i

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

vi

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

vii

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

viii

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

ix

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

x

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

xi

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

xii

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.

1

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,

2

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

3

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

4

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

5

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?

6

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.

7

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.

8

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

9

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,

10

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 (%)

11

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

12

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.

13

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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51

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