecting assets from equity because they require assets
18
to operate in order to achieve their mission. Firms are also aspired to produce returns
from equity investments.
The return investors collect from stocks consists of two components:
1) Yield: Firms at times of their choosing award their equity investors with
cash payments from the firms‟ treasury. These payments are called cash
dividends. Dividend payments are decided and declared by the board of
directors and range from zero to any amount the corporation decided (Jones,
2010). However, firms do not have any binding obligation to produce
dividend payments to their investors.
2) Capital Gain (Loss): Another component of returns on stock is the
appreciation (or depreciation) of stock price in the stock market (secondary
market). This type of return does not including transfer of money or other
assets from the companies to their investors. The return is obtained because
the value of the stock is more than the value it was in the beginning of the
period therefore if the investors realize the return by selling the specific
stocks, the investors would gain wealth from the investment. Investors also
have the choice to keep their stocks and therefore have their gain unrealized
for the amount of time they choose and still be considered to have acquired
capital gain. The calculation of capital gain for a period is the subtraction of
the beginning price of the stock from its price in the end of the period
divided by the beginning price.
19
Equation 7: Capital Gain
The combinations of the two components of returns collected by investors are
called Total Return. Total return is stated in percentage of the investment made to the
firm and is called Stock Return. The following is the general equation for calculating
Stock Return (Jones, 2010; Hartono, 2010)
Equation 8: Total Stock Return
where:
Rt = Stock Return in period t
Dt = Cash Dividend(s) paid during period t
PE= Price of stock at the end of period t
PB= Price of stock at the beginning of period t
2.2 Hypotheses Development
2.2.1 Effect of EVA on Stock Return
Empirical evidence on the effect of Economic Value Added on the market
value has been provided by both academics and practitioners. In the academic wagon,
there are de Wet and du Toit (2007) and Lehn and Makhija (1996) (Hartono &
Chendrawati, 1999). In the practitioner wagon, there is James Meenan of AT&T and
executives from Coca-Cola which said that near perfect correlation were found
between EVA and Stock Return of their companies (Tully, 1993).
20
Based on the explanation and empirical studies, the formulation of the first
hypothesis of the study is as follows:
H1: EVA has positive effect on Stock Return
2.2.2 Effect of EVA Spread on Stock Return
Studies concerning the ability of EVA Spread in explaining Stock Return have
only been explored at a minimum level. The study of EVA Spread has only been
done by de Wet and du Toit (2007). The study was done whit a sample of 83
corporations listed in the Johannesburg Securities Exchange. The study showed that
EVA Spread has superior capability in explaining the Stock Return with an r2 of only
0.154 than the traditionally acclaimed Return on Equity.
The argument of EVA Spread to be conceptually superior to EVA in
explaining Stock Return is that EVA Spread is a standardized unit just like Stock
Return, and EVA is not. Both Stock Return and EVA Spread is size neutral. EVA is
not. A large company would be expected to have larger EVA for the same number of
profitability than a small company. This is not the case with EVA Spread. EVA
Spread can be used to directly compare corporations with different sizes of invested
capital
Based on the explanation and empirical study, the formulation of the first
hypothesis of the study is as follows:
21
H2: EVA Spread has positive effect on Stock Return
2.2.3 Effect of EVA Momentum on Stock Return
EVA Momentum has never been empirically tested since its initial publication
by Stewart (2009). The publication itself only provides calculations of EVA
Momentum for a number of firms listed in the American stock exchanges such as
Google. Nevertheless EVA Momentum has attempted to provide the necessity tool to
assess the predicaments stated by Ramezani, Soenen, and Jung (2002) about finding
the optimum growth for a firm. By following EVA Momentum, firms are given a tool
to guide itself to ensure that all sales growths are value creating. Hence it will always
support the firms‟ goal to create wealth for the shareholder.
Based on the explanation, the formulation of the first hypothesis of the study
is as follows:
H3: EVA Momentum has positive effect on Stock Return
2.2.4 Effect of Return on Asset on Stock Return
Although EVA had been increasingly popular, Dodd and Chen (1996) which
studied the financial statements and market data of US companies from the data of
Stern Stewart (a consulting firm on EVA) and Hartono and Chendrawati (1999)
which studied the companies of the Indonesian LQ45 index empirically proved that
Return on Asset has a greater portion of explaining Stock Return variations than
EVA. ROA is a measuring tool that can be broken down with the du Pont formula
which then can be translated into pragmatic measures for middle and lower
management (Rothschild, 2006). It is also the single ratio that pictures the whole
22
operation of a firm without a way for managers to cheat the numbers with, for
instance increase financial leverage like in ROE.
Based on the explanation, the formulation of the first hypothesis of the study
is as follows:
H4: Return on Asset has positive effect on Stock Return
23
CHAPTER III: RESEARCH METHOD
3.1 Research Data and Sample
This study uses data from firms listed the Indonesia Stock Exchange. Data is
categorized into firms‟ financial data and stock prices data. Firms‟ financial data are
acquired through the OSIRIS database. Data regarding stock prices are acquired from
Yahoo Finance. In addition to that data of the prices and characteristics of the
Indonesian Government Bond are acquired through the website of Bank Indonesia.
The selection of samples to be studied is based on judgment (purposive sampling).
These firms are all listed in the LQ 45 Index in the second semester (semester when
financial report is published) consecutively from 2004 to 2008. The use of firms
listed in the LQ 45 Index has been done in studies conducted by various authors such
as Hartono and Chendrawati (1999), Sumiyana (2007; 2009), and Sugiharto, Inaga,
and Sembel (2007). The LQ45 Index is an Index of 45 firms filtered based on the
most actively traded stocks in the Indonesia Stock Exchange (Indonesia Stock
Exchange, 2010). This selection of sample is also to avoid bias from firms that are
rarely traded. All financial service firms are excluded from the sample.
Table 1: Company List
No. Company Name
1 2 3 4 5 6 7 8
Astra Agro Lestari Tbk Astra International Tbk Pt Astra Otoparts Tbk Bakrieland Development Tbk Bank Lippo Tbk. Bentoel Internasional Investama Tbk Ciputra Surya Tbk Gajah Tunggal Tbk
24
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Hanjaya Mandala Sampoerna Holcim Indonesia Tbk Indah Kiat Pulp & Paper Corporation Indofarma Tbk Indofood Sukses Makmur International Nickel Indonesia Tbk Kawasan Industri Jababeka Tbk Kimia Farma (Persero) Tbk Limas Centric Indonesia Tbk Matahari Putra Prima Tbk Medco Energi Internasional Tbk Mobile-8 Telecom Tbk Pabrik Kertas Tjiwi Kimia Tbk Polychem Indonesia Tbk PP London Sumatra Indonesia Tbk PT Aneka Tambang Tbk PT Arpeni Pratama Ocean Line Tbk PT Astra Graphia Tbk PT Bakrie & Brothers Tbk PT Bakrie Sumatera Plantations Tbk PT Bakrie Telecom Tbk PT Barito Pacific Tbk PT Berlian Laju Tanker Tbk PT Bukit Asam (Persero) Tbk PT Bumi Resources Tbk PT Charoen Pokphand Indonesia Tbk PT Ciputra Development Tbk PT Citra Marga Nusaphala Persada Tbk PT Energi Mega Persada Tbk PT Enseval Putera Megatrading Tbk PT Global Mediacom Tbk PT Gudang Garam Tbk PT Indocement Tunggal Prakarsa Tbk PT Indosat Tbk PT Indosiar Karya Media Tbk PT Jakarta International Hotels & Development Tbk PT Kalbe Farma Tbk PT Lippo Karawaci Tbk PT Polaris Investama Tbk PT Semen Gresik (Persero) Tbk PT Sumalindo Lestari Jaya Tbk PT Telekomunikasi Indonesia Tbk PT Trias Sentosa Tbk PT Unilever Indonesia Tbk PT United Tractors Tbk Ramayana Lestari Sentosa Tbk Summarecon Agung Tbk Tempo Scan Pacific Tbk Timah Tbk Total Bangun Persada Tbk Truba Alam Manunggal Engineering Tbk Tunas Baru Lampung Tbk
25
To control the events of stock splits and dividends, adjusted stock prices
which adhere to the standards of Center for Research on Security Prices (CRSP) of
the University Of Chicago Booth School Of Business are employed in all calculations
regarding stock prices. The adjustments are to integrate both stock split and dividends
in to the price. The base price for adjusted prices is the latest price on the data for
each stock. On the events of stock splits, adjusted closing price before the events
shows numbers which are the unadjusted price multiplied by the inverse of the split
term. On the case of dividend distribution, adjusted closing prices before the events
show numbers which are the unadjusted prices divided by one plus the percentage of
dividend distribution relative to unadjusted price of the time of the distribution of the
dividends. For stocks that have undergone multiple splits, dividends, and or splits and
dividends the multiplication and or division are compounded for all inverse values of
the stock splits and or division of one plus the percentage of dividend distribution
calculated for each event of stock split and dividend distribution after the specific
closing price.
3.2 Calculating Research Variables
3.2.1 Calculating EVA, EVA Spread, and EVA Momentum
To acquire Economic Value Added (EVA), EVA Spread, and EVA
Momentum, calculations to find EVA needs to be conducted prior to these variables.
The calculation of Economic Value Added used in this study uses similar method as
done by Hartono and Chendrawati (1999) with minor modifications: (1) the use of
Indonesian Government Bond Yield as a proxy for risk free rate of return and (2) the
26
use of adjusted closing stock price to accommodate stock splits and dividend
distributions which was originally neglected. To calculate EVA, a number of
components are required: cost of debt (kd), cost of equity (ke), weight of debt (wd),
weight of equity (we), Net Operating Profit After Tax, and Invested Capital. The steps
on calculating EVA is as follows:
1. Calculating Cost of Debt ( )
2. Calculating Cost of Equity (ke)
3. Calculating weight of debt (wd) and weight of equity (we)
4. Calculating Weighted Average Cost of Capital (WACC)
5. Calculating EVA
3.2.1.1 Calculating Cost of Debt ( )
Calculating EVA Cost of Debt of a firm is acquired by dividing the firm‟s
monetary interest expenses with the firm‟s total debt stated in the financial statement.
This calculation does not include debt to its subsidiaries.
Equation 9: Cost of Debt
Where
kdt = cost of debt of period t
3.2.1.2 Calculating Cost of Equity (ke)
Cost of Equity is calculated using Capital Asset Pricing Model (CAPM)
which components was developed independently by Sharpe (1964) and Treynor
27
(1961) (French, 2002), and extended and clarified by Lintner (1965a; 1965b). The
formula of the model is shown bellow.
Equation 10: Cost of Equity
where
ke1 = Cost of Equity for stock i
Rf = Risk free rate of return
βi = Beta of stock i
Rm = Market return
To calculate the cost of equity using CAPM, the following calculations are
needed: (1) Periodic Stock Return, (2) Periodic Market Return, (3) Beta Coefficient
off all stocks in the sample, and (4) Risk Free Rate of Return.
1. Periodic Stock Return
The ideal period length in the Indonesian market in calculating return
for the Capital Asset Pricing Model is one week. Weekly period is considered
ideal because it shows enough variations of prices yet not so short that it will
create bias. Daily periods in calculating return is not recommended because in
emerging markets where the number of trading is limited, beta score resulted
in daily period calculation will be biased and yearly or monthly period is not
sufficient to capture the movement of the stock prices for short period of times
(Brown & Warner, 1985; in Hartono & Chendrawati, 1999).
28
This periodic Stock Return is only for the purpose of calculating the
cost of equity and is different from the Stock Return calculation that will be
the dependent variable of this study. To control the events of stock split and
dividends, adjusted stock price is used in the calculation. The adjustments are
to integrate both stock split and dividends in to the price. The following is the
formula for calculating periodic return of stocks.
Equation 11: Periodic Stock Return
Where
Rt =Stock Return in period t
PE = Adjusted price of stock at the end of period t
PB = Adjusted price of stock at the beginning of period t
2. Periodic Market Return
To calculate Periodic Market Return, the LQ45 Index is used as a
proxy for market price index (Pm). The use of LQ45 index as a proxy for
market is used due to its superior ability in predicting the systemic risks bared
by the component stocks shown by the smaller prediction error than of the
Indonesia Stock Exchange Composite Index (IHSG) (Putra, 2001; Sugiharto,
Inanga, & Sembel, 2007). The LQ45 is also perceived by most fund managers
trading in the Jakarta Stock Exchange (former name of Indonesia Stock
Exchange) as the better index in analyzing the market of the Jakarta Stock
29
Exchange (Sugiharto, Inanga, & Sembel, 2007). The following is the formula
to calculate periodic market return
Equation 12: Market Return
where
Rmt = Market return of period t
Pmt = Market price index for period t
Pmt-1 = Market price index for period t-1
3. Beta Coefficient
Beta coefficients of stocks in the sample are calculated with the
following formula.
Equation 13: Beta Coefficient
where
βi = Beta coefficient of stock i in period t
Rit = Return of stock i in period t
Rmt = Return of market in period t
4. Risk Free Rate of Return
The proxy for Risk Free Rate of Return in this study is the 10 year
Indonesia Government Bond Yield provided in the website of Bank Indonesia
(www.bi.go.id).
30
3.2.1.3 Calculating Weighted Average Cost of Capital
Weighted Average Cost of Capital (WACC) are calculated using components
of capital structure cost of debt and cost of equity and the tax rate of income imposed
to the firms. Tax rate imposed to corporations by the Indonesian Government is 30%
for the years this study is using. Capital structure (wd and we) are obtained from the
respective firm‟s financial reports. Data of the capital structure of firms in the sample
are acquired from the financial statements of each firm. The calculation of WACC
uses the following formula.
Equation 14: Weighted Average Cost of Capital
WACCi = wdkd x (1 – Tax Rate) +weke
3.2.1.4 Final Calculations of EVA, EVA Spread, and EVA Momentum
Calculating EVA requires the knowledge of firms‟ Earnings Before Interest
and Taxes (EBIT), Weighted Average Cost of Capital, Corporate Income Tax Rate,
and the amount of Capital Employed. EBIT and Corporate Income Tax Rate are
needed to calculate firms‟ Net Operating Profit After Tax. Weighted Average Cost of
Capital is acquired by the calculation explained previously. Firms‟ Earnings Before
Interest and Taxes and Capital Employed are acquired from the information published
in the firms‟ respective financial statements. The following is the formula to acquire
EVA of a firm.
Equation 15: EVA of Period t
EVAt = EBITt x (1 – Tax Rate) – WACC x Capitalt
To calculate EVA Spread, EVA of a period is divided by the Capital
Employed by the firm in the beginning of that period. The following is the formula
31
for calculating EVA Spread (Abate, Grant, & Stewart III, 2004; de Wet & du Toit,
2007).
Equation 16: EVA Spread of Period t
To calculate EVA Momentum, and are acquired using
calculations of acquiring EVA and Sales of the previous period is acquired from the
data shown in the financial statements of the firms. The following is the formula for
calculating EVA Momentum (Stewart, 2009).
Equation 17: EVA of Period t
3.2.2 Calculating Return on Asset
Calculation of Return on Asset requires information of Net Income and the
Total Assets Employed by the firm. Both components are acquired through the
information shown in the firms‟ financial statements. The following are the formula
for calculating Return on Asset (Damodaran, 2002)
Equation 18: ROA of Period t
32
3.2.3 Calculating Stock Return
Stock Return calculations require information of stock prices in the
beginning and in the end of a period. The following is the formula for calculating
periodic return of stocks.
Equation 19: Stock Return of Period t
where
Rt = Stock Return in period t
Pt = Adjusted price of stock at the end of period t
Pt-1 = Adjusted price of stock at the beginning of period t
Because audited financial report is announced annually, this study calculates
Stock Return using time period of one year per period. To ensure that information in
the financial statement is reflected in the stock price being calculated, this study uses
the closing stock price of the trading days after the last day (March 31st) in which the
firms listed in the Indonesia Stock Exchange are required to announce their audited
financial statement. Simple Moving Average of daily closing stock price of all
trading days in the month of April following the announcement of the audited
financial statement is used as a proxy of the end stock price of the preceding period
and the beginning price for following period. The use of simple moving average price
is necessary to avoid bias coming from the short term fluctuations of stock prices due
to various market activity such as profit taking.
33
3.3 Research Model
This study is conducted to separately test the effects of EVA, EVA Spread,
EVA Momentum, and ROA (independent variables) on Stock Return (dependent
variables) using four Simple Linear Regressions. The test will be done by conducting
regressions of the independent variables against the dependent variable. The
following are the regression models of the study:
(1)
(2)
(3)
(4)
where
R = Stock Return
= Interception
β1 = Regression coefficient
EVA Spread = EVA Spread
EVA Momentum = EVA Momentum
ROA = Return on Asset
To test the hypotheses, this study conducts a test of significance (the t-test).
The t test is a procedure by which sample results of regressions are used to verify the
truth or falsify the null hypothesis (Gujarati & Porter, 2009). Hypotheses being tested
using the t-test are Hx0 (null hypotheses) in which they are tested whether their is
equal to zero.
34
Hx0: βi = 0
When the test accepts H0, a specific independent variable does not explain the
dependent variable significantly. The alternative hypotheses (Ha) state that is not
equal to zero.
Hx0: βi ≠ 0
When the test accepts H0, a specific independent variable does explain the dependent
variable significantly.
The test is conducted by comparing the statistical probability of the regression
coefficient with the degree of significance required (Gujarati & Porter, 2009). The
null hypothesis is accepted if the probability is larger than required significant value.
The null hypothesis is rejected if the probability is smaller than the required
significant value. This study uses 10% as the required significant value of probability.
3.4 Goodness of Fit
To give an understanding of how well the Ordinary Least Squared Regression
fits the data, this study calculates the coefficient of determination. The calculation is
also to find which predictor best fits the data of the sample in explaining the Stock
Return. Coefficient of determination, also known as R2, is the portion of the sample
variation of the dependent variable that is explained by the independent variable or
technically is the ratio of the explained variation compared to the total variation
keeping in mind that the intersect of the regression is estimated along with the slope
(Wooldridge, 2009). The value of R2 is between 1 and 0. Values close to 1 are
interpreted that the independent variable are able to explain most of the variations in
35
the dependent variable. Values close to 0 are interpreted that the independent
variables have very limited ability in explaining the dependent variable.
The basic weakness of R2 is that it would increase as the number of predictor
in the model is increased. The coefficient would rise with the inclusion of additional
independent variable regardless whether or not the additional variable gives more
explanation of the model on the dependent variable. This bias then makes the R2 less
preferable to find which model fits the data better.
To overcome this bias, many studies employ adjusted R2 which unlike regular
R2, it does not increase simply because an additional variable is added to the model.
The coefficient of adjusted R2 only rises when the extra predictor added helped the
model in explaining the variations of the dependent variable. The coefficient of
adjusted R2 would decrease when the extra predictor does not help the model in
explaining the variations of the dependent variable (Gujarati & Porter, 2009). Even
though this study only uses one independent variable for all its models, it uses
adjusted R2 to measure the goodness of fit and find the best model that fits the data as
it is the preferred coefficient.
3.5 Assessing Classical Assumptions
To ensure that our model can be inferred into the real world population of
data, a number of assumptions are made according to the classical linear regression
model. Before the hypotheses are tested using the data, classical assumption tests are
36
conducted. These tests are normality test, autocorrelation test, heteroscedasticity test,
and multicollinearity test.
3.5.1 No Autocorrelation
The classical linear regression model requires the assumption that there is no
autocorrelation between the disturbances or symbolically cov(ui,uj|Xi,Xj)=0 (Gujarati
& Porter, 2009). To detect the existence of autocorrelation, the Durbin-Watson test is
employed.
3.5.2 Homoscedasticity
Homoscedasticity is the assumption where the variance of errors of all value
of the independent variable is the same. If the variance of the errors of the value of
independent variable is not the same, then the data have heteroscedasticity
characteristics (Gujarati & Porter, 2009). However, heteroscedasticity is not a reason
to invalidate an econometric model (Mankiw, 1990; Gujarati & Porter, 2009, p. 400).
Thus, the homoscedasticity assumption will not be assessed
37
CHAPTER IV: DATA ANALYSIS
4.1 Descriptive Statistics
Descriptive statistics shows the characteristics of the data being used in the
study. EVA is a numerical data representing a currency unit while the rest are ratios.
The main descriptive data shown are mean, median, standard deviation, maximum,
and minimum.
Table 2: Descriptive Statistics
N Minimum Maximum Mean
Std.
Deviation
Stock Return 204 -.8718 7.6706 .566545 1.1658345
EVA (th IDR) 204 -1.0919E10 1.7917E10 5.661598E8 2.1880542E9
EVA Spread 204 -.7724 .5834 .081511 .1360381
ROA 204 -.6137 .8883 .098131 .1548129
EVA
Momentum 160 -9.49 1.18 -.0415 .78740
Valid N (list
wise) 160
All units other than EVA, which is in thousand IDR currency units, are in
ratios. The high value of Stock Returns (mean of 0.566545 and maximum) is caused
by high capital flow coming in to the Indonesian stock market from foreign investors.
There are even firms that have multiplied their stock values in one year because of
this. The figure for EVA which is shown in thousand IDR currency units is showing
an average return of 566 billion IDR, minimum of -10 trillion IDR and maximum of
38
17 trillion IDR. As written in Table 1, the average EVA Momentum is -0.0415 with a
maximum of 1.18 and minimum of -9.49. This shows that the firms are producing an
average lower EVA year by year. The ROA shows an average of 0.098131 with a
maximum of 0.8883 and minimum of -0.6137. The figures of EVA Spread seem
similar to ROA with average of 0.081511, maximum of 0.5834, and minimum -.7724.
4.2 Classical Assumption Tests
4.2.1 No Autocorrelation
To detect for autocorrelations, Durbin Watson Test is employed. The
regression showed that:
Table 3: Durbin Watson Test
Durbin
Watson
Value N dL dU
2.137 204 1.664 1.684
2.101 204 1.664 1.684
2.158 160 1.611 1.637
2.094 204 1.664 1.684
Based on the Durbin Watson tests conducted to all the models, none of the
models falls in the category of having autocorrelation. Thus, these models fulfill the
no autocorrelation assumption.
39
4.3 Hypotheses Testing
The previous chapters have discussed that the hypotheses will be tested using
t-tests on the results of simple linear regressions of the independent variables the tests
using Simple Linear Regressions are conducted with an aid of SPSS 16.0 statistics
software. The software conducts the regressions and the t-tests to find whether the
null hypotheses is preserved or rejected. The regressions are conducted separately for
the four independent variables against the dependent variables.
After the regression is conducted, t-tests are conducted to find whether the
beta produced by the regression is different from zero. The null hypotheses will be
rejected if the significance value of the t-test shows a number of 0.10 or less which
means that the probability of the beta to be statistically equal to zero is 0.10 or less.
4.3.1 Effect of EVA on Stock Return
This study puts EVA as the basis for the two other independent variables
because EVA Spread and EVA Momentum contain the calculations of EVA in them.
The regression of EVA against Stock Return shows a Beta Coefficient of -0.026. This
result suggests that the higher the Economic Value Added of a company in a certain
period, the lower its Stock Return will be. The significance of this regression for the
beta of EVA is 0.712 which is in the zone of preserving the null hypothesis. This
study shows that the null hypothesis is failed to be rejected. The result seems contrary
to the argument that beliefs that EVA is the best measure of wealth creation. The
result shows that EVA is actually wealth destroying in an insignificant manner.
40
Table 4: EVA Coefficient
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .574 .085 6.797 .000
EVA (th
IDR) -1.386E-8 .000 -.026 -.370 .712
a. Dependent Variable: Stock Return
The probable cause for this result is that the variables in the model are not
standardized models. Standardized variables need to be used in regressions. However,
previous studies explained in the preceding chapters also use the unstandardized
variables in their models.
This result is particularly similar to the study conducted by Hartono and
Chendrawati (1999) and Sartono and Setiawan (1999) which also find the beta
coefficient for EVA to be insignificant when regressed against Stock Return in the
Indonesian capital market. The result of this study is not in line with Lehn and
Makhija (1996).
H10: EVA has no significant effect on Stock Return
Not rejected
4.3.2 Effect of EVA Spread on Stock Return
The result of regression of EVA Spread against Stock Return shows a Beta
coefficient of 0.157. This number suggests that each increase of EVA Spread, Stock
Return will increase by as much as 0.157 of the increase of EVA Spread. The Beta
41
coefficient for the regression of EVA Spread against Stock Return has a t-value of
2.262 which is significant at α = 5%. This means that the probability of the effect of
EVA Spread to be 0 on Stock Return is only 2.5%.
Table 5: EVA Spread Coefficient
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .457 .094 4.845 .000
EVA
Spread 1.347 .595 .157 2.262 .025
This result supports the study conducted in the Johannesburg capital market
conducted by de Wett and du Toit (2007) which finds EVA Spread able to explain
Stock Return and arguments by Abate, Grant, and Stewart (2004). At the moment,
there has been no published study that has gone against the hypotheses of EVA
Spread as a factor that affects Stock Return.
H20: EVA Spread has no significant effect on Stock Return
Rejected
4.3.3 Effect of EVA Momentum on Stock Return
EVA Momentum seems to perform very insignificantly in the regression
against Stock Return. Both the beta and the t-value of EVA Momentum are very low.
Each movement of EVA Momentum will only affect 0.021 of its magnitude to the
movement of Stock Return. The significance score of EVA Momentum is also far
42
from the point of rejecting the null hypothesis. EVA Momentum has 78.9%
probability of not affecting Stock Return.
Table 6: EVA Momentum Coefficient
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .574 .099 5.772 .000
EVA
Momentum .034 .127 .021 .268 .789
a. Dependent Variable: Stock Return
This result does not support the statements of Abate, Grant, and Stewart
(2004) and Stewart (2009). This result does not suggest that the growth and
profitability dilemma can be solved.
H30: EVA Momentum has no significant effect on Stock Return
Not Rejected
4.3.4 Effect of Return on Asset on Stock Return
Result of regression on ROA is much similar to results of EVA Spread. The
Beta Coefficient of ROA is 0.174 which means that the movement in ROA affects to
movement of Stock Return as big as 0.174 of its magnitude. The significance level of
ROA as an independent variable on Stock Return is enough to be significant at α=5%.
This number means that the probability of ROA has no affect on Stock Return is
1.3%.
43
Table 7: ROA Coefficient
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .438 .095 4.589 .000
ROA 1.309 .522 .174 2.508 .013
a. Dependent Variable: Stock Return
This result where there is a significant effect of ROA on Stock Return
supports Dodd and Chen (1996). However, it does not support Lehn and Makhija
(1996) Milunovich & Tsuei (1996) and Finegan (1991).
H40: Return on Asset has no significant effect on Stock Return
Rejected
4.4 Goodness of Fit
Goodness of fit as explained in the previous chapter is a measure which tries
to identify which model is the best in explaining the realities of Stock Return. To
assess this matter, Adjusted R2 is calculated from all the models in this study to be
compared.
44
Table 8: Adjusted R2
Model Variable Adjusted R2
EVA
EVA Spread
EVA Momentum
ROA
-0.004
0.020
-0.006
0.025
This study shows that with 0.027, EVA Spread is able to explain the variance
of Stock Return better than the other variables of this study. However, the value of
the Adjusted R2
is still very small compared to EVA Spread in the study of de Wett
and du Toit (2007). This shows that only 2.7% of the variance in the Stock Return
can be explained by the EVA Spread.
45
CHAPTER V: CONCLUSION
5.1 Conclusion
This study concludes that EVA Spread and ROA have significant effects on
Stock Return. Meanwhile EVA and EVA Momentum do not have significant effects
on Stock Return. The result on EVA Spread is consistent with the study conducted in
Johannesburg by de Wett and du Toit (2007) and the result on EVA and ROA are
consistent with Hartono and Chendrawati (1999). ROA has the highest ability in
explaining Stock Return compared to the other methods.
5.2 Implication
The implication of this study is that investors and managers should not use
EVA and EVA Momentum as a measure for evaluating firms‟ performance. If EVA
is really the best measure of wealth creation in a firm given that the calculation of off
balance sheet assets (e.g. good will, patents, research and development results) are
also calculated, then public investors need to be informed of the value of these off
balance sheet assets. The public cannot use public information alone to forecast the
resulting EVA. Investors need to find better methods of calculating EVA.
5.3 Limitation
This study is bounded by a number of limitations. The limitations among
others are:
46
1. This study uses 63 firms listed in the Indonesian Stock Exchange as a sample
using purposive sampling. The use of more firms to be examined will increase
the probability that the study will yield better results which explain the
relationships between the independent and dependent variables better.
2. This study uses data from a very limited amount of time (5 years) due to the few
issuance of Indonesian Government Bond which were only issued between 2004
and 2008.
3. The characteristic of the Indonesian Stock Exchange is that it is an emerging
capital market where most of the funds invested are foreign funds and the
capitalization and daily trading are relatively low. This might limit the ability of
this study in capturing the true effects of the independent variables on the
dependent variables
4. The Indonesian Government Bond, although issued by the highest governing
body in the nation which also regulates banks, is not, in the period of the study,
rated in the investment grade. Thus, it‟s robustness as a risk free rate of return is
debatable.
5.4 Suggestions
Based on the experiences in conducting this study, a number of suggestions
arise for future studies in the subject.
47
1. Managers should pay attention to EVA Spread and ROA projections when
deciding to launch a project
2. Investors should pay attention to EVA Spread and ROA in deciding which firms
they want to invest.
3. Future studies are suggested to use more firms to be examined from a wider
range of industries would most likely result in better understanding of the subject
4. Future studies are suggested to examine a longer timeframe of observation as
more government bonds which can be better proxy for risk free rate of interest
are issued.
5. Future studies are suggested to examine larger and more advanced capital
markets as they have more liquidity and capitalization and hence give a richer
data on the subject.
48
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APPENDIX
Appendix 1: Summary of EVA Regression on Return
( )
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .026a .001 -.004 1.1683213 2.137
a. Predictors: (Constant), EVA
b. Dependent Variable: Stock Return
Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .574 .085 6.797 .000
EVA -1.386E-11 .000 -.026 -.370 .712
a. Dependent Variable: Stock Return
52
Appendix 2: Summary of EVA Spread Regression on Return
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .157a .025 .020 1.1541875 2.101
a. Predictors: (Constant), EVA Spread
b. Dependent Variable: Stock Return
Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .457 .094 4.845 .000
EVA
Spread 1.347 .595 .157 2.262 .025
a. Dependent Variable: Stock Return
53
Appendix 3: Summary of EVA Momentum Regression on Return
(
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .021a .000 -.006 1.2564843 2.158
a. Predictors: (Constant), EVA Momentum
b. Dependent Variable: Stock Return
Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .574 .099 5.772 .000
EVA
Momentum .034 .127 .021 .268 .789
a. Dependent Variable: Stock Return
54
Appendix 4: Summary of ROA on Return
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
Durbin-
Watson
1 .174a .030 .025 1.1509330 2.094
a. Predictors: (Constant), ROA
b. Dependent Variable: Stock Return
Coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .438 .095 4.589 .000
ROA 1.309 .522 .174 2.508 .013
a. Dependent Variable: Stock Return